Neuro Informatics 2019

September 1-2 Warsaw, Poland

PROGRAM BOOK What is INCF?

About INCF

INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology in Stockholm, Sweden. INCF currently has Governing and Associate Nodes spanning 4 continents, with an extended network comprising organizations, individual researchers, industry, and publishers.

INCF promotes the implementation of neuroinformatics and aims to advance data reuse and reproducibility in global brain research by:

• developing and endorsing community standards and best practices • leading the development and provision of training and educational resources in neuroinformatics • promoting open science and the sharing of data and other resources • partnering with international stakeholders to promote neuroinformatics at global, national and local levels • engaging scientific, clinical, technical, industry, and funding partners in colla- borative, community-driven projects

INCF supports the FAIR (Findable Accessible Interoperable Reusable) principles, and strives to implement them across all deliverables and activities.

Learn more: incf.org neuroinformatics2019.org

2 Welcome

Welcome to the 12th INCF Congress in Warsaw!

It makes me very happy that a decade after the 2nd INCF Congress in Plzen, Czech Republic took place, for the second time in Central Europe, the meeting comes to Warsaw.

The global neuroinformatics scenery has changed dramatically over these years. With the European Human Brain Project, the US BRAIN Initiative, Japanese Brain/ MINDS initiative, and many others world-wide, neuroinformatics is alive more than ever, responding to the demands set forth by the modern brain studies. I am looking forward to the exciting program this year. We focus this year on connectomics, neuroinformatics workflows, brain computer interfaces, challenges in behavior, building open science communities, and the future of academic publishing. Two sessions reflect INCF interests of today; global brain projects and training in neuroinformatics, which is further highlighted with a discussion panel on higher education and industry needs, and with three day-long courses on different aspects of neuroinformatics. Whether you are more excited by the neuro- or the -informatics parts of the field, you will find something for yourself in this meeting. Neuroinformatics 2019 is organized by INCF with the Polish INCF node, hosted by the Department of Physics of the University of Warsaw, supported by the Nencki Institute and the Polish Society for Neuroscience.

Warsaw is a vibrant city with bustling life, many cultural events, and great restaurants. May this combination of a great location with excellent research leave you all with great memories.

Welcome!

Daniel Wójcik Program Committte Chair

3 Program at a glance Congress program at a glance

DAY 1 SEPTEMBER 1

09:00-09:15 Welcome Opening remarks by the Program committee and the Local Organizing Committee

09:15-09:45 About INCF

09:45-10:30 Plenary lecture

10:30-11:00 Session1: Comparative and predictive connectomics

11:00-11:40 Investigator presentations

11:40-12:00 Discussions

12:00-13:20 LUNCH

13:20-14:05 Plenary lecture

14:15-14:45 Parallel sessions 2A: Neuroinformatics workflows 2B: Building open science communitites

14:45-15.25 Investigator presentations

15:25-15:45 Discussions both sessions

15:45-16:15 COFFEE BREAK

16:15-17:15 Session 3: Training in neuroinformatics

17:15-17:45 Discussion panel: Higher education and industry needs

17:45-19:45 Poster presentation and reception

4 Program at a glance Congress program at a glance

DAY 2 SEPTEMBER 2

09:00-09:35 Polish Node presentation

09:35-10:20 Plenary lecture

10:30-11:00 Parallel sessions 4A: Brain Computer Interface 4B: Neuroinformatics challenges in behavioral research

11:00-11:40 Investigator presentations

11:40-12:00 Discussions both sessions

12:00-13:20 LUNCH

13:20-14:05 Plenary lecture

14:05-14:35 Session 5: Future of academic publishing

14:35-15:15 Investigator presentations

15:15-15:35 Discussions

15:35-16:05 COFFEE BREAK

16:05-17:35 Session 6: Global brain projects

17:35-17:55 Discussions

17:55-18:00 Closing

18:20-20:20 Poster presentation and reception

5 General information

Opening hours of the registration desk The registration desk will be open as follows: September 1: 08:00 - 18:30 September 2: 08:00 - 13:30 Internet There will be an open network and the eduroam network available. Lunch Lunch and coffee will be served according to the program schedule.

Conference coordinator on site Lotta Johansson +46 760506069 [email protected] Participation, name tags Official conference name tags will be handed out at registration and will be required for admission to all conference functions. Participants who lose their name tags will have to pay a fee of 25EUR to obtain a replacement tag. Neuroinformatics mingle Participants can enjoy lighter food and drinks during the poster sessions on Sunday, September 1, from 17:45 and Monday, September 2, from 18:00.

6 Neuro SAVE THE DATE! Informatics 2020 Seattle, USA | August 17-21

Join the global INCF community for keynotes | panel discussions | posters | demos | socials

neuroinformatics2020.org Sponsors

8 Sponsors

9 Program

Sunday, September 1, 2019

09:00 WELCOME

09:15 ABOUT INCF

09:45 PLENARY LECTURE Specifics of the primate connectome Henry Kennedy, INSERM

10:30 SESSION 1: COMPARATIVE AND PREDICTIVE CONNECTOMICS

10:30 KEYNOTE: Converging spaces of the human connectome Daniel Margulies, CNRS

11:00 INVESTIGATOR PRESENTATIONS

11:40 DISCUSSIONS

12:00 LUNCH

13:20 PLENARY LECTURE FAIRy stories: tales from the building the FAIR Research Commons Carole Goble, University of Manchester

14:15 SESSION 2A: NEUROINFORMATICS WORKFLOWS

14:15 KEYNOTE: SANKET: The Signaling and Neurophysiology Knowledge-resource for Experiments and Theory Upinder Bhalla, National Centre for Biological Sciences

14:45 INVESTIGATOR PRESENTATIONS

15:25 DISCUSSIONS

10 Program

Sunday, September 1, 2019

14:15 SESSION 2B: BUILDING OPEN SCIENCE COMMUNITIES

14:15 KEYNOTE: 10 simple rules for running an open and inclusive project online Kirstie Whitaker, University of Cambridge

14:45 INVESTIGATOR PRESENTATIONS

15:25 DISCUSSIONS

15:45 COFFEE BREAK

16:15 SESSION 3: TRAINING IN NEUROINFORMATICS

16:15 KEYNOTE 1: Preparing students and a workforce for the big data tsunami: summary of reccomendations from the iNEURO project workshop William Grisham, UCLA

16:35 KEYNOTE 2: Training in neuroinformatics from the bachelor’s level at FUW Jaroslaw Zygierwwicz, University of Warsaw

16:55 KEYNOTE 3: Training in the era of brain observatories: data science education and collaboration Ariel Rokem, University of Washington

17:15 DISCUSSION PANEL: HIGHER EDUCATION AND INDUSTRY NEEDS

17:45 POSTER SESSION AND RECEPTION

11 Program

Monday, September 2, 2019

09:00 POLISH NODE PRESENTATION

09:35 PLENARY LECTURE Machine learning and tensor networks and their applications, in Brain Computer Interface, neurofeedback and recognition of human emotions Andrzej Cichocki, Skolkovo Institute of Science and Technology, Moscow and Artificial Intelligence project, Riken, Japan

10:30 SESSION 4A: BRAIN COMPUTER INTERFACE

10:30 KEYNOTE: Towards personalized brain-computer interfaces Maureen Clerc, Inria

11:00 INVESTIGATOR PRESENTATIONS

11.40 DISCUSSIONS

10:30 SESSION 4B: NEUROINFORMATICS CHALLENGES IN BEHAVIORAL RESEARCH

10:30 KEYNOTE: Computational models of decision making: a bridge between behavioral data and neurobiology Rafal Bogacz, University of Oxford

11:00 INVESTIGATOR PRESENTATIONS

11:40 DISCUSSIONS

12:00 LUNCH

13:20 PLENARY LECTURE Theta rhytms in the hippocampus - being clear and open Frances Skinner, University of Toronto

12 Program

Monday, September 2, 2019

14:05 SESSION 5: FUTURE OF ACADEMIC PUBLISHING

14:05 KEYNOTE: Preprinting for neuroinformatics: where are we at? Naomi Penfold, ASAPbio (Accelerating Science and Publication in Biology)

14:35 INVESTIGATOR PRESENTATIONS

15:15 DISCUSSIONS

15:35 COFFEE BREAK

16:05 SESSION 6: GLOBAL BRAIN PROJECTS

16:05 KEYNOTE 1: A user-driven data sharing and data management infrastructure for neuroscience Jan Bjaalie, University of Oslo

16:35 KEYNOTE 2: Brain atlasing and databasing in the Brain/MINDS project Alexander Woodward, RIKEN CBS

17:05 KEYNOTE 3: Community resources for single cell and cell type data in the brain Michael Hawrylycz, Allen Institute for Brain Science

17:35 DISCUSSIONS

17:55 CLOSING REMARKS

18:00 POSTER SESSION AND RECEPTION

13 14 Connect with us

Join INCF at SfN in Chicago on October 19-23!

Our activities this year includes community neuroinformatics demos, a science management workshop, and a sponsored Open, FAIR, and Reproducible Neuroscience Social! Make sure to follow us on social media and incf.org for more details!

We’re looking forward to seeing you in booth 2117!

The INCF booth at SfN 2018 in San Diego

Sign up for our newsletter

Scan the QR code to sign up for the quarterly INCF newsletter to get the latest updates on neuroscience and neuroinformatics activities, and the latest news from INCF.

You can also find us on social media: • Twitter • Facebook • YouTube • LinkedIn

15 Abstracts

ABSTRACT INFORMATION Abstracts will be presented outside the lecture halls. The abstract list is sorted in alphabetical order by the corresponding author’s last name.

D Demo P Poster IP Investigator presentation

SESSION 1 On September 1, abstracts with odd numbers will be presented.

SESSION 2 On September 2, abstracts with even numbers will be presented.

16 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Abraham, Sanu Ann ReproSchema and Library: A JSON-LD schema to D3 harmonize behavioral, cognitive, and neuropsych assessments Abrams, Mathew TrainingSpace: neuroeducation without borders D1

Andersson, Krister A Neuroinformatics platform for making neuroscience P66 data Findable, Accessible, Interoperable, and Reuseable Aswendt, Markus Automated atlas-based image analysis of in vivo MRI P21 and ex vivo histology

Bakker, Rembrandt Scalable Brain Atlas Composer: API for collaborative P56 brain atlasing and visualization workflows

Bednarek, Sylwia Computational framework for whole volume analysis P16 of optically cleared brains Bhalla, Upinder SANKET: The Signaling and Neurophysiology P9 Singh Knowledge-resource for Experiments and Theory

Brůha, Petr BASIL BCI project - experiments performed in a P49 hospital

Crook, Sharon NeuroML-DB: A model sharing resource that P73 & IP promotes rapid selection and reuse Dagar, Snigdha A process for skeletonizing and parameterizing P40 astrocyte reconstructions

De Bonis, Giulia Slow Waves Analysis Pipeline for extracting features P25 of slow oscillations from the cerebral cortex of anesthetized mice Dichter, Benjamin K Extending Neurodata Without Borders: P27 Neurophysiology for Electrocorticography

Duda-Goławska, Exploratory methods of EEG/MEG data signal P41 Joanna analysis Dudziak, Martin Investigations in dysfunctional geometry and P11 Joseph boundary-space perception as as avenue for understanding brain connectome network dynamics

17 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Dudziak, Martin Requirements and bidirectional contributions of P32 Joseph clinical neurology with brain function research

Durka, Piotr BCI illiteracy: technical flaws turned into an urban D14 legend Dvoretskii, Stefan Modeling neural development with Braitenberg D10 Vehicles P43 & IP Dzik, Jakub Really reproducible behavioural paper Finc, Karolina FMRIDenoise: tool for automatic denoising and D6 quality control of functional connectivity data Fraga, Francisco J Metrics for skills of users of brain computer P48 interfaces in the motor imagery paradigm Futagi, Daiki A spike sorting method using differencing-based P28 feature extraction and HDBSCAN Garcia, Pedro Overview of test suites for validation of data-driven P39 models in neuroscience Gerkin, Richard C SciDash: A web portal for model validation using D11 SciUnit P12 Goulas, Alexandros A blueprint of mammalian cortical connectomes Grethe, Jeffrey S Building a FAIR Data Commons: The Open Data D17 & IP Commons for Spinal Cord Injury (ODC-SCI) Gutzen, Robin Evaluating neural network models within a formal P71 & IP validation framework

Hagen Blixhavn, Infrastructure and workflow for integrating and Camilla navigating multi-scale and multi-modal murine P67 neuroscience data using 3D digital brain reference atlases Halchenko, Yaroslav O ReproNim/containers and DataLad to make D19 containers FAIR for posterity

Hilgetag, Claus C Comprehensive computational modelling of the P75 & IP development of characteristic brain connectivity

18 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Hjorth, Johannes “Snudda” a framework for reconstructing and P42 simulating neuronal microcircuitry Hovhannisyan, Role of neurotrophic factors in memory formation P36 Arman Artak processes Hovhannisyan, Changes of glial markers in white rats hippocampus P38 Arman Artak during cycloheximide injection induced memory loss Hovhannisyan, Changes of colony stimulatory factors in white rats D9 Arman Artak hippocampus during cycloheximide induced memory loss Ikeno, Hidetoshi Estimation of synaptic connections between neurons responsive to waggle dance vibration signals in the P17 primary mechanosensory center of the honeybee brain Iyer, Vijay Combining commercial and community- D4 driven foundations to address challenges in neuroinformatics Jabłońska, Judyta Computational modelling of decision-making in a P 70 & IP Magdalena reinforcement learning task without time constraints

Jarecka, Dorota Nipype 2.0: A new Python-based ecosystem for D5 neuroscientific workflows and reproducibility Ježek, Petr Title: BASIL Project - Visual workflow designer for D15 signal processing pipelines in a cloud infrastructure Jurkiewicz, Gabriela Guidelines on how to use mpPAC - a novel EEGLAB P3 Justyna toolbox for detection of phase-amplitude coupling Jurkiewicz, Gabriela Phase-amplitude cross-frequency coupling: P4 Justyna physiological or epiphenomenal? Jędrzejewska-Szmek, pyEcoHAB: a Python library for analysis of rodent P5 Joanna behavioral data recorded with Eco-HAB Jędrzejewska-Szmek, Kernel current source density revisited P24 Joanna Jędrzejewska-Szmek, Neuromodulatory control of long-term synaptic P26 Joanna plasticity in CA1 pyramidal neurons

19 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Kaminski, Maciej Calculation of causal coupling between BOLD, heart P45 rate interval and breathing signals may help to localize a “central pacemaker” in the brain stem. Karbowski, Jan The cost of synaptic learning and memory using P18 proteomic data and computations. Kbah, Sadeem Controlling a servo motor using EEG signals from the P50 Nabeel primary motor cortex Kerrien, Samuel Blue Brain Nexus - A knowledge graph for data- P59 driven science Kim, Yongsoo Enhanced and unified anatomical labeling for a P10 common mouse brain atlas Klonowski, Novel analytics for clinical data - simple digital P60 Wlodzimierz pathology method for computer-aided analysis of virtual whole slide images (WSI) Komorowski, Michał Spectral Fingerprints: identification accuracy of P72 Konrad regions of the human brain varies with an analyzed dataset - a MEG and EEG data study Konorski, Piotr Adiutis - a prototype for simultaneous electrodermal D8 activity and heart rate variability measurements Koudelka, Vlastimil Singular value optimization for electrode positioning P29 in electrical source imaging Kowalska, Marta kernel Electrical Source Imaging (kESI) - P77 reconstruction of sources of brain electric activity in realistic brain geometries Krześniak, Alicja Adaptive-kernel method of brain gyrification P19 analysis is more spatially precise than a standard Freesurfer approach - blind and sighted study. Kuřátko, D Influence of electric conductivity and frequency on P6 rat’s head forward modelling Kuznietsov, Illia Microstate analysis of pre- and post- alpha- P35 neurofeedback training erps LaMuth, John E A cartesian coordinate system for the human P13 forebrain Legault, Mélanie Using the DATS model to describe datasets in the D20 Canadian Open Neuroscience Platform

20 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Lu, Hanna LANDSCAPE: An MRI-based project for P33 computational neuromodulation Majka, Piotr Marmoset Brain Connectivity Atlas. An open access D7 & IP resource for cellular-resolution analysis of the primate cortex Martone, Maryann E A FAIR knowledge model for classifying neuronal P1 types

Mohácsi, Máté A unified framework for the application and P44 evaluation of different methods for neural parameter optimization Mouček, Roman Towards independent home brain-computer P51 interfaces: Czech-Bavarian BASIL project Nakamura, Takashi Identification of phase space manifolds of brain P52 dynamics from EEG signals Nowak, Maciej A From synaptic interactions to collective dynamics in P2 random neuronal networks models: critical role of eigenvectors and transient behavior Ochab, Jeremi K Non-linear functional co-activations: directed, P20 dynamic, and delayed

P30 Osetrova, Maria Lipidomic map of healthy adult human brain Paiva, Santiago Dissemination of FAIR datasets and analysis D24 & IP pipelines using the Canadian Open Neuroscience Platform (CONP) Perens, Johanna A dedicated light sheet fluorescence microscopy P74 atlas for mapping neuronal activity and genetic markers in the mouse brain D12 Piotrowski, Tomasz Jan supFunSim: spatial filtering toolbox for EEG Prochoroff, Zofia Machine learning based approach to identify brain P22 regions affected by active smoking Raamana, Pradeep Comprehensive kernel methods library for advanced D22 Reddy machine learning applications in neuroscience Ramanathan, Shalini Neuron Nascent: hypergraph simulation framework P7 for neurogenesis

21 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Ratajczak, Ewa Novel quantitative approach to HRV-biofeedback P31 Katarzyna training quality assessment: the spectral YETI index Rogers, Christine Incorporating EEG quantitative analysis into the MNI D2 Neuroinformatics ecosystem Saleh, Mai Sabry Psychological model describing brain circuits P37 involved in perceiving and encoding an event Schmitt, Oliver Exploring reaction diffusion models in weighted and P14 directed connectomes Serap, Sinem A neuroethics framework for global brain projects P8 and neurotechnology Sokołowska, Beata Machine Learning algorithms to study of laterality in P54 Virtual Reality Sonntag, Michael From metadata to the semantic web: services for P63 data annotation and findable data Sonntag, Michael Microservice infrastructure for continuous P68 validation, processing and indexing of research data on an open platform Stróż, Anna Multimodal assessment of circadian rhythms and P34 sleep quality in disorders of consciousness (DOC) Sy, Mohameth François Neuroshapes: Open data models for FAIR scientific P64 data and knowledge in neuroscience at web scale Szczepanik, Michał Data export plugin and automated pipeline based on BIDS - expanding and utilising free software P62 to improve fMRI data flow in Laboratory of Brain Imaging. Tanaka, Keita Deep convolutional neural network for classification P55 octave illusion using MEG data Thompson, Carol L Ontology development to support data integration P57 for the Brain Initiative Cell Census Network in the Cell Registry and Allen Brain Explorer. Tiesinga, Paul Predicting the mesoconnectome using multimodal P15 data integration Urbschat, Annika Quantifying the cortical representations of cognitive P23 processes during speech comprehension

22 Reference index for abstracts

ABSTRACT CORRESPONDING AUTHOR ABSTRACT TITLE NO Vařeka, Lukáš Evaluation of public EEG datasets for deep learning P53 research Vivar Rios, Carlos PIPES, a module-based pipeline framework to P58 accelerate reproducible data analysis in astrocyte research. Vohra, Sumit Kumar Active segmentation for cell segmentation and D18 classification

Wachtler, Thomas Utilizing open source tools for reproducibility and P61 efficient data sharing - a use case of collaborative analysis of electrophysiological data P46 Wang, Hao Multiphysics of neural signals

Weitz, Andrew Tools and data resources from the NIH Stimulating P65 Peripheral Activity to Relieve Conditions (SPARC) program Yates, Sharon Christine Batch quantification and spatial analysis of labelling D13 in microscopic rodent brain sections

D 23 & IP Yatsenko, Dimitri Neuroscience data pipelines with DataJoint Żebrowska, Małgorzata Simple moving average as a method to remove P47 artifact from EEG recordings during periorbital sinusoidal alternating current stimulation Zehl, Lyuba Minimum Information for Neuroscience Data Sets P69 (MINDS): The metadata standard of the Human Brain Project data sharing platform.

23 Abstracts Posters

Posters

[P 01] A FAIR Knowledge Model for Classifying Neuronal Types

Thomas H Gillespie1, Shreejoy Tripathy2, Francoise Mohammed Sy3, Maryann E Martone1, Sean L Hill3,4 1. Neurosciences, University of California San Diego, San Diego, CA 92093-0608, USA 2. Krembil Centre for Neuroinformatics, CAMH, University of Toronto, Toronto, Canada 3. Blue Brain Project, EPFL, Geneva, Switzerland 4. Psychiatry, CAMH, University of Toronto, Toronto, Canada The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project and others are generating large amounts of data and systematically characterizing large numbers of neurons from throughout the nervous system. We propose a standardized approach to cell naming using community ontology identifiers as a common language, a framework for systematically organizing knowledge about cellular properties, and interoperability with existing commonly used neuron naming schemes. The Neuron Phenotype Ontology models neuron types as extensible collections of phenotypic prop- erties. The approach is similar to that proposed by Shepherd et al. (2019) but the data model is supported by computational tools that enable individual researchers to compose a complex phenotype out of any number of individual phenotypes tightly linked to individual data sets and analyses. To bridge new knowledge emerging from new techniques with that gathered over the last 100 years, we include three branches: 1. phenotypic representations of common neuron types derived largely from classical morphological and physiological studies over the past 100 years (common usage types); 2. Classification models arising from newer technologies supported by the BRAIN initiative (evidence based models); 3) Experimental instances of single cell descriptions. The ontology and code are available at: https://github.com/SciCrunch/NIF-Ontology. Acknowledgements Supported by NIMH U24 MH114827, NIBIB P41 EB019936, NIDA U24 DA039832 (MM) and was funded in part by the EPFL Blue Brain Project Fund and the ETH Board Funding to the Blue Brain Project (SH). References 1 Shepherd et al., 2019 doi: 10.3389/fnana.2019.00025

©(2019) Gillespie TH, Tripathy S, Sy FM, Martone ME, Hill SL Cite as: Gillespie TH, Tripathy S, Sy FM, Martone ME, Hill SL (2019) A FAIR Knowledge Model for Classifying Neuronal Types. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0001

25 Posters

[P 02] From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior

Ewa Gudowska-Nowak1, Maciej A. Nowak1, Dante R. Chialvo2, Jeremi K. Ochab1, Wojciech Tarnowski1 1. Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, S. Łojasiewicza 11, 30-348 Kraków, Poland 2. Center for Complex Systems and Brain Sciences, Universidad National de San Martin, 25 de Mayo 1169, Buenos Aires, Argentina The study of neuronal interactions is currently at the center of several neuroscience big collaborative projects (including the Human Connectome, the Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the entire brain matrix. Under certain constraints, mathematical theory can advance predictions of the expected neural dynamics based solely on the statistical properties of such synaptic interaction matrix. This work explores the application of free random variables (FRV) to the study of large synaptic interaction matrices. Besides recovering in a straightforward way known results on eigenspectra of neural networks, we extend them to heavy-tailed distributions of interactions. More importantly, we derive analytically the behavior of eigenvector overlaps, which determine stability of the spectra. We observe that upon imposing the neuronal excitation/inhibition balance, although the eigenvalues remain unchanged, their stability dramatically decreases due to strong non-orthogonality of associated eigenvectors. It leads us to the conclusion that the understanding of the temporal evolution of asymmetric neural networks requires considering the entangled dynamics of both eigenvectors and eigenvalues, which might bear consequences for learning and memory processes in these models. We hope that the results presented here will foster additional applications of FRV techniques in the area of brain sciences. Acknowledgements The research was supported by the MAESTRO DEC-2011/02/A/ST1/00119 grant of the National Center of Science. WT also appreciates the financial support from the Polish Ministry of Science and Higher Education through “Diamond Grant” 0225/DIA/2015/44. References 1 arXiv:1805.03592v1 https://arxiv.org/pdf/1805.03592.pdf

©(2019) Gudowska-Nowak E, Nowak MA, Chialvo DR, Ochab JK, Tarnowski W Cite as: Gudowska-Nowak E, Nowak MA, Chialvo DR, Ochab JK, Tarnowski W (2019) From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0002

26 Posters

[P 03] Guidelines on how to use mpPAC - a novel EEGLAB toolbox for detection of phase-amplitude coupling

Gabriela Justyna Jurkiewicz1, Jarosław Żygierewicz1 1. Faculty of Physics, Institute of Experimental Physics, Division of Biomedical Physics, University of Warsaw, Ludwika Pasteura 5, 02-093 Warsaw, Poland Recent studies indicate that analysis of phase-amplitude coupling (PAC) - a phenomenon where the phase of low-frequency oscillation modulates the amplitude of high-frequency oscillation - provide valuable information on many cognitive processes i.e. learning, memory and spatial navigation [1,2]. There are few widely used methods for detection of PAC. However, most of them do not differentiate between physiological coupling derived from an interaction between two separate oscillations and epiphenomenal coupling derived from the specific non-sinusoidal waveform [3]. We present a novel EEGLAB toolbox for detection of phase-amplitude coupling that allows the user to investigate the origins of coupling. The proposed method is based on analysis of time-frequency representation of signals aligned to a given phase in the low-frequency band. Low-frequency wave is obtained with the Matching Pursuit algorithm. The time-frequency representation of the signal’s energy density is derived from a continuous wavelet transform. Next, the representation is thresholded at critical values obtained from the distributions of surrogate data. The resulting maps are used to compute comodulograms. The effects presented in the comodulograms are validated with extreme value statistics. This poster is a tutorial on how to use the mpPAC toolbox. We present the description of PAC phenomena, detection methodology, interpretation of the results and the features of the framework. Acknowledgements The work was partly supported from grant 2014/13/B/HS6/03155 OPUS 7 NCN and form Polish funds for science. References 1 Canolty R, Edwards E, High gamma power is phase-locked to theta oscillations in human neocortex, Science, 2006, (313):1626–8 doi: 10.1126/science.1128115 2 Tort AB, Komorowski RW, Manns JR, Kopell NJ, Eichenbaum H. Theta-gamma coupling increases during the learning of item-context associations. Proc Natl Acad Sci USA. 2009, (106):20942–20947 doi: 10.1073/pnas.0911331106 3 Aru J, Aru J, Priesemann V, Wibral M, Lana1 L, Pipa G, Singer W, Vicente R, Untangling cross-frequency coupling in neuroscience, Current Opinion in Neurobiology, 2015, 31:51–61 doi: 10.1016/j.conb.2014.08.002

©(2019) Jurkiewicz GJ, Żygierewicz J Cite as: Jurkiewicz GJ, Żygierewicz J (2019) Guidelines on how to use mpPAC - a novel EEGLAB toolbox for detection of phase-amplitude coupling. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0003

27 Posters

[P 04] Phase-amplitude cross-frequency coupling: physiological or epiphenomenal?

Gabriela Justyna Jurkiewicz1, Jarosław Żygierewicz1 1. Faculty of Physics, Institute of Experimental Physics, Division of Biomedical Physics, University of Warsaw, Ludwika Pasteura 5, 02-093 Warsaw, Poland It is proposed, based on many experimental studies, that phase-amplitude cross-frequency coupling (PAC) provides valuable information on cognitive processing i.e. learning, working memory and spatial navigation [1,2]. PAC is a phenomenon where the phase of low-frequency oscillation modulates the amplitude of high-frequency oscillation. There are few widely used methods for detection of PAC. However, most of them do not differentiate between physiological coupling derived from an interaction between two separate oscillations and epiphenomenal coupling derived from the specific non-sinusoidal waveform [3]. The purpose of this study was to test a novel mpPAC and widely used Canolty’s method for detection of PAC in terms of a distinction between physiological and epiphenomenal origins. The methods were tested both on synthetic and real signals. The synthetic signals gave us the possibility to examine the sensitivity, specificity, and robustness to sharp-edge signal structures (which are known to produce epiphenomenal PAC) in a well-controlled manner. The real signals are the LFP recordings from rat’s olfactory bulb after injection of ketamine which induces the high-frequency oscillations that are coupled with theta band. We show that the proposed approach correctly detects proper PAC in synthetic and real data, it is more specific than the Canolty’s method, and it allows the user to investigate the origins of the cross-frequency coupling as physiological or epiphenomenal. Acknowledgements The work was partly supported from grant 2014/13/B/HS6/03155 OPUS 7 NCN and form Polish funds for science. References 1 Canolty R, Edwards E, High gamma power is phase-locked to theta oscillations in human neocortex, Science, 2006, (313):1626–8 doi: 10.1126/science.1128115 2 Tort AB, Komorowski RW, Manns JR, Kopell NJ, Eichenbaum H. Theta-gamma coupling increases during the learning of item-context associations. Proc Natl Acad Sci USA. 2009;(106):20942–20947 doi: 10.1073/pnas.0911331106 3 Aru J, Aru J, Priesemann V, Wibral M, Lana1 L, Pipa G, Singer W, Vicente R, Untangling cross-frequency coupling in neuroscience, Current Opinion in Neurobiology, 2015, 31:51–61 doi: 10.1016/j.conb.2014.08.002

©(2019) Jurkiewicz GJ, Żygierewicz J Cite as: Jurkiewicz GJ, Żygierewicz J (2019) Phase-amplitude cross-frequency coupling: physiological or epiphenomenal?. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0004

28 Posters

[P 05] pyEcoHAB: a Python library for analysis of rodent behavioral data recorded with Eco-HAB

Joanna Jędrzejewska-Szmek1, Jan Mąka2, Szymon Łęski1, Maciej Winiarski3, Daniel K Wójcik1, Ewelina Knapska3 1. Laboratory of Neuroinformatics, Nencki Institute, Pasteura 3, 02-093 Warszawa, Poland 2. Biomedical Physics Division, Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warszawa, Poland 3. Laboratory of Emotions Neurobiology, Nencki Institute, Pasteura 3, 02-093 Warszawa, Poland It is a truth universally acknowledged, that a behavioral experiment must be in want of standarisation of experimental conditions, minimal human interference and automatic data analysis. Eco-HAB [1], a system for automated measurement of social preference and in-cohort sociability in mice, provides a solution for the first two issues. Eco-HAB closely follows murine ethology, providing a 4-compartment apparatus with narrow tunnels, and minimizes contact between the experimenter and tested animals. Since manual data analysis is unreliable as well as inefficient, pyEcoHAB, a Python package, has been developed to allow for automatic data analysis. pyEcoHAB provides methods for assessment of mice social behavior, such as approach to social odor, total time spent by each pair of mice together in each compartment (in-cohort sociability), number of times each mouse follows other mice in narrow tunnels (following), and, also, number of time each mouse pushes other mice out of a narrow tunnel (behavior similar to the tube dominance test). Acknowledgements This work was supported by the Polish National Science Centre grant 2017/27/B/NZ4/02025. References 1 Puścian A, Łęski S, Kasprowicz , Winiarski M, Borowska J, Nikolaev T, Boguszewski PM, Lipp HP, Knapska E. Eco-HAB as a fully automated and ecologically relevant assessment of social impairments in mouse models of autism. Elife. 2016;5. pii: e19532. doi: 10.7554/eLife.19532

©(2019) Jędrzejewska-Szmek J, Mąka J, Łęski S, Winiarski M, Wójcik DK, Knapska E Cite as: Jędrzejewska-Szmek J, Mąka J, Łęski S, Winiarski M, Wójcik DK, Knapska E (2019) pyEcoHAB: a Python library for analysis of rodent behavioral data recorded with Eco-HAB. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0005

29 Posters

[P 06] Influence of Electric Conductivity and Frequency on Rat’s Head Forward Modelling

D. Kuřátko1, Z. Raida1, J. Láčik1, D. K. Wójcik1,2, V. Koudelka3 1. Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic 2. Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland 3. National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic Reliable inverse imaging of source currents in rat’s brain requires accurate models of fields and interfaces. Accuracy of field models depends on equations used for the forward problem solving [2] and on dielectric properties of modelled brain tissues [6–9] which are frequency dependent. Usually, the frequency dependence of brain tissues in the forward model is completely ignored [1-3]. Here, the full-wave formulation of Maxwell’s equations is used and the effect of the electric conductivity and frequency on the distribution of electric field intensity in the rat’s head is studied in the CST EM Studio [5]. For modelling, a three-dimensional model of rat’s head [4] comprising the brain, the cerebrospinal fluid, and the skull was used. The following two configurations of the simulation process are considered: • the invariant frequency f = 10 Hz and the variable electric conductivity σ in the range from 0.1 S/m to 1.5 S/m; • the invariant electric conductivity of the brain tissue σ = 0.33 S/m [6–9] and the variable frequency f in the range from 10 Hz to 200 Hz. The conductivity of the cerebrospinal fluid and the skull for all simulations are 1.79 S/m and 0.0174 S/m, respectively. Our numerical investigations indicate that the frequency dependent electric conductivity of brain tissues significantly affects the forward problem solution (as demonstrated in Fig. 1) and consequently the accuracy of inverse imaging. Detailed results will be presented at the conference.

Top: the measuring trajectory p1 represented by the blue line. Bottom left: electric field intensity along measuring trajectory p1 for varying electric conductivity with invariant frequency f = 10 Hz. Bottom right: electric field intensity along p1 for varying f with invariant σ = 0.33 S/m.

Acknowledgements The presented work was supported by the Czech Science Foundation under the grant no. 18-16218S. Simulations were performed in the SIX Research Centre thanks to the support of the National Sustainability Program, the grant no. LO1401. References 1 HE, B. Neural engineering. 1st ed., rev. New York (U.S.): Kluwer Academic/Plenum, 2005. ISBN: 978-0-306-48609-8 2 HALLEZ, H., VANRUMSTE, B., GRECH, R., et all. Review of solving the forward problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation, 2007, vol. 4, no. 1 doi: DOI: 10.1186/1743-0003-4-46 30 Posters

3 NUNEZ, P. L., SRINIVASAN, R. Electric fields of the brain: the neurophysics of EEG, 2nd ed. rev. New York (U.S): Oxford University Press, 2006. ISBN 9780195050387 4 KURATKO, D., RAIDA, Z., CUPAL, M., et. all, Electromagnetic modelling of rat’s brain: comparison of models and solvers, Proceedings of International Workshop on Computing, Electromagnetics, and Machine Intelligence (CEMi 2018). Stellenbosch (South Africa), 2018. 5 –, CST Studio Suite 2018 [online]. Cited 2018-11-12. https://www.cst.com/ 6 GUTIERREZ, Z., NEHORAI, A., MURAVCHIK CH. Estimating brain conductivities and dipole source signals with EEG arrays. IEEE Transaction on Biomedical Engineering, 2004, vol. 51, no. 12, p. 2113 to 2122 doi: DOI: 10.1109/TBME.2004.836507 7 ANDREUCCETTI, D., FOSSI, R., PETRUCCI, C. An Internet resource for the calculation of the dielectric properties of body tissues in the frequency range 10 Hz - 100 GHz [online]. IFAC-CNR. Florence (Italy), 1997. http://niremf.ifac.cnr.it/tissprop/ 8 LAI, Y., DRONGELEN, VAN W., DING, L., et al. Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings. Clinical Neurophysiology, 2005, vol. 116, no. 2, p. 456–465 doi: DOI: 10.1016/j.clinph.2004.08.017 9 GOTO, T., HATANAKA, R., OGAWA, T., et al. An evaluation of the conductivity profile in the somatosensory barrel cortex of Wistar rats. Journal of Neurophysiology, 2010, vol. 104, no. 6, p. 3388-3412 doi: DOI: 10.1152/jn.00122.2010

©(2019) Kuřátko D, Raida Z, Láčik J, Wójcik DK, Koudelka V Cite as: Kuřátko D, Raida Z, Láčik J, Wójcik DK, Koudelka V (2019) Influence of Electric Conductivity and Frequency on Rat’s Head Forward Modelling. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0006

[P 07] Neuron Nascent: Hypergraph Simulation Framework for Neurogenesis

Shalini Ramanathan1, Mohan Ramasundaram1 1. Dept. of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Trichy,TN, India The robust network of brain can be viewed as a brawny Hypergraph Structure. Mapping hypergraph for the simulation of single neuron and multiple neuron construction is effortless, because the hypergraph contains many nodes in a single edge. Every brain region is considered as a single edge(Ri) and the cells/neurons in the brain region is considered as vertices(Ni),where i represents the count of vertices and edges. Here, The Hypergraph Framework simulates the life cycle of multi-level neuron construction. Once construction of N number of edges and vertex were over. Hypergraph Coloring is implemented to differentiate the region of brain. Transversal hypergraph is implemented for the information flow from one neuron to another along the pathway or highway. Removing the vertex or the neurons without pathway shows the death of neuron. To differentiate the information flow among the same and different region of the brain, here, the communication of neurons among the same region is called as pathway and among different region is called highway. The stem cell’s lineage is also shown using the dual hypergraph. The hypergraph is implemented using python and the visualization of the brain network is shown using PyLaTeX. This framework can helps the neuroscientists to better understand and visualize the neuron developments and its behaviors. 31 Posters

Ensample of simulation architecture of neurons in brain region

References 1 Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol. 2008;4(6):e1000100. Published 2008 Jun 27. doi: 10.1371/journal.pcbi.1000100 2 Kim SJ, Ha JW, Zhang BT. Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning. BMC Syst Biol. 2013;7:47. Published 2013 Jun 19. doi: 10.1186/1752-0509-7-47 3 Ritz A, Avent B, Murali TM. Pathway Analysis with Signaling Hypergraphs. IEEE/ACM Trans Comput Biol Bioinform. 2017;14(5):1042–1055. doi: 10.1109/TCBB.2015.2459681 4 R M, R S (2018) Neuron Reconstruction Framework using Parallel Random Walk on Hypergraph. Neuroinformatics 2018 Abstract. doi: 10.12751/incf.ni2018.0099 5 PyLaTeX,https://pypi.org/project/PyLaTeX/0.7.1/ https://pypi.org/project/PyLaTeX/0.7.1/

©(2019) Ramanathan S, Ramasundaram M Cite as: Ramanathan S, Ramasundaram M (2019) Neuron Nascent: Hypergraph Simulation Framework for Neurogenesis. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0007

[P 08] A Neuroethics Framework for Global Brain Projects and Neurotechnology

Sinem Serap1 1. Biomedical Engineering, Yeditepe University, Istanbul, Turkey During the last two decades, with the development of technology, the small steps in brain science turned into the giant leap for mankind. Millions of bytes of data generated all over the world to break the brain’s code, to understand inner workings of brain function and hope to find treatments to cognitive diseases. Seven brain initiatives launched around the globe, but no single initiative was able to tackle those challenges. In December 2017, seven initiatives came together and formed International Brain Initiative to combine their effort and accelerate the progress of neurotechnological findings. Implementing data sharing and standardization mechanism was one of the intents of the initiatives. Human body is known as the biggest data platform but human brain homes to most valuable data. All human behaviour could be understood under the illuminated light of this neurodata. Merging of brain initiatives also brings the question of who will capture this neurodata and how to maintain medical ethics. Neuroethics could be classified under the umbrella of bioethics but it should also be diverse from the point that our minds are the true representatives of who we are. In this poster I will discuss the neuroethical standards that are proposed by brain initiatives so far and discuss the challenges in neuroethics from the window of neurotechnology and 32 Posters

brain-computer interfaces. I aim to recommend some new insights for ethical principles for the effective protection of human subjects.

Brain Initiatives and Related Programs Around the World

References 1 https://www.internationalbraininitiative.org/ https://www.internationalbraininitiative.org/ 2 Clausen, J., & Levy, N. (Eds.). (2015). Handbook of Neuroethics. Dordrecht: Springer. 3 https://www.brainalliance.org.au/learn/media-releases/worlds-brain-initiatives-move-forward-together/ https://www.brainalliance.org.au/learn/media-releases/worlds-brain-initiatives-move-forward-together/

©(2019) Serap S Cite as: Serap S (2019) A Neuroethics Framework for Global Brain Projects and Neurotechnology. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0008

[P 09] SANKET: The Signaling and Neurophysiology Knowledge-resource for Experiments and Theory

G.V Harsha Rani1, Surbhit Wagle2, Upinder Singh Bhalla1 1. Neurobiology, NCBS-TIFR, Bangalore, India 2. Neurobiology, InStem, Bangalore, India Complex multiscale signaling underlies many aspects of neural function in health and disease. The SANKET consortium (Sanket means Signal in Sanskrit) brings together researchers who address neural function at multiple scales. SANKET links together key tools, data resources, a portal, and several consortium projects. The tools include MOOSE for simulations, FINDSIM for structuring experiment definitions, and HOSS to perform optimization. The data resources are a database of structured experiments utilized by FINDSIM; signaling databases such as DOQCS and BioModels, and an an evolving set of optimized reference models. Consortium projects include signaling in autism, postynaptic plasticity, and neural-astrocyte interactions. The typical workflow in each project is 1. Identify signaling and neurophysiological mechanisms core to the system of interest, 2. Develop a multiscale model (typically combining biochemical signaling, trafficking, protein synthesis, and neurophysiology) to represent the system. 3. Perform experiments, and mine the literature, to assemble a dataset of structured experiment definitions. 4. To constrain and optimize the model using this dataset and the HOSS tools. 5. To analyze the model(s) for better understanding the system, and to motivate further experimental exploration. Overall, the SANKET consortium is an open, data-driven, and principled effort to reproducibly develop a better understanding of complex neuronal processes. 33 Posters

Acknowledgements BLIFE grant from the Department of Biotechnology, India, and NCBS core support. Nisha Viswan for discussion. SANKET Consortium members especially Aditi Bhattacharya and James Clement References 1 SANKET: The Signaling and Neurophysiology Knowledge-resource for Experiments and Theory. 2 MOOSE: Multiscale Object-Oriented Simulation Environment https://moose.ncbs.res.in 3 FINDSIM: Framework for Integrating Neuronal Data and SIgnaling Models https://github.com/BhallaLab/FindSim 4 HOSS: Hierarchical Optimization for Systems Simulations

©(2019) Rani GH, Wagle S, Bhalla US Cite as: Rani GH, Wagle S, Bhalla US (2019) SANKET: The Signaling and Neurophysiology Knowledge- resource for Experiments and Theory. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0009

[P 10] Enhanced and Unified Anatomical Labeling for a Common Mouse Brain Atlas

Uree Chon1, Daniel J Vanselow2, Keith C Cheng2, Yongsoo Kim1 1. Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, United States 2. Pathology, College of Medicine, Penn State University, Hershey, PA, United States Anatomical atlases in standard coordinates are necessary for the interpretation and inte- gration of research findings in a common spatial context1. However, the two most-used mouse brain atlases, the Franklin and Paxinos (FP) and the common coordinate frame- work (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopted the FP labels into the CCF to merge two labels in the single atlas framework. We used cell type specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, new segmentations were added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we have digitized our anatomical labels based on the Allen ontology, created a web-interface for visualization, and provided tools for comprehensive comparisons between the Allen and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas. Acknowledgements This project was made possible by a NIH grant (R01MH116176) and Tobacco Cure Funds from the Pennsylvania Department of Health to Y.K. and facilitated by NIH grant 1R24OD18559-01-A2 to K.C.

©(2019) Chon U, Vanselow DJ, Cheng KC, Kim Y Cite as: Chon U, Vanselow DJ, Cheng KC, Kim Y (2019) Enhanced and Unified Anatomical Labeling for a Common Mouse Brain Atlas. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0010

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[P 11] Investigations in dysfunctional geometry and boundary-space perception as as avenue for understanding brain connectome network dynamics

Martin Joseph Dudziak1 1. Memorial Healthcare Institute for Neurosciences and Multiple Sclerosis, Owosso, Michigan, United States Several neurological diseases and disorders share remarkably common although eti- ologically and neurologically distinct sources of dysfunction in perception of spatial relationships including but not limited to distance, area, similarity, congruency and geometrical object-fitting. These disorders and objects of intense clinical focus in our work include multiple sclerosis, stroke, and traumatic brain injury. The "dysfunctional geometry problem" has two important consequences of particular significance to patient health, rehabilitation and both interpersonal social and occupational function. One con- cerns the disposition and probability of further serious injury through falls and accidents with household or other equipment. The other concerns issues of judgment regarding boundary spaces and limitations of more abstract psychological and interpersonal natures which can lead to additional psychological adversities including depression, anxiety and even violence. We examine how these differences in perception and motor action are linked, empirically, through observed multi-spectral measurements and inferred functional relationships, and these findings can serve as important new sources of information and direction for basic theoretical, computational and experimental brain research that is focused upon building a more comprehensive and accurate connectomic model of the central nervous system.

©(2019) Dudziak MJ Cite as: Dudziak MJ (2019) Investigations in dysfunctional geometry and boundary-space perception as as avenue for understanding brain connectome network dynamics. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0011

[P 12] A blueprint of mammalian cortical connectomes

Alexandros Goulas1, Piotr Majka2,3, Marcello GP Rosa2,4, Claus C Hilgetag1,5 1. University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, Germany 2. Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Australia 3. Nencki Institute of Experimental Biology of Polish Academy of Sciences, Laboratory of Neuroinformatics, Poland 4. Department of Physiology, Biomedicine Discovery Institute, Australia 5. Department of Health Sciences, Boston University, United States of America The cerebral cortex of mammals exhibits intricate interareal wiring. Moreover, mam- malian cortices differ vastly in size, cytological composition, and phylogenetic distance. Given such complexity and pronounced species differences, it is a considerable challenge to decipher organizational principles of mammalian connectomes. Here, we demonstrate species-specific and species-general unifying principles linking the physical, cytological, and connectional dimensions of architecture in the mouse, cat, marmoset, and macaque monkey. The existence of connections is related to the cytology of cortical areas, in addition to the role of physical distance, but this relation is attenuated in mice and marmoset monkeys. The cytoarchitectonic cortical gradients, and not the rostrocaudal axis of the cortex, are closely linked to the laminar origin of connections, a principle 35 Posters

that allows the extrapolation of this connectional feature to humans. Lastly, a network core, with a central role under different modes of network communication, characterizes all cortical connectomes. We observe a displacement of the network core in mammals, with a shift of the core of cats and macaque monkeys toward the less neuronally dense areas of the cerebral cortex. This displacement has functional ramifications but also entails a potential increased degree of vulnerability to pathology. In sum, we sketch out a blueprint of mammalian connectomes consisting of species-specific and species-general principles.

Unifying principles of mammalian connectomes and their common and diverse mani- festation across species.

Acknowledgements This work is supported by the Alexander von Humboldt Foundation (Humboldt Research Fellowship) (AG), DFG (SFB 936/A1, Z3; TRR169/A2; SPP 2041 HI 1286/7-1) (CCH), Australian Research Council (CE140100007) and the International Neuroinformatics Coordination Facility (Seed grant scheme) (PM,MGPR).

©(2019) Goulas A, Majka P, Rosa MG, Hilgetag CC Cite as: Goulas A, Majka P, Rosa MG, Hilgetag CC (2019) A blueprint of mammalian cortical connectomes. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0012

[P 13] A Cartesian Coordinate System for the Human Forebrain

John E. LaMuth1 1. Division of Biomedical Sciences, American University of Sovereign Nations, Sacaton, Arizona, USA The first Periodic Table for the Human Forebrain utilizes the dual parameters of input specificity and phylogenetic age. The precise number of parameter values has accurately been determined for both basic forebrain parameters. Sanides (1970) proposed that the human cortex evolved as a sequence of five concentric growth rings comprising a medio-lateral hemisphere gradient. Furthermore, the interoceptive, exteroceptive and proprioceptive input categories each project to its own four-part complex of cortical bands that define an antero-posterior hemisphere gradient. When the para-coronal variable of phylogenetic age is plotted as the ordinate and the para-sagittal parameter of input specificity charted as the abscissa in a Cartesian coordinate system, the resulting dual parameter grid is spatially oriented in a pattern analogous to the standard cortical representation. Each cortical area described by Brodmann and von Economo corresponds to schematically unique age/input parameter coordinates. Furthermore, each affiliated thalamic nucleus of specific age and input coordinates projects to that cortical area 36 Posters

comprising identical pair-coordinate values, implying that the evolution of the thalamus and cortex are similarly defined in terms of the dual parameter grid: offering an overarching framework for understanding circuit functionality within an evolutionary context. More at forebrain.org

Acknowledgements Warm appreciation is extended to Darryl Macer Ph.D, Provost at AUSN References 1 Sanides, F. 1970. Functional architecture of motor and sensory cortices in primates in the light of a new concept of neocortex evolution. doi: Adv. in Primatology. 1:137-208.

©(2019) LaMuth JE Cite as: LaMuth JE (2019) A Cartesian Coordinate System for the Human Forebrain. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0013

[P 14] Exploring reaction diffusion models in weighted and directed connectomes

Oliver Schmitt1, Peter Eipert1, Christian Nitzsche1 1. Department of Anatomy, University of Rostock, Gertrudenstr. 9, Germany The weighted structural connectome (SC) of the rat nervous system was reconstructed by collating neuronal connectivity information from tract tracing publications [1] in neuroVIISAS [2]. The data allows the structual and dynamic analysis of complete peripheral-central pathways. The spread of diseases is simulated by newly implemented reaction diffusion models. The propagation of signals derived from basic diffusion processes and a cellular automata model [3], the Gierer-Meinhardt (GM) [4], Gray- Scott (GS) [5] and Mimura-Murray (MM) [6] reaction diffusion simulations (RDS) was investigated. The models have been adapted to a weighted and directed connectome. The application of RDS in SCs exhibit a lower complexity by contrast with coupled single neuron models (FitzHugh Nagumo (FHN)) [3] or models of spiking leaky integrate and fire populations. Modeling of diseases like Alzheimer and Parkinson as well as multiple sclerosis (MS) in SC helps to understand spreading of pathology and predicting changes of white and gray matter [7-9]. The reduction of connection weights by modeling reflect the effect of myelin degeneration in MS (Figure). A reduction of diffusion was observed in GM and MM following linear and nonlinear reduction of connectivity weights. The 37 Posters change of weights of critical connections show diffusibility reduction in the motoric pathway as well what coincides with clinical observations (paresthesias, spaticity) in the somatosensory and somatomotoric system.

A: Small MS-connectome with atlas localozations (A1-A3), B, B1: midscale MS- connectome, C: FHN coactivation matrix of controll connectome, D: coactivation matrix after spinocuneate weight reduction.

References 1 [1] doi: 10.1007/s00429-014-0936-0 2 [2] doi: 10.1007/s12021-012-9141-6 3 [3] doi: 10.1038/srep07870 4 [4] doi: 10.1007/BF00289234 5 [5] doi: 10.1016/0378-4754(95)00044-5 6 [6] doi: 10.1038/nphys1651 7 [7] doi: 10.1002/hbm.24444 8 [8] doi: 10.1016/j.nicl.2019.101690 9 [9] doi: 10.1002/hbm.23993

©(2019) Schmitt O, Eipert P, Nitzsche C Cite as: Schmitt O, Eipert P, Nitzsche C (2019) Exploring reaction diffusion models in weighted and directed connectomes. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0014

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[P 15] Predicting the mesoconnectome using multimodal data integration

Nestor Timonidis1, Rembrandt Bakker1, Paul Tiesinga1 1. Neuroinformatics, Donders Institute, Radboud University, Heyendaalseweg 135, Nijmegen, Netherlands Reconstructing brain connectivity at sufficient resolution for computational brain models is extremely challenging. A mesoconnectome that includes cell-type specificity and laminar resolution would be a major step forward. We report our analyses and tool chain towards this goal. We first analyzed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns. The volumetric gene expression data [1] and connectivity data was preprocessed and subjected to various regression algorithms. For the prediction of the connection strength, we found ridge-regularized linear regression performed best with a median r2 value of 0.54. For binary prediction (connected or not), logistic regression was best performing with median area under ROC 0.89. Next, we compared these predictions to those derived from single-cell reconstructed axonal projection patterns [2] and found interesting deviations, which shed new light on the meaning of the connection strength value in the published mesoconnectome [3]. Finally, we made voxel-to-voxel predictions at a resolution of 200 microns and found them much less accurate than area-to-area predictions. The predictions were generated using a jupyter notebook, made publicly accessible via the HBP collab (https://collab.humanbrainproject.eu/#/collab/8650/nav/299443). Taken together, we have demonstrated a prediction pipeline for performing multimodal data integration to improve the accuracy of the mesoconnectome. Acknowledgements Supported by European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 785907 (Human Brain Project SGA2) and FLAG ERA project FIIND (NWO 054-15-104) References 1 Lein, E.S. et al (2007) Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445, 168-176. doi: 10.1038/nature05453 2 Economo, M.N., et al (2016) A platform for brain-wide imaging and reconstruction of individual neurons. Elife, 5, e10566. doi: 10.7554/eLife.10566 3 Oh, S.W et al. (2014) A mesoscale connectome of the mouse brain. Nature, 508, 207-214. doi: 10.1038/nature13186

©(2019) Timonidis N, Bakker R, Tiesinga P Cite as: Timonidis N, Bakker R, Tiesinga P (2019) Predicting the mesoconnectome using multimodal data integration. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0015

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[P 16] Computational framework for whole volume analysis of optically cleared brains

Sylwia Bednarek1, Natalia Jermakow1, Monika Pawłowska2, Marzena Stefaniuk2, Rida Rehman3, Francesco Roselli3, Daniel Krzysztof Wójcik1, Piotr Majka1 1. Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, Warsaw, Poland 2. Laboratory of Neurobiology, BRAINCITY, Nencki Institute of Experimental Biology, Warsaw, Poland 3. Department of Neurology, Ulm University, Ulm, Germany Recent development of potent tissue clearing methods, coupled with the resurgence of light-sheet fluorescence microscopy (LSFM), enabled efficient single-cell resolution imaging of intact rodent brains. The resulting high-resolution images, while offering unique insight into neural activity patterns in brain-wide context, are often challenging to analyze without novel, high-throughput methods and dedicated tools. We present a computational pipeline developed to facilitate such large-volume analyzes. The data are first preprocessed to remove common artifacts, then compressed to Hierarchical Data Format 5 (HDF5) container, together with essential metadata which enables flexible access to subsets of image volume at different resolutions. To obtain high accuracy registration to a reference atlas, we employed Deep Convolutional Neural Network which segments out characteristic features of the brain, such as main white matter tracts which then serve as label maps guiding the registration process. The resulting transformations allow for atlas-based segmentation. Additionally, we used synthetic data to train UNet DCNN to identify and count labeled neuronal nuclei in iDISCO-cleared brains. Finally, by combining the atlas registration and the cell detection routines, we were able to map neuronal activity in experiments interrogating neurobiology of alcohol addiction, and aversive and appetitive learning in mice.

A: LSFM imaging, B: acquired data are preprocessed to remove imaging artifacts, and compressed with lsfmPy to HDF5 format (C), This allows for further manipulation of the data by exporting (D) required information for the purpose of semantic segmentation (E), registration (F), or cell detection (G).

Acknowledgements This project is supported by ERA-NET NEURON grant from the National Centre for Research and Development (ERA-NET-NEURON/17/2017) and by the G2631 grant from The National Science Center.

©(2019) Bednarek S, Jermakow N, Pawłowska M, Stefaniuk M, Rehman R, Roselli F, Wójcik DK, Majka P Cite as: Bednarek S, Jermakow N, Pawłowska M, Stefaniuk M, Rehman R, Roselli F, Wójcik DK, Majka P (2019) Computational framework for whole volume analysis of optically cleared brains. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0016

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[P 17] Estimation of synaptic connections between neurons responsive to waggle dance vibration signals in the primary mechanosensory center of the honeybee brain

Hidetoshi Ikeno1, Ajayrama Kumaraswamy2, Thomas Wachtler2, Kazuki Kai3, Hiroyuki Ai4 1. School of Human Science and Environment, University of Hyogo, Japan 2. Department Biologie II, Ludwig-Maximilians-Universität München, Germany 3. Faculty of Science, Hokkaido University, Japan 4. Department of Earth System Science, Fukuoka University, Japan Honeybees use the unique behavior “waggle dance” to inform spatial information of feeding site to their hive mates. The duration of the waggle phase changes linearly with the distance to the feeding site. During the waggle phase, the dancer beats her wings to produce trains of air vibration and dance followers detect trains of vibration pulses by Johnston’s organ on the antennae. However, it is still unknown how they decode information from the trains of vibration pulses. Recently, we identified several interneurons with specific response patterns to waggle dance-like vibration stimuli (Ai et al., 2017). We analyzed their response properties and morphologies, however their synaptic connections were still unrevealed. In this study, we reconstructed morphologies of vibration-sensitive interneurons in the primary mechanosensory center (PMC) of the honeybee, Apis mellifera (Ikeno et al., 2018). The reconstructed neurons were registered on the most vividly observable PMC image, with the common region shape (Fig.1). A range of constant distance from the terminal segment of each neuron was taken as the possible synapse sphere region (PSSR). The strength of connectivity among the interneurons was described by synaptic weight, which was obtained from overlapping volumes of PSSR. Based on the synaptic weight values, we estimated the possible neural circuit in the PMC, consistent with a putative disinhibitory network for processing of vibration signals (Ai et al., 2017).

Registered reconstructed interneurons in the PMC (Posterior view). Green: DL-Int1, red: unilateral interneuron, blue: bilateral interneuron. These neurons arborize in the DL and dSEG.

Acknowledgements This work was supported by JST (Grant 22570079, 25330342, 15K14569, 18K06345), Fukuoka University 41 Posters

(Grant 151031) and BMBF (Grant 01GQ1116) References 1 Ai et al. doi: 10.1523/JNEUROSCI.0044-17.2017 2 Ikeno et al. doi: 10.3389/fninf.2018.00061

©(2019) Ikeno H, Kumaraswamy A, Wachtler T, Kai K, Ai H Cite as: Ikeno H, Kumaraswamy A, Wachtler T, Kai K, Ai H (2019) Estimation of synaptic connections between neurons responsive to waggle dance vibration signals in the primary mechanosensory center of the honeybee brain. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0017

[P 18] The cost of synaptic learning and memory using proteomic data and computations.

Jan Karbowski1 1. Dept. of Mathematics, University of Warsaw, Poland Learning and memory are one the most important brain functions, yet we do not know their metabolic cost. Moreover, we do not know if synaptic plasticity underlying these processes can be observable using in vivo imaging tools. These study estimates the cost of synaptic learning and its accuracy using a combination of proteomic data for synaptic molecules, and computations based on thermodynamic principles and information theory. It is found that, at baseline, synaptic plasticity uses a marginal amount of energy. However, activated plasticity can use substantial amounts of energy, but still accounting for only about 10% of the energy used for fast synaptic transmission. These results indicate that synaptic plasticity, and thus, learning and memory are relatively cheap, but they might be observable at the active phase. Acknowledgements The work was supported by the Polish National Science Centre (NCN) grant no. 2015/17/B/NZ4/02600. References 1 Karbowski J (2019) Metabolic constraints on synaptic learning and memory. Journal of Neurphysiol. (accepted) 2 Karbowski J (2019) Energy cost of BCM type synaptic plasticity and storing of accurate information. PLoS Comput. Biol. (accepted)

©(2019) Karbowski J Cite as: Karbowski J (2019) The cost of synaptic learning and memory using proteomic data and computations.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0018

42 Posters

[P 19] Adaptive-kernel method of brain gyrification analysis is more spatially precise than a standard Freesurfer approach - blind and sighted study.

Alicja Krześniak1,2, Gabriela Dzięgiel3, Joanna Plewko3, Katarzyna Rączy1,4, Maria Zimmermann1, Jacek Matuszewski2, Maksymilian Korczyk1, Kinga Izdebska1, Przemysław Adamczyk1, Katarzyna Jednoróg3, Artur Marchewka2, Marcin Szwed1 1. Department of Psychology, Jagiellonian University, Ingardena 6 30-060 Kraków, Poland 2. Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3 02-093 Warszawa, Poland 3. Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3 02-093 Warszawa, Poland 4. Biological Psychology and Neuropsychology Department, Hamburg University, Von-Melle-Park 11 20146 Ham- burg, Germany The blind brain is different in several aspects of its function and connectivity from the sighted brain [1]. The structural underpinnings of these differences remain largely unknown, although there have been reports of greater visual cortex thickness in the blind [2]. Here, we applied two gyrification measurement methods to assess structural differences between the sighted and the blind brain. We used a standard Freesurfer (FS) pipeline which calculates spherical-kernel local Gyrification Index (GI) and a new adaptive-kernel method, introduced by Lyu et. al. [3]. Preliminary results from 24 blind and 24 controls are shown in Fig. 1 (results with 120 subjects are in preparation). The right calcarine sulcus is less gyrificated in the blind group. This result is more circumscribed and significant in the new method (z-score difference equals 1.57). Gyrification measurements, therefore, complement existing (sMRI, qMRI, DTI, rs fMRI) methods that can be applied to the study of the blind brain and offer possible applications beyond this field. The GI has been used as a feature for differentiating healthy and schizophrenic patients [4] and in the algorithms classifying Alzheimer‘s disease [5,6]. We believe that these classification methods could benefit from the adaptive-kernel approach as it appears to yield sharper and more significant results than the standard FS pipeline.

Local GI group comparison maps of differences between blind and sighted subjects. Column A: adaptive-kernel method, column B: Freesurfer pipeline. Both metod’s parameters: vertex-wise and cluster-wise thresholds 0.01, smoothing fwhm 5 mm. Colourscale: -log10(p) values with contrast blind – sighted. 43 Posters

Acknowledgements We thank the participants for taking part in our study. We thank LOBI and MCB for technical assistance during scanning. Study supported by an NCN grant no 2015/19/B/HS6/01256 and an internal grant from the Department of Psychology of the Jagiellonian University. References 1 Hirsch, G. V, Bauer, C. M., & Merabet, L. B. (2015). Using structural and functional brain imaging to uncover how the brain adapts to blindness. Annals of Neuroscience and Psychology, 2 doi: 10.7243/2055-3447-2-7 2 Jiang, J., Zhu, W., Shi, F., Liu, Y., Li, J., Qin, W., . . . Jiang, T. (2009). Thick Visual Cortex in the Early Blind. Journal of Neuroscience, 29(7), 2205–2211 doi: 10.1523/JNEUROSCI.5451-08.2009 3 Lyu, I., Kim, S. H., Girault, J. B., Gilmore, J. H., & Styner, M. A. (2018). A cortical shape-adaptive ap- proach to local gyrification index. Medical Image Analysis, 48, 244–258 doi: 10.1016/J.MEDIA.2018.06.009 4 Palaniyappan, L., Park, B., Balain, V., Dangi, R., & Liddle, P. (2015). Abnormalities in structural covariance of cortical gyrification in schizophrenia. Brain Structure and Function, 220(4), 2059–2071 doi: 10.1007/s00429-014-0772-2 5 Lahmiri, S., & Shmuel, A. (2018). Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomedical Signal Processing and Control doi: 10.1016/J.BSPC.2018.08.009 6 Zheng, W., Yao, Z., Xie, Y., Fan, J., & Hu, B. (2018). Identification of Alzheimer’s Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(10), 887–897 doi: 10.1016/J.BPSC.2018.06.004

©(2019) Krześniak A, Dzięgiel G, Plewko J, Rączy K, Zimmermann M, Matuszewski J, Korczyk M, Izdebska K, Adamczyk P, Jednoróg K, Marchewka A, Szwed M Cite as: Krześniak A, Dzięgiel G, Plewko J, Rączy K, Zimmermann M, Matuszewski J, Korczyk M, Izdebska K, Adamczyk P, Jednoróg K, Marchewka A, Szwed M (2019) Adaptive-kernel method of brain gyrification analysis is more spatially precise than a standard Freesurfer approach - blind and sighted study.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0019

[P 20] Non-linear functional co-activations: directed, dynamic, and delayed

Maria T Miller Flores1, Ignacio Cifre2, Jeremi K Ochab3, Dante R Chialvo1 1. Center for Complex Systems & Brain Sciences (CEMSC3), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina 2. Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain 3. Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, ul. Łojasiewicza 11, PL30-348 Kraków, Poland Pearson correlation is the most frequently used framework for (dynamic) functional connectivity (FC). An alternative introduced in [1,2] emphasized that the strong Blood Oxygen Level Dependent (BOLD) activations contain most of the relevant FC information. Here we investigate novel properties of these activations, including a) directionality in the co-activations between areas; b) lags between the events; and c) obtaining task-triggered co-activations. As a test-bed, we use the Austism ABIDE database [3] containing resting state scans of patients (AU) and healthy subjects (HS). Our method [2,1] starts by selecting from each BOLD time-series the events (usually 8-15 seconds) surpassing a given threshold (usually 1 SD), see Figure. These events for each (source) time-series are stored with the signal sampled at the same times in other (target) voxels. They are further used to compute correlations between source and targets, their direction, temporal delay, etc. We replicated the findings in [4] where HS showed higher correlation between ventral agranular insula L and Precuneus L and R, and revealed that identical results are obtained by event correlations. In addition, we find a clear asymmetry in the direction of the 44 Posters co-activations between Insula and Fusiform, and that AU have higher delays between the events of Insula and Precuneus. The results show the ability of our method for inspecting relevant events going beyond the classical measures of resting state FC.

For a source region (A), we define BOLD triggered events (*) by the BOLD crossing a threshold. To each source event correspond target events sampled at the same time intervals (vertical lines) in regions of interest (B-C). They can be averaged (E-F) to compute correlations, delays or directionality.

Acknowledgements Work supported by the MICINN (Spain) grant PSI2017-82397-R, the National Science Centre (Poland) grant DEC-2015/17/D/ST2/03492, and by CONICET (Argentina) and Escuela de Ciencia y Tec- nología, UNSAM. Work conducted under the auspice of the Jagiellonian University-UNSAM Cooperation Agreement. References 1 E. Tagliazucchi et al, “Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis,” Front. Physiol., vol.3, pp. 15, 2012. doi: 10.3389/fphys.2012.00015 2 E. Tagliazucchi et al. “Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state,” Neurosci. Lett., vol. 488, no. 2, pp. 158–163, Jan. 2011. doi: 10.1016/j.neulet.2010.11.020 3 H. Chen, et al, “Intrinsic functional connectivity variance and state-specific under-connectivity in autism,” Hum. Brain Mapp., vol. 38, no. 11, pp. 5740–5755, 2017. doi: 10.1002/hbm.23764 4 J. Xu et al., “Both hypo-connectivity and hyper-connectivity of the insular subregions associated with severity in children with autism spectrum disorders,” Front. Neurosci., vol 12,pp 1–9, 2018. doi: 10.3389/fnins.2018.00234

©(2019) Miller Flores MT, Cifre I, Ochab JK, Chialvo DR Cite as: Miller Flores MT, Cifre I, Ochab JK, Chialvo DR (2019) Non-linear functional co-activations: directed, dynamic, and delayed. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0020

45 Posters

[P 21] Automated atlas-based image analysis of in vivo MRI and ex vivo histology

Niklas Pallast1, Frederique Wieters1, Dirk Wiedermann2, Gereon Rudolf Fink1,3, Mathias Hoehn3, Markus Aswendt1 1. Neurology, University Hospital Cologne, Kerpener Strasse 62, 50937 Cologne, Germany 2. In-vivo-NMR, Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany 3. Cognitive Neuroscience (INM-3), Research Centre Juelich, 52425 Juelich, Germany Objective The correlation of mouse brain in vivo imaging and ex vivo histology is essential to better understand processes underlying brain function and disease. Brain atlases play a key role in that analysis, however, most studies still rely on a manual region-of-interest analysis. We present pipelines for the Atlas-based Image Data Analysis (AIDA) of magnetic resonance imaging (MRI) and histology. Methods MRI sequences for fiber tracking (diffusion tensor imaging, DTI) and functional connectivity (resting-state functional MRI) were acquired using a 9.4T Bruker scanner. For histology, 20 µm brain cryosections were stained for various glial and neuronal marker and cell nuclei counterstained with DAPI. Whole brain slice images were acquired using an EVOS fluorescence microscope. Image analysis software was developed in Python 3.6 including algorithms for registration (with the Allen Mouse brain atlas, ccf3), region-wise connectivity measures and cell segmentation (https://github.com/maswendt/). Results The developed workflows are user-friendly, semi-automatic, and time-efficient. We validated the accuracy of registration and cell detection in an extensive comparison to two manual expert raters who assigned regions of interest and counted cells, respectively. The registration and counting procedure outperformed other approaches and software. These tools will facilitate data processing in large cohorts, multi-center studies and provide cross-modality correlations. Acknowledgements This work was financially supported by the Friebe Foundation 1339 (T0498/28960/16).

©(2019) Pallast N, Wieters F, Wiedermann D, Fink GR, Hoehn M, Aswendt M Cite as: Pallast N, Wieters F, Wiedermann D, Fink GR, Hoehn M, Aswendt M (2019) Automated atlas-based image analysis of in vivo MRI and ex vivo histology. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0021

46 Posters

[P 22] Machine learning based approach to identify brain regions affected by active smoking

Zofia Prochoroff1, Michal Lipinski1, Michal Pietruszka1, Dawid Drozdziel2, Adriana Zlahoda-Huzior3, Marcin Szwed1, Tomasz Pieciak3 1. Jagiellonian University, Krakow, Poland 2. Polish Academy of Sciences, Warsaw, Poland 3. AGH University of Science and Technology, Krakow, Poland Studies that analyzed grey matter volume using statistical inference have regularly identified differences in brain structure among smokers [1, 2, 3]. This work presents a novel approach, where Machine Learning (ML) is used to classify samples from a large structural MRI data set to identify smokers based on volumetric and geometric brain features. High-resolution T1-weighted scans were acquired using a MPRAGE sequence. Individual scans were segmented into 31 regions per hemisphere using the Desikan-Killiany-Tourville cortical labeling protocol [4]. Each region was characterized by its volume, cortical thickness, surface area, and mean curvature. Our classification framework consisted of preliminary feature selection using a Random Forest Variable Importance (RFVI) metric, followed by classification using a Support-vector Machine (SVM) model. The SVM classifier was fit and trained in a 10-fold cross validation scheme and applied to an independent validation set to assess its performance. At the optimal number of features (85), the SVN classifier achieves an average of 67.2% accuracy (±6.0%), and the area under the curve equals 0.67 (Fig1a, b). The most discrminating ROIs referred to grey matter (GM) volume, and were located in the inferior temporal gyrus and the inferior frontal gyrus (pars orbitalis and pars triangularis), and the total cortical GM volume (Fig1c). Attained results imply that ML techniques may possibly reveal smoking related effects on human brain anatomy.

(a) Accuracy of the SVM classifier based on the number of features compared to the random forest and logistic regression techniques, and (b) ROC for SVM classifier with 85 features. (c) The most important features detected in the analysis. The box plots are represented in a normalized scale.

References 1 T.R. Franklin, et al (2014). The effects of chronic cigarette smoking on gray matter volume: influence of sex. PloS one 9.8: e104102. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0104102 2 X. Ding et al. (2015), Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Hum. Brain Mapp., 36: 4869-4879. doi: 10.1002/hbm.22956

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3 S. Karama et al. (2015), Cigarette smoking and thinning of the brain’s cortex. Mol. Psychiatry, 20.6: 778-785 doi: 10.1038/mp.2014.187 4 R.S. Desikan et al. (2006), An automated labeling system for subdividing the human cere- bral cortex on MRI scans into gyral based regions of interest. Neuroimage, 36: 968–980. doi: 10.1016/j.neuroimage.2006.01.021

©(2019) Prochoroff Z, Lipinski M, Pietruszka M, Drozdziel D, Zlahoda-Huzior A, Szwed M, Pieciak T Cite as: Prochoroff Z, Lipinski M, Pietruszka M, Drozdziel D, Zlahoda-Huzior A, Szwed M, Pieciak T (2019) Machine learning based approach to identify brain regions affected by active smoking. Neuroinfor- matics 2019 Abstract. doi: 10.12751/incf.ni2019.0022

[P 23] Quantifying the Cortical Representations of Cognitive Processes During Speech Comprehension

Annika Urbschat1,2, Stefan Uppenkamp1,2, Jörn Anemüller1,2 1. Medical Physics, Carl von Ossietzky University, 26111 Oldenburg, Germany 2. Hearing4All, Germany Speech comprehension comprises multiple overlapping cognitive processes. Functional MRI represents a superposition of these effects in activation maps. To disentangle the diverse cognitive processes, BOLD responses to acoustic stimuli holding specific speech properties to different extent need to be compared. In a Multi-Voxel Pattern Analysis (MVPA) study we investigated the differences in local BOLD patterns during the pro- cessing of speech stimuli with semantic content compared to speech-like stimuli without semantic content but with natural speech properties, represented by the International Speech Test Signal, employing the Searchlight Analysis approach. Cortical responses to these stimuli were additionally compared to responses to speech simulated noise and noise vocoded speech, resulting in a composition of test-stimuli that differ in the presence of pitch, modulations and the presence of semantic content. To account for effects that disappear in classical group analyses, we examined classification performance results in anatomical regions of single-subject maps, determined by the automated anatomical labeling (AAL) template and quantitatively analyzed to what extent the properties pitch, modulation, and semantic content are represented in the investigated BOLD responses. Results indicate a dominant effect caused by amplitude modulations rather than pitch and semantic content in a corpus of known auditory areas.

©(2019) Urbschat A, Uppenkamp S, Anemüller J Cite as: Urbschat A, Uppenkamp S, Anemüller J (2019) Quantifying the Cortical Representa- tions of Cognitive Processes During Speech Comprehension. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0023

48 Posters

[P 24] Kernel Current Source Density revisited

Chaitanya Chintaluri1, Marta Kowalska2, Michał Czerwiński2, Władysław Średniawa2, Joanna Jędrzejewska-Szmek2, Jakub M Dzik2, Daniel K Wójcik2 1. Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford, OX1 3SR, United Kingdom 2. Laboratory of Neuroinformatics, Nencki Institute, Pasteura 3, 02-093 Warszawa, Poland Extracellular recordings reflecting transmembrane currents of neural and glial cells have been the foundation of neural activity measurements. Reconstruction of current source density (CSD), the potential’s local origin, greatly facilitates data interpretation. Unfortunately, interpretation of recorded potentials is straightforward only when one neuron contributes. The long-range of the electric field leads to significant correlations between recordings at distant sites, complicating the analysis. Kernel Current Source Density method (KCSD) [1] is a model-based method, which allows for current source estimation from potentials recorded by arbitrarily distributed electrodes. Overfitting is prevented by constraining complexity of the inferred CSD model. Here, we revisit KCSD to present a new, open-source, Python implementation, which includes new functionality and several additional diagnostic tools. We have added 1) analysis of spectral properties of the method; 2) error map generation for assessment of reconstruction accuracy; 3) L-curve, a method for estimation of optimum reconstruction parameters. The re- implementation allows for CSD reconstruction from potentials measured by 1D, 2D, and 3D experimental setups for a) sources distributed in the entire tissue, b) in a slice, or c) in a single cell with known morphology, provided the potential is generated by that cell. The toolbox and tutorials are available at https://github.com/Neuroinflab/kCSD-python Acknowledgements This work was supported the Polish National Science Centre’s SYMFONIA (2013/08/W/NZ4/00691) and OPUS (2015/17/B/ST7/04123) grants. References 1 Potworowski J, Jakuczun W, Le¸ski S, Wójcik D. Kernel current source density method. Neural Comput. 2012;24(2):541-75. doi: 10.1162/NECO_a_00236

©(2019) Chintaluri C, Kowalska M, Czerwiński M, Średniawa W, Jędrzejewska-Szmek J, Dzik JM, Wójcik DK Cite as: Chintaluri C, Kowalska M, Czerwiński M, Średniawa W, Jędrzejewska-Szmek J, Dzik JM, Wójcik DK (2019) Kernel Current Source Density revisited. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0024

49 Posters

[P 25] Slow Waves Analysis Pipeline for extracting features of slow oscillations from the cerebral cortex of anesthetized mice

Giulia De Bonis1, Miguel Dasilva2, Antonio Pazienti3, Maria Victoria Sanchez-Vives2, Maurizio Mattia3, Pier Stanislao Paolucci1 1. Istituto Nazionale di Fisica Nucleare (INFN), p.le Aldo Moro 2, Roma, Italy 2. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Carrer del Rosselló 149, Barcelona, Spain 3. Istituto Superiore di Sanità (ISS), Viale Regina Elena 299, Roma, Italy Cortical slow oscillations are an emergent property of the cortical network and a hallmark of low complexity brain states like sleep. Here, we present a methodological approach for quantifying the spatial and temporal properties of this activity. We improved and enriched a robust analysis procedure adding new tools and methods, validated by inspecting the electrocorticography (ECoG) traces recorded from a custom 32-channel multi-electrode array in wild-type isoflurane-anesthetized mice. The enhanced analysis pipeline, named SWAP (Slow Waves Analysis Pipeline), detects Up and Down states, enables the characterization of the spatial dependency of their statistical properties, and supports the comparison of different subjects. The SWAP is implemented in a data-independent way, allowing its application to other data sets (acquired from different subjects, or with different recording tools), as well as to the outcome of numerical simulations. By using SWAP, we report statistically significant differences in the observed slow oscillations (SO) across cortical areas and cortical sites. We give evidence of gradients at the global scale along an oblique axis directed from fronto-lateral towards occipito-medial regions, further highlighting some heterogeneity within cortical areas. The results obtained will be essential for producing data-driven brain simulations and for triggering a discussion on the role of, and the interplay between, the different regions in the cortex. Acknowledgements This study was carried out in the framework of the Human Brain Project (https://www.humanbrainproject.eu/en/), funded under Specific Grant Agreements No. 785907 (HBP SGA2) and No. 720270 (HBP SGA1), in particular within activities of sub-project SP3 ("Systems and Cognitive Neuroscience") References 1 Steriade M, Nunez A, Amzica F. A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. https://www.ncbi.nlm.nih.gov/pubmed/8340806 2 Volgushev M, Chauvette S, Mukovski M, Timofeev I. Precise long-range synchronization of activity and silence in neocortical neurons during slow-wave sleep doi: 10.1523/JNEUROSCI.0279-06.2006 3 Seamari Y, Narva’e JA, Vico FJ, Lobo D, Sanchez-Vives MV. Robust off- and online separation of intracellularly recorded up and down cortical states doi: 10.1371/journal. pone.0000888 4 Mukovski M, Chauvette S, Timofeev I, Volgushev M. Detection of Active and Silent States in Neo- cortical Neurons from the Field Potential Signal during Slow-Wave Sleep doi: 10.1093/cercor/bhj157 5 Jercog D, Roxin A, Barth’o P, Luczak A, Compte A, de la Rocha J. Up-down cortical dynamics reflect state transitions in a bistable network doi: 10.7554/eLife.22425 6 Ruiz-Mejias M, Ciria-Suarez L, Mattia M, Sanchez-Vives MV. Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse doi: 10.1152/jn. 00440.2011 7 Mattia M, Pani P, Mirabella G, Costa S, Del Giudice P, Ferraina S. Heterogeneous attractor cell assemblies for motor planning in premotor cortex doi: 10.1523/ JNEUROSCI.4664-12.2013 8 Capone C, Rebollo B, Munoz A, Illa X, Del Giudice P, Sanchez-Vives MV, et al. Slow waves in cortical slices: How spontaneous activity is shaped by laminar structure. doi: 10.1093/cercor/bhx326 9 Ruiz-Mejias M, Perez-Mendez L, Ciria-Suarez L, Martinez de Lagran M, Gener T, Sancristobal B, et al. Over-expression of Dyrk1A, a Down syndrome candidate, decreases excitability and impairs gamma oscillations in the pre-frontal Cortex doi: 10.1523/JNEUROSCI. 2517-15.2016 10 Pazzini L, Polese D, Weinert JF, Maiolo L, Maita F, Marrani M, et al. An ultra-compact integrated system for brain activity recording and stimulation validated over cortical slow oscillations in vivo and in vitro. doi: 10.1038/s41598-018-34560-y 11 Paolucci PS, Ammendola R, Biagioni A, Frezza O, Lo Cicero F, Lonardo A, et al. Distributed simulation of polychronous and plastic spiking neural networks: strong and weak scaling of a representative mini- application benchmark executed on a small-scale commodity cluster https://arxiv.org/abs/1310.8478 12 Pastorelli E, Paolucci PS, Ammendola R, Biagioni A, Frezza O, Lo Cicero F, et al. Impact of exponential long range and Gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses https://arxiv.org/abs/1512.05264 50 Posters

13 Pastorelli E, Paolucci PS, Simula F, Biagioni A, Capuani F, Cretaro P, et al. Gaussian and exponential lateral connectivity on distributed spiking neural network simulation. doi: 10.1109/PDP2018.2018.00110 14 Mattia M, Ferraina S, Del Giudice P. Dissociated multi-unit activity and local field potentials: a theory inspired analysis of a motor decision task 15 Lidierth M. sigTOOL: A MATLAB-based environment for sharing laboratory-developed software to analyze biological signals doi: 10.1016/j.jneumeth.2008.11. 004. 16 Brown EN, Lydic R, Schiff R. General anesthesia, sleep, and coma. doi: 10.1056/NEJMra0808281 17 Akeju O, Brown EN. Neural oscillations demonstrate that general anesthesia and sedative states are neurophysiologically distinct from sleep doi: 10.1016/j. conb.2017.04.011 18 Jones E, Oliphant T, Peterson P, et al. SciPy: Open source scientific tools for Python http: //www.scipy.org/ 19 Seabold S, Perktold J. Statsmodels: Econometric and statistical modeling with python 20 Mattia M, Sanchez-Vives MV. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity. doi: 10.1007/s11571-011-9179-4 21 Sanchez-Vives MV, Mattia M, Compte A, Perez-Zabalza M, Winograd M, Descalzo VF, et al. Inhibitory modulation of cortical up states doi: 10. 1152/jn.00178.2010 22 Massimini M, Huber R, Ferrarelli F, Hill SL, Tononi G. The sleep slow oscillation as a traveling wave. doi: 10.1523/JNEUROSCI.1318-04.2004 23 Stroh A, Adelsberger H, Groh A, Rühlmann C, Fischer S, Schierloh A, et al. Making Waves: Initiation and Propagation of Corticothalamic Ca2+ Waves In Vivo. doi: 10.1016/j.neuron.2013.01.031 24 Mohajerani MH, Chan AW, Mohsenvand M, LeDue J, Liu R, McVea Da, et al. Spontaneous cortical activity alternates between motifs defined by regional axonal projections. doi: 10.1038/nn.3499 25 Vyazovskiy VV, Olcese U, Hanlon EC, Nir Y, Cirelli C, Tononi G. Local sleep in awake rats doi: 10.1038/nature10009 26 Nir Y, Staba RJ, Andrillon T, Vyazovskiy VV, Cirelli C, Fried I, et al. Regional slow waves and spindles in human sleep. doi: 10.1016/j.neuron.2011.02.043 27 Sanchez-Vives MV, Massimini M, Mattia M. Shaping the default activity pattern of the cortical network doi: 10.1016/j.neuron.2017.05.015 28 Sancristobal B, Rebollo B, Boada P, Sanchez-Vives MV, Garcia-Ojalvo J. Collective stochastic coherence in recurrent neuronal networks. doi: 10.1038/nphys3739. 29 Chan AW, Mohajerani MH, LeDue JM, Wang YT, Murphy TH. Mesoscale infraslow sponta- neous membrane potential fluctuations recapitulate high-frequency activity cortical motifs. doi: 10.1038/ncomms8738

©(2019) De Bonis G, Dasilva M, Pazienti A, Sanchez-Vives MV, Mattia M, Paolucci PS Cite as: De Bonis G, Dasilva M, Pazienti A, Sanchez-Vives MV, Mattia M, Paolucci PS (2019) Slow Waves Analysis Pipeline for extracting features of slow oscillations from the cerebral cortex of anesthetized mice. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0025

[P 26] Neuromodulatory control of long-term synaptic plasticity in CA1 pyramidal neurons

Joanna Jędrzejewska-Szmek1, Ziemowit T Sławiński1, Daniel K Wójcik1 1. Laboratory of Neuroinformatics, Nencki Institute, ul. Pasteura 2, 02-093 Warszawa, Poland We had the description of long-term synaptic plasticity (LTSP), bit by bit, elicited by various experimental paradigms, and as generally happens in such cases, each time it involved different molecular requirements and differed in its neuromodulatory dependence. One distinct example is the involvement of dopaminergic (D1R) activation in spike-timing dependent plasticity (STDP) [1] and beta-adrenergic (βAR) [2] activation in LTSP evoked by rate-dependent paradigms (RTP), which is even more puzzling because both D1R and βAR activate cAMP and signaling pathways downstream. To investigate how differences in temporal patterns of synaptic and neuronal activation determine activation of signaling pathways we use two detailed, multi-compartmental, morphologically realistic models of the CA1 neuron: a conductance-based model neuron [3] and a stochastic reaction-diffusion model of calcium-, noradrenaline- and dopamine-activated signaling 51 Posters

pathways underlying LTSP. Concentrating on the role of modulation of dendritic ion channels by cAMP targets such as proteine kinase A (PKA) we explain the difference in neuromodulatory requirements of STDP and RTP. By evaluating activity of key molecules, such as calcium/calmodulin-dependent protein kinase II (CaMKII) and PKA, involved in spine-specific and dendrite-specific changes required by LTSP we predict STDP duration, namely if it can last more than an hour. References 1 Foncelle A, Mendes A, Jędrzejewska-Szmek J, Valtcheva S, Berry H, Blackwell KT, Venance L. Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models. Front Comput Neurosci. 2018;12:49 doi: 10.3389/fncom.2018.00049 2 Je¸drzejewska-Szmek J, Luczak V, Abel T, Blackwell KT. β-adrenergic signaling broadly contributes to LTP induction. PLoS Comput Biol. 2017;13(7):e1005657. doi: 10.1371/journal.pcbi.1005657 3 ombe CL, Canavier CC, Gasparini S. Intrinsic Mechanisms of Frequency Selectivity in the Proximal Den- drites of CA1 Pyramidal Neurons. J Neurosci. 2018;38(38):8110-8127. doi: 10.1523/JNEUROSCI.0449- 18.2018

©(2019) Jędrzejewska-Szmek J, Sławiński ZT, Wójcik DK Cite as: Jędrzejewska-Szmek J, Sławiński ZT, Wójcik DK (2019) Neuromodulatory control of long-term synaptic plasticity in CA1 pyramidal neurons. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0026

[P 27] Extending Neurodata Without Borders: Neurophysiology for Electrocorticography

Benjamin K Dichter1,2, Konstantinos Nasiotis3, Oscar Woolnough4, Vyassa Baratham5, Maximilian Dougherty5, Andrew Tritt2, Oliver Ruebel2, Kristofer Bouchard2,5, Nitin Tandon4, Edward Chang1 1. Department of Neurological Surgery, University of California San Francisco, Box 0112 505 Parnassus Ave, Room M779 San Francisco, CA 94143, USA 2. Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road Mail Stop 50F1650 Berkeley, CA 94720, USA 3. Department of Neurology & Neurosurgery, McGill University, McGill University 3801 University St., #894 Montreal, Quebec, Canada 4. The Vivian L. Smith Department of Neurosurgery, University of Texas Health Center at Houston, 6431 Fannin St, Suite MSB G550D, Houston, TX 77030, USA 5. Biological Systems Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road Mail Stop 50F1650 Berkeley, CA 94720, USA Electrocorticography (ECoG) provides a rare opportunity to examine scientific questions that pertain to quintessentially human experiences; however, sharing these datasets with the community is nascent. Since ECoG is also increasingly being used in non-human species, there is a need for a cross-species neural data standard. Here, we present the result of a cross-institution collaboration, an extension to the Neurodata Without Borders: Neurophysiology (NWB:N) 2.0 format1 for storage of ECoG data. In human ECoG data, the precise position of each electrode with respect to the gyral patterns of the cortex is essential information, and since gyral patterns differ from subject to subject, the shape of the cortex must be stored for each. We parameterize surfaces as a triangular mesh, which can be stored by specifying two matrices: one for vertices in 3D space, and one for triplets of indices of vertices to join to create triangles. 52 Posters

The extension allows for storage of cortical and subcortical surfaces as triangular meshes, and of images of the subject’s brain, alongside recordings from the ECoG electrodes. We worked with established ECoG visualization libraries in MATLAB and python to build importers for NWB. We demonstrate the use of Brainstorm2, a MATLAB toolbox, to perform cross-species time-frequency analysis and rendering of a 3D animation of this activity on the surface of a brain. We also show high-quality rendering of ECoG data using the img_pipe python package3.

A, example electrodes and cortical surface. B, schematic of a triangular mesh, which consists of vertices and faces. C, example data for storing a triangular mesh in NWB:N 2.0

References 1 Ruebel, O., Tritt, A., Dichter, B., Braun, T., Cain, N., Clack, N., Davidson, T.J., Dougherty, M., Fillion-Robin, J.C., Graddis, N. and Grauer, M., 2019. NWB: N 2.0: An Accessible Data Standard for Neurophysiology. doi: 10.1101/523035 2 Nasiotis, K., Cousineau, M., Tadel, F., Peyrache, A., Leahy, R.M., Pack, C.C. and Baillet, S., 2019. Integrated Open-Source Software for Multiscale Electrophysiology. BioRxiv, p.584185. doi: 10.1101/584185 3 Hamilton, L.S., Chang, D.L., Lee, M.B. and Chang, E.F., 2017. Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography. Frontiers in neuroinformatics, 11, p.62. doi: 10.3389/fninf.2017.00062

©(2019) Dichter BK, Nasiotis K, Woolnough O, Baratham V, Dougherty M, Tritt A, Ruebel O, Bouchard K, Tandon N, Chang E Cite as: Dichter BK, Nasiotis K, Woolnough O, Baratham V, Dougherty M, Tritt A, Ruebel O, Bouchard K, Tandon N, Chang E (2019) Extending Neurodata Without Borders: Neurophysiology for Electrocorticography. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0027

53 Posters

[P 28] A spike sorting method using differencing-based feature extraction and HDBSCAN

Daiki Futagi1, Katsunori Kitano2 1. Global Innovation Research Organization, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, Japan 2. College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, Japan Neural systems are formed by various types of neurons. For understanding the functional meaning of neural circuit structure, it is effective to record and analyze the electrical activities of multiple cells by using MEA [1]. However, time series data recorded by single micro-electrode are most likely to contain a mixture of spikes of several unspecified neurons. Therefore, it is necessary to perform spike sorting that classifies the mixed spike waveforms into several clusters based on the similarity of their features [2]. In general, the spike sorting procedure consists of three steps: spike detection, feature extraction, and clustering. In this study, we evaluate the usability of differencing-based feature extraction and HDBSCAN clustering [3]. The feature extraction algorithm is simply that obtains difference sequence of spike waveform as feature and compresses it by PCA. By using grand-truth datasets of spike waveforms [4], we confirmed the data distribution in feature space. As a result, the higher the order of differencing, the more the clusters were clearly isolated from one another, but the cluster shape tended to be like crescent. It is generally difficult to detect complex shaped clusters. However, because HDBSCAN is based on data point density in feature space, it could perform clustering with high precision. In addition, the combination of differencing and HDBSCAN has few parameters to be tuned by a user and is low arbitrariness, and therefore would be useful. References 1 Spira, M. E., & Hai, A. (2013). Multi-electrode array technologies for neuroscience and cardiology. Nature nanotechnology, 8(2), 83. 2 Lewicki, M. S. (1998). A review of methods for spike sorting: the detection and classification of neural action potentials. Network: Computation in Neural Systems, 9(4), R53-R78. 3 Campello, R. J., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining (pp. 160-172). Springer, Berlin, Heidelberg. 4 Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural computation, 16(8), 1661-1687.

©(2019) Futagi D, Kitano K Cite as: Futagi D, Kitano K (2019) A spike sorting method using differencing-based feature extraction and HDBSCAN. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0028

54 Posters

[P 29] Singular value optimization for electrode positioning in electrical source imaging

Vlastimil Koudelka1, Tomas Palenicek1, Zbynek Raida2, Daniel Wójcik2 1. National Institute of Mental Health in Klecany, Topolova 748, 25067 Klecany, Czech Republic 2. Brno University of Technology, Technicka 12, 61600 Brno, Czech Republic In this contribution, an approach to reach the best possible conditioning an of inverse problem in distributed electrical source imaging (ESI) is discussed. Considering a fixed set of candidate brain sources, geometry, and electric parameters of tissues, an optimal set of electrode positions can be found in sense of optimal conditioning of the leadfield matrix inversion. This work is motivated by development of EEG system in rats designed for ESI. A recent paper [1] validating an EEG electrode system in rats addressed rather an optimal density of regular grid of electrodes than specific electrode positions and its relation to the inverse solution. The evaluation was base on the concept of half sensitivity volume (HSV) [2]. In this contribution, the key idea is to find a set of electrode positions which optimizes decay of singular values (SVs) of the decomposed leadfield matrix. This approach was recently applied in field of inverse electromagnetic scattering [3]. The goal is to find electrode positions providing the flattest behavior of the SVs. A 2D version of three shell spherical model corresponding to [2] was considered. The Picard plots were computed for both optimized and equidistant electrode positions in case of noisy potentials. Combination of TSVD and SLORETA [4] methods was implemented to evaluate localization precision and accuracy. The optimized electrode positions shown to provide better conditioning of the system.

Comparison of equidistant (A) and optimimized (B) electrode positions. Electrodes are depicted by colored squares corresponding to projected potencial from a dipole, candidate sources are depicted by blue dots (top), the Picard plots contain SV, noisy right hand side, and solution energy (bottom).

Acknowledgements This work was supported by the grant GACR 18-16218Sand project LO1611 under the NPU I program. References 1 VALDÉS-HERNÁNDEZ, Pedro A., et al. Validating non-invasive EEG source imaging using optimal electrode configurations on a representative rat head model. Brain topography, 2016, 1-26. doi: 10.1007/s10548-016-0484-410.1007/s10548-016-0484-4 2 MALMIVUO, Jaakko; SUIHKO, Veikko; ESKOLA, Hannu. Sensitivity distributions of EEG and MEG measurements. IEEE Transactions on Biomedical Engineering, 1997, 44.3: 196-208 doi: 10.1109/10.554766

55 Posters

3 CAPOZZOLI, A.; CURCIO, C.; LISENO, A. Singular value optimization in inverse electromag- netic scattering. IEEE Antennas and Wireless Propagation Letters, 2016, 16: 1094-1097. doi: 10.1109/LAWP.2016.2622713 4 GRECH, Roberta, et al. Review on solving the inverse problem in EEG source analysis. Journal of neuroengineering and rehabilitation, 2008, 5.1: 25. doi: 10.1186/1743-0003-5-25

©(2019) Koudelka V, Palenicek T, Raida Z, Wójcik D Cite as: Koudelka V, Palenicek T, Raida Z, Wójcik D (2019) Singular value optimization for electrode positioning in electrical source imaging. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0029

[P 30] Lipidomic map of healthy adult human brain

Maria Osetrova1 1. Skolkovo Institute of Science and Technology, Moskovskiy, Russia The lipid composition of brain anatomical structures remains poorly understood, par- ticularly in human. In our work we describe the generation and analysis of a lipidome atlas of the adult human brain. Lipid profiles of 344 lipid species were measured for 75 brain regions of 4 healthy individuals, gene expression was measured for 35 of them. Clustering of brain regions based on their lipidomic profiles produced a stable separation of prefrontal and primary motor regions from the rest of neocortex, whereas in terms of gene expression, all neocortical regions clustered together. Total lipid content was in good agreement with T1-weighted MRI data. Subset of genes, highly correlating with lipidomic profiles, was enriched with synaptic transmission and vesicle formation gene ontology terms. Comparison with connectivity data revealed higher correlation of relative distances between regions for lipids and functional connectivity and for gene expression and structural connectivity. Similarity to functional connectivity of the brain makes lipids a potential target for mental disorders diagnosis. Lipid intensity differences between schizophrenia patients and control individuals were previously reported in prefrontal cortex area of the brain. Additional analysis of blood plasma lipidome revealed a subset of potential schizophrenia lipid biomarkers. Interestingly, most of lipid classes showed significant inverse correlation with the differences found in brain.

©(2019) Osetrova M Cite as: Osetrova M (2019) Lipidomic map of healthy adult human brain. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0030

56 Posters

[P 31] Novel quantitative approach to HRV-biofeedback training quality assessment: the spectral YETI index

Ewa Katarzyna Ratajczak1, Marcin Hajnowski2 1. Deptartment of Informatics, Nicolaus Copernicus University, Grudziącka 5, 87-100 Toruń, Poland 2. Faculty of Humanities, Nicolaus Copernicus University, Fosa Staromiejska 1a, 87-100 Toruń, Poland Heart rate variability biofeedback (HRV-BFB) training is based on a robust and widely used psychophysiological marker, HRV [1]. Despite its growing popularity, only very few studies investigated its effects on brain function [2,3], while investigation of the effectiveness of HRV-BFB is hindered by several methodological problems. The aim of this study is to address a few of such methodological gaps. 61 healthy individuals (age 22.38±3.27) were semi-randomly divided into two groups: HRV-BFB (N=28) and novel control group: sham HRV-BFB (N=33). Both groups underwent 2 x 10 training sessions, and a ECG-EEG pre-, mid- and post-test. PPT signal collected during sessions served to calculate a novel Yield Efficiency of Training Index (YETI). ECG signal analysis focused on 4 HRV indices (SDNN, LF, HF and TP). EEG-based brain activity was assessed as signal power within standard power bands, and signal complexity calculated as the HFD index. Results suggest that perception of the two conditions is very similar however, each influences human physiology differently. Application of the YETI criterion to training data allowed for (1) precise qualitative assessment of training quality and comparison among subjects and groups, (2) separation of subjects based on training performance, not assigned group into de facto training and not training clusters, and (3) rejection of participants based on YETI as an inclusion criterion, improving reliability and specificity of results in final groups.

Yield Efficiency of Training Index (YETI)

Acknowledgements We would like to thank Prof. Włodzisław Duch and Dr Joanna Dreszer for supervision, mgr Michał Meina and Dr Tomasz Piotrowski for critical comments. Many thanks to the BrainHeart team - mgr Julita Fojutowska, mgr Mateusz Stawicki, mgr Piotrek Szczęsny -for all the hard work we went through together!

©(2019) Ratajczak EK, Hajnowski M Cite as: Ratajczak EK, Hajnowski M (2019) Novel quantitative approach to HRV-biofeedback training qual- ity assessment: the spectral YETI index. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0031

57 Posters

[P 32] Requirements and bidirectional contributions of clinical neurology with brain function research

Martin Joseph Dudziak1 1. Memorial Healthcare Institute for Neurosciences and Multiple Sclerosis, Owosso, Michigan, United States Current trends in brain function research make extensive use of enhanced neural mapping technologies, converting image and spectroscopy data into models of neural dynamics. Understandably, the majority of studies employ samples and subjects for the data collection process that provide primarily average, normal and otherwise statistically and experimentally manageable sources. Within the world of clinical neuromedicine there are strong requirements to advance the capabilities of early warning, detection and prediction of several neural disorders and diseases, particularly multiple sclerosis and forms of dementia, and dominated by autoimmune reactions and frequently centered upon myelin sheath and related axonal degeneration. Discernment of the effectiveness and basic utility of certain pharmacological as well as biomagnetic, cognitive and physical therapies can be enhanced significantly through incorporation of existing and emerging multimodal "data fusion" from both experimental and computational neural research. Simultaneously, research programs in brain network dynamics and more comprehensive connectome studies can benefit immensely from deeper and more extensive incorporation of pathological data derived from all stages of clinical examination, therapy and monitoring. We describe current strategies and practices in these regards that are underway as part of a comprehensive bidirectional integration of basic research with neurological medical practices.

©(2019) Dudziak MJ Cite as: Dudziak MJ (2019) Requirements and bidirectional contributions of clinical neurology with brain function research. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0032

[P 33] LANDSCAPE: An MRI-based Project for Computational Neuromodulation

Hanna Lu1,2 1. Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China 2. The Affiliated Brain Hospital of Guangzhou Medical University, China Background: Neuromodulation is increasingly used as a probe of brain function and potential therapeutics in experimental neuroscience and neurorehabilitation. Scalp to cortex distance (SCD), as a key parameter, has been shown to potentially impact on the neuromodulation-induced electric field. This study aimed to examine the region-specific SCD in the context of age-related brain atrophy. Methods: We launched an MRI-based project named as “Localized Analysis of Normalized Distance from Scalp to Cortex and Personalized Evaluation” (LANDSCAPE). We analysed the SCD of left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in 643 cognitively normal adults from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). Computational head model was developed to simulate the impact of SCD on the electric field. Results: We found age-related increased SCD in the left DLPFC (p < 0.001), but not M1 (p = 0.134). The electric field induced by stimulation was consequently decreased with the increased SCD across normal aging individuals. Conclusion: Age have differential 58 Posters

impacts on the SCDs of left DLPFC and M1. The findings suggest that it is important to be aware of region-specific distance measures when conducting neuromodulation in individuals with old age.

Figure 1. Illustration of three-dimensional scalp-to-cortex distance (SCD), including (a) the stimulation target on the scalp and cortical surface, (b) distance measure from scalp to cortex, and (c) the anatomical layers of SCD.

Acknowledgements The authors would like to thank the University of Cambridge for providing clinical and imaging data and Professor Lorraine K Tyler and the whole team of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study (http://www.cam-can.org/).

©(2019) Lu H Cite as: Lu H (2019) LANDSCAPE: An MRI-based Project for Computational Neuromodulation. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0033

[P 34] Multimodal assessment of circadian rhythms and sleep quality in disorders of consciousness (DOC)

Anna Stróż1, Magdalena Zieleniewska1, Anna Duszyk1, Marta Bogotko2, Piotr Biegański1, Marcin Pietrzak1, Piotr Durka1 1. Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland 2. Prof. Jan Bogdanowicz Children Hospital, Niekłańska 4/24, 03-924 Warsaw, Poland Diagnosis of disorders of consciousness (DOC) is one of the major challenges of contem- porary medicine. Conscious processing is directly assessed in behavioural or psychophys- iological paradigms, involving patient’s participation. However, due to fluctuating attention of DOC patients and subjectivity of behavioural scales, these measures are prone to errors [1], so we need also more objective, passive approaches. One of the hallmarks of proper functioning of the brain is the preservation of circadian rhythm and sleep architecture. We hypothesise that differences between patients in unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) are reflected in circadian activity, as these groups differ in the severity of brain damage. Actigraphy was used in [3] to distinguish between MCS and UWS groups of adult patients. Indicators of periodicity in overnight EEG recordings were used to differentiate between UWS and MCS patients in [4]. Usability of melatonin and cortisol measurements in DOC was described in [5, 6]. We present sleep-wake patterns of pediatric patients from “The Alarm Clock Clinic” [2], obtained via four complementary, non-invasive measurements: actigraphy, polysomnography, and cortisol and melatonin levels. Expected benefit of parallel combination of abovementioned methods lies in a robust assessment of circadian rhythms, which may provide a valuable aid in DOC diagnostics. 59 Posters

An example of circadian rhythm reflected in two of the discussed approaches: A.—three- day (72 hours) actigraphy; B.—profile of slow wave activity (SWA) energy in consecu- tive 20 s epochs in one overnight (11 hours) EEG recording. Data of DOC patient from [2].

Acknowledgements We thank “Akogo” foundation, staff of “The Alarm Clock Clinic”, and parents of the patients, for understanding the importance of the study and kind cooperation. This research was partly supported by the Polish National Science Centre grants UMO-2015/17/N/ST7/03784 and UMO-2015/17/N/ST7/03769. References 1 Childs, N. L., Mercer, W. N., & Childs, H. W. (1993). Accuracy of diagnosis of persistent vegetative state. Neurology, 43(8), 1465-1465. doi: 10.1212/WNL.43.8.1465 2 The Alarm Clock Clinic, model hospital for children with severe brain damage, Warsaw, Poland. http://www.klinikabudzik.pl/en 3 Cruse, D., Thibaut, A., Demertzi, A., Nantes, J. C., Bruno, M. A., Gosseries, O., ... & Laureys, S. (2013). Actigraphy assessments of circadian sleep-wake cycles in the vegetative and minimally conscious states. BMC medicine, 11(1), 18. doi: 10.1186/1741-7015-11-18 4 Zieleniewska, M., Duszyk, A., Różański, P., Pietrzak, M., Bogotko, M., & Durka, P. (2019). Paramet- ric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness. International Journal of Neural Systems, 29(3), 1850049-1850049. doi: 10.1142/S0129065718500491 5 Guaraldi, P., Sancisi, E., La Morgia, C., Calandra-Buonaura, G., Carelli, V., Cameli, O., ... & Piperno, R. (2014). Nocturnal melatonin regulation in post-traumatic vegetative state: a pos- sible role for melatonin supplementation? Chronobiology international, 31(5), 741-745. doi: 10.3109/07420528.2014.901972 6 Sato, M., Sugimoto, M., Yamaguchi, K., & Kawaguchi, T. (2017). Evaluation of nursing inter- ventions using minimally invasive assessments methods for patients in a persistent vegetative state. Psychogeriatrics, 17(6), 406-413. doi: 10.1111/psyg.12265

©(2019) Stróż A, Zieleniewska M, Duszyk A, Bogotko M, Biegański P, Pietrzak M, Durka P Cite as: Stróż A, Zieleniewska M, Duszyk A, Bogotko M, Biegański P, Pietrzak M, Durka P (2019) Multimodal assessment of circadian rhythms and sleep quality in disorders of consciousness (DOC). Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0034

60 Posters

[P 35] MICROSTATE ANALYSIS OF PRE- AND POST- ALPHA-NEUROFEEDBACK TRAINING ERPS

Tetiana Kachynska1, Illia Kuznietsov1, Olha Abramchuk1, Oleksandr Zhuravlov1, Sergii Kryzhanovskyi2, Irma Khachidze3, Teona Gubianuri3, Ihor Zyma4, Oleksiy Shpenkov4, Mariia Chernykh4, Pawel Herman5 1. Lesya Ukrainka Eastern European University, Ukraine 2. Institute of Gerontology National Academy of Medical Sciences of Ukraine, Ukraine 3. Beritashvili Centre of Experimental Biomedicine, Georgia 4. National Taras Shevchenko University of Kyiv, Ukraine 5. KTH Royal Institute of Technology, Sweden Microstate analysis is a prospective method frequently used for assessing brain dynamics reflected in the electroencephalogram (EEG) under varying conditions and brain states [1]. However, there has been limited research on microstates analysis based on event- related potentials (ERPs) in the context of neurofeedback training. In our study we recorded EEG activity from 7 subjects performing 10 alpha-neurofeedback sessions, each lasting 10 minutes and conducted on a separate day during 3-weeks period, to examine the effect of neurofeedback training on ERP-dependent microstates. The purpose of training was to facilitate the upregulation of the oscillatory alpha-band (8-12 Hz) EEG activity in the frontal brain region (Fz) with the help of visual feedback. In line with prior expectations, the total duration of microstates with expressed frontal activity increased after training, implying in our opinion the enhanced capability of the subjects to exhibit an executive self-control function. Moreover, total duration of microstates with activity in parietal areas also increased. We hypothesize that this reflects the increased performance of thalamo-cortical interactions and improved signal/noise ratio during visual stimuli processing [2]. In summary, microstate analysis in the ERP domain provides insight into dynamical mechanisms involved in the regulation of neurofeedback dependent self-control. Acknowledgements This research was supported by Swedish Research Links grant VR-2016-05871 awarded by the Swedish Research Council (Vetenskapsrådet). References 1 C. M. Michel and T. Koenig, “EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review,” NeuroImage, vol. 180. Academic Press, pp. 577–593, 15-Oct-2018. doi: 10.1016/j.neuroimage.2017.11.062. 2 D. Güntensperger, C. Thüring, M. Meyer, P. Neff, and T. Kleinjung, “Neurofeedback for Tinnitus Treatment – Review and Current Concepts,” Front. Aging Neurosci., vol. 9, p. 386, Dec. 2017. doi: 10.3389/fnagi.2017.00386

©(2019) Kachynska T, Kuznietsov I, Abramchuk O, Zhuravlov O, Kryzhanovskyi S, Khachidze I, Gubianuri T, Zyma I, Shpenkov O, Chernykh M, Herman P Cite as: Kachynska T, Kuznietsov I, Abramchuk O, Zhuravlov O, Kryzhanovskyi S, Khachidze I, Gubianuri T, Zyma I, Shpenkov O, Chernykh M, Herman P (2019) MICROSTATE ANALYSIS OF PRE- AND POST- ALPHA-NEUROFEEDBACK TRAINING ERPS. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0035

61 Posters

[P 36] Role of neurotrophic factors in memory formation processes

Arman Artak Hovhannisyan1, Gagik Avetik Hoveyan1, Garnik Ara Avetisyan2 1. Medical Chemistry, Yerevan State Medical University, 2 Koryun Street, Armenia 2. Radiology, Yerevan State Medical University, 2 Koryun Street, Armenia There are various factors, which are influencing on memory formation processes.Shortage of physical activity and genetic factors are increasing risk of neurodegenerative processes and various forms of amnesia. Neurotrophic factors are supporting brain homeostasis and memory formation via direct influence on biochemical processes which are based on memory formation. Nerve growth factor and its precursor are modulating neuronal survival and migration processes. In our experiment reelin decreased by 37.5%, 45.2% and 48.7%, while nerve growth factor decreased by 52.1% on 60th day(p<0.01). proNGF increased by 58.3% and 60% on 40th and 60th days(p<0.001).Decrease of migration and differentiation of neural stem cells leads to disruption of memory formation, which is bad predictor of health state. From the other hand, decrease of neurotrophic factors stimulates activation of microglial cells and astrocytes and leads to brain damage and development of neurological deficit. Increase of pro-forms of neurotrophic factors leads to apoptosis of mature neurons and neural stem cells. From the other hand current neurotrophic factors are preventing brain from survival of damaged neural stem cells with furthermore formation of pathologic synapses in brain. References 1 Life Sci. 2002 Jan 4;70(7):735-44. doi: Life Sci. 2002 Jan 4;70(7):735-44. 2 Neuropharmacology. 2012 Nov;63(6):1085-92. doi: 10.1016/j.neuropharm.2012.06.050. Epub 2012 Jul 4. 3 Mol Neurobiol. 2016 Oct;53(8):5692-700. doi: 10.1007/s12035-015-9459-9. Epub 2015 Oct 21 4 Neuron Glia Biol. 2008 Aug;4(3):259-70. doi: 10.1017/S1740925X09990184. Epub 2009 Aug 13.

©(2019) Hovhannisyan AA, Hoveyan GA, Avetisyan GA Cite as: Hovhannisyan AA, Hoveyan GA, Avetisyan GA (2019) Role of neurotrophic factors in memory formation processes. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0036

[P 37] Psychological model describing brain circuits involved in perceiving and encoding an event

Mai Sabry Saleh1 1. Environmental and Occupational Nedicine, National Research Centre, 33 Elbehouth street, Dokki, Egypt This work is a psychological model describing perception and encoding of events through different memory systems. It introduces four neural pathways/brain circuits that describe perception styles identified by the Intellectual Style Inventory (ISI). Where each style is rooted in one of the four cortical lobes of the human cortex and is characterized by its own unique psychological functions. The model states that encoding process of an event is determined by the predominant perception style used by everyone that follows the law of preference and hence four types of schemas could be formed. Memory systems involved in such encoding processes are described by the Integrated Model of Mind. As shown in the attached figure, there are four different pathways/ brain circuits involved in perception, encoding and storage of sensory stimuli related to an event. In the first pathway, the front left cortical lobe (FL) is assumed to be the predominant perception style as assessed by ISI scoring system. It directs focus of attention towards 62 Posters semantic meanings and knowledge contained in the perceived event through the short term sensory store (STSS). Semantics are then verbally encoded by the phonological loop of the working memory by the help of semantic long term memory (LTM). A verbal semantic image schema is formed that is stored in the semantic LTM and could be reversibly processed with the perceptual representation system later on. And similarly it works for the other three pathways.

The present figure shows a psychological model describing four different pathways/ brain circuits involved in perception, encoding and storage of sensory stimuli related to an event.

References 1 M. S. Saleh, (2015). The Four Isomorphic Couplets Passive Mind/Active Mind, Definition/Syllogism, Tasawwur/Tasdiq and Perception/Thinking. American Journal of Applied Psychology, 3(2), 43-46. 2 M.S. Saleh, "IMM: A Multisystem Memory Model for Conceptual learning," American Journal of Psychology, Vol. 127, No. 4, Winter 2014. 3 M.S. Saleh, Z. M. Monir, A. Saad-Hussein, S. M. Moustafa, "Intellectual Style Inventory (ISI): Learning Style Assessment after Cortical Functional Specialization," British Journal Of Education, Society and Behavioral Science, Vol. 4 No. 7, 2014, pp.987-1005.

©(2019) Saleh MS Cite as: Saleh MS (2019) Psychological model describing brain circuits involved in perceiving and encoding an event. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0037

63 Posters

[P 38] Changes Of Glial Markers In White Rats Hippocampus During Cycloheximide Injection Induced Memory Loss

Arman Artak Hovhannisyan1,2, Artem Sergey Grigoryan1, Garnik Ara Avetisyan2 1. Pathophysiology, Yerevan State Medical University, 2 Koryun Street, Armenia 2. Radiology, Yerevan State Medical University, 2 Koryun Street, Armenia Introduction Revealing of mechanisms of memory formation is important issue of modern neuroscience. Wide spectrum neurodegenerative disorders as well as Alzheimer’s disease are are damaging brain, eventually leading to disruption of memory formation. Materials and Methods Experiments were carried out on 60 white male rats(n=15). Animals were kept in general vivarium states with free access of food and water. All procedures were carried out under intraperitoneal injection of 40mg/kg Nembutal for anesthesia. Injection of cycloheximide was performed according to current protocols; (100µg/25µl lateral ventricles(AP=-0.8; L=1.3-1.5; H=3.8)). After decapitation hippocampus was extracted and homogenized in 50mM Tris HCl containing buffer with furthermore centrifugation by 6000g 60min 40C.Determination of markers was performed by ELISA method (Sigma). Statistics was performed by SPSS 21.0 program. Results Results show that glial fibrillary acidic protein increased by 56.2% , 61% and 66.7% during experiment, while S100b increased by 62.6% 72% and 77.25% (p<0.01). Therefore neural cell adhesion molecule decreased by 45% on 60th day(p<0.001) Conclusion Results testify that increase of GFAP protein carries compensatory role, while increase of S100b protein marks about neuroinflammation and neuronal damage which is performed due to secretion of large amount of pro-inflammatory cytokines. References 1 Neuron. 2014 Nov 19;84(4):753-63. doi: 10.1016/j.neuron.2014.09.039. Epub 2014 Oct 30. doi: DOI: 10.1016/j.neuron.2014.09.039 2 J Neurosci. 2010 Nov 17;30(46):15573-7. doi: 10.1523/JNEUROSCI.3229-10.2010. doi: DOI: 10.1523/JNEUROSCI.3229-10.2010 3 Proc Natl Acad Sci U S A. 2008 Dec 30;105(52):20976-81. doi: 10.1073/pnas.0810119105. Epub 2008 Dec 15. doi: 10.1073/pnas.0810119105. Epub 2008 Dec 15.

©(2019) Hovhannisyan AA, Grigoryan AS, Avetisyan GA Cite as: Hovhannisyan AA, Grigoryan AS, Avetisyan GA (2019) Changes Of Glial Markers In White Rats Hippocampus During Cycloheximide Injection Induced Memory Loss. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0038

64 Posters

[P 39] Overview of Test Suites for Validation of Data-Driven Models in Neuroscience

Pedro Garcia-Rodriguez1, Shailesh Appukuttan1, Lungsi Sharma1, Andrew Davison1 1. ICN, CNRS, CNRS FRE - 3693, 1 Avenue de la Terrasse, 91198 Gif-sur-Yvette, France., France The field of computational neuroscience has flourished rapidly in the past few decades. An unintended consequence is the lack of systematic statistical validation of these models, and the consequent inability to compare and contrast the various models. This is augmented by differences in their internal structures, the language used and/or the simulator employed. As part of the Human Brain Project, we have developed a software framework for quantitative validation testing that explicitly supports applying a given validation test to different models. The framework consists of a set of Python modules, building on the SciUnit package, and a web service. SciUnit employs the concept of capabilities, standardized interfaces to which models must adhere, to decouple individual validation tests from any specific model and rendering them model agnostic. HippoUnit, CerebUnit and BasalUnit are validation suites, consisting of several individual tests, focusing on neuronal activity in hippocampus, cerebellum and basal ganglia, while MorphoUnit targets testing of neuronal reconstructions and network structures. Each test comprises a specification of the required model capabilities, the location of the reference dataset, and data analysis code to transform the recorded parameters (e.g. membrane potential) into a format (e.g. histogram) that allows results to be statistically compared to reference data. This poster gives an overview of the validation framework and its various test suites.

The framework consists of a set of Python modules, and a web service comprising of three primary components: model catalog, test library, and results database. These can be accessed either via apps in the HBP Collaboratory, or through a Python client which enables automation of tasks.

65 Posters

References 1 Omar et al., 2014 doi: 10.1145/2591062.2591129

©(2019) Garcia-Rodriguez P, Appukuttan S, Sharma L, Davison A Cite as: Garcia-Rodriguez P, Appukuttan S, Sharma L, Davison A (2019) Overview of Test Suites for Validation of Data-Driven Models in Neuroscience. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0039

[P 40] A process for skeletonizing and parameterizing astrocyte reconstructions

Snigdha Dagar1, Daniya Boges2, Kalpana Kare2, Corrado Cali2, Marwan Abdellah1, Daniel Keller1 1. Blue Brain Project, EPFL, Switzerland 2. Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Saudi Arabia Astrocytes are star-shaped cells ubiquitous in the central nervous system. They provide energy and support for neurons. In order to model them, the structures should be geometrically equivalent to biological cells in key aspects such as shape, volume, and surface area. In order to do this, we developed a pipeline that automatically reduces a complex three-dimensional mesh representation of astrocytes, obtained by reconstructing EM stack images, into a curved skeleton. The curved skeleton represents the cell morphology and is comprised of connected nodes, with cross-sectional diameters and perimeters associated with each node. This reduced skeleton and the morphological features it stores provide enough information to approximate the original shape. The skeletonization step uses a laplacian contraction approach. In a parallel step, the mesh is also voxelized to obtain a volume. Furthermore, a skeleton-to-surface mapping and a skeleton-to-voxel mapping is made. The diameter and the perimeter is calculated at each node, in each section, by assuming each segment to be a truncated cone. We show that the end product retains the surface area and volume of the original cell within reasonable error. References 1 Oscar Kin-Chung Au, Chiew-Lan Tai, Hung-Kuo Chu, Daniel Cohen-Or, and Tong-Yee Lee. 2008. Skeleton extraction by mesh contraction. ACM Trans. Graph. 27, 3, Article 44 (August 2008) doi: 10.1145/1360612.1360643

©(2019) Dagar S, Boges D, Kare K, Cali C, Abdellah M, Keller D Cite as: Dagar S, Boges D, Kare K, Cali C, Abdellah M, Keller D (2019) A process for skeletonizing and pa- rameterizing astrocyte reconstructions. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0040

66 Posters

[P 41] Exploratory methods of EEG/MEG data signal analysis

Joanna Duda-Goławska1, Jarosław Żygierewicz1 1. Faculty of Physics, Biomedical Physics Division , Institute of Experimental Physic, University of Warsaw, Warsaw, Poland Currently, the most popular approach of the EEG/MEG data analysis is an approach based on the detailed hypotheses regarding the effects of a priori assumed locations and latencies. Using standard methods of analysis, i.e. looking for activity changes only in components commonly known from the literature, there is a risk of omitting new effects. In order to check which method allows to better match the analysis, we compared the data-driven approach with the classical approach using data from experiments. In the time domain, the exploratory approach will be GFP (Global Field Power), calculated as spatial standard deviation. The analysis of the GFP curve is used to determine the time periods in which we then perform the next level of the analysis. A similar tool in the time-frequency field for identifying new regions of interest will be analyzed using non-parametric statistics. In electrophysiological experiments, we are looking for effects consisting of differences in the processing of stimuli or the processing of a task that would be correlates of changes in independent factors controlled in the experiment. This approach consists of a two-step analysis. The first step will be to determine if and where changes are shown relative to the reference period, using the non-parametric cluster statistics method and the method of massive univariate statistics. In the second stage, the differences between experimental conditions will be investigated.

©(2019) Duda-Goławska J, Żygierewicz J Cite as: Duda-Goławska J, Żygierewicz J (2019) Exploratory methods of EEG/MEG data signal analysis. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0041

[P 42] “Snudda” a framework for reconstructing and simulating neuronal microcircuitry

Johannes Hjorth1, Alexander Kozlov1,2, Johanna Frost Nylén2, Robert Lindroos2, Ilaria Carannante1, Yvonne Johansson2, Gilad Silberberg2, Sten Grillner2, Jeanette Hellgren Kotaleski1,2 1. Department of Computer Science and Technology, Royal Institute of Technology, Tomtebodavägen 23a, 171 65 Solna, Sweden 2. Department of Neuroscience, Karolinska Institute, Solnavägen 9, 171 65 Solna, Sweden We present a bottom-up framework implemented in Python for creating neuronal micro-circuitry. From reconstructed neuron morphologies, RNA sequencing and electro- physiological data detailed multi-compartmental models are optimised using BluePyOpt [1]. The neurons are placed within a 3D mesh of the brain structure, resulting in realistic densities and distributions of the neuronal cell bodies. To place synapses a voxel based touch detection algorithm is used, allowing the morphologies of the neurons to constrain where the synapses can be located. To reproduce connection probabilities seen in exper- imental pairwise recordings a set of pruning rules is applied. Tsodyks-Markram synapse models [2,3] are optimised to match facilitation and depression seen in experimental recordings. The resulting network is then simulated on Cray XC40 machine using Parallel Neuron [4]. 67 Posters

We use the framework to create the connectivity in a striatal micro-circuitry with 1.7 million neurons [5], and five different neuronal types. Network simulations were performed using a reduced set of neurons. A network of 120,000 neurons with 145 million synapses takes around 5 hours on a 3.6GHz 6 core Xeon PC.

Acknowledgements This study is funded by: Horizon2020 grant agreement 720270 and 785907 (Human Brain Project, SGA1 and SGA2); The Swedish Research Council; Swedish e-Science Research Center. The network model is simulated on a Cray supercomputer provided by the Swedish National Infrastructure for Computing (SNIC) at References 1 Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol JD, Muller EB, Schürmann F, Segev I, Markram H. (2016) BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience. Front Neuroinform. doi: 10.3389/fninf.2016.00017 2 Tsodyks M, Pawelzik K, Markram H (1998) Neural networks with dynamic synapses. Neural Comput 10:821–835. doi: 10.1162/089976698300017502 3 Planert H, Szydlowski SN, Hjorth JJJ, Grillner S, Silberberg G. (2010) Dynamics of Synaptic Transmission between Fast-Spiking Interneurons and Striatal Projection Neurons of the Direct and Indirect Pathways. doi: 30 (9) 3499-3507 4 Migliore M, Cannia C, Lytton WW, Markram H, Hines ML. (2006) Parallel network simulations with NEURON. J Comp Neurosci. doi: 10.1007/s10827-006-7949-5 5 Rosen GD, Williams RW. (2001) Complex trait analysis of the mouse striatum: independent QTLs modulate volume and neuron number, BMC Neurosci. doi: 10.1186/1471-2202-2-5

©(2019) Hjorth J, Kozlov A, Frost Nylén J, Lindroos R, Carannante I, Johansson Y, Silberberg G, Grillner S, Hellgren Kotaleski J Cite as: Hjorth J, Kozlov A, Frost Nylén J, Lindroos R, Carannante I, Johansson Y, Silberberg G, Grillner S, Hellgren Kotaleski J (2019) “Snudda” a framework for reconstructing and simulating neuronal microcircuitry. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0042

[P 44] A unified framework for the application and evaluation of different methods for neural parameter optimization

Máté Mohácsi1,2, Márk Patrik Török1,2, Sára Sáray1,2, Tamás Freund1,2, Szabolcs Káli1,2

1. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-1083 Budapest, Práter utca 50/a, Hungary 2. Institute of Experimental Medicine, Hungarian Academy of Sciences, H-1083 Budapest, Szigony utca 43, Hungary Automated parameter fitting is becoming a widely used methodology in neuroscientific research. Several software packages have been successfully used for this purpose, but most of them are relatively difficult to master for non-specialists, and no systematic evaluation of their applicability to neural optimization problems has been performed. We have created an extended and enhanced version of our previously described neural optimization tool (Friedrich et al., 2014). The new version has an updated graphical user interface, which guides the user through the steps of defining a parameter fitting problem, running the optimization, and evaluating the results. The software provides 68 Posters

a unified interface to many different algorithms for parameter optimization (including single- and multi-objective methods), implemented by four external packages. For many algorithms, parallel execution is also supported. We used our unified framework to perform an extensive comparison of different algorithms and packages on a set of benchmark problems in neural optimization. Certain types of evolutionary and related algorithms provided the most consistently satisfactory results, but the relative performance of the different methods depended significantly on the nature of the problem, and sometimes also on the implementation. Our software tool and benchmarking results should make it easier for neuroscientists to select and use state-of-the-art parameter tuning methods for their research.

New graphical user interface and generation plot of algorithms

Acknowledgements Funding from the EU FP7 and Horizon 2020 grants 720270 and 785907 (Human Brain Project SGA1 and SGA2), and from Széchenyi 2020 Program of the Human Resource Development Operational Program, and of the PITI in Central-Hungary (EFOP-3.6.2-16-2017-00013 and 3.6.3-VEKOP- 16-2017-00002). References 1 Friedrich P, Vella M, Gulyás AI, Freund TF and Káli S (2014) A flexible, interactive software tool for fitting the parameters of neuronal models. Front. Neuroinform. 8:63 doi: 10.3389/fninf.2014.00063

©(2019) Mohácsi M, Török MP, Sáray S, Freund T, Káli S Cite as: Mohácsi M, Török MP, Sáray S, Freund T, Káli S (2019) A unified framework for the application and evaluation of different methods for neural parameter optimization. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0043

69 Posters

[P 45] Calculation of causal coupling between BOLD, heart rate interval and breathing signals may help to localize a “central pacemaker” in the brain stem.

Gert Pfurtscheller1,2, Maciej Kaminski3, Wolfgang Klimesch4, Andreas Schwerdtfeger2,5, Alexandre Andrade6, Christoph Stefan Aigner2,7, Katarzyna J. Blinowska3,8 1. Institute of Neural Engineering, Graz University of Technology, Austria 2. BioTechMed Graz, Austria 3. Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Poland 4. Centre of Cognitive Neuroscience, University of Salzburg, Austria 5. Department of Psychology, University of Graz, Austria 6. Faculdade de Ciências da Universidade de Lisboa, Instituto de Biofísica e Engenharia Biomédica, Portugal 7. Institute of Medical Engineering, Graz University of Technology, Austria 8. Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Poland The goal of our study was to gain understanding of the generation mechanisms of slow rhythms in body signals: heart rate beat-to-beat intervals (RRI) and respiration (resp). We aimed to verify the hypothesis of a slow frequency oscillations (LFO) pacemaker in the brain stem. LFO have been identified in different electrophysiological and hemodynamic signals. Two hypotheses were proposed concerning their generation: one assumes a central peacemaker, possibly located in the brain stem; the other proposes the baroreflex loop able to produce low-frequency oscillations. We studied five healthy subjects during 5 minutes of rest. For each subject 3 Regions of Interest were chosen involving cerebellum/brain stem and prefrontal cortex. The causal coupling between the signals was found by means of full frequency Directed Transfer Function (ffDTF) fitted to RRI, respiration and three BOLD signals. The recordings were divided into 40 s non-overlapping windows and we calculated ffDTF-based time-frequency plots of transmissions. Transmissions were calculated in frequency bands around 0.1 and 0.15 Hz. In three subjects, it was possible to find support for the existence of a central pacemaker in the brain stem, as a driving force for slow RRI oscillations. In two other subjects, the driving driving force for slow RRI oscillations was localized in the prefrontal cortex. Our results provided evidence for the influence of the central nervous system on the LFO in peripheral body signals. Acknowledgements We like to thank Thomas Zussner, David Fink and Karl Koschutnig for fMRI data acquisition and Clemens Brunner, all University of Graz, for organizing the data transfer with University of Warsaw. References 1 De Boer, R.W., Karemaker, J.M., Strackee, J., 1985. Relationships between short-term blood-pressure fluctuations and heart-rate variability in resting subjects II: a simple model Med. Biol. Eng. Comput. 23(4), 359–364. doi: 10.1007/BF02441589 2 Julien, C., 2006. The enigma of Mayer waves: facts and models. Cardiovasc. Res. 70, 12–21. doi: 10.1016/j.cardiores.2005.11.008 3 Kaminski, M., Blinowska, K.J., 1991. A new method of the description of the information flow in the brain structures. Biol. Cybern. 65(3), 203–210. doi: 10.1007/BF00198091 4 Korzeniewska, A., Manczak, M., Kaminski, M., Blinowska, K.J., Kasicki S., 2003. Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J. Neurosci. Meth. 125, 195–207. doi: 10.1016/S0165-0270(03)00052-9 5 P. Lachert, J. Zygierewicz, D. Janusek, P. Pulawski, P. Sawosz, M. Kacprzak, A. Liebert, K.J. Blinowska, Causal Coupling Between Electrophysiological Signals, Cerebral Hemodynamics And Systemic Blood Supply Oscillations In Mayer Wave Frequency Range, Intern. J of Neural Systems 2018, 1850033 doi: 10.1142/S012906571850033 6 Pfurtscheller, G., Schwerdtfeger, A., Seither-Preisler, A., Brunner,C., Aigner, C., Brito, J., Carmo, M.P., Andrade, A., 2017. Brain-heart communication: Evidence for “central pacemaker” oscillations with a dominant frequency at 0.1 Hz in the cingulum. Clin. Neuroph. 128, 183–193. doi: 10.1016/j.clinph.2016.10.097 7 Pfurtscheller, G., Schwerdtfeger, A., Seither-Preisler, A., Brunner, C., Aigner, C.S., Calisto, J., Gens, J., Andrade, A., 2018. Synchronization of intrinsic 0.1-Hz blood-oxygen-level-dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety. Eur. J. Neurosci. doi: 70 Posters

10.1111/ejn.13845 8 Rassler, B., Schwerdtfeger, A., Aigner, C.S., Pfurtscheller, G., 2018. “Switch-off” of respiratory sinus arrhythmia can occur in a minority of subjects during functional magnetic resonance imaging (fMRI), Front. Physiol. 9, 1688 doi: 10.3389/fphys.2018.01688 9 Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B. Joliot, M., 2002. Automated Anatomical Labeling of Activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage 15, 273–289. doi: 10.1006/nimg.2001.0978

©(2019) Pfurtscheller G, Kaminski M, Klimesch W, Schwerdtfeger A, Andrade A, Aigner CS, Blinowska KJ Cite as: Pfurtscheller G, Kaminski M, Klimesch W, Schwerdtfeger A, Andrade A, Aigner CS, Blinowska KJ (2019) Calculation of causal coupling between BOLD, heart rate interval and breathing signals may help to localize a “central pacemaker” in the brain stem.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0044

[P 46] Multiphysics of neural signals

Hao Wang1 1. ECE, National University of Singapore, SIngapore A Multiphysics neural model is proposed by accounting two reactive components in neural systems: the inductive coil structure of the myelin sheath and the piezoelectric effect of the plasma membrane. The model reveals the interaction of electrical, magnetic and mechanical mechanisms in the generation of the neural signal and its transit along axonal tracts. A novel neural circuit with an RLC configuration is presented and is shown to provide an excellent fit to neural stimulation experiments. With this idea of the presence of reactive components in neuronal models, a number of phenomena can be explained, such as the opposite spiraling orientations of adjacent myelin sheaths, the experimental observations from magnetic nerve stimulation, the mechanism of acoustic nerve stimulation and the mechanical wave accompanying the action potential.

Fig. 1. The two mechanisms to generate the inductive reactance in neural systems and the multiphysics of neural signals.

Acknowledgements This work was supported by grants from the National Research Foundation Competitive research 71 Posters

programme (NRF-CRP) ‘Peripheral Nerve Prostheses: A Paradigm Shift in Restoring Dexterous Limb Function’ (NRF-CRP10-2012-01).

©(2019) Wang H Cite as: Wang H (2019) Multiphysics of neural signals. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0045

[P 47] Simple moving average as a method to remove artifact from EEG recordings during periorbital sinusoidal alternating current stimulation

Małgorzata Żebrowska1, Piotr Dzwiniel1, Wioletta Joanna Waleszczyk1 1. 1Laboratory of Visual Neurobiology, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Pasteura 3, 02-093 Warsaw, Poland Periorbital sinusoidal alternating current stimulation (pACS) seems to be a promising method in the treating of the neurological visual dysfunctions. Due to the stimulation artifact, however, analysis of the simultaneous EEG recordings remains problematic. The aim of the study was an analysis of the effectiveness of the method for the stimulation artifact removal by creating its template with the simple moving average (SMA). The algorithm of the artifact removal has been tested on simulated and real data obtained from two healthy volunteers: man and woman (age 30 and 23 years, respectively). During 5 min of pACS (stimulation frequency: 10 Hz and amplitude: 20 µA) participants either gazed at a black point on a whiteboard (low activity alpha condition) or remained with eyes closed (high activity alpha condition). Simulated data was prepared by adding 5, 10, or 15 Hz sinusoidal artifact to the EEG signal during sham pACS sessions. The effect of SMA filtration depended on two averaging parameters: the number of sinusoidal periods in one segment and the number of segments chosen to construct one artifact template. The spectrum power difference (SPD) between original and filtered signal was the smallest in one period filtration, which was characterized by the plateau phase. In this case, the SPD values stabilized in the plateau phase at about 6% in both alpha activity conditions, using the possibly lowest number of segments equal to 2.5% of the entire stimulation signal. References 1 Helfrich, R.F., Schneider, T.R., Rach, S., Trautmann-Lengsfeld, S.A., Engel, A.K., Herrmann, C.S., 2014b. Entrainment of Brain Oscillations by Transcranial Alternating Current Stimulation. Current Biology 24, 333–339 doi: 10.1016/j.cub.2013.12.041 2 Sun, L., Hinrichs, H., 2016. Moving average template subtraction to remove stimulation artefacts in EEGs and LFPs recorded during deep brain stimulation. Journal of Neuroscience Methods 266, 126–136 doi: 10.1016/j.jneumeth.2016.03.020 3 Dowsett, J., Herrmann, C.S., 2016. Transcranial Alternating Current Stimulation with Sawtooth Waves: Simultaneous Stimulation and EEG Recording. Frontiers in Human Neuroscience 10 doi: 10.3389/fnhum.2016.00135

©(2019) Żebrowska M, Dzwiniel P, Waleszczyk WJ Cite as: Żebrowska M, Dzwiniel P, Waleszczyk WJ (2019) Simple moving average as a method to remove artifact from EEG recordings during periorbital sinusoidal alternating current stimulation. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0046

72 Posters

[P 48] Metrics for Skills of Users of Brain Computer Interfaces in the Motor Imagery Paradigm

Paulo V. Ascenção1, Eliana M. Santos1, Luciano H. Araújo1, Francisco J. Fraga1 1. Engineering, Modelling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), Santo Andre, SP, Brazil Brain-computer interfaces (BCI) are communication systems that allow users to interact with the environment using only brain activity (usually EEG). It is well-known that proper training is indispensable for BCI users to acquire the required skills to control the system, particularly for BCI based on motor imagery (MI-BCI). However, in order to assess the effectivity of the training procedure, it is necessary to evaluate separately the classification algorithm and the BCI user skills [1]. Recently, Lotte and Jeunet proposed new performance metrics [2] to quantify MI-BCI skills regardless the classification algorithm, by defining class distinctiveness (CD) metrics and trial stability (TS) metrics based on Riemann’s distance [3]. In this study, we calculated such metrics for two balanced datasets (UFABC and GIGASCIENCE) containing 27-channel EEG recorded with slightly different protocols from 30 age-, gender- and education-matched subjects during motor execution (ME) and MI of right- and left-hand movements. The protocols revealed almost no difference regarding the class distinctiveness metrics, but showed great difference when it comes to the TS metrics (Figure 1). In summary, our findings have shown that both training strategies (ME before MI for UFABC and classification accuracy feedback for GIGASCIENCE) were roughly equivalent for average classification accuracy and CD, but the UFABC training strategy for MI-BCI skills learning was clearly superior for trial stability.

Average Class Stability metric for each run and database

Acknowledgements This work was financed by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) through PhD and MSc scholarships and the National Council for Scientific and Technological Development (CNPq) through an undergraduate research scholarship. References 1 C. Jeunet, A. Cellard, S. Subramanian, M. Hachet, B. N’Kaoua, and F. Lotte, “How Well Can We Learn With Standard BCI Training Approaches ? A Pilot Study.,” 6th Int. Brain-Computer Interface Conf., no. Sepy, pp. 1–5, 2014. doi: 10.3217/978-3-85125-378-8-83 2 F. Lotte and C. Jeunet, “Defining and Quantifying Users ’ Mental Imagery-based BCI skills : a first step”, Journal of Neural Engineering, vol 15, 4, pp. 1–37, 2018. doi: 10.1088/1741-2552/aac577 3 A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Classification of covariance matrices using a Riemannian-based kernel for BCI applications,” Neurocomputing, vol. 112, pp. 172– 178, 2013. doi: 10.1016/j.neucom.2012.12.039

©(2019) Ascenção PV, Santos EM, Araújo LH, Fraga FJ Cite as: Ascenção PV, Santos EM, Araújo LH, Fraga FJ (2019) Metrics for Skills of Users of Brain Computer Interfaces in the Motor Imagery Paradigm. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0047

73 Posters

[P 49] BASIL BCI project - experiments performed in a hospital

Petr Brůha1, Roman Mouček1, Lukáš Vařeka1, Petr Ježek1 1. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, Pilsen, Czech Republic The Brain-Driven Computer Assistance System for People with Limited Mobility (BASIL) project aims to provide disabled patients with a mean for basic independent communi- cation (i.e. a simple BCI). Besides laboratory testing, it is also important to test related hardware and software in the hospital on the target group of patients. Although the experiment design has been the same as it has been in lab conditions, we have had to cope with several difficulties based on the variability of the patients’ environments and their current state of health. Outside the laboratory, experimenters have to deploy the whole architecture in a small space at hospital units, respect a patient’s current state, deal with various light conditions and process data locally. This way of testing makes the BCI system robust and usable under different conditions. In Figure 1, the schema of the BCI experiment performed in a hospital is shown. Here, seven electrodes and a classic matrix experiment design were used. In the right upper corner, one of six presentation modes of the matrix of nine symbols can be seen. Each symbol represents an activity or requirement the user of the BASIL project needs to perform. During the stimulation process, one row or one column always flashes in random order. The assistance system was successfully tested on 20 patients in Czech hospitals.

BASIL experiment performed in a hospital

Acknowledgements This work was supported by ERDF and Ministry of Regional Development of the Czech Republic within the project INTERREG V-A 85.

©(2019) Brůha P, Mouček R, Vařeka L, Ježek P Cite as: Brůha P, Mouček R, Vařeka L, Ježek P (2019) BASIL BCI project - experiments performed in a hospital. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0048

74 Posters

[P 50] Controlling a Servo Motor Using EEG Signals from the Primary Motor Cortex

Sadeem Nabeel Kbah1 1. Biomedical Eng., University of Baghdad, Baghdad, Iraq Brain Computer Interface means the group of processes in which the generated signals of EEG in the human brain can be interpreted and transferred to control an external device. In this paper, a novel method is adopted to control the rotation of a servo motor via EEG signals extracted from the human brain cortex. These signals have to pass through a processing procedure consisting mainly noise filtration and signal normalizing. The processed signal is fed to Arduino, which in turn controls the servo motor rotation. References 1 M. Teplan, FUNDAMENTALS OF EEG MEASUREMENT, Neural Prostheses: Linking Brain Signals to Prosthetic Devices, Institute of Measurement Science, Slovak Academy of Sciences, 9, 841 04 Bratislava, Slovakia MEASUREMENT SCIENCE REVIEW, Volume 2, Section 2, 2002. 2 Carlos Pedreira, Juan Martinez and Rodrigo Quian Quiroga, Neural Prostheses: Linking Brain Signals to Prosthetic Devices. Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil, SBC Journal on 3D Interactive Systems, volume 4, number 1, 3 Alessandro L. Stamatto Ferreira, Leonardo Cunha de Miranda, Erica E. Cunha de Miranda, Sarah Gomes Sakamoto, A Survey of Interactive Systems based on Brain-Computer Interfaces. 4 P. M. Shende and V. S. Jabade, "Literature review of brain computer interface (BCI) using Elec- troencephalogram signal," Pervasive Computing (ICPC), 2015 International Conference on, Pune, 2015, pp. 1-5. doi: 10.1109/PERVASIVE.2015.7087109, IEEE. 5 Bernhard Graimann, Gert Pfurtscheller, Brendan Allison, Brain-Computer Interfaces, ISBN: 978-3-642- 02090-2 (Print) 978-3-642-02091-9 (Online), Book, The Frontiers Collection, 2010, Revolutionizing Human-Computer Interaction, “PDCA12-70 data sheet,” Opto Speed SA, Mezzovico, Switzerland. 6 C. Harrison, D. Tan, and D. Morris, “Skinput: appropriating the body as an input surface,” in Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI’10), ACM, 2010, pp. 453– 462, doi: 10.1145/1753326.1753394.

©(2019) Kbah SN Cite as: Kbah SN (2019) Controlling a Servo Motor Using EEG Signals from the Primary Motor Cortex. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0049

[P 51] Towards independent home brain-computer interfaces: Czech-Bavarian BASIL project

Roman Mouček1, Lukáš Vařeka1, Petr Ježek1, Petr Brůha1, Tomáš Prokop1, Pavel Mautner1 1. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic Number of studies on brain-computer interfaces (BCIs) has been increasing for decades. Despite all the research effort, BCIs mainly remain constricted to laboratory settings and suffer from problems that prevent their widespread use among the target users (such as patients with neuromuscular disorders). There are several factors that contribute to the current state: high cost of EEG-related hardware, low accuracy, low transfer bit-rates and long training times. The Czech Bavarian project BASIL aims to address these issues by designing and implementing a new prototype of the home BCI system providing an assistance to people with limited mobility. Advances in building the prototype include the development of a system for EEG signal acquisition and wireless transfer of the EEG signal (both developed 75 Posters

by the Bavarian partners), design and implementation of stimulation protocols, storing data in standardized formats, development of a remote cloud-based infrastructure for experimental data, results and computational tools, development of visual workflow designer for creating and running signal processing pipelines, and use of machine learning including deep learning methods for classification of brain patterns. The BASIL prototype has been deployed and verified in hospitals and home environment. Acknowledgements This work was supported by ERDF and Ministry of Regional Development of the Czech Republic within the project INTERREG V-A 85.

©(2019) Mouček R, Vařeka L, Ježek P, Brůha P, Prokop T, Mautner P Cite as: Mouček R, Vařeka L, Ježek P, Brůha P, Prokop T, Mautner P (2019) Towards independent home brain-computer interfaces: Czech-Bavarian BASIL project. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0050

[P 52] Identification of Phase Space Manifolds of Brain Dynamics from EEG signals

Takashi Nakamura1, Scott Hagan1 1. Mathematics, British Columbia Institute of Technology, Burnaby, BC, Canada In this presentation, we outline our attempt to identify the nonlinear manifolds where the phase space trajectories of brain dynamics reside. Our aim is to develop an algorithm to recognise various brain dynamics by identifying the geometries of their phase space manifolds. Takens’ delay embedding theorem [Packard, et al.] states that the phase space trajectory can be reconstructed by using time-lagged variables created from the observed single time series. Since the EEG signal can be considered a projection of higher dimensional dynamics of the brain, it is expected that time-lagged variables should give approximate reconstruction of phase space trajectories. Reconstructed trajectories, however, are contaminated by large noise in the EEG signal. In order to tease out meaningful patterns buried in noise, we use the diffusion map analysis [Coifman, et al.] which can extract highly nonlinear manifolds where data points (time-lagged variables) cluster. A diffusion map maps the original data points to points in the space spanned by the eigenvectors of the graph Laplacian of the diffusion kernel constructed from the data. Taking only a few dominant eigenvectors we have highly correlated signals that are practically noise-free. We thus obtain a nonlinear manifold spanned by a few eigenvectors of the Laplacian, in which the phase trajectory is confined. Our algorithm is demonstrated using a simple toy model, and some preliminary results of analyzing real EEG data are presented. References 1 Packard, et al. doi: 10.1103/PhysRevLett.45.712 2 Coifman, et al. doi: 10.1073/pnas.0500334102

©(2019) Nakamura T, Hagan S Cite as: Nakamura T, Hagan S (2019) Identification of Phase Space Manifolds of Brain Dynamics from EEG signals. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0051

76 Posters

[P 53] Evaluation of public EEG datasets for deep learning research

Lukáš Vařeka1, Jan Kebrle2, Roman Mouček1 1. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic 2. Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic Automatic evaluation and classification of electroencephalographic (EEG) data are currently essential subjects of research. A well-known example is a brain-computer interface (BCI) [1], which allows immobile persons to operate devices only by analyzing their EEG signal without the need for muscle involvement. Improving BCIs is one of the main aims of the current Brainwave driven digital assistance system for motor-impaired people (BASIL) project. Another application could be the analysis of pathological EEG signal, e.g. in patients with epilepsy [2]. In this case, the common goal of analysis and classification is to recognize the upcoming seizure on time. In both cases, classification is challenging because of the low signal to noise ratio typical for EEG signals. Nowadays, the field of deep learning is rapidly developing and has led to significantly better classification results than other existing state-of-the-art algorithms in many research areas [3]. It can be hypothesized that it is not feasible to obtain large enough training datasets from a single subject when applying complex deep learning methods. Therefore, to achieve a similar breakthrough in EEG classification, neural networks should be trained using a large multi-subject dataset [4]. The ongoing work aims to evaluate existing public EEG datasets suitable for deep learning. Improved neural network training would lead to improved BCI performance or more reliable automated EEG-based diagnosis. Acknowledgements This work was supported by ERDF and Ministry of Regional Development of the Czech Republic within the project INTERREG V-A 85. References 1 D. J. McFarland and J. R. Wolpaw, Brain-computer interfaces for communication and control, Commun. ACM, vol. 54, no. 5, 2011. doi: 10.1145/1941487.1941506 2 F. Mormann, R. G. Andrzejak, Seizure prediction: making mileage on the long and winding road, Brain, vol. 139, 2016. doi: 10.1093/brain/aww091 3 L. Deng and D. Yu, Deep learning: Methods and applications, Foundations and Trends in Signal Processing, vol. 7, 2013. https://ieeexplore.ieee.org/document/8187230 4 F. Lotte et al., A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update, J Neural Eng, vol. 15, 2018. doi: 10.1088/1741-2552/aab2f2

©(2019) Vařeka L, Kebrle J, Mouček R Cite as: Vařeka L, Kebrle J, Mouček R (2019) Evaluation of public EEG datasets for deep learning research. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0052

77 Posters

[P 54] Machine Learning algorithms to study of laterality in Virtual Reality

Beata Sokołowska1, Ewa Sokołowska2 1. Bioinformatics Laboratory, Mossakowski Medical Research Centre Polish Academy of Sciences, 02-106 Warsaw, 5 Pawińskiego Street, Poland 2. Faculty of Social Science, Department of Clinical Psychology, John Paul II Catholic University of Lublin, 20-950 Lublin, 14 Aleje Racławickie Street, Poland Laterality is the preference that most people show for one side of the body over the other. Examples include right or left-handedness and can also refer to the primary use of the left or right hemisphere of the brain. Laterality of motor and sensory control has been the subject of the recent intense research [1]. The aim of the study is to evaluate the handedness for cognitive tasks of training sessions using the NEUROFORMA’s Virtual Reality system (http://www.neuroforma.pl/en/) [2, 3]. The test sessions included virtual tasks performed with one hand and then with the other. Handedness has been recognized by machine learning algorithms of supervised learning classification and regression [4]. In the set of cognitive tasks, the adult healthy volunteers achieved lower results at higher levels of difficulty, especially in the case of lexical exercises, and more often made mistakes in arithmetical calculations. In summary, the results for the dominant hand were better than those for the non-dominant one. These differences increased at higher levels, and the effects of fatigue were more pronounced in the non-dominant hand, decreasing the results.

The illustration of cognitive tasks such as recognition of balls or Polish words, and arithmetic calculations in NEUROFORMA’s Virtual Reality

Acknowledgements The study was carried out using the computational infrastructure of the Biocentrum-Ochota project (POIG.02.03.00-00-0030 / 09) and applying the software of Titanis’ technologies. References 1 Harrison D.W., Brain Asymmetry and Neural Systems. Foundations in Clinical Neuroscience and Neuropsychology. Springer International Publishing Switezerland 2015 2 Sharman W.R., Craig W.R., Understanding Virtual Reality. Interface, application, and design, Elsevier Science (USA) 2003 3 Riener R., Harders M., Virtual Reality in Medicine, Springer-Verlag London 2012 4 Jain A.K., Duin R.P.W., Mao J., Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 2000, pp. 1-37

©(2019) Sokołowska B, Sokołowska E Cite as: Sokołowska B, Sokołowska E (2019) Machine Learning algorithms to study of laterality in Virtual Reality. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0053 78 Posters

[P 55] Deep convolutional neural network for classification octave illusion using MEG data

Keita Tanaka1, Nina Pilyugina2 1. Department of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hikigun, Saitama, 350- 0394, Japan 2. Department of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hikigun, Saitama, 350- 0394, Japan The octave illusion occurs when two tones with one-octave difference alternately played to both ears at the same time. The aim of this study is to classify participants into the illusion group and without group by applying the convolutional neural network application. Brain activity data was recorded by magnetoencephalography (MEG) and analyzed the activation level between those two groups. The study proposed a method for developing several convolutional layers of learning units for comparing activity in the same brain regions for groups with illusion and without. To the best of the author’s knowledge, it is the only study attempting to classify octave illusion data using deep learning techniques.

©(2019) Tanaka K, Pilyugina N Cite as: Tanaka K, Pilyugina N (2019) Deep convolutional neural network for classification octave illusion using MEG data. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0054

[P 56] Scalable Brain Atlas Composer: API for collaborative brain atlasing and visualization workflows

Rembrandt Bakker1,2, Paul Tiesinga1 1. Neuroinformatics department, Donders Institute for Brain, Cognition and Behaviour, Radboud University Ni- jmegen, Nijmegen, The Netherlands 2. Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany We present SBA Composer, a web-based service for visualizing brain volumes, tissue sections, region meshes, neuron morphologies and other 3d geometries inside a brain atlas. SBA Composer can be used directly to view files from local storage, but offers most flexibility when controlled by an external website (Javascript) or Jupyter notebook (Python). A typical user scenario is that the external application first sends summary data to SBA Composer, and then sends additional details when the user interacts with it. For example, the summary data can display all somata from the Mouselight neuron morphology database [1] and respond to a mouse click by sending a fully reconstructed neuron to the viewer [2,3]. Thanks to this interplay, SBA Composer can focus on visualization and atlas registration, whereas the researcher can use Jupyter notebooks to build a documented and reproducible pipeline for data analysis, which can be shared via online notebook providers like the HBP Collab or Google Colabs to form a collaborative workflow. The benefits of interacting with SBA Composer beyond generating eye-candy are (1) SBA Composer has built-in atlases with region meshes and anatomical data; (1) spatial misalignment of imported data is immediately visible; (2) SBA Composer has tools for manual data alignment and scene composition; (3) the staged summary/full data display facilitates data exploration and multiple source integration. API documentation is found at [4], source code at [5]. 79 Posters

Use case: spheres represent Mouselight neurons with axon terminals in Primary Motor Cortex. On mouse click, the corresponding full reconstruction is retrieved.

Acknowledgements Supported by European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 785907 (Human Brain Project SGA2) and FLAG ERA project FIIND (NWO 054-15-104) References 1 http://mouselight.janelia.org/ http://mouselight.janelia.org/ 2 https://neuroinformatics.nl/HBP/mouselight-viewer https://neuroinformatics.nl/HBP/mouselight- viewer 3 https://neuroinformatics.nl/HBP/allen-connectivity-viewer https://neuroinformatics.nl/HBP/allen- connectivity-viewer 4 https://scalablebrainatlas.incf.org/composer/docs https://scalablebrainatlas.incf.org/composer/docs 5 https://github.com/rbakker/sba-composer https://github.com/rbakker/sba-composer

©(2019) Bakker R, Tiesinga P Cite as: Bakker R, Tiesinga P (2019) Scalable Brain Atlas Composer: API for collaborative brain atlasing and visualization workflows. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0055

[P 57] Ontology development to support data integration for the Brain Initiative Cell Census Network in the Cell Registry and Allen Brain Explorer.

Carol L. Thompson1, David Feng1, Thomas Gillespie2, Nika Hejazinia1, Gautham Acharya1, Tim Dolbeare1, Tyler Mollenkopf1, Shoaib Mufti1, Allison Indyk1, Florence D’Orazi1, Maryann E. Martone2, Michael J Hawrylycz1, Lydia Ng1 1. Allen Institute for Brain Science, United States 2. University of California San Diego, United States The US BRAIN Initiative Cell Census Network (BICCN) is creating a detailed census of cell type proper-ties in the brain. The BRAIN Cell Data Center is developing the community resource for integrated cell-centric data, including molecular, anatomical and physiological annotations of cell types in mouse and human. The integrated data framework assimilates data from >30 laboratories, and implements a unified data schema with supporting ontologies across all teams and datasets. Essential to integration is the adop-tion of semantic standards, spatial registration to common coordinate systems and standardized data processing pipelines and normalization for cross-modality comparison and analysis. The ontology tracks provenance and version for the data life cycle from data generation, feature ex-traction, analysis and the knowledge gain about cell types and brain organization, anchored around the PROV community standard pattern to encapsulate the generation, transform and modification of enti-ties by various activities of the consortium. Deep data interoperability is achieved by representing computed features in standardized grammar and to map and project them in known and newly gener-ated taxonomies. The ontology and integrated data framework support the BICCN 80 Posters

Cell Registry which provides a centralized and archival record of all cells profiled or imaging experiments generated and points to data archive locations for high resolution data and protocols using FAIR principles. Acknowledgements This work was supported by NIH award 1U24MH114827-01 (PIs: Hawrylycz and Ng) and by the Allen Institute. We wish to thank the Allen Institute founders, Paul G. Allen, for his vision, encouragement and support.

©(2019) Thompson CL, Feng D, Gillespie T, Hejazinia N, Acharya G, Dolbeare T, Mollenkopf T, Mufti S, Indyk A, D’Orazi F, Martone ME, Hawrylycz MJ, Ng L Cite as: Thompson CL, Feng D, Gillespie T, Hejazinia N, Acharya G, Dolbeare T, Mollenkopf T, Mufti S, Indyk A, D’Orazi F, Martone ME, Hawrylycz MJ, Ng L (2019) Ontology development to support data integration for the Brain Initiative Cell Census Network in the Cell Registry and Allen Brain Explorer.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0056

[P 58] PIPES, a module-based pipeline framework to accelerate reproducible data analysis in astrocyte research.

C. Vivar Rios1 1. DNF, UNIL, Rue de Bugnon, 9, Lausanne, Suisse Astrocytes produce complex intracellular spatiotemporal Ca2+ signals at different scales that play a relevant role in information processing via exocytosis of modulating ligands. In order to completely understand the calcium code that underlie the integration and processing of the Ca2+ signals, multiple techniques and expertise are required, as a consequence the nature of the data produced in this research field is big and highly heterogeneous—i.e., multidimensional (1D, 2D and 3D) multiphoton recordings, electrophysiological data, microendoscopes recordings, gene expression or behavior. As a solution we develop PIPES, a python-based tool that manage data storage, analysis pipelines and provide interaction solutions with the outputs. This is done considering each pipeline as a set of independent modules concatenated and checking the integrity of inputs and outputs according to a user-specified standard. Reproducibility and reusability of the data is improved by keeping track of all analysis steps done including parameters values and manual decisions. Furthermore, due to the standardization of the outputs, the experts can generate analysis modules that can extend or modify the existing pipelines so experimentalist can sketch a preliminary analysis in parallel to the development of other compatible analysis modules. With PIPES this can be done in a highly efficient manner by running only those modules affected and comparing the differences in the outcomes.

©(2019) Vivar Rios C Cite as: Vivar Rios C (2019) PIPES, a module-based pipeline framework to accelerate reproducible data analysis in astrocyte research.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0057

81 Posters

[P 59] Blue Brain Nexus - A Knowledge Graph for Data-driven Science

Samuel Kerrien1, Mohameth Francois Sy1, Bogdan Roman1, Wojciech Wajerowicz1, Didac Montero1, Henry Genet1, Michael Dupont1, Kenneth Pirman1, Julien Machon1, Anna-Kristin Kaufmann1, Pierre-Alexandre Fonta1, Jonathan Lurie1, Huanxiang Lu1, Sean Hill1,2 1. Blue Brain Project, EPFL, Chemin des Mines 9, 1202 Genève, Switzerland 2. Krembil Centre for Neuroinformatics, CAMH, Toronto, Canada Blue Brain Nexus (https://bluebrainnexus.io) is an open source, domain agnostic, scalable data management platform that enables any data-driven organisation to organise and share their digital assets following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. At the heart of Nexus lies a Knowledge Graph that enables scientists to share their data as well as describe it with rich metadata using JSON- LD and including detailed provenance (W3C PROV-O). Data quality can be enforced through the use of Shapes Constraint Language (W3C SHACL) as a primary schema and validation language. Both the Blue Brain Project and the EU Human Brain Project, use Nexus to flexibly organise a broad variety of Neuroscience data and enable scientists to accelerate their research. Blue Brain Nexus v1.0 was publicly released early 2019 and bring numerous new capabilities such as a new REST API, tools to interface with the platform (Python/Javascript SDK, Command Line Interface), enhanced security model, scalability backed by performance benchmark, ability to deploy on private cloud as well as commercial provider such as Amazon AWS. Furthermore, an in-depth tutorial has been made available to enable scientists to learn how to leverage Nexus. In order to enable and ease the Nexus technology adoption in the Neuroscience community, an open community effort to develop standard data models has been established (https://incf.github.io/neuroshapes) for a variety of data types.

Poster ©(2019) Kerrien S, Sy MF, Roman B, Wajerowicz W, Montero D, Genet H, Dupont M, Pirman K, Machon J, Kaufmann A, Fonta P, Lurie J, Lu H, Hill S Cite as: Kerrien S, Sy MF, Roman B, Wajerowicz W, Montero D, Genet H, Dupont M, Pirman K, Machon J, Kaufmann A, Fonta P, Lurie J, Lu H, Hill S (2019) Blue Brain Nexus - A Knowledge Graph for Data-driven Science. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0058 82 Posters

[P 60] Novel analytics for clinical data - simple digital pathology method for computer-aided analysis of virtual whole slide images (WSI)

Wlodzimierz Klonowski1 1. Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, 4 Ks.Trojdena St., Poland Most of the image analysis methods in digital pathology attempt to emulate a pathol- ogist’s way of thinking. These methods use for example different models of neural networks. Such algorithms before they might be incorporated in clinical practice need to be trained on large datasets of images while in medicine datasets are rather small. Moreover, traditional evaluation of tissue images by pathologists lead to interobserver differences and even intraobserver variations in the assessment. Thus, simple and fast computerized methods that give reproducible results are essential for medical applications. We propose a novel simple method of image analysis, in particular of histopathological images, based on color filtration pixel-by-pixel (CFPP) of even very big whole slide images (WSI) or their fragments. We have demonstrated that this method enables to quantify the proliferation of lymphomas and shows good agreement with such quantification made by a trained pathologist. The method is rapid and does not require the time-consuming step of selecting regions of interest (ROIs) manually nor it needs computationally complicated detection of so-called hot-spots, both of which attempt to emulate a pathologist’s way of thinking. The method is still significantly improved by applying a sliding window approach, so enabling to assess local image properties. Progress does not consist in making calculations more complicated but in making calculations simpler. References 1 W.Klonowski, A.Korzynska, R.Gomolka. Computer analysis of histopathological images for tumor grading, Physiol. Meas. 39 (2018) 034002 (8pp),

©(2019) Klonowski W Cite as: Klonowski W (2019) Novel analytics for clinical data - simple digital pathology method for computer-aided analysis of virtual whole slide images (WSI). Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0059

83 Posters

[P 61] Utilizing open source tools for reproducibility and efficient data sharing - a use case of collaborative analysis of electrophysiological data

Ajayrama Kumaraswamy1, Kazuki Kai2, Hiroyuki Ai3, Hidetoshi Ikeno4, Thomas Wachtler1

1. Department of Biology II, Ludwig-Maximilians-Universität München, Germany 2. Faculty of Science, Hokkaido University, Japan 3. Department of Earth System Science, Fukuoka University, Japan 4. School of Human Science and Environment, University of Hyogo, Japan Collaborative research requires efficient exchange of data. We demonstrate the use of open source tools to achieve reproducible analysis, presenting a use case in which electrophysiology data was shared between labs, organized and analyzed to investigate the neural basis of honeybee communication[1]. Intracellularly recorded data[2] were read from SMR files using Neo[3] (RRID:SCR_000634). Metadata were converted from spreadsheets into an organized structure according to odML[4] (RRID:SCR_001376) and were stored with the data in the NIX format[5] (RRID:SCR_016196). This achieved a meaningful grouping of data linked with metadata and tagging of relevant parts of the data such as stimulus intervals, which made it easy for collaborators to understand the data and to quickly browse by NixView[6]. Importantly, it enabled automated selection of subsets of data from within analysis scripts, which ensured reproducibility and made it possible to perform systematic comparison with simulation results[7]. Data sharing was done using the GIN platform[8] (RRID:SCR_015864), which enabled all collaborators to work on the data together while keeping track of the state of the datasets. It further facilitated publishing the data[9] with a button click, without the need of further preparation or data transfer. Our presentation demonstrates how open-source tools can be used to establish reproducible framework and clear organization of data, facilitating collaborative research and re-use of data. Acknowledgements We are grateful to the G-Node team for advice and discussions. Supported by MEXT (Grants 22570079, 17K00414, 18K160345), JST (Grant 15K14569), Fukuoka University (Grant 151031) and BMBF (Grants 01GQ1116 and 01GQ1302). References 1 Ai et al (2017) Interneurons in the honeybee primary auditory center responding to waggle dance-like vibration pulses. J Neurosci 37:10624-10635 doi: 10.1523/JNEUROSCI.0044-17.2017 2 Kumaraswamy et al (2018) Adaptations during maturation in an identified honeybee interneuron responsive to waggle dance vibration signals. bioRxiv 469502 doi: 10.1101/469502 3 http://neuralensemble.org/neo http://neuralensemble.org/neo 4 http://www.g-node.org/odml http://www.g-node.org/odml 5 http://www.g-node.org/nix http://www.g-node.org/nix 6 http://bendalab.github.io/NixView http://bendalab.github.io/NixView 7 Kumaraswamy et al (2017) Network simulations of interneuron circuits in the honeybee primary auditory center. bioRxiv 159533 doi: 10.1101/159533 8 http://www.g-node.org/gin http://www.g-node.org/gin 9 https://gin.g-node.org/The_Ginjang_Project https://gin.g-node.org/The_Ginjang_Project

©(2019) Kumaraswamy A, Kai K, Ai H, Ikeno H, Wachtler T Cite as: Kumaraswamy A, Kai K, Ai H, Ikeno H, Wachtler T (2019) Utilizing open source tools for reproducibility and efficient data sharing - a use case of collaborative analysis of electrophysiological data. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0060

84 Posters

[P 62] Data export plugin and automated pipeline based on BIDS - expanding and utilising free software to improve fMRI data flow in Laboratory of Brain Imaging.

Michał Szczepanik1, Bartosz Kossowski1 1. Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Str. 02-092 Warsaw, Poland Brain Imaging Data Structure (BIDS) is a standard for organisation of neuroimaging data and metadata. One of its aims was facilitating creation of automated tools for data processing. Since the publication of BIDS specification version 1.0 in 2016 [1], a significant ecosystem of BIDS software, such as converters and data analysis pipelines, has indeed emerged. There are two key elements to this poster. First, we describe horos-bids-output plugin, a data export extension created by us for the Horos DICOM browser. According to our knowledge, it is the first GUI-based tool which can be used to automatically create BIDS datasets. The plugin provides a graphical interface where user can describe detected sequence names and view the resulting file names before exporting. It utilises Horos’ API for DICOM queries and dcm2niix for conversion to NIfTI. It is available at github.com/mslw/horos-bids-output. Second, we describe how we combined a set of existing tools with lua, python and bash scripting to improve the data flow in our laboratory, most notably making quality control automated and easily accessible. After completion of scanning, dicom data are copied to a dedicated server, converted to BIDS using heudiconv, and submitted to MRIQC. They can be further stored for download or submitted to additional processing. Together, these free software based tools greatly improved the work in our laboratory, where multiple projects are conducted by internal and external users.

MRI data are automatically submitted to a quality control pipeline after each scanning session. They can be also manually exported and organised using a GUI plugin which we created for Horos browser.

References 1 Gorgolewski et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments doi: 10.1038/sdata.2016.44

©(2019) Szczepanik M, Kossowski B Cite as: Szczepanik M, Kossowski B (2019) Data export plugin and automated pipeline based on BIDS - expanding and utilising free software to improve fMRI data flow in Laboratory of Brain Imaging.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0061

85 Posters

[P 63] From metadata to the semantic web: services for data annotation and findable data

Michael Sonntag1, Achilleas Koutsou1, Jan Grewe2, Thomas Wachtler1 1. German Neuroinformatics Node, Ludwig-Maximilians-Universität München, Martinsried, Germany 2. Institute for Neurobiology, Eberhard-Karls-Universität Tübingen, Tübingen, Germany Annotation of research data with metadata is crucial to provide context for analysis and re-use. The odML[1] format (RRID:SCR_001376) offers a flexible and comprehensive solution for the scientist to collect and organize metadata in a structured form that is both human readable and machine actionable[2] for documentation and automated analysis. To further support the FAIR principles[3], we present tools to export metadata from odML to RDF[4], which opens metadata up to semantic web services. The G- Node SPARQL server[5] is aimed at providing searchable whole metadata sets for meta analyses and also providing links to the actual published scientific data set. Scientists can upload their metadata to make their data findable and accessible even if it was a data publication or if it is an unpublished data set. Furthermore, the GIN[6] data hosting service (RRID:SCR_015864) provides an opt-in feature to automatically update the metadata service when changes to a dataset occur, to ensure the metadata is always up- to-date. Finally with a metadata resource service the G-Node hosts a platform providing terminologies for metadata annotation and features a forum for general feedback, usage discussions and exchange of metadata templates with the scientific community. Acknowledgements Supported by BMBF (Grants 01GQ1302 and 01GQ1509) References 1 Grewe et al (2011). A bottom-up approach to data annotation in neurophysiology. Front. Neuroinform. 5:16. doi: 10.3389/fninf.2011.00016 2 Zehl et al (2016) Handling metadata in a neurophysiology laboratory. Frontiers in Neuroinformatics 10:26. doi: 10.3389/fninf.2016.00026 3 Wilkinson et al (2016) The FAIR guiding principles for scientific data management and stewardship. Scientific Data 3:160018. doi: 10.1038/sdata.2016.18 4 https://www.w3.org/RDF/ https://www.w3.org/RDF/ 5 https://meta.g-node.org https://meta.g-node.org 6 https://gin.g-node.org https://gin.g-node.org

©(2019) Sonntag M, Koutsou A, Grewe J, Wachtler T Cite as: Sonntag M, Koutsou A, Grewe J, Wachtler T (2019) From metadata to the semantic web: services for data annotation and findable data. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0062

86 Posters

[P 64] Neuroshapes: Open data models for FAIR scientific data and knowledge in neuroscience at web scale

Mohameth François Sy1, Anna-Kristin Kaufmann1, Jean-Denis Courcol1, Andrew P. Davison2, Genrich Ivaska1, Huanxiang Lu1, Pierre-Alexandre Fonta1, Samuel Kerrien1, Sean L. Hill1,3 1. Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Biotech Campus, 1202 Geneva, Switzer- land 2. Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique UPR 3293, Gif-sur-Yvette, France 3. Krembil Centre for Neuroinformatics, CAMH, Toronto, Canada The FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles are key foundations for managing and publishing scientific data. While many data models and repositories enable data accessibility and findability, interoperability and reusability are more challenging. We present the v1.0 release of Neuroshapes (neuroshapes.org), a community-driven initiative defining a set of open data models, design patterns and tools for a comprehensive support of the FAIR principles. It extends schema.org and the W3C PROV-O initiatives with neuroscience types enabling thus the usage of structured metadata markups and linked data principles for publishing data at web scale. Neuroshapes expresses the key scientific and technical activities as well as the agents involved in the data generation using provenance-based data models. Versioned and with persistent identifiers, they capture contextual information necessary to interpret the scientific meaning of the data, infer data types, evaluate trust and quality, ensure attribution of all contributors, and support data integration, interoperability, reuse and longevity. Currently, Neuroshapes addresses single cell electrophysiology, neuron morphology, brain atlasing and computational modeling workflows and supports search, provenance tracking, publishing and data-driven modeling workflows in the Blue Brain Project and the EU Human Brain Project. An INCF SIG was launched to collaboratively develop additional data models.

©(2019) Sy MF, Kaufmann A, Courcol J, Davison AP, Ivaska G, Lu H, Fonta P, Kerrien S, Hill SL Cite as: Sy MF, Kaufmann A, Courcol J, Davison AP, Ivaska G, Lu H, Fonta P, Kerrien S, Hill SL (2019) Neuroshapes: Open data models for FAIR scientific data and knowledge in neuroscience at web scale. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0063

87 Posters

[P 65] Tools and Data Resources from the NIH Stimulating Peripheral Activity to Relieve Conditions (SPARC) Program

Andrew Weitz1, Felicia Qashu2, Michael Wolfson1, Kristina Faulk2, Gene Civillico2 1. National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 6707 Democracy Blvd, Suite 200, Bethesda, MD 20817, USA 2. Office of Strategic Coordination, Office of the Director, National Institutes of Health, 6001 Executive Blvd, Suite 8190, Rockville, MD 20892, USA The autonomic nervous system plays a critical role in the regulation of organ function. Peripheral nerves innervating these organs are promising neuromodulation targets to treat various conditions such as heart failure, gastrointestinal disorders, and diabetes. However, anatomical and mechanistic knowledge of the specific neural circuits involved is lacking. The Stimulating Peripheral Activity to Relieve Conditions (SPARC) Program at the National Institutes of Health aims to advance the neuromodulation field toward precise treatment of diseases and conditions for which conventional therapies fall short. This program will improve our understanding of peripheral nerve-organ interactions by creating anatomical and functional maps and predictive simulations of the neural control of organ function, and by developing cutting-edge tools and technologies to interrogate and modulate the peripheral nervous system. The data, maps, models, and tools generated by SPARC will be available to the research community through an online platform and will ultimately serve as a resource for neuromodulation target development and validation. Critical to these efforts is a robust informatics framework that brings anatomy and physiology up to the ontological standards that have developed in the multiomics space.

©(2019) Weitz A, Qashu F, Wolfson M, Faulk K, Civillico G Cite as: Weitz A, Qashu F, Wolfson M, Faulk K, Civillico G (2019) Tools and Data Resources from the NIH Stimulating Peripheral Activity to Relieve Conditions (SPARC) Program. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0064

88 Posters

[P 66] Neuroinformatics platform for making neuroscience data Findable, Accessible, Interoperable, and Reuseable

Krister A. Andersson1, Camilla H. Blixhavn1, Heidi Kleven1, Lyuba Zehl2, Ingvild Bjerke1, Oliver Schmidt3, Martin Øvsthus1, Ida Aasebø1, Sharon C. Yates1, Ulrike Schlegel1, Elodie Legouee4, Mathew Abrams5, Maja A. Puchades1, Andrew Davison4, Timo Dickscheid2, Trygve Leergaard1, Jan G. Bjaalie1 1. Neural Systems Laboratory, Institute for Basic Medical Sciences, University of Oslo, Oslo, Norway 2. Institute for Neuroscience and Medicine (INM-1), Jülich Research Centre, Jülich, Germany 3. Human Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Geneva, Switzerland 4. Unité de Neurosciences, nformation et Complexité, Centre National de la Recherche Scientifique UPR 3293, Gif-sur-Yvette, France 5. International Neuroinformatics Coordinating Facility Secretariat (INCF), Stockholm, Sweden There is growing awareness that sharing of scientific data is required to increase reproducibility and transparency, reflected in the widely endorsed FAIR principles, stating that data should be Findable, Accessible, Interoperable and Reusable [1]. Anticipating this need, the EU Human Brain Project has developed a generic system for FAIR sharing of neuroscience research data. The new infrastructure offers tools, workflows, and data curation services aimed at making heterogeneous, multimodal data discoverable, interpretable, and possible to access and re-use. Data producers submitting their data can benefit from a 3-tier curation process. In Tier 1, the data are tagged with standardised basic metadata. In Tier 2, spatial information, based on standardised brain atlases, is added to define where in the brain the data are located. In Tier 3, in-depth metadata for the particular research domain are added. These metadata are organised in the HBP Knowledge Graph and made searchable through a web-based user interface, as well as through programmatic APIs. Data are stored at HPC centres, close to compute resources. All data are tagged with a Creative Commons license, chosen by the data provider, thereby allowing researchers to define under which conditions their data is shared. Data are made citable through DOIs. We demonstrate the current workflow and provide examples showing the added values of sharing data through the new infrastructure. Acknowledgements This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and 785907 (HBP SGA2). References 1 Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, et al. 2016. “The FAIR Guiding Principles for scientific data management and stewardship.” Scientific Data 3 (1): 160018. doi: 10.1038/sdata.2016.18

©(2019) Andersson KA, Blixhavn CH, Kleven H, Zehl L, Bjerke I, Schmidt O, Øvsthus M, Aasebø I, Yates SC, Schlegel U, Legouee E, Abrams M, Puchades MA, Davison A, Dickscheid T, Leergaard T, Bjaalie JG Cite as: Andersson KA, Blixhavn CH, Kleven H, Zehl L, Bjerke I, Schmidt O, Øvsthus M, Aasebø I, Yates SC, Schlegel U, Legouee E, Abrams M, Puchades MA, Davison A, Dickscheid T, Leergaard T, Bjaalie JG (2019) Neuroinformatics platform for making neuroscience data Findable, Accessible, Interoperable, and Reuseable. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0065

89 Posters

[P 67] Infrastructure and workflow for integrating and navigating multi-scale and multi-modal murine neuroscience data using 3D digital brain reference atlases

Camilla Hagen Blixhavn1, Krister Andreas Andersson1, Heidi Kleven1, Ulrike Schlegel1, Maja Amedjkouh Puchades1, Jan Gunnar Bjaalie1, Trygve Brauns Leergaard1 1. Neural Systems Laboratory, Institute for Basic Medical Sciences, University of Oslo, Norway A grand challenge in neuroscience is the management, integration and analysis of the rapidly growing body of heterogeneous, multi-scale and multi-modal experimental data. Standardised parameters are needed to connect relevant data across methodologies, scales and modalities. The EU Human Brain Project (HBP) is establishing a new infrastructure of Neuroinformatics tools and data curation services, through which disparate neuroscience data can be shared, used and analysed. Three-dimensional (3D) open access brain reference atlases provide the anatomical context for all data shared via the HBP infrastructure, easing comparison and interpretation of findings. We here present the workflows used to assign location metadata to different types of murine neuroscience data, enabling researchers to combine and co-visualise morphological features from multiple datasets in a common 3D anatomical brain atlas viewer system. We further show how accumulation of data in a common reference space opens new avenues for discovery and analysis of brain data. The Human Brain Project now invites the community to use the new research infrastructure to share, find and use open access research data. Acknowledgements This research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1) and Specific Grant Agreement No. 785907 (Human Brain Project SGA2).

©(2019) Blixhavn CH, Andersson KA, Kleven H, Schlegel U, Puchades MA, Bjaalie JG, Leergaard TB Cite as: Blixhavn CH, Andersson KA, Kleven H, Schlegel U, Puchades MA, Bjaalie JG, Leergaard TB (2019) Infrastructure and workflow for integrating and navigating multi-scale and multi-modal murine neuroscience data using 3D digital brain reference atlases. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0066

[P 68] Microservice infrastructure for continuous validation, processing and indexing of research data on an open platform

Michael Sonntag1, Achilleas Koutsou1, Christian Garbers1, Jiří Vaněk1, Thomas Wachtler1

1. German Neuroinformatics Node, Ludwig-Maximilian-Universität München, Martinsried, Germany Research requires data to continuously be processed, analyzed and visualized; data needs to be quality checked, verified and backed up. Data and metadata need to be made publicly available in an easy to find and use manner. Many of these tasks can be automated, which usually leads to fewer errors and a higher results quality. To facilitate these tasks, we introduce a suite of microservices for the G-Node data infrastructure (GIN)[1], an open platform for collaboration and sharing of research data and code. - gin-valid is a service for validating files in GIN repositories based on various format standards. Once a GIN repository is registered with the validation service, every update to the data automatically triggers validators for the supported types and the results 90 Posters

are made available to the owner. The service covers the BIDS[2], NIX[3] and odML[4] formats, validation of CSV tables based on goodtables[5] is in development. - gin-proc automates data processing, based on SnakeMake[6] and Drone CI[7] making workflows reproducible. Much like gin-valid, a registered repository will execute a defined data processing workflow whenever data changes occur. - gin-dex automatically extracts metadata information from supported file types and provides extensive search across GIN repositories, making data easily findable and accessible. These new microservices for the GIN framework enable achieving higher quality and FAIRness of data while reducing workload for scientists. Acknowledgements Supported by BMBF (Grants 01GQ1302 and 01GQ1509) References 1 GIN (RRID:SCR_015864) https://gin.g-node.org 2 BIDS (RRID:SCR_016124) http://bids.neuroimaging.io 3 NIX (RRID:SCR_016196) http://www.g-node.org/nix 4 odML (RRID:SCR_001376) http://www.g-node.org/odml 5 goodtables https://goodtables.io 6 SnakeMake (RRID:SCR_003475) doi: 10.1093/bioinformatics/bts480 7 DroneCI https://drone.io

©(2019) Sonntag M, Koutsou A, Garbers C, Vaněk J, Wachtler T Cite as: Sonntag M, Koutsou A, Garbers C, Vaněk J, Wachtler T (2019) Microservice infrastructure for continuous validation, processing and indexing of research data on an open platform. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0067

[P 69] Minimum Information for Neuroscience Data Sets (MINDS): The metadata standard of the Human Brain Project data sharing platform.

Lyuba Zehl1, Sara Zafarnia1, Stefan Köhnen1, Anna Hilverling1, Krister Andersson2, Elodie Legouée3, Camilla Hagen Blixhavn2, Ida E.J. Aasebø2, Maja A. Puchades2, Ulrike Schlegel2, Heidi Kleven2, Oliver Schmid4, Tom Gillespie5, Mathew B. Abrams5, Andrew Davison3, Trygve B. Leergaard2, Katrin Amunts1,6, Jan G. Bjaalie2, Timo Dickscheid1 1. Institute for Neuroscience and Medicine (INM-1), Jülich Research Centre, Jülich, Germany 2. Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway 3. Department for Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience UMR 9197 CNRS/Université Paris Sud, France 4. École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland 5. International Neuroinformatics Coordinating Facility (INCF), Karolinska Institutet, Stockholm, Sweden 6. Cécile and Oskar Vogt Institute of Brain Research, Heinrich Heine University Düsseldorf, University Hospital Düsseldorf, Düsseldorf, Germany The Human Brain Project (HBP) is building a unique data sharing framework that provides access to neuroscience data of different modalities. For this, corresponding metadata are registered into the HBP Knowledge Graph (KG). In order to perform data queries across scales and modalities via the KG, the registered metadata have to capture experiment-specific aspects, yet guarantee comparability across experimental data. To this end, the HBP curation team implemented the HBP metadata standard MINDS (Minimum Information for Neuroscience Data Sets). MINDS (v0.3, in dev) is a flexible, ontology-based metadata standard consisting of 13 information blocks (Fig. 1) that can be used to describe the origin, context, content, and physical location of individual data in a modular fashion. It allows users to successively increase the level 91 Posters of detail for data descriptions in the KG, and to link to additional in-depth metadata. To ensure interoperability, metadata in the MINDS information blocks are linked to existing neuroscience ontologies. Nonetheless, anticipating that available ontologies do not completely cover terminologies for all neuroscience data, metadata entries are not forced to strictly match ontology terms. In fact, user-defined entries are used by the HBP data sharing framework to trigger the advancement of existing ontologies. We present here the implementation of MINDS and how this unique metadata standard can be used to describe and link neuroscience data in the HBP KG.

Basic information blocks of the HBP MINDS. According to its name, each block can be used to collect a set of related metadata. In order to describe a data set, suitable MINDS blocks need to be generated and filled with corresponding metadata, as well as linked according to their data set connection.

Acknowledgements This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and 785907 (HBP SGA2).

©(2019) Zehl L, Zafarnia S, Köhnen S, Hilverling A, Andersson K, Legouée E, Hagen Blixhavn C, Aasebø IE, Puchades MA, Schlegel U, Kleven H, Schmid O, Gillespie T, Abrams MB, Davison A, Leergaard TB, Amunts K, Bjaalie JG, Dickscheid T Cite as: Zehl L, Zafarnia S, Köhnen S, Hilverling A, Andersson K, Legouée E, Hagen Blixhavn C, Aasebø IE, Puchades MA, Schlegel U, Kleven H, Schmid O, Gillespie T, Abrams MB, Davison A, Leergaard TB, Amunts K, Bjaalie JG, Dickscheid T (2019) Minimum Information for Neuroscience Data Sets (MINDS): The metadata standard of the Human Brain Project data sharing platform.. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0068

92 Posters

[P 72] Spectral Fingerprints: identification accuracy of regions of the human brain varies with an analyzed dataset - a MEG and EEG data study

Michał Konrad Komorowski1,2, Jakub Wojciechowski3, Jan Nikadon1,2, Tomasz Jan Piotrowski1,2, Joanna Dreszer2,4, Włodzisław Duch1,2 1. Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Poland 2. Centre for Modern Interdisciplinary Technologies„ Nicolaus Copernicus University in Toruń, Poland 3. Bioimaging Research Centre, Institute of Physiology and Pathology of Hearing in Kajetany, Poland 4. Faculty of Humanities, Nicolaus Copernicus University in Toruń, Poland Each region of a human brain tends to preserve its own natural frequency of oscillations. This allows identifying its activity among other regions via the so-called brain Spectral Fingerprint (SF) - a data-driven model that captures dynamic features of different brain regions from MEG / EEG signal. The original method was proposed by Keitel and Gross in 2016 and tested on magnetoencephalographic (MEG) data recorded during the resting period. It is based on the clustering activity of each region of interest (ROI) as measured by the MEG source-reconstructed activity. 116 ROIs were selected according to the Automated Anatomical Labeling Atlas. As a result, a group-level spectral representation of the dynamic behavior of the human cortex and deep sources is obtained. SF representation enables fairly accurate identification of ROIs. Moreover, using only functional data, the algorithm linked ROIs to clusters that correspond well to the large-scale anatomical parcellations of the cortex. We have recreated and adapted automatic analysis pipeline to investigate resting-state MEG data from the Human Connectome Project and in-house recorded EEG datasets. Among many questions that we try to answer is how a choice of EEG instead of MEG will influence the accuracy of the ROI identification, how various source reconstruction methods and clusterization techniques will affect the results and what metric should be used to compare SF from different datasets.

EEG Spectral Fingerprint of a left medial part of the Cerebellum is presented as a clustered power spectra in source space in comparison to the whole brain. The legend shows the corresponding duration of each pattern. Shaded error bars illustrate the standard error of the mean (N=12).

Acknowledgements Study was supported by the National Science Center Poland (UMO-2016/20/W/NZ4/00354) grant. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium. Calculations were carried out at the Academic Computer Centre in Gdańsk. We thank A.Keitel and J.Gross for their support.

93 Posters

References 1 A. Keitel and J. Gross, “Individual human brain areas can be identified from their characteristic spectral activation fingerprints,” PLoS Biol, vol. 14, no. 6, p. e1002498, 2016. doi: 10.1371/jour- nal.pbio.1002498 2 A. Puce and M. S. Hämäläinen, “A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies,” Brain Sci, vol. 7, no. 6, May 2017. doi: 10.3390/brainsci7060058 3 W. O. Tatum, Handbook of EEG interpretation. New York [N.Y.: Demos Medical Pub., 2013. https://www.springerpub.com/handbook-of-eeg-interpretation-second-edition-9781620700167.html 4 R. A. Poldrack, J. A. Mumford, and T. E. Nichols, Handbook of functional MRI data analysis. Cam- bridge New York Melbourne Madrid: Cambridge University Press, 2011. https://www.amazon.co.uk/ Handbook-Functional-MRI-Data-Analysis/dp/0521517664 5 S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic brain mapping,” IEEE Signal Processing Magazine, vol. 18, no. 6, pp. 14–30, Nov. 2001. doi: 10.1109/79.962275 6 M. X. Cohen, Analyzing neural time series data: theory and practice. Cambridge, Massachusetts: The MIT Press, 2014. https://mitpress.mit.edu/books/analyzing-neural-time-series-data 7 J. G. Proakis and D. K. Manolakis, Digital Signal Processing, 4 edition. Upper Saddle River, N.J: Pearson, 2006. https://www.amazon.com/Digital-Signal-Processing-John-Proakis/dp/0131873741 8 B. D. Van Veen, W. Van Drongelen, M. Yuchtman, and A. Suzuki, “Localization of brain electrical activity via linearly constrained minimum variance spatial filtering,” IEEE Transactions on biomedical engineering, vol. 44, no. 9, pp. 867–880, 1997. doi: 10.1109/10.623056 9 C. Wolters and J. Munck, “Volume conduction,” Scholarpedia, vol. 2, no. 3, p. 1738, 2007. (10.4249/scholarpedia.1738) https://www.google.com/search?hl=en&source=hp&q=C.+Wolters+ and+J.+Munck%2C+%E2%80%9CVolume+conduction%2C%E2%80%9D+Scholarpedia%2C+vol.+ 2%2C+no.+3%2C+p.+1738%2C+2007.+(10.4249%2Fscholarpedia.1738)

©(2019) Komorowski MK, Wojciechowski J, Nikadon J, Piotrowski TJ, Dreszer J, Duch W Cite as: Komorowski MK, Wojciechowski J, Nikadon J, Piotrowski TJ, Dreszer J, Duch W (2019) Spectral Fingerprints: identification accuracy of regions of the human brain varies with an analyzed dataset - a MEG and EEG data study. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0069

[P 74] A Dedicated Light Sheet Fluorescence Microscopy Atlas for Mapping Neuronal Activity and Genetic Markers in the Mouse Brain

Johanna Perens1 1. Gubra, Hørsholm Kongevej 11B 2970 Hørsholm, Denmark The brain is the most complex organ in the body consisting of billions of neurons that are connected in a complex functional network of both long and short distance activating or inhibitory signals over long distances. Registration of anatomical features, neuronal connectivity and genetic markers into a common coordinate framework (CCF) has therefore greatly improved our understanding of the brain but also highlighted the spatial complexity and the need for high quality 3D reference maps. In mice, Allen’s Institute of Brain Science has pioneered this effort by generating annotated average brain atlases based on Nissl staining’s and two-photon microscopy images. However, due to differences in tissue processing such atlases are not always suitable for registration of data obtained using other imaging modalities. Therefore, we developed a digital mouse brain atlas for automated analysis of intact iDISCO cleared brains scanned with light sheet fluorescence microscopy (LSFM). The digital LSFM mouse brain atlas incorporates a variational average LSFM mouse brain image with 20 µm isotropic resolution, which was generated from LSFM autofluorescence images of 162 individual mice brains, and anatomical annotations of the brain regions. The variational LSFM mouse brain image 94 Posters was created through iterative multi-resolution image registration algorithm in order to avoid the bias towards a chosen reference brain and account for morphological differences between individual mouse brains.

©(2019) Perens J Cite as: Perens J (2019) A Dedicated Light Sheet Fluorescence Microscopy Atlas for Mapping Neuronal Activity and Genetic Markers in the Mouse Brain. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0070

[P 77] kernel Electrical Source Imaging (kESI) - reconstruction of sources of brain electric activity in realistic brain geometries

Marta Kowalska1, Jakub M. Dzik1, Chaitanya Chintaluri1,2, Daniel K. Wójcik1 1. Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland 2. Department of Physiology Anatomy and Genetics, University of Oxford, Sherrington Building, Parks Road Oxford OX1 3PT, United Kingdom Epilepsy affects around 50 million people worldwide. Every third patient suffers from intractable seizures, in spite of efficiency and a steady development of pharmacological treatments. In these patients surgical intervention may be the only solution. To identify the epileptogenic zone, neurosurgeons implant electrodes on the cortex or deep in the brain. The measured potentials are used in localizing the epileptic source. We argue that reconstruced source of this brain activity may lead to more precise localization of seizures’ origin and better surgical outcome. Here we present a method - kernel Electrical Source Imaging (kESI) - to localize multiple sources in the brain. kESI allows arbitrary electrode positions which makes it applicable for a specific patient’s case. The advantage of kESI over previous work (Potworowski et al., 2012) is that it accounts for spatial variations of brain conductivity and brain anatomy. In the poster we present our most recent state of development of kESI method for reconstruction of brain activity from cortical and depth recordings in rodents and humans. Acknowledgements Project funded from the Polish National Science Centre’s OPUS grant (2015/17/B/ST7/04123). References 1 Potworowski, J., Jakuczun, W., Łęski, S. & Wójcik, D. (2012) Kernel current source density method. Neural Comput 24(2), 541-575. doi: 10.1162/NECO_a_00236

©(2019) Kowalska M, Dzik JM, Chintaluri C, Wójcik DK Cite as: Kowalska M, Dzik JM, Chintaluri C, Wójcik DK (2019) kernel Electrical Source Imaging (kESI) - reconstruction of sources of brain electric activity in realistic brain geometries. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0071

95 Demos

Demos

[D 01] TrainingSpace: neuroeducation without borders

Pradeep George1, Malin Sandström1, Lotta Johansson1, Mathew Abrams1 1. INCF, Nobels väg 15 A, Sweden TrainingSpace (TS) is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community developed in collaboration with INCF, HBP, SfN, FENS, IBRO, IEEE, BD2K, and iNeuro Initiative. As a hub, TS provides users with access to: Multimedia educational content from courses, conference lectures, and laboratory exercises from some of the world’s leading neuroscience institutes and societies - Study tracks to facilitate self-guided study - Tutorials on tools and open science resources for neuroscience research - A Q&A forum - A neuroscience encyclopedia that provides users with access to over 1.000.000 publicly available datasets as well as links to literature references and scientific abstracts Topics currently included in TS include: general neuroscience, clinical neuroscience, computational neuroscience, neuroinformatics, computer science, data science, and open science. All courses and conference lectures in TS include a general description, topics covered, links to prerequisite courses if applicable, and links to software described in or required for the course, as well as links to the next lecture in the course or more advanced related courses. In addition to providing resources for students and researchers, TS also provides resources for instructors, such laboratory exercises, open science services, and access to publicly available datasets and models. To learn more about TrainingSpace, see: https://training.incf.org/ Acknowledgements INCF Training and Education Committee for thought leadership and oversight

©(2019) George P, Sandström M, Johansson L, Abrams M Cite as: George P, Sandström M, Johansson L, Abrams M (2019) TrainingSpace: neuroeducation without borders. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0072

96 Demos

[D 02] Incorporating EEG quantitative analysis into the MNI Neuroinformatics ecosystem

Jorge Bosch-Bayard1,2,3, Christine Rogers1, Eduardo Aubert-Vazquez3, Shawn T Brown1, Greg Kiar1, Tristan Glatard4, Lalet Scaria4, Lidice Galan-Garcia3, Maria-Luisa Bringas- Vega2,3, Trinidad de los Virues-Alba3, Armin Taheri1, Samir Das1, Cecile Madjar1, Zia Mohades1, Leigh MacIntyre1, Alan C Evans1, Pedro A Valdes-Sosa1,2,3 1. McGill Centre for Integrative Neuroinformatics, Montreal Neurological Institute, McGill University, 3801 Uni- versity St, Montreal, Canada 2. The Clinical Hospital of Chengdu Brain Sciences Institute, University of Electronic Science and Technology of China UESTC, Chengdu, China 3. Cuban Neuroscience Center, Havana, Cuba 4. Concordia University, Montreal, Canada Integration of sophisticated EEG analysis with high-performance computing is pivotal to growing standardized applications in different research and clinical settings. This objective has been undertaken in a joint collaboration between the Cuban Neuroscience Center (CNEURO) and the University of Electronic Science and Technology of China led by Pedro Valdes-Sosa, with the McGill Centre for Integrative Neuroscience at Canada’s Montreal Neurological Institute (MNI) led by Alan Evans. To fully incorporate advanced EEG analysis into a single neuroinformatics “ecosystem”, quantitative EEG methods developed by CNEURO have been integrated into LORIS and CBRAIN platforms developed at the MNI. This software “ecosystem” was developed in parallel by the Evans group since the early 1990s to standardize and expand methodologies in neuroimaging analysis and to share tools through “open science”. Recently, the Tomographic Quantitative Electroencephalography (qEEGt) toolbox was released via CBRAIN. It provides registered users open access to EEG frequency domain source spectra, a variety of descriptive EEG parameters, comparison of z spectra with respect to the Cuban normative database, visualization and more. This Statistical Parametric Mapping of functional brain data is extensible by design to other neuroimaging modalities, to improve methods and solutions that will lead to a wider understanding of how the brain works, and to aid clinical diagnosis.

©(2019) Bosch-Bayard J, Rogers C, Aubert-Vazquez E, Brown ST, Kiar G, Glatard T, Scaria L, Galan- Garcia L, Bringas-Vega M, de los Virues-Alba T, Taheri A, Das S, Madjar C, Mohades Z, MacIntyre L, Evans AC, Valdes-Sosa PA Cite as: Bosch-Bayard J, Rogers C, Aubert-Vazquez E, Brown ST, Kiar G, Glatard T, Scaria L, Galan- Garcia L, Bringas-Vega M, de los Virues-Alba T, Taheri A, Das S, Madjar C, Mohades Z, MacIntyre L, Evans AC, Valdes-Sosa PA (2019) Incorporating EEG quantitative analysis into the MNI Neuroinformatics ecosystem. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0073

97 Demos

[D 03] ReproSchema and Library: A JSON-LD schema to harmonize behavioral, cognitive, and neuropsych assessments

Sanu Ann Abraham1, Anisha Keshavan2, Zaliqa Rosli3, Jon Clucas2, Arno Klein2, Satrajit Ghosh1, Samir Das3 1. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA 2. Child Mind Institute, NY, NY, USA 3. McGill Center for Integrative Neuroscience, McGill University, Montreal, QC, Canada Cognitive and clinical assessments are used throughout neuroscience, but little consis- tency exists in assessment data acquisition or response representation across studies. Harmonizing data after acquisition is resource intensive. Currently, the NIMH Data Archive (NDA) [1] enforces harmonization during data submission. This approach can create a mismatch between collected and submitted data. Reverse engineering NDA data dictionaries to their original assessments using a tool like Brainverse [2] can be tedious. To enforce consistency at the data acquisition stage, we created a standard schema and a set of reusable common assessments. The schema extends and modifies the CEDAR [3] metadata representation. Using JSON-LD [4], we represent Items or Scores (elements of individual assessments), Activities (individual assessments), and Activity sets (collections of activities performed by a participant). An implementation of the schema can specify scoring logic, branching logic, and user interface rendering options. The schema allows internationalization (multiple languages), is implementation agnostic, and tracks variations in assessments (e.g., PHQ-9, PHQ-8). This open and accessible schema library with appropriate conversion (e.g., to RedCap) and data collection tools (e.g., MindLogger [5], LORIS [6], RedCap [7]) enables more consistent acquisition across projects, with results being harmonized by design.

Left: snapshot of jsonld for PHQ-9 assessment. Right: data collection user-interface rendering the assessment in Reproschema format

Acknowledgements We acknowledge NIH grants (P41EB019936, R01EB020740). This work has also been continuously supported and made possible by the International Neuroinformatics Coordinating Facility (INCF) metadata working group. References 1 NIMH Data Archives https://ndar.nih.gov/ 2 BrainVerse https://github.com/ReproNim/brainverse 3 CEDAR https://link.springer.com/chapter/10.1007/978-3-319-49004-5_49 4 JSON-LD https://json-ld.org/spec/latest/json-ld/ 5 MindLogger https://mindlogger.info/

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6 LORIS https://www.frontiersin.org/articles/10.3389/fninf.2011.00037/full 7 RedCap https://projectredcap.org/

©(2019) Abraham SA, Keshavan A, Rosli Z, Clucas J, Klein A, Ghosh S, Das S Cite as: Abraham SA, Keshavan A, Rosli Z, Clucas J, Klein A, Ghosh S, Das S (2019) ReproSchema and Library: A JSON-LD schema to harmonize behavioral, cognitive, and neuropsych assessments. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0074

[D 04] Combining commercial and community-driven foundations to address challenges in neuroinformatics

Vijay Iyer1, Lisa Kempler2 1. MathWorks, Inc., Natick, MA, USA 2. MathWorks, Inc., Natick, MA, USA Informatics practices in neuroscience are changing rapidly, including practices for scientific computing, code availability, and data sharing. Scientific compute cycles increasingly execute on remote servers to provide end-users with advantages ranging from portability, scalability, GPU acceleration (e.g. for deep learning), software configuration, pipeline management, and proximity to large datasets. Scientific code and data are increasingly shared with colleagues, thus increasing the global impact of intricate analyses and hard-won datasets produced by individual labs and institutes. The approach of MathWorks for these neuroinformatics needs is to serve as both a commercial tool provider and an active participant in community-driven initiatives [1]. We show here how new tools such as Live Editor, MATLAB Online, GPU Coder, and MATLAB Parallel Server can address many of these new requirements. We demonstrate how our community tool site, the File Exchange, is evolving to better facilitate code sharing, discovery, and collaboration, including peering with GitHub. We also highlight community-driven efforts for neuroinformatics use cases, such as Neurodata without Borders [2, 3] for data exchange standardization and Neuroscience Gateway [4] for broadened and streamlined cluster computing access. Integration of a commercial product (MATLAB) with community efforts provides neuroscientists a stronger platform for tapping into their network of code, data, and compute resources. Acknowledgements MATLAB interface to Neurodata without Borders (NWB): contributions by Vidrio Technologies, funding from the Kavli Foundation and Simons Foundation. References 1 http://mathworks.com/solutions/neuroscience http://mathworks.com/solutions/neuroscience 2 http://nwb.org/ http://nwb.org/ 3 https://github.com/NeurodataWithoutBorders/matnwb https://github.com/NeurodataWithoutBorders/ matnwb 4 https://www.nsgportal.org/ https://www.nsgportal.org/

©(2019) Iyer V, Kempler L Cite as: Iyer V, Kempler L (2019) Combining commercial and community-driven foundations to address challenges in neuroinformatics. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0075

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[D 05] Nipype 2.0: A new Python-based ecosystem for neuroscientific workflows and reproducibility

Dorota Jarecka1, Christopher J. Markiewicz2, Mathias Goncalves1, Jakub Kaczmarzyk1, Oscar Esteban2, Krzysztof J. Gorgolewski2, Satrajit Ghosh1 1. McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA 2. Psychology, Stanford University, Stanford, CA, USA Data from neuroimaging experiments require complex analyses to interpret. These analyses often utilize algorithms from various software packages, and consistency and reproducibility demand they be organized into automated pipelines. Nipype has been helping scientists build workflows for almost a decade, providing a uniform interface to such neuroimaging packages as FSL, ANTs, AFNI, FreeSurfer and SPM. This flexibility has made it an ideal basis for popular preprocessing tools, such as fMRIPrep and C-PAC. Here, we present the next generation of the Nipype ecosystem, which provides additional flexibility and is designed to easily adapt to any other scientific domain. Nipype 2.0 is being developed with reproducibility, ease of use, and scalability in mind. The main elements of its modular design: Pydra - a workflow engine; TestKraken - parameterized testing; Neurodocker - reproducible environments supporting Docker and Singularity; Niflows - reusable, importable and well-tested workflows. The core package in the ecosystem, Pydra, is a workflow engine implementing: 1 Simplified semantics of computational objects: a) Task combines the functionality of Interfaces and Nodes; b) Splitting/combining semantics replace iterables, JoinNodes and MapNodes while providing increased runtime flexibility; c) Consistent API for Task and Workflow; 2. Conditional execution; 3. Global cache support to reduce recomputation; 4. Support for execution of Tasks and Workflows in isolated environments. Acknowledgements We acknowledge NIH grants (P41EB019936, R01EB020740) and the neuroimaging community for feedback.

©(2019) Jarecka D, Markiewicz CJ, Goncalves M, Kaczmarzyk J, Esteban O, Gorgolewski KJ, Ghosh S Cite as: Jarecka D, Markiewicz CJ, Goncalves M, Kaczmarzyk J, Esteban O, Gorgolewski KJ, Ghosh S (2019) Nipype 2.0: A new Python-based ecosystem for neuroscientific workflows and reproducibility. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0076

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[D 06] FMRIDenoise: tool for automatic denoising and quality control of functional connectivity data

Karolina Finc1, Kamil Bonna1,2, Mateusz Chojnowski1,2, Włodzisław Duch1,2 1. Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Wileńska 4, Poland 2. Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziądzka 5/7, Poland Functional connectivity (FC) became a prominent method in functional MRI (fMRI) studies. Before estimating FC, fMRI data have to be denoised to minimize the effect of motion and physiological processes via regressing out potentially confounding variables from the fMRI time-series. Great variability in denoising strategy choices used by researchers makes comparisons across FC studies hardly possible. This problem raises the necessity to develop tools supporting reproducible fMRI data processing. One good example is the fMRIPrep tool for preprocessing fMRI data that requires minimal user input and provides detailed reports for further data quality control (Esteban et al., 2019). Here we present fMRIDenoise, a tool for automatic denoising and denoising strategies comparison, working directly on fMRIPrep derivatives. The tool performs denoising using common denoising strategies and provides the output including: (1) subject-level reports and the information which subjects should be excluded from further analyses due to high motion, (2) FC quality control benchmarks estimated on group level (Parkes et al., 2018), (3) recommendation of the best-performing pipeline given the data. We believe that fMRIDenoise can make a selection of the denoising strategy more objective, help researchers to obtain FC quality control metrics with almost no effort, and improve the reproducibility of the FC research. Acknowledgements K.F. acknowledges support from the Foundation for Polish Science, Poland (START 23.2018). References 1 Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Oya, H. (2019). FMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111. doi: 10.1038/s41592-018-0235-4 2 Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage, 171, 415-436. doi: 10.1016/j.neuroimage.2017.12.073

©(2019) Finc K, Bonna K, Chojnowski M, Duch W Cite as: Finc K, Bonna K, Chojnowski M, Duch W (2019) FMRIDenoise: tool for automatic denoising and quality control of functional connectivity data. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0077

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[D 08] Adiutis - a prototype for simultaneous electrodermal activity and heart rate variability measurements

Piotr Konorski1 1. Sybilla Technologies, Toruńska 59/004, 85-023 Bydgoszcz, Poland Adiutis is a prototype of a versatile device designed primarily for tightly-syncronized multi- channel monitoring of electrodermal activity (EDA) and heart rate variability (HRV) simultaneously, in real-time. Its lightweight and ergonomic construction allow for practical and non-disturbing everyday use, which combined with high cadence of data recording (100 Hz) and excellent data accuracy ( 1%) make Adiutis a unique tool for both non- and stationary measurements. The device, equipped with embedded WiFi transceiver specifically for mobile applications, encompasses a set of electronics connecting optimally- placed sensors attached to shirt/sock/pants/headband textiles for comfortable wearing while providing necessary contact points with the skin, assuring strong and reliable signals. Additionally, a dedicated software suite for data reduction, analysis and visualisation has been developed allowing for validation of a proposed solution under rigorously-designed psychophysiological experiments mainly focused on emotion/stress level recognition, for which Adiutis, supported by adopted machine learning algorithms, achieved accuracies exceeding 80% on average. Adiutis was developed under European Space Agency contract reaching TRL of 3 (partially reaching TRL-4), confirming its applicability for scientific research and commercial market, serving the purpose of an important psychophysiological measurement and reference tool.

Adiutis device ©(2019) Konorski P Cite as: Konorski P (2019) Adiutis - a prototype for simultaneous electrodermal activity and heart rate variability measurements. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0078

102 Demos

[D 09] Changes Of Colony Stimulatory Factors In White Rats Hippocampus During Cycloheximide Induced Memory Loss

Arman Artak Hovhannisyan1, Artem Sergey Grigoryan1, Garnik Ara Avetisyan2 1. Pathophysiology, Yerevan State Medical University, 2 Koryun Street, Armenia 2. Radiology, Yerevan State Medical University, 2 Koryun Street, Armenia Introduction There are several pathways, which are involved in memory formation processes as well as migration of bone marrow stem cells to current areas of brain and differentiation to mature neurons. Brain is major initiator of this process by production of stromal derived factor-1, granulocyte macrophage colony stimulatory factor, granulocyte colony stimulatory factor and macrophage colony stimulatory factors. Materials and Methods Experiments were carried out on 60 white male rats(n=15). Animals were kept in general vivarium states with free access of food and water. All procedures were carried out under intraperitoneal injection of 40mg/kg Nembutal for anesthesia. Hippocampus was extracted and homogenized in 50mM Tris HCl containing buffer with furthermore centrifugation by 6000g 60min 4 0C.Determination of colony stimulatory factors was performed by ELISA method (Sigma). Statistics was performed by SPSS 21.0 program. Results Results testify that SDF-1 decreased by 55.2% on 60th day of experiment, while G-CSF decreased by 44.1% and 57% on 40th and 60th days(p<0.01) GM-CSF decreased by 53.4% 61% and 65.4% during experiment, while M-CSF factor decreased by 38.5% and 42% on 40th and 60th days(p<0.001) Conclusion Decrease of SDF-1 in later stages testifies about activation of compensatory mechanisms which are preventing damage of bone marrow stem cell migration processes to brain. . Therefore, decrease of colony stimulatory factors stimulates toxic effects of IL-6 on brain References 1 Int J Mol Sci. 2010 Jan; 11(1): 222–232. doi: 10.3390/ijms11010222 2 Neuron. 2014 Apr 16;82(2):380-97 doi: 10.1016/j.neuron.2014.02.040. 3 Dev Biol. 2012 Jul 15;367(2):100-13 doi: 10.1016/j.ydbio.2012.03.026. Epub 2012 Apr 19. 4 Send to Trends Neurosci. 2016 Jun;39(6):378-393. doi: 10.1016/j.tins.2016.03.005. Epub 2016 Apr 12. 5 Curr Top Dev Biol. 2017;123:229-275. doi: 10.1016/bs.ctdb.2016.10.004. Epub 2016 Dec 1.

©(2019) Hovhannisyan AA, Grigoryan AS, Avetisyan GA Cite as: Hovhannisyan AA, Grigoryan AS, Avetisyan GA (2019) Changes Of Colony Stimulatory Factors In White Rats Hippocampus During Cycloheximide Induced Memory Loss. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0079

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[D 10] Modeling Neural Development with Braitenberg Vehicles

Stefan Dvoretskii1, Bradly Alicea2 1. Department of Informatics, Technical University of Munich, Boltzmannstraße 3, 85748 Garching bei München, Germany 2. Orthogonal Research and Education Lab In the second half of the twentieth century, the psychologist Valentino Braitenberg proposed a thought experiment in which embodied simulations of natural cognitive processes are being created. This is achieved by building so-called vehicles out of primitive blocks, like sensor-motor connected pairs, or signal threshold activation devices (cp. ReLU). Although there have been some attempts to make computer simulations implementing “Vehicles” mind experiment alone, our project proposes a way to provide these vehicles a “brain” - neural networks connecting different parts of vehicle’s body with each other. We then evolve these neural networks in sense of adding new neurons and connections to a vehicle’s connectome. We parametrize the Genetic Algorithm we use to evolve the networks, and in particular the fitness function, which allows the conduction of custom simulation experiments. We build our simulation on top of Simbrain neural networks simulation tool (simbrain.net). The neurons form different connectivity patterns that influence vehicle behaviour. We hope to observe some natural behavioural patterns as well as some connectome motifs seen in nature. This would allow us to reproduce the very same behaviours’ simulation, as well as hypothesize on correspondence of connectome motifs to specific behaviours. The project results have possible applications in brain development studies, transferring synthesized models to robots andenriching virtual embodied systems’ intelligence.

A brief sketch of the development plan of the simulation

Acknowledgements We are thankful to Google as well as Google Summer of Code for providing the stipend to work on this project. References 1 V. Braitenberg Vehicles: Experiments in Synthetic Psychology, Philosophical Review 95 (1):137-139 (1986) doi: 10.2307/2185146 2 Gisiger T., Dehaene S. and Changeux, J.-P. Computational models of association cortex, Current Opinion in Neurobiology 2000, 10:250–259 doi: 10.1016/S0959-4388(00)00075-1 3 Vlada Kugurakova, Maxim Talanov, Denis Ivanov, Neurobiological Plausibility as Part of Criteria for Highly Realistic Cognitive Architectures, Procedia Computer Science, Volume 88, 2016, Pages 217-223 doi: 10.1016/j.procs.2016.07.428. 4 Sporns O, Ko¨tter R (2004) Motifs in brain networks. PLoS Biol 2(11): e369 doi: 10.1371/jour- nal.pbio.0020369

©(2019) Dvoretskii S, Alicea B Cite as: Dvoretskii S, Alicea B (2019) Modeling Neural Development with Braitenberg Vehicles. Neu- roinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0080

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[D 11] SciDash: A web portal for model validation using SciUnit

Richard C Gerkin1, Sharon M Crook1,2, Justas Birgiolas1, Giovanni Idili3 1. School of Life Sciences, Arizona State University, Tempe, AZ, USA 2. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA 3. MetaCell, LLC, Oxford, OX2 0HJ, UK Neuron models span many programming languages, levels of implementation, and scientific goals. Regardless of a model’s form, we may care most about one thing: does it recapitulate the key facts, features, and behaviors of the biological system? SciUnit[1,2] facilitates the creation of “unit tests” for models, abstracting away im- plementation details to focus on model/data agreement. In conjunction with the neuroscience-specific module NeuronUnit[3], models described in NeuroML[4] can be tested against several dozen unit tests of neuron behavior, each of which can encode observed data from The Human Brain Project, The Allen Institute, Neuroelectro.org[5], or custom data sources. Each test compares model output (e.g. spike trains, IV curves, or oscillation power spectra) against experimental observations. Models can be evaluated according to their performance on such tests. To make this process and its outcomes more public, we developed SciDash[6], a web platform for creating, running, and visualizing tests of models described in NeuroML. Users can instantiate, schedule, and run tests on the site, or upload locally generated SciUnit test scores via API. Individual models, tests, and scores can be searched, sorted, and filtered to identify those of interest, for example those corresponding to a specific cell type. The performance of many models against many tests can be visualized in a “score matrix”, transparently summarizing the merits and limitations of a panoply of models.

A “score matrix” from the SciDash website, showing the performance of each of 7 olfactory bulb mitral cell models on each of 6 tests derived from mitral cell data available on neuroelectro.org. This figure illustrates how well each of these models recapitulates a given feature of experimental data.

Acknowledgements We would like to acknowledge financial support from NIH (NIBIB R01EB021711 and NIMH R01MH106674) and the Human Brain Project (Infrastructure Voucher Programme). We would also like to recognize the high quality development work implemented by Metacell, LLC.

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References 1 Omar C, Aldrich J, Gerkin RC. Collaborative Infrastructure for Test-driven Scientific Model Validation. Companion Proceedings of the 36th International Conference on Software Engineering. New York, NY, USA: ACM; 2014. pp. 524–527. doi: 10.1145/2591062.2591129 2 SciUnit: A Python framework for test-driven validation of scientific models https://github.com/scidash/sciunit 3 NeuronUnit: A package for data-driven validation of neuron and ion channel models using SciUnit https://github.com/scidash/neuronunit 4 Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, et al. NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol. 2010;6: e1000815. doi: 10.1371/journal.pcbi.1000815 5 Tripathy SJ, Savitskaya J, Burton SD, Urban NN, Gerkin RC. NeuroElectro: a window to the world’s neuron electrophysiology data. Front Neuroinform. 2014;8: 40. doi: 10.3389/fninf.2014.00040 6 SciDash: A web portal for model validation using SciUnit http://dash.scidash.org

©(2019) Gerkin RC, Crook SM, Birgiolas J, Idili G Cite as: Gerkin RC, Crook SM, Birgiolas J, Idili G (2019) SciDash: A web portal for model validation using SciUnit. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0081

[D 12] supFunSim: spatial filtering toolbox for EEG

Krzysztof Rykaczewski1, Jan Nikadon1, Włodzisław Duch1, Tomasz Jan Piotrowski1 1. Nicolaus Copernicus University Recognition and interpretation of brain activity patterns from EEG or MEG signals is one of the most important tasks in cognitive neuroscience, requiring sophisticated methods of signal processing. We present supFunSim: a new Matlab toolbox which generates accurate EEG forward models and implements a collection of spatial filters for EEG source reconstruction, including linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) and directed transfer function (DTF) measures. The supFunSim library is based on the well-known FieldTrip toolbox for EEG and MEG analysis and is written using object-oriented programming paradigm. The resulting modularity of the toolbox enables its simple extensibility. Acknowledgements National Science Center Poland, grant no. UMO-2016/20/W/NZ4/00354 References 1 K Rykaczewski, J Nikadon, W Duch, T Piotrowski doi: 10.1101/618694

©(2019) Rykaczewski K, Nikadon J, Duch W, Piotrowski TJ Cite as: Rykaczewski K, Nikadon J, Duch W, Piotrowski TJ (2019) supFunSim: spatial filtering toolbox for EEG. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0082

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[D 13] Batch quantification and spatial analysis of labelling in microscopic rodent brain sections

Sharon Christine Yates1, Nicolaas Groeneboom1, Gergely Csucs1, Trygve B Leergaard1, Maja A Puchades1, Jan G Bjaalie1 1. University of Oslo, Molecular medicine, Institute of Basic Medical Science, Norway Experimental research and intervention studies in small animal disease models depend on quantitative comparisons of cellular and molecular measures in large groups of specimens, requiring efficient and reproducible methods. We present a new workflow for automated quantification and spatial analysis of labelling in large series of section images from mouse and rat brains, using Human Brain Project (HBP) tools and procedures. To overcome the limitations of traditional semi-automated approaches that rely on manual anatomical feature delineation combined with either stereology or feature extraction by segmentation, the new workflow combines: 1. image registration to 3D atlases with the QuickNII tool, defining anatomical regions of interest and generating atlas maps; 2. advanced automated image segmentation using machine learning with ilastik based on Random Forest Algorithm for supervised classification. 3. analysis of the segmented images at the whole brain and regional level, using the registered atlas maps (Nutil software). Thereby, the number of objects (extracted features) and areas occupied by the objects are identified at the whole brain and region level, enabling quantitative analysis. This method yielded results comparable to expert manual delineations and to the output of a stereological method.

Workflow for automated quantification and spatial analysis of labelled objects in serial 2D rodent brain section images

Acknowledgements The tools development received support from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1) and Specific Grant Agreement No. 785907 (Human Brain Project SGA2).

©(2019) Yates SC, Groeneboom N, Csucs G, Leergaard TB, Puchades MA, Bjaalie JG Cite as: Yates SC, Groeneboom N, Csucs G, Leergaard TB, Puchades MA, Bjaalie JG (2019) Batch quantification and spatial analysis of labelling in microscopic rodent brain sections. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0083

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[D 14] BCI illiteracy: technical flaws turned into an urban legend

Marian Dovgialo1, Anna Chabuda1, Anna Duszyk1, Anna Stróż1, Maciej Pawlisz2, Piotr Durka1,2 1. Faculty of Physics, University of Warsaw, http://www.fuw.edu.pl, Pasteura 5, 02-093 Warsaw, Poland 2. BrainTech Ltd., https://braintech.pl, Jeziorna 23, 02-911 Warsaw, Poland “BCI illiteracy” is a term used in scientific papers and books; e.g. in [1] Authors quote a common belief that “about 20% of subjects are not proficient with a typical BCI system”. In a recent study [2] we implemented binary BCI based on either SSVEP or visual, audio or tactile P300. Each of the 30 participants was able to efficiently communicate at least in one of these modalities. These results suggest, that the term “BCI illiteracy” should be either used solely in relation to given paradigm, or abandoned at all as misleadingly suggesting neurophysiological rather than technical barriers. In this demo we invite you to test the two most efficient paradigms from [2], based upon visual P300 and high-frequency SSVEP. Apart from novel software solutions, like the BCI Control Panel (Fig. 1), these systems are based on state of the art hardware: 1. wireless EEG headset with water-based electrodes [3] improves ease of application and comfort, 2. stimuli for high-frequency SSVEP are rendered on Blinker [4]—successor of the BCI Appliance [5]. Each of its 320 fields can flicker with frequencies controlled separately by dedicated electronics, highlighting selected areas of the overlaid LCD screen. This approach greatly improves usability and unobtrusiveness of SSVEP-based BCIs, and opens new research possibilities in other fields, allowing to monitor the focus of attention in psychophysiological experiments, games, or future interactive movies.

Screenshot from https://youtu.be/10FGlCgRh8I—previous non-interactive presenta- tion of the BCI system used in [2]. Central picture presents the BCI Control Panel, allowing to trace online the progress of a BCI session and avoid some of the common failures, sometimes attributed to “BCI illiteracy”.

Acknowledgements This research was partly supported by the Polish National Science Centre (UMO-2015/17/N/ST7/03784) and NCBiR (POIR.01.01.01-00.0573/15-00) grants, awarded to the University of Warsaw and BrainTech Ltd., respectively. References 1 Allison, B. Z., & Neuper, C. (2010). Could anyone use a BCI? In D. S. Tan & N. Anton (Eds.), Brain-computer interfaces (pp. 35–54). Springer. 2 A. Chabuda, M. Dovgialo, A. Duszyk, A. Stróż, M. Pawlisz and P. Durka. BCI communication via high-frequency SSVEP or visual, audio or tactile P300 works for each of 30 volunteers and one DOC patient. Under revision in Acta Neurobiologiae Experimentalis since March 2019 3 BrainTech EEG headset https://braintech.pl/hardware/#headset-eeg

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4 Blinker—hardware solution for flexible rendering of stimuli for hi-freq SSVEP https://braintech.pl/ hardware/#blinker 5 P. J. Durka, R. Kuś, J. Żygierewicz, M. Michalska, P. Milanowski, M. Łabęcki, T. Spustek, D. Laszuk, A. Duszyk, M. Kruszyński. User-centered design of brain-computer interfaces: OpenBCI.pl and BCI Appliance. Bulletin of the Polish Academy of Sciences, vol. 60, No 3, Sept. 2012, pp. 427-433

©(2019) Dovgialo M, Chabuda A, Duszyk A, Stróż A, Pawlisz M, Durka P Cite as: Dovgialo M, Chabuda A, Duszyk A, Stróż A, Pawlisz M, Durka P (2019) BCI illiteracy: technical flaws turned into an urban legend. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0084

[D 15] Title: BASIL Project - Visual workflow designer for signal processing pipelines in a cloud infrastructure

Petr Ježek1, Lukáš Vařeka1, Roman Mouček1, Petr Brůha1 1. NTIS - New Technologies for the Information Society, University of West Bohemia, Univerzitní 8 306 14 Plzeň, Czech Republic The current Brain-driven computer assistance system for people with limited mobility (BASIL) project aims at building a brain-computer interface (BCI) system for controlling home environment devices using brain activity. Classifiers typically trained on specific users constitute the core part of any BCI system. They process online EEG data streams to provide feedback to the controlled system. The developed BCI is controlled by a small portable Raspberry Pi microcomputer not suitable for performing massive parallel operations required for the training of classifiers. Therefore, a remote cloud-based infrastructure for experimental data, results and computational tools is implemented. It brings a higher availability, sustainability, and performance of the whole system. Processing data in a BCI system requires calling of various methods connected in a workflow. Each method is defined by its inputs, outputs, parameters, and executive code. The Workflow Designer is a web-based application allowing convenient drag-and-drop creating, editing, and running workflows from a predefined library of methods. Moreover, any workflow can be exported or imported in JSON format to ensure reusability and local execution of exported JSON configurations. The Workflow Designer can be applied to any general computation if the custom library of methods is available.

An example of a BCI processing workflow.

Acknowledgements This work was supported by ERDF and Ministry of Regional Development of the Czech Republic within the project INTERREG V-A 85.

©(2019) Ježek P, Vařeka L, Mouček R, Brůha P Cite as: Ježek P, Vařeka L, Mouček R, Brůha P (2019) Title: BASIL Project - Visual workflow designer for signal processing pipelines in a cloud infrastructure. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0085

109 Demos

[D 18] Active Segmentation for Cell Segmentation and Classification

Sumit Kumar Vohra1, Hans-Christian Hege1, Dimiter Prodanov2,3 1. Visual, ZIB, Berlin, Germany 2. NERF, IMEC, Leuven, Belgium 3. EHS, Imec, Leuven, Belgium With the advances in microscopy and imaging, the automated segmentation of cells and the classification of their phenotypes is becoming increasingly interesting for researchers. There are several scenarios where researchers are more interested in classification of regions of interest or whole images rather than individual pixels. While image statistics using, e.g. image moments, like Zernike moments [1] or Legendre moments [2], as well as texture statistics [3] has been proven highly successful to capture global features on an image. In order to deal with complex cell morphologies, several tools for segmentation and classification, like ilastik [4] and Weka segmentation [5] have been developed in recent years. In the scope of the Google Summer of Code, we have developed a generic framework for region-based classification that encompasses three different disciplines: feature extraction based on image moments, machine learning, and expert input based on domain knowledge. We efficiently compute the nth order Legendre moments [1] and Zernike moments [2] by approximation in order to build a multiscale feature space, that is then used to learn characteristic “fingerprints” of the objects of interests. As a particular application, we classified the blood cells into four different cell types i.e. Eosinophil, Lymphocyte, Monocyte, and Neutrophil [6] with 70% accuracy. We are also in process of classifying the astrocytes and glial cells in two-photon images. Acknowledgements The authors would like to acknowledge the Google Summer of Code 2016 – 2018 and the International Neuroinformatics Coordinating Facility for supporting the development of the platform. References 1 Amayeh G., Erol A., Bebis G. and Nicolescu M., “Accurate and Efficient Computation of High Order Zernike Moments.” In: Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804, pp. 462-469. Springer, Berlin, Heidelberg, (2005) 2 Pew-Thian Yap and R. Paramesran, “An efficient method for the computation of Legendre moments,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1996-2002, Dec. 2005. 3 Robert M. Haralick, K. Shanmugam, and Its’hak Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, 3(6), pp. 610–621, 1973 4 Christoph Sommer, Christoph Straehle, Ullrich Köthe and Fred A. Hamprecht, “ilastik: Interactive Learning and Segmentation Toolkit”, in: Eighth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). Proceedings, 230-233, (2011) 5 Arganda-Carreras, I.; Kaynig, V. & Rueden, C. et al., “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.”, Bioinformatics (Oxford Univ Press) 33 (15), (2017) 6 Kaggle’s Blood Cells Dataset https://www.kaggle.com/paultimothymooney/blood-cells

©(2019) Vohra SK, Hege H, Prodanov D Cite as: Vohra SK, Hege H, Prodanov D (2019) Active Segmentation for Cell Segmentation and Classification. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0086

110 Demos

[D 19] ReproNim/containers and DataLad to make containers FAIR for posterity

Yaroslav O Halchenko1, Kyle Meyer1, Benjamin Poldrack2, Michael Hanke2,3 1. Psychology and Brain Sciences, Dartmouth College, Hanover, NH, USA 2. Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany 3. Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany FAIR principles impose requirements on data resource providers and in turn on data itself to facilitate data efficient access and reuse. Containers are large black boxes of “executable data”. Their findability is typically through documentation and availability rests upon the hope of persistency of public container hubs free storage. Here we present the ReproNim/containers[1]. Like any typical software[2] and data[3] distribution it collates containers from different sources into a unified distribution with unambiguous versioning with DataLad[4]. It includes support for the datalad-container[5] extension to make those containers readily usable in a setting maximizing reproducibility. It can be used independently to simplify access to containers or incorporated as a component within other datasets to achieve modular organization for efficient and reproducible computation via DataLad and ReproMan[6]. Because of unambiguous versioning and support for redundant availability, in contrast to public container hubs we can provide expected version of the container necessary to reproduce your finding without requiring any changes to users’ workflows. At the moment we provide Singularity images for BIDS apps and ReproNim projects and plan to include select Boutique, Flywheel, and BrainLife images. We work to support execution of Singularity images on non-Linux platforms through Docker, and to extract relevant metadata to make those containers easier to find as our collection grows. Acknowledgements We are grateful to teams developing containerization platforms, all Free and Open Source developers which make those containers useful, and support from NIH (1P41EB019936-01A1), and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2). References 1 ReproNim/containers doi: 10.5281/zenodo.3236408 2 NeuroDebian doi: 10.3389/fninf.2012.00022 3 datasets.datalad.org Data Distribution http://datasets.datalad.org 4 DataLad doi: 10.5281/zenodo.808846 5 datalad-container extension doi: 10.5281/zenodo.2431914 6 ReproMan doi: 10.5281/zenodo.2403222

©(2019) Halchenko YO, Meyer K, Poldrack B, Hanke M Cite as: Halchenko YO, Meyer K, Poldrack B, Hanke M (2019) ReproNim/containers and DataLad to make containers FAIR for posterity. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0087

111 Demos

[D 20] Canadian Open Neuroscience Platform

Mélanie Legault1, Jennifer Tremblay-Mercier2, Santiago Paiva1, Christine Rogers1, Samir Das1, Tristan Glatard3, Ali Khan4, Erin E. Dickie5, Jeffrey S. Grethe6, Charles S. Evans1, Jean-Baptiste Poline1 1. Faculty of Medicine, McGill University, Montréal, Québec, Canada 2. Douglas Hospital, Montréal, Québec, Canada 3. Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada 4. Department of Medical Biophysics, Western University, London, Ontario, Canada 5. Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada 6. Center for Research in Biological Systems, UCSD, La Jolla, CA, USA As open data sharing grows across neuroscientific research, the challenge of find- ing datasets to match a researcher’s specific needs remains. The Canadian Open Neuroscience Platform[1] (CONP.ca) promotes data discovery across neuroimaging, behavioral-clinical and genetic collections. As part of its mandate, it addresses the common problem of impoverished indexing and data organization[2], enabling advanced search and re-use of appropriate populations of interest. CONP’s interactive hub links a network of open repositories meeting the FAIR [3] princi- ples. Researchers can publish datasets including standardized metadata via integrations of DataLad and Git-Annex, optimizing exchange of large datasets and data descriptors. Automated scripts extract metadata from databases including LORIS [4] in standardized file formats (JSON), facilitating search indexing. The CONP portal launched in early 2019 links 6 pilot datasets including the PREVENT- AD study[5] on Alzheimer’s Disease (232 subjects,1071 MRI sessions spanning 11 scan types). A human phantom MRI collection (521 scans from 160 sessions) and genetic datasets count among its growing set of open data assets. CONP continues to extend its neuroinformatics tools for open collaboration, providing multi-modal datasets enriched with detailed and well-curated metadata. These combined features facilitate powerful search capacity for pinpointing populations of interest amid an increasing stream of open science resources. References 1 Dyke et al 2018 doi: 10.12751/incf.ni2018.0073 2 Poline 2019 doi: 10.12688/mniopenres.12772.2 3 Wilkinson et al 2016 doi: 10.1038/sdata.2016.18 4 Legault et al 2018 doi: 10.12751/incf.ni2018.0088 5 Breitner et al 2016 doi: 10.14283/jpad.2016.121

©(2019) Legault M, Tremblay-Mercier J, Paiva S, Rogers C, Das S, Glatard T, Khan A, Dickie EE, Grethe JS, Evans CS, Poline J Cite as: Legault M, Tremblay-Mercier J, Paiva S, Rogers C, Das S, Glatard T, Khan A, Dickie EE, Grethe JS, Evans CS, Poline J (2019) Canadian Open Neuroscience Platform. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0088

112 Demos

[D 21] Using the DATS model to describe datasets in the Canadian Open Neuroscience Platform

Mélanie Legault1,2, Jennifer Tremblay-Mercier3, Santiago Paiva1,2, Christine Rogers1,2, Samir Das1,2, Tristan Glatard4, Ali Khan5, Erin W Dickie6,7, Jeffrey S Grethe8, Jean- Baptiste Poline2,9,10, Alan C Evans1,2 1. McGill Center for Integrative Neuroscience, McGill University, Montréal, Québec, Canada 2. Faculty of Medecine, McGill University, Montréal, Québec, Canada 3. Douglas Hospital, Montréal,Québec, Canada 4. Department of Computer Science and Software Engineering, Concordia University, Montréal,Québec, Canada 5. Department of Medical Biophysics, Western University, London, Ontario, Canada 6. Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada 7. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada 8. Center for Research in Biological Systems, UCSD, La Jolla, California, USA 9. McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada 10. Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada As open data sharing grows across neuroscientific research, so does the challenge of finding datasets to match a researcher’s specific needs. As part of its mandate, the Canadian Open Neuroscience Platform [1] (CONP.ca) addresses the common problem of impoverished indexing and data organization [2], to promote re-use of published datasets across neuroimaging, cellular, behavioral-clinical and genetic collections. CONP’s metadata schema and powerful search capacity provide centralized access to information across a range of repositories, enabling users to rapidly pinpoint datasets of interest. It serves as an interactive hub linking a network of open repositories meeting the FAIR [3] principles, linked via DataLad, Git and Git-Annex to optimize exchange of large datasets and data descriptors. Data contributions to CONP must include well-curated metadata, based on the DATS [4] schema, describing each dataset in detail. Automated scripts extract the metadata from individual databases including LORIS [5] in standardized file formats (JSON), indexing them in the searchable CONP Portal. CONP continues to extend its neuroinformatics tools for open collaboration, providing multi-modal datasets enriched with detailed and well-curated metadata. These combined features facilitate data discovery by both human and machine, reducing effort required to find and access datasets amid an increasing demand for open science resources. References 1 Dyke et al 2018 doi: 10.12751/incf.ni2018.0073 2 Poline 2019 doi: 10.12688/mniopenres.12772.2 3 Wilkinson et al 2016 doi: 10.1038/sdata.2016.18 4 Sansone et al 2017 doi: 10.1038/sdata.2017.59 5 Legault et al 2018 doi: 10.12751/incf.ni2018.0088

©(2019) Legault M, Tremblay-Mercier J, Paiva S, Rogers C, Das S, Glatard T, Khan A, Dickie EW, Grethe JS, Poline J, Evans AC Cite as: Legault M, Tremblay-Mercier J, Paiva S, Rogers C, Das S, Glatard T, Khan A, Dickie EW, Grethe JS, Poline J, Evans AC (2019) Using the DATS model to describe datasets in the Canadian Open Neuroscience Platform. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0089

113 Demos

[D 22] Comprehensive kernel methods library for advanced machine learning applications in neuroscience

Pradeep Reddy Raamana1 1. Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst st, Toronto, ON, Canada kernelmethods is a pure python library defining modular classes that provides basic kernel methods and an intuitive interface for advanced functionality such as composite and hyper kernels. This library fills an important void in the ever-growing python-based machine learning ecosystem, where users are limited to few predefined kernels without the ability to customize or extend them for their own applications. This library defines the KernelMatrix class that is central to all the kernel methods. As it is a key bridge between input data and kernel learning algorithms, it is designed to be highly usable and extensible to different applications and data types. Kernel operations implemented are normalization, centering, product, alignment, linear combination and ranking. Convenience classes, such as KernelSet,Bucket, are designed for easy management of a large collection of kernels. Dealing with diverse kernels and their fusion is necessary for automatic kernel selection in applications such as Multiple Kernel Learning. Besides numerical kernels, we designed this library to provide categorical, string and graph kernels, with the same attractive properties of intuitive and highly-testable API. Besides non-numerical kernels, we aim to provide a deeply extensible framework for arbitrary input data types, such as sequences and trees, via pyradigm. Moreover, drop-in Estimator classes are provided for seamless usage in scikit-learn ecosystem. URL: github.com/raamana/kernelmethods Acknowledgements I am grateful for the support of the Canadian Biomarker Integration Network for Depression (CAN-BIND) and Ontario Neurodegenerative Disease Research Initiative (ONDRI), which are two integrated discovery programs of the Ontario Brain Institute (OBI), Canada.

©(2019) Raamana PR Cite as: Raamana PR (2019) Comprehensive kernel methods library for advanced machine learning applications in neuroscience. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0090

114 Poster and Investigator Presentations

Poster and Investigator Presentations

[PI 43] Really reproducible behavioural paper

Jakub Mateusz Dzik1, Alicja Puścian2, Zofia Harda a.k.a. Mijakowska3, Kasia Radwanska3, Szymon Łęski1 1. Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology PAS, Pasteura 3; 02-093 WARSZAWA, Poland 2. Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, United States 3. Laboratory of Molecular Basis of Behavior, Nencki Institute of Experimental Biology PAS, Pasteura 3; 02-093 WARSZAWA, Poland So called "replication crisis" in life sciences has received significant attention recently. Many factors can affect reproducibility, such as lack of standardisation of experimental conditions or human errors. One way to mitigate this is to use highly standardized systems for automated phenotyping, such as IntelliCage (Codita 2012). Nevertheles, even if data acquisition is automatic, manual analysis, which often follows, may still suffer from human errors. A convenient countermeasure to this issue is automation of data analysis with a non-interactive computer program (Buckheit 1995). The PyMICE library (RRID:nlx_158570) has been developed to facilitate development of Python programs for automated analysis of mice behavioural data obtained from IntelliCage system. The title paper presents the library to the scientific community (Dzik 2017). It has been written according to literate programming paradigm (Knuth 1984), thus the presented results are highly reproducible and transparent. All programs used for analysing the example experimental data are embedded in the source code of the paper itself. The source code of the title paper is available at: https://github.com/Neuroinflab/PyMICE_SM/

Thumbnail of the poster

Acknowledgements Project funded from the Polish National Science Centre’s SYMFONIA (2013/08/W/NZ4/00691) grant.

115 Poster and Investigator Presentations

References 1 Buckheit JB, Donoho DL (1995) WaveLab and Reproducible Research. Lecture Notes in Statistics doi: 10.1007/978-1-4612-2544-7_5 2 Codita A, Mohammed AH, Willuweit A, Reichelt A, Alleva E, Branchi I, Cirulli F, Colacicco G, Voikar V, Wolfer DP, Buschmann FJU, Lipp H-P, Vannoni E, Krackow S (2012) Effects of Spatial and Cognitive Enrichment on Activity Pattern and Learning Performance in Three Strains of Mice in the IntelliMaze doi: 10.1007/s10519-011-9512-z 3 Dzik JM, Puścian A, Mijakowska Z, Radwanska K, Łęski S (2017) PyMICE: A Python library for analysis of IntelliCage data doi: 10.3758/s13428-017-0907-5 4 Knuth DE (1984) Literate Programming doi: 10.1093/comjnl/27.2.97

©(2019) Dzik JM, Puścian A, Harda a.k.a. Mijakowska Z, Radwanska K, Łęski S Cite as: Dzik JM, Puścian A, Harda a.k.a. Mijakowska Z, Radwanska K, Łęski S (2019) Really reproducible behavioural paper. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0091

[PI 70] Computational modelling of decision-making in a reinforcement learning task without time constraints

Judyta Magdalena Jabłońska1, Łukasz Szumiec1, Jan Rodriguez Parkitna1 1. Maj Institute of Pharmacology Polish Academy of Sciences, Smętna 12, Kraków Learning to choose actions that maximize rewards and minimize punishments only by optimizing decision-making policy, according to the outputs of performed actions, is referred to as reinforcement learning (RL). Due to its relevance to human psychopathol- ogy, it is extensively studied with the use of unsupervised ML methods - RL algorithms. The aspect, which still has not been fully explored yet, is the natural timing of decision- making, determined neurobiologically by memory processes. To address this issue, we present our new behavioral model of the probabilistic reversal learning task without time constraints and a new extension to the RL Q-learning algorithm, accounting for a memory decay effect. The experimental task was based on the IntelliCage system, where a group of female mice chose freely between two alternatives with different probabilities (0.3/0.9) of receiving access to saccharin (0.1% w/v) being reversed every 2 days. In line with expectations, mice preferred the alternative with a greater probability of reward. Surprisingly we also noticed a tendency toward repeating the same choice at longer intervals from a previous decision. We found the observed behavior to be adequately explained by models incorporating a function of the interval. Based on our results, we report that the interval between choices affects the decisions taken. Thus, an adjustment to models accounting for memory processes is necessary while studying biological underpinnings of RL.

©(2019) Jabłońska JM, Szumiec Ł, Rodriguez Parkitna J Cite as: Jabłońska JM, Szumiec Ł, Rodriguez Parkitna J (2019) Computational modelling of decision- making in a reinforcement learning task without time constraints. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0092

116 Poster and Investigator Presentations

[PI 71] Evaluating neural network models within a formal validation framework

Robin Gutzen1,2, Michael von Papen1, Guido Trensch3, Pietro Quaglio1,2, Sonja Grün1,2, Michael Denker1 1. Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA- Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany 2. Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany 3. Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Jülich Research Centre, Jülich, Germany To bridge the gap between the theory of neuronal networks and findings obtained by the analysis of experimental data, advances in computational neuroscience rely heavily on simulations of neuronal network models. The verification of model implementations and validation of its simulation results is thus an indispensable part of any simulation workflow. Moreover, in face of the heterogeneity of models and simulators, approaches to enable the comparison between model implementations is an issue of increasing importance which calls for the establishment of a formalized validation scheme. Although the bottom-up validation of cell response properties is important, it does not automatically entail the validity of the simulation dynamics on the network scale. Here, we discuss a set of tests to assess the network dynamics to attain a quantified level of agreement with a given reference. We developed NetworkUnit (RRID:SCR_016543; github.com/INM- 6/NetworkUnit) as a Python library, built on top of the SciUnit (RRID:SCR_014528) framework [1,2], as formal implementation of this validation process for network-level validation testing, which complements NeuronUnit (RRID:SCR_015634) for the single cell level. The toolbox Elephant (RRID:SCR_003833) provides the foundation to extract well-defined and comparable features of the network dynamics. We demonstrate the use of the library in a validation testing workflow [3,4] using a worked example involving the SpiNNaker neuromorphic system.

Network-level validation techniques applied to compare simulations

Acknowledgements Funding by: EU’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant 117 Poster and Investigator Presentations

Agreements No. 720270 and No. 785907 (HBP SGA1, SGA2); the Helmholtz Association Initiative and Networking Fund, No. ZT-I-0003; and HDSLEE. References 1 Omar, C., Aldrich, J., and Gerkin, R. C. (2014). “Collaborative infrastructure for test-driven scientific model validation”. Companion Proceedings of the 36th International Conference on Software Engineering - ICSE Companion 2014, pages 524–527. doi: 10.1145/2591062.2591129 2 Sarma, G. P., Jacobs, T. W., Watts, M. D., Ghayoomie, S. V., Larson, S. D., and Gerkin, R. C. (2016). “Unit testing, model validation, and biological simulation”. F1000 Research, 5:1946. doi: 10.12688/f1000research.9315.1 3 Gutzen, R., von Papen M., Trensch G., Quaglio P., Grün S., and Denker M. (2018). “Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data”. Frontiers in Neuroinformatics 12:90. doi: 10.3389/fninf.2018.00090 4 Trensch, G., Gutzen R., Blundell I., Denker M., and Morrison A. (2018). “Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data”. Frontiers in Neuroinformatics 12:81. doi: 10.3389/fninf.2018.00081

©(2019) Gutzen R, von Papen M, Trensch G, Quaglio P, Grün S, Denker M Cite as: Gutzen R, von Papen M, Trensch G, Quaglio P, Grün S, Denker M (2019) Evaluat- ing neural network models within a formal validation framework. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0093

[PI 73] NeuroML-DB: A model sharing resource that promotes rapid selection and reuse

Justas Birgiolas1, Richard C Gerkin1, Sharon Crook1,2 1. School of Life Sciences, Arizona State University, Tempe, Arizona, USA 2. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA As large-scale data-driven neuroscience models become tractable, the ability to efficiently leverage previously developed models is paramount. However, it is difficult to rapidly evaluate whether a model is appropriate for reuse due to a lack of easily accessible data about a published model’s behavior and complexity. Even if an existing model is determined to be suitable, the use of heterogeneous simulators makes it difficult to easily reuse models or model components like ion channels or synapses. These two issues of model selection and reuse hinder scientific progress. Ideally, scientists should be able to simultaneously evaluate many existing models and then easily select models or their components for reuse. To make progress towards this vision, we have developed a web-accessible database cataloging over 1,500 previously published models that have been translated to NeuroML [1]. Model search and characterization features allow researchers to evaluate and select models for reuse. The focus on models described using the simulator-independent NeuroML format facilitates the automated simulation, visualization, and analysis of models ranging in scales from ion channels to large networks. In addition to providing code for running models using multiple simulators, NeuroML-DB provides the results of systematic characterizations of the electrophysiology, morphology, 118 Poster and Investigator Presentations

and computational complexity of each model, making it easier to find, evaluate, select, and reuse existing models. Acknowledgements This work was supported by the National Institutes of Health through 1F31DC016811 to JB, R01MH10664 to SC, and R01EB021711 to RCG. References 1 Gleeson P, et al. (2010) NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLOS Computational Biology. 6(6):e1000815.

©(2019) Birgiolas J, Gerkin RC, Crook S Cite as: Birgiolas J, Gerkin RC, Crook S (2019) NeuroML-DB: A model sharing resource that promotes rapid selection and reuse. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0094

[PI 75] Comprehensive computational modelling of the development of characteristic brain connectivity

Claus C Hilgetag1,2, Sarah F Beul1, Richard F Betzel3, Alexandros Goulas1 1. Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany 2. Department of Health Sciences, Boston University, Boston, USA 3. Department of Bioengineering, University of Pennsylvania, Philadelphia, USA The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including features such as modules and hubs. However, the mechanisms resulting in the observed inter-regional wiring principles and characteristic network topology of brains are unknown. With the aid of systematic computational modeling studies [1,2], we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients and the rule of “what develops together, wires together", without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles and global network topology of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious and plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains. Acknowledgements Supported by a Humboldt Research Fellowship from the Alexander von Humboldt Foundation (A.G.) as well as grants from the German Research Council DGF SFB 936/A1, TRR 169/A2 and SPP 2041/ HI 1286/7-1 (C.C.H.) References 1 Beul SF, Goulas A, Hilgetag CC (2018) Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle. PLoS Comput Biol 14(11): e1006550 doi: 10.1371/journal.pcbi.1006550 2 Goulas A, Betzel RF, Hilgetag CC, Spatiotemporal ontogeny of brain wiring, Sci Adv, in press

©(2019) Hilgetag CC, Beul SF, Betzel RF, Goulas A Cite as: Hilgetag CC, Beul SF, Betzel RF, Goulas A (2019) Comprehensive computational mod- elling of the development of characteristic brain connectivity. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0095

119 Demo and Investigator Presentations

Demo and Investigator Presentations

[DI 07] Marmoset Brain Connectivity Atlas. An Open Access Resource for Cellular-Resolution Analysis of the Primate Cortex

Piotr Majka1,2,3, Shi Bai1,2, Jonathan M. Chan1,2, Natalia Jermakow3, Marcello G.P. Rosa1,2 1. Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia 2. Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia 3. Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland Understanding the structural principles of the processes such as perception, action, and cognition is a core objective of present-day neuroscience. To help addressing these problems, we propose the Marmoset Brain Connectivity Atlas (RRID: SCR_015964): an open resource available through the http://marmosetbrain.org portal hosting results of 143 monosynaptic retrograde tracer injections into marmoset cerebral cortex. The repository provides quantified and directional data on connections between the injected areas, information on the stereotaxic location of each labeled cell, its location relative to the granular cell layer of the cortex, and extensive metadata. Moreover, it also offers access to the underlying experimental data. Thanks to this multifaceted nature, the platform opens new avenues of exploring the connectivity data, enables comparisons with results of other studies, and allows for future reanalysis in the light of evolving knowledge. The potential of the resource as a medium for discoveries has been already demonstrated by recent studies that investigated the spatial distribution of labeled neurons (Majka, Rosa et al. 2019) or explored the connectivity network properties (Goulas et al. 2019, Buckner and Marguiles 2019). Here we showcase the capabilities of the connectome by testing the hypothesis that the structural connectivity distance depends on how far the regions being studied are located from primary cortical areas (Oligschläger et al. 2019).

Medial (top left) and lateral (top right) mid-thickness projections on the template brain, with the locations of the injections indicated. The tracers used in each injection are coded by color. (bottom) Cortico-cortical matrix showing the strength and the direction of connections between areas. 120 Demo and Investigator Presentations

Acknowledgements The project was supported by the INCF Seed Funding grant scheme, Australian Research Council (DP110101200, DP140101968, CE140100007), and the National Health and Medical Research Council (APP1003906, APP1020839, APP1083152, APP1082144, APP1122220). References 1 Majka, P., Rosa, M. G. P. et al. (2019). Unidirectional monosynaptic connections from auditory areas to the primary visual cortex in the marmoset monkey. Brain Struct. Funct., 224(1), 111–131 doi: 10.1007/s00429-018-1764-4 2 Goulas, A., Majka, P., Rosa, M. G. P., and Hilgetag, C. C. (2019). A blueprint of mammalian cortical connectomes. PLOS Biol., 17(3), e2005346 doi: 10.1371/journal.pbio.2005346 3 Buckner, R. L., and Margulies, D. S. (2019). Macroscale cortical organization and a default-like apex transmodal network in the marmoset monkey. Nat. Commun., 10(1), 1976 doi: 10.1038/s41467-019- 09812-8 4 Oligschläger, S., Xu, T., Baczkowski, B. M., Falkiewicz, M., Falchier, A., Linn, G., and Margulies, D. S. (2019). Gradients of connectivity distance in the cerebral cortex of the macaque monkey. Brain Struct. Funct., 224(2), 925–935 doi: 10.1007/s00429-018-1811-1

©(2019) Majka P, Bai S, Chan JM, Jermakow N, Rosa MG Cite as: Majka P, Bai S, Chan JM, Jermakow N, Rosa MG (2019) Marmoset Brain Connectivity Atlas. An Open Access Resource for Cellular-Resolution Analysis of the Primate Cortex. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0096

[DI 17] Building a FAIR Data Commons: The Open Data Commons for Spinal Cord Injury (ODC-SCI)

Jeffrey S Grethe1, Carlos Almeida2, Michael Chiu1, J. Russell Huie2, Jessica L Nielson3, Troy Sincomb1, Abel Torres-Espin2, Karim Fouad4, Maryann E Martone1, Adam R Ferguson2 1. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA 2. Brain and Spinal Injury Center (BASIC), University of California, San Francisco, San Francisco, CA, USA 3. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA 4. Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada Data shared in publications about preclinical research, represent only a fraction of the data produced. For example, data that do not fit the ‘story’ of a manuscript and experiments with so-called “negative” outcomes are often not published at all. These ‘dark data’ are estimated to make up 85% of all data collected (Ferguson et al., 2014), which creates various problems including publication bias and the inability to perform replications and independent validations. Collecting a large body of data would allow a new form of meta-analysis based on raw data, thereby facilitating new discoveries and translation (Nielson et al., 2015). A major barrier to realizing this potential of data science has been a cultural skepticism to data sharing. In the past decade the spinal cord injury (SCI) research community has demonstrated a strong willingness to share data and work collaboratively. More recently, we have been involved in building a data sharing platform: the Open Data Commons for Spinal Cord Injury (http://ODC-SCI.org) that is meant to provide a useful and FAIR data platform for the broad preclinical SCI community. The ODC-SCI will serve to integrate data across many disciplines engaged in SCI research and allow for analysis of shared preclinical data. This demonstration will provide an overview of the ODC-SCI and highlight ODC-SCI’s implementation of a FAIR Data Commons designed to shepherd datasets from use in a laboratory to broader community sharing and then publication. 121 Demo and Investigator Presentations

A Commons for Spinal Cord Injury data

Acknowledgements Craig H. Neilsen Foundation, Wings for Life Foundation, International Spinal Research Trust, Rick Hansen Institute, International Neuroinformatics Coordinating Center, UCSF-BASIC, University of Alberta, NIH (NS088475, NS106899), US Department of Veterans Affairs (I01RX002245-01, I01RX002787) References 1 Ferguson AR, Nielson JL, Cragin MH, Bandrowski AE, Martone ME (2014) Big data from small data: data-sharing in the ’long tail’ of neuroscience. Nat Neurosci. 2014 Nov;17(11):1442-7 doi: 10.1038/nn.3838 2 Nielson, J. L., Paquette, J., Liu, A. W., Guandique, C. F., Tovar, C. A., Inoue, T., . . . Ferguson, A. R. (2015). Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury. Nature Communications, 6(1): 8581 doi: 10.1038/ncomms9581

©(2019) Grethe JS, Almeida C, Chiu M, Huie JR, Nielson JL, Sincomb T, Torres-Espin A, Fouad K, Martone ME, Ferguson AR Cite as: Grethe JS, Almeida C, Chiu M, Huie JR, Nielson JL, Sincomb T, Torres-Espin A, Fouad K, Martone ME, Ferguson AR (2019) Building a FAIR Data Commons: The Open Data Commons for Spinal Cord Injury (ODC-SCI). Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0097

[DI 23] Neuroscience Data Pipelines with DataJoint

Dimitri Yatsenko1,2, Shan Shen1,2, Edgar Y. Walker1,2, Thinh Nguyen2, Jacob Reimer1,2, Andreas Tolias1,2 1. Neuroscience, Baylor College of Medicine, Houston, Texas, USA 2. DataJoint Neuro, Vathes LLC, Houston, TX, USA Solving hard questions in neuroscience requires a major boost in the sophistication and rigor of data science in collaborative research. DataJoint (https://datajoint.io) is an open-source framework for rigorous data management, distributed computation, flexible queries, and workflow management. It combines databases, bulk storage, and computation in a uniform framework for building scientific data pipelines in collaborative projects directly from MATLAB and Python. Since its initial release several years ago, dozens of top research teams have adopted DataJoint. These teams include large multi- lab projects funded by BRAIN Initiative U19 grants, IARPA, the Wellcome Trust and the Simons Foundation. Their experiments include diverse data modalities: multielectrode electrophysiology, calcium imaging, light and electron microscopy, behavioral monitoring, sensory stimulation, transcriptomics, and optogenetics. In these complex data ecosystems, DataJoint allows groups of scientists to collaborate with a clear workflow, sharing data continuously and preserving data integrity. We will demonstrate the basics of building 122 Demo and Investigator Presentations

shared data pipelines for several modalities of cellular neurophysiology experiments with examples and references from completed and ongoing projects. Acknowledgements IARPA D16PC00003; Princeton Neuroscience Institute; DARPA D17PC00162 References 1 Yatsenko, Dimitri, Jacob Reimer, Alexander S. Ecker, Edgar Y. Walker, Fabian Sinz, Philipp Berens, Andreas Hoenselaar, Ronald James Cotton, Athanassios S. Siapas, and Andreas S. Tolias. "DataJoint: managing big scientific data using MATLAB or Python." BioRxiv (2015): 031658. doi: 10.1101/031658 2 Yatsenko, Dimitri, Edgar Y. Walker, and Andreas S. Tolias. "DataJoint: A Simpler Relational Data Model." arXiv preprint arXiv:1807.11104 (2018). https://arxiv.org/abs/1807.11104

©(2019) Yatsenko D, Shen S, Walker EY, Nguyen T, Reimer J, Tolias A Cite as: Yatsenko D, Shen S, Walker EY, Nguyen T, Reimer J, Tolias A (2019) Neuroscience Data Pipelines with DataJoint. Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0098

[DI 24] Dissemination of FAIR Datasets and Analysis Pipelines Using the Canadian Open Neuroscience Platform (CONP)

Santiago Paiva1,2, Shawn T. Brown1,2, Brenden Behan3, Pierre Bellec4, Krishna Chatpar1,2,5, Candice Czech1,2, Samir Das1,2, Moyez Dharsee6, Erin Dickie7, Stephanie O.M. Dyke1,2, Simon Duchesne8, Matthieu Dupuis1,2, Alan C. Evans1,2, Tristan Glatard9, Thomas Gee6, Justin Kat1,2, Ali Khan10, Gregory Kiar1,2, Xavier Lecours-Boucher1,2, Mélanie Legault1,2, Paolo V. Leone11, Derek Lo1,2, Dave MacFarlane2, Cecile Madjar2,12, Naser Muja1, Emmet A. O’Brien2, Paul Pavlidis13, Darcy Quesnel1,2, Christine Rogers1,2, Dylan Roskams-Edris14, David Rotenberg15, Dawn Smith7, Nikola Stikov16, Ted Strauss17, Armin Taheri1,2, Adrian Thorogood14, Jennifer Tremblay-Mercier1,2,12, Alizée Wickenheiser1,2, Jean-Baptiste Poline1 1. Montreal Neurological Institute, McGill University, Montreal, Canada 2. McGill Center for Integrative Neuroscience (MCIN), McGill University, Montreal, Canada 3. Ontario Brain Institute (OBI), Toronto, Canada 4. Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM), Montreal, Canada 5. Tanenbaum Open Science Institute (TOSI), McGill University, Montreal, Canada 6. Indoc Research, Toronto, Canada 7. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada 8. MEDICS Laboratory, Quebec City Mental Health Institute, Quebec, Canada 9. PERFORM Centre, Concordia University, Montreal, Canada 10. Robarts Research Institute, Western University, London, Canada 11. Desautels Faculty of Management, McGill University, Montreal, Canada 12. Douglas Mental Health University Institute, McGill University, Montreal, Canada 13. Dept. Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver, Canada 14. Centre of Genomics and Policy, McGill University, Montreal, Canada 15. Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada 16. École Polytechnique, Montreal, Canada 17. McConnell Brain Imaging Center (BIC), McConnell Brain Imaging Center (BIC), Montreal, Canada The Canadian Open Neuroscience Platform (CONP) is a decentralized open platform enabling researchers to explore, search, and share research including datasets, analytic tools (pipelines), standardized metadata, and links to computational resources. The platform is built on the FAIR principles [1], using Datalad [2] and Git-Annex [3] to create and access metadata from data repositories, facilitating the transfer of large documents and standardized metadata file formats to the end user’s local machine, through a multi-tier access model to allow appropriate researcher access to data, respecting ethical-legal restrictions. 123 Demo and Investigator Presentations

CONP currently indexes seven datasets, such as PREVENT-AD [4]. Metadata files and search tools allow researchers to navigate easily across datasets, pipelines, and notebooks. Users can query datasets using DATS model descriptors [5] and search for data pipelines and execute them on Compute Canada’s infrastructure via CBRAIN [6]. Pipelines are published and retrieved in the Boutiques format [7] through the Zenodo data repository. To promote accessibility and training, the platform also incorporates NeuroLibre, a collection of Jupyter notebooks developed for publishing interactive tutorials and innovative research objects such as text, code, and figures. As the CONP platform grows to incorporate and index new datasets and pipelines, researchers will be able to share resources to reuse existing data and accelerate the rate of scientific discovery.

The Canadian Open Neuroscience Platform (CONP) Data Portal

Acknowledgements This work was supported in part by funding provided by Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative References 1 Wilkinson et al. "The FAIR Guiding Principles for scientific data management and stewardship" Scientific Data volume 3, Article number: 160018 (2016) doi: 10.1038/sdata.2016.18 2 Halchenko et al. "Datalad" (2018) https://zenodo.org/record/1000098#.XO79qNNKjfY 3 Ram, Karthik. "Git can facilitate greater reproducibility and increased transparency in science." Source code for biology and medicine 8.1 (2013): 7. doi: 10.1186/1751-0473-8-7

124 Demo and Investigator Presentations

4 J.C.S. Breitner ; J. Poirier ; P.E. Etienne ; J.M. Leoutsakos for the PREVENT-AD Research Group (2016): Rationale and Structure for a New Center for Studies on Prevention of Alzheimer’s Disease (StoP-AD). The Journal of Prevention of Alzheimer’s Disease (JPAD) doi: 10.14283/jpad.2016.121 5 Sansone, Susanna-Assunta, Alejandra Gonzalez-Beltran, Philippe Rocca-Serra, George Alter, Jeffrey S. Grethe, Hua Xu, Ian M. Fore, et al. 2017. “DATS, the Data Tag Suite to Enable Discoverability of Datasets.” Scientific Data 4 (June): 170059. doi: 10.1038/sdata.2017.59 6 Sherif, T., Rioux, P., Rousseau, M. E., Kassis, N., Beck, N., Adalat, R., ... & Evans, A. C. (2014). CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research. Frontiers in neuroinformatics, 8, 54 doi: 10.3389/fninf.2014.00054 7 Glatard, T., Kiar, G., Aumentado-Armstrong, T., Beck, N., Bellec, P., Bernard, R., ... & Das, S. (2018). Boutiques: a flexible framework to integrate command-line applications in computing platforms. GigaScience, 7(5), giy016 doi: 10.1093/gigascience/giy016

©(2019) Paiva S, Brown ST, Behan B, Bellec P, Chatpar K, Czech C, Das S, Dharsee M, Dickie E, Dyke SO, Duchesne S, Dupuis M, Evans AC, Glatard T, Gee T, Kat J, Khan A, Kiar G, Lecours-Boucher X, Legault M, Leone PV, Lo D, MacFarlane D, Madjar C, Muja N, O’Brien EA, Pavlidis P, Quesnel D, Rogers C, Roskams-Edris D, Rotenberg D, Smith D, Stikov N, Strauss T, Taheri A, Thorogood A, Tremblay-Mercier J, Wickenheiser A, Poline J Cite as: Paiva S, Brown ST, Behan B, Bellec P, Chatpar K, Czech C, Das S, Dharsee M, Dickie E, Dyke SO, Duchesne S, Dupuis M, Evans AC, Glatard T, Gee T, Kat J, Khan A, Kiar G, Lecours-Boucher X, Legault M, Leone PV, Lo D, MacFarlane D, Madjar C, Muja N, O’Brien EA, Pavlidis P, Quesnel D, Rogers C, Roskams-Edris D, Rotenberg D, Smith D, Stikov N, Strauss T, Taheri A, Thorogood A, Tremblay-Mercier J, Wickenheiser A, Poline J (2019) Dissemination of FAIR Datasets and Analysis Pipelines Using the Canadian Open Neuroscience Platform (CONP). Neuroinformatics 2019 Abstract. doi: 10.12751/incf.ni2019.0099

125

Index

127 127 AUTHORS

Authors

Aasebø I, 89 Chojnowski M, 101 Aasebø IE, 91 Chon U, 34 Abdellah M, 66 Cifre I, 44 Abraham SA, 98 Civillico G, 88 Abramchuk O, 61 Clucas J, 98 Abrams M, 89, 96 Courcol J, 87 Abrams MB, 91 Crook S, 118 Acharya G, 80 Crook SM, 105 Adamczyk P, 43 Csucs G, 107 Ai H, 41, 84 Czech C, 123 Aigner CS, 70 Czerwiński M, 49 Alicea B, 104 Almeida C, 121 D’Orazi F, 80 Amunts K, 91 Dagar S, 66 Andersson K, 91 Das S, 97, 98, 112, 113, 123 Andersson KA, 89, 90 Dasilva M, 50 Andrade A, 70 Davison A, 65, 89, 91 Anemüller J, 48 Davison AP, 87 Appukuttan S, 65 De Bonis G, 50 Araújo LH, 73 de los Virues-Alba T, 97 Ascenção PV, 73 Denker M, 117 Aswendt M, 46 Dharsee M, 123 Aubert-Vazquez E, 97 Dichter BK, 52 Avetisyan GA, 62, 64, 103 Dickie E, 123 Dickie EE, 112 Bai S, 120 Dickie EW, 113 Bakker R, 39, 79 Dickscheid T, 89, 91 Baratham V, 52 Dolbeare T, 80 Bednarek S, 40 Dougherty M, 52 Behan B, 123 Dovgialo M, 108 Bellec P, 123 Dreszer J, 93 Betzel RF, 119 Drozdziel D, 47 Beul SF, 119 Duch W, 93, 101, 106 Bhalla US, 33 Duchesne S, 123 Biegański P, 59 Duda-Goławska J, 67 Birgiolas J, 105, 118 Dudziak MJ, 35, 58 Bjaalie JG, 89–91, 107 Dupont M, 82 Bjerke I, 89 Dupuis M, 123 Blinowska KJ, 70 Durka P, 59, 108 Blixhavn CH, 89, 90 Duszyk A, 59, 108 Boges D, 66 Dvoretskii S, 104 Bogotko M, 59 Dyke SO, 123 Bonna K, 101 Dzięgiel G, 43 Bosch-Bayard J, 97 Dzik JM, 49, 95, 115 Bouchard K, 52 Dzwiniel P, 72 Bringas-Vega M, 97 Brown ST, 97, 123 Eipert P, 37 Brůha P, 74, 75, 109 Esteban O, 100 Evans AC, 97, 113, 123 Cali C, 66 Evans CS, 112 Carannante I, 67 Chabuda A, 108 Faulk K, 88 Chan JM, 120 Feng D, 80 Chang E, 52 Ferguson AR, 121 Chatpar K, 123 Finc K, 101 Cheng KC, 34 Fink GR, 46 Chernykh M, 61 Fonta P, 82, 87 Chialvo DR, 26, 44 Fouad K, 121 Chintaluri C, 49, 95 Fraga FJ, 73 Chiu M, 121 Freund T, 68 128 AUTHORS

Frost Nylén J, 67 Jurkiewicz GJ, 27, 28 Futagi D, 54 Kachynska T, 61 Galan-Garcia L, 97 Kaczmarzyk J, 100 Garbers C, 90 Kai K, 41, 84 Garcia-Rodriguez P, 65 Káli S, 68 Gee T, 123 Kaminski M, 70 Genet H, 82 Karbowski J, 42 George P, 96 Kare K, 66 Gerkin RC, 105, 118 Kat J, 123 Ghosh S, 98, 100 Kaufmann A, 82, 87 Gillespie T, 80, 91 Kbah SN, 75 Gillespie TH, 25 Kebrle J, 77 Glatard T, 97, 112, 113, 123 Keller D, 66 Goncalves M, 100 Kempler L, 99 Gorgolewski KJ, 100 Kerrien S, 82, 87 Goulas A, 35, 119 Keshavan A, 98 Grethe JS, 112, 113, 121 Khachidze I, 61 Grewe J, 86 Khan A, 112, 113, 123 Grigoryan AS, 64, 103 Kiar G, 97, 123 Grillner S, 67 Kim Y, 34 Groeneboom N, 107 Kitano K, 54 Grün S, 117 Klein A, 98 Gubianuri T, 61 Kleven H, 89–91 Gudowska-Nowak E, 26 Klimesch W, 70 Gutzen R, 117 Klonowski W, 83 Knapska E, 29 Köhnen S, 91 Hagan S, 76 Komorowski MK, 93 Hagen Blixhavn C, 91 Konorski P, 102 Hajnowski M, 57 Korczyk M, 43 Halchenko YO, 111 Kossowski B, 85 Hanke M, 111 Koudelka V, 30, 55 Harda a.k.a. Mijakowska Z, 115 Koutsou A, 86, 90 Hawrylycz MJ, 80 Kowalska M, 49, 95 Hege H, 110 Kozlov A, 67 Hejazinia N, 80 Kryzhanovskyi S, 61 Hellgren Kotaleski J, 67 Krześniak A, 43 Herman P, 61 Kuřátko D, 30 Hilgetag CC, 35, 119 Kumaraswamy A, 41, 84 Hill S, 82 Kuznietsov I, 61 Hill SL, 25, 87 Hilverling A, 91 Láčik J, 30 Hjorth J, 67 LaMuth JE, 36 Hoehn M, 46 Lecours-Boucher X, 123 Hoveyan GA, 62 Leergaard T, 89 Hovhannisyan AA, 62, 64, 103 Leergaard TB, 90, 91, 107 Huie JR, 121 Legault M, 112, 113, 123 Legouée E, 91 Idili G, 105 Legouee E, 89 Ikeno H, 41, 84 Leone PV, 123 Indyk A, 80 Łęski S, 29, 115 Ivaska G, 87 Lindroos R, 67 Iyer V, 99 Lipinski M, 47 Izdebska K, 43 Lo D, 123 Lu H, 58, 82, 87 Jabłońska JM, 116 Lurie J, 82 Jarecka D, 100 Jednoróg K, 43 MacFarlane D, 123 Jędrzejewska-Szmek J, 29, 49, 51 Machon J, 82 Jermakow N, 40, 120 MacIntyre L, 97 Ježek P, 74, 75, 109 Madjar C, 97, 123 Johansson L, 96 Majka P, 35, 40, 120 Johansson Y, 67 Mąka J, 29 129 AUTHORS

Marchewka A, 43 Ramanathan S, 31 Markiewicz CJ, 100 Ramasundaram M, 31 Martone ME, 25, 80, 121 Rani GH, 33 Mattia M, 50 Ratajczak EK, 57 Matuszewski J, 43 Rehman R, 40 Mautner P, 75 Reimer J, 122 Meyer K, 111 Rodriguez Parkitna J, 116 Miller Flores MT, 44 Rogers C, 97, 112, 113, 123 Mohácsi M, 68 Roman B, 82 Mohades Z, 97 Rosa MG, 35, 120 Mollenkopf T, 80 Roselli F, 40 Montero D, 82 Roskams-Edris D, 123 Mouček R, 74, 75, 77, 109 Rosli Z, 98 Mufti S, 80 Rotenberg D, 123 Muja N, 123 Ruebel O, 52 Rykaczewski K, 106 Nakamura T, 76 Nasiotis K, 52 Saleh MS, 62 Ng L, 80 Sanchez-Vives MV, 50 Nguyen T, 122 Sandström M, 96 Nielson JL, 121 Santos EM, 73 Nikadon J, 93, 106 Sáray S, 68 Nitzsche C, 37 Scaria L, 97 Nowak MA, 26 Schlegel U, 89–91 Schmid O, 91 O’Brien EA, 123 Schmidt O, 89 Ochab JK, 26, 44 Schmitt O, 37 Osetrova M, 56 Schwerdtfeger A, 70 Øvsthus M, 89 Serap S, 32 Sharma L, 65 Shen S, 122 Paiva S, 112, 113, 123 Shpenkov O, 61 Palenicek T, 55 Silberberg G, 67 Pallast N, 46 Sincomb T, 121 Paolucci PS, 50 Sławiński ZT, 51 Pavlidis P, 123 Smith D, 123 Pawlisz M, 108 Sokołowska B, 78 Pawłowska M, 40 Sokołowska E, 78 Pazienti A, 50 Sonntag M, 86, 90 Perens J, 94 Średniawa W, 49 Pfurtscheller G, 70 Stefaniuk M, 40 Pieciak T, 47 Stikov N, 123 Pietruszka M, 47 Strauss T, 123 Pietrzak M, 59 Stróż A, 59, 108 Pilyugina N, 79 Sy FM, 25 Piotrowski TJ, 93, 106 Sy MF, 82, 87 Pirman K, 82 Szczepanik M, 85 Plewko J, 43 Szumiec Ł, 116 Poldrack B, 111 Szwed M, 43, 47 Poline J, 112, 113, 123 Prochoroff Z, 47 Prodanov D, 110 Taheri A, 97, 123 Prokop T, 75 Tanaka K, 79 Puchades MA, 89–91, 107 Tandon N, 52 Puścian A, 115 Tarnowski W, 26 Thompson CL, 80 Thorogood A, 123 Qashu F, 88 Tiesinga P, 39, 79 Quaglio P, 117 Timonidis N, 39 Quesnel D, 123 Török MP, 68 Tolias A, 122 Raamana PR, 114 Torres-Espin A, 121 Rączy K, 43 Tremblay-Mercier J, 112, 113, 123 Radwanska K, 115 Trensch G, 117 Raida Z, 30, 55 Tripathy S, 25 130 AUTHORS

Tritt A, 52

Uppenkamp S, 48 Urbschat A, 48

Valdes-Sosa PA, 97 Vaněk J, 90 Vanselow DJ, 34 Vařeka L, 74, 75, 77, 109 Vivar Rios C, 81 Vohra SK, 110 von Papen M, 117

Wachtler T, 41, 84, 86, 90 Wagle S, 33 Wajerowicz W, 82 Waleszczyk WJ, 72 Walker EY, 122 Wang H, 71 Weitz A, 88 Wickenheiser A, 123 Wiedermann D, 46 Wieters F, 46 Winiarski M, 29 Wojciechowski J, 93 Wójcik D, 55 Wójcik DK, 29, 30, 40, 49, 51, 95 Wolfson M, 88 Woolnough O, 52

Yates SC, 89, 107 Yatsenko D, 122

Zafarnia S, 91 Żebrowska M, 72 Zehl L, 89, 91 Zhuravlov O, 61 Zieleniewska M, 59 Zimmermann M, 43 Zlahoda-Huzior A, 47 Żygierewicz J, 27, 28, 67 Zyma I, 61

131 Neuro Informatics 2019

INCF Secretariat, Karolinska Institutet, Nobels väg 15 A, SE-171 77 Web: incf.org Stockholm, Sweden incf.org/blog Tel: +46 8 524 870 93 Fax: +46 8 524 870 94 twitter.com/INCForg E-mail:INCF Secretariat [email protected] Tel: +46 youtube.com/user/INCForg 8 524 870 93 Karolinska Institutet, Nobels väg 15 A E-mail: facebook.com/INCForg [email protected] ©SE-171 INCF 772011 Stockholm2 Web: incf.org Sweden © INCF 2019