4th INCF Japan Node International Workshop Advances in Neuroinformatics 2016 and 14th INCF Nodes Workshop
Program and Abstracts
May 28-29, 2016 Suzuki Umetaro Hall, RIKEN 2-1, Hirosawa, Wako, Saitama, Japan RIKEN Brain Science Institute JAPAN
Organized by RIKEN Brain Science Institute International Neuroinformatics Coordinating Facility (INCF)
In Cooperation with The Japanese Society for Artificial Intelligence Japanese Neural Network Society The Japan Neuroscience Society
※This Workshop is a part of the RIKEN Symposium Series.
AINI2016 ＆ INCF Nodes Workshop
Table of Contents
Message from Chairs ...... 3 Workshop Organizing Committee ...... 4 General Information ...... 5 Program at a Glance ...... 6 Program ...... 7 Keynote Lecture Abstracts ...... 12 Oral Session I Whole Brain Imaging for Approaches to Functions and Anatomy Abstracts ...... 15 Oral Session II Multi-dimensional Theoretical Neuroscience Abstracts ...... 19 Oral Session III Asian and Pacific Neuroinformatics I Abstracts ...... 23 Oral Session IV Asian and Pacific Neuroinformatics II Abstracts ...... 26 Oral Session V Utilization of Ontology for Data Sharing and Mining Abstracts ...... 29 Oral Session VI International Neuroinformatics Abstracts ...... 33 Poster Session Abstracts ...... 36 Demonstration Session Abstracts ...... 56 Floor Plan ...... 65 RIKEN Campus Map ...... 67 Access to Venue ...... 68
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Message from Chairs
The Japan Node of the International Neuroinformatics Coordinating Facility (INCF) Japan Node is pleased to announce the 4th international workshop in the series – Advances in Neuroinformatics, AINI2016 at Wako, Japan on May 28 and 29, 2016.
We welcome researchers in all fields related to neuroscience, including: interdisciplinary and multi-dimensional researchers of the nervous system at the molecular, cellular, neural circuitry and behavioral levels; analysis tools, neural models, computation, and simulation; brain-machine interfaces and brain-inspired information and communication technology.
This year’s special topic is "Integration of Multi-Dimensional Neuroscience". The program includes keynote lectures, organized sessions, workshops, and general presentations. A number of prominent invited speakers from overseas and Japan are scheduled to participate in the workshop. In last year’s AINI2015, many researchers and young students from various countries attended and discussed the current state and future of neuroinformatics. Thus, the workshop will be beneficial for anyone who is developing or is interested in neuroscientific tools and methods, atlasing and visualization of the brain, integrating and databasing interdisciplinary and multi-dimensional data, and modeling and simulation.
See you in early summer, a beautiful season in Japan.
AINI 2016 Workshop Chairs
Teiichi FURUICHI Yoko YAMAGUCHI Professor, Science and Technology Neuroinformatics Japan Center (NIJC) Director Tokyo University of Science RIKEN Brain Science Institute (BSI)
We are delighted to co-host the 14th INCF Nodes workshop with the INCF Japan Node and their Advances in Neuroinformatics workshop. The focus of the Nodes workshops is on developing international collaborations between INCF Nodes. This meeting promises to be an inspiring and fruitful scientific event with speakers from Asia, Australia, Europe and North America, poster and demonstration sessions, as well as time devoted to collaboration discussions. The format is planned as a great opportunity for building and extending collaborations between groups in INCF’s Asian Nodes, and the whole international INCF network.
Thanks to the INCF Japan Node for hosting this event. We look forward to seeing you in Tokyo!
INCF Nodes Workshop Chair
Linda LANYON INCF Executive Director International Neuroinformatics Coordinating Facility (INCF)
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Workshop Organizing Committee
Honorary Chairs Shun-ichi AMARI RIKEN Brain Science Institute Shiro USUI RIKEN Brain Science Institute Keiji TANAKA RIKEN Brain Science Institute
AINI 2016 Workshop Chairs Teiichi FURUICHI Science and Technology, Tokyo University of Science Yoko YAMAGUCHI RIKEN Brain Science Institute
AINI 2016 Program Committee Akiko AIZAWA National Institute of Informatics Hideki KASHIOKA National Institute of Information and Communications Technology Yoshiyuki ASAI Okinawa Institute of Science and Technology Graduate University Tsuyoshi MIYAKAWA Institute for Comprehensive Medical Science, Fujita Health University Yuko Okamura-OHO RIKEN Center for Advanced Photonics/Brain Research Network (NPO BReNt) Hayato OHWADA Science and Technology, Tokyo University of Science Yo SHINODA Science and Technology, Tokyo University of Science Akira SATO Science and Technology, Tokyo University of Science
INCF Nodes Workshop Organizing Committee Linda LANYON INCF Executive Director Malin SANDSTRÖM INCF Secretariat Yoko YAMAGUCHI RIKEN Brain Science Institute
Organized by RIKEN Brain Science Institute (BSI) International Neuroinformatics Coordinating Facility (INCF)
In Cooperation with The Japanese Society for Artificial Intelligence Japanese Neural Network Society The Japan Neuroscience Society
AINI 2016 Secretariat Neuroinformatics Japan Center (NIJC), RIKEN BSI 2-1 Hirosawa Wako Saitama JAPAN 351-0198
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AINI2016 ＆ INCF Nodes Workshop
General Information
Venue Reference index for Abstracts The Keynote lectures and Oral sessions KL: Keynote Lecture will take place at Bioscience Bldg. (S01), OS: Oral Session Suzuki Umetaro Hall, RIKEN. Poster and PS: Poster Session Demonstration sessions will take place in DS: Demo Session the Lobby (2F・3F) . Abstracts Online publication Entrance Card All presented Abstracts (including attached All participants except for RIKEN figures) in the Workshop will be published employees need to get an entrance card at on-line with DOI at INCF Japan Node portal the guard station near the west gate of website. http://www.neuroinf.jp/aini2016 RIKEN. Please get the card at the guard Lunch station every morning and return it to the As the cafe, restaurants and the shops in guard station every evening. this venue will be closed on weekends, it is Pre-registered participants through strongly recommend to bring your own on-line may receive this entrance card on lunch during the workshop. Lunch eating arrival at the guard station that has been areas are available at Room No. N-351, issued beforehand. Please mention your S-310. name upon your arrival. Coffee・Tea Service Opening Hours of the Reception desk 1F: Workshop hours excluding Poster and The Entrance Lobby 1F, Bioscience Bldg. Demonstration Core Times (S01) 2F・ 3F: Poster and Demonstration Core May 28 9:00-17:30 Times only May 29 9:00-17:00 Social Event Internet & Charging Devices RIKEN Campus Tour WIFI is available at the venue. ※Advance registration required. Electrical sockets are available at the Date & Time: May 28, 17:30-18:40 Poster and Demonstration area, in the Meeting Place: Reception Desk at Lobby (2F・3F) for charging devices for Bioscience Bldg. (S01) Free. RIBF Cyclopedia To Speakers in Oral Sessions (Keynote RIKEN Nishina Center for Lectures and Oral Sessions) Accelerator-Based Science Nishina • You are requested to check your own RIBF Building (E01) computer by connecting to the Brain Box projector in the venue at / or earlier RIKEN Brain Science Institute, Central than the break time prior to your talk Building 1F (C51) session. Please contact to the reception RIKEN Gallery desk on details of the check. AV Hall, 1F (C04) To Poster and Demonstration Social Gathering Presenters ※Advance registration required. • Poster Size: A0 (841 mm W and 1,189 Date & Time: May 28, 19:00-20:30 mm H) Place: La Pasta Tou • All posters should be put up in the 1-7 2F, Honcho, Wako-shi, Saitama Lobby (2F ・ 3F) from 9:00 until Tel: 048-451-0150 the morning break on May 28, Fee: Regular 4,000yen, Student and removal should be done after the 2,000yen end of the Poster and Demonstration Please pay at the reception desk at Core Time II on May 29. Bioscience Bldg. (S01) on arrival. The poster boards will be marked with • Contact numbers referring to those stated in the AINI 2016 Secretariat abstract book. Neuroinformatics Japan Center (NIJC) At Poster and Demonstration Core Time • RIKEN Brain Science Institute (BSI) at least one presenter should stand by 2-1 Hirosawa Wako Saitama 351-0198 the poster. JAPAN Odd ID numbers are the posters on the • e-mail: [email protected] Core Time I. Even ID numbers are the Tel: 048-467-6454 posters on the Core Time II.
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Program at a Glance
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Program
Workshop Day 1, Saturday, May 28, 2016
Workshop Day 2, Sunday, May 29, 2016
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Nodes member Program at RIKEN BSI, May 29, 2016
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Poster and Demonstration
Odd ID No. Core Time I, May 28 12:55-13:55 Even ID No. Core Time II, May 29 12:40-13:40
Posters
PS-1 Visiome Platform Shunji SATOH
PS-2 BCI Developmental Tools Available for Online EEG Data Acquisition and Time-Frequency Analyses by Using the Wavelet Lifting Scheme Balbir SINGH
PS-3 A Simultaneous Recording of EEGs and the Eye-Tracking System based on Multitask Operation Mayu ICHIKI
PS-4 Linking INCF Japan Node Platforms with Application Networks based on Garuda Platform Yoshiyuki ASAI
PS-5 Ontology-Based Search Server (ObSS) Extends Availability of Neuroinformatics Data by Listing Relevant Metadata if it is Unfamiliarly Indexed Yoshiyuki ASAI
PS-6 ERP Topography of the effects of Music Therapy for children with severe neurological disorders Maria L. BRINGAS
PS-7 On the neural circuits mediating sense to action - Insight from physiology and numerical modelling Andreas Antonios KARDAMAKIS
PS-8 Community Detection and Mean Field Approximation for Dimension Reduction of Spiking Network Models Carlos Enrique GUTIERREZ
PS-9 A Strategy to infer hidden sources behind multiple neural recording data for neural circuit inference Ildefons MAGRANS DE ABRIL
PS-10 Analysis on the mouse gait patterns by using a neuro-musculoskeletal model Satoshi OOTA
PS-11 A novel approach for repetitive sequential activity detection Keita H. WATANABE
PS-12 Cancelled
PS-13 Efficient numerical simulation of neuron models with spatial structure on graphics processing units Tsukasa TSUYUKI
PS-14 Honeybee neurons responsive to the pulsed vibration produced by waggle dance Hiroyuki AI
PS-15 The Invertebrate Brain Platform (IVB-PF) -updates of IVBPF in 2016- Daisuke MIYAMOTO
PS-16 The Cerebellar Platform and its recent innovation Shinji KAKEI
PS-17 Development of an on-line simulation environment for computational neuroscience Hidetoshi IKENO
PS-18 Cloud computing for clinical brain analytics Eric Tatt Wei HO
PS-19 Brain decoding can be improved by diffusion imaging based registration Takuya FUCHIGAMI
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PS-20 Lending Global Fiber-Tracking to the Tracing Of Dense Neurites in Fluorescence Micrographs Henrik SKIBBE
PS-21 Cancelled
PS-22 Recent activities of Neuroimaging Platform Hideki KASHIOKA
PS-23 Mathematical modeling and dynamical analysis using structural and functional connectivity Hiromichi TSUKADA
PS-24 Parallelization of the registration method for ISH images into the 3D standard brain atlas Daisuke MIYAMOTO
PS-25 RIKEN MetaDatabase: a life-science database integration and publication platform based on the Semantic Web Norio KOBAYASHI
PS-26 Hypotheses of proximity-rule generation during visual search tasks Akihiro MASAOKA
PS-27 Theory of STDP in Dynamic Boltzmann Machines for Learning Temporal Patterns Sakyasingha DASGUPTA
PS-28 Transcriptional signatures of connectome topology in the mouse brain Ben David FULCHER
PS-29 Brain Transcriptome Database (BrainTx) Akira SATO
PS-30 Finding unknown disease-related genes by comparing Random Forest Results to Secondary Data in Medical Science Study Takahiro KOIWA
PS-31 Finding a Disease-Related non-coding RNA Using Random Forest Hirumu IDE
PS-32 The report from Hackathon working group in the J-node: INCF Japan Node Hackathon 2016 April Tomoki KAZAWA
PS-33 INCF Japan Node Software Center Takayuki KANNON
PS-34 Development of Research Portal for Multi-scale Heterogeneous Brain Data of Brain/MINDS Project Yoko MORII
PS-35 Data Analysis Pipeline Environment on the Brain/MINDS Research Platform Masahide MAEDA
PS-36 Big Data Transfer Tool on Brain/MINDS Research Portal Takeshi TAKEUCHI
PS-37 Brain Science Dictionary Yasunori HAYASHI
PS-38 Developing large scale simulation of silkmoth brain using super computer Tomoki KAZAWA
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Demonstrations
DS-1 HI-brain: a software platform for numerical simulation of the complex vision system by connecting and reusing existing models Kazuki URABE
DS-2 A Workflow to Build and Validate the BCI System Architecture Using the SSVEP Stimulator and Robotic Care Devices Archived in the Neuroinformatics Platform Kiyohisa NATSUME
DS-3 An Explorative Framework to Accelerate Collaborative Data Analysis of Medical Time Series Supported by the J-Node Platform Data Conversion Service Hiroaki WAGATSUMA
DS-4 Synchronized Motion Generation Derived from Implemented Neurodynamics based on Coupling Nonlinear Oscillators Toward Perspectives of Suitable Human-Robot Interactions Motoharu HAGIO
DS-5 Analysis of motor function of arm movements on Kinect v2 Takeru HONDA
DS-6 ViBrism DB Platform Yuko Okamura-OHO
DS-7 Marmoset Atlas with Web 3D Viewer Alexander WOODWARD
DS-8 Computational Methods For Accurate 3D Digital Marmoset Brain Atlas Creation From Histology and MRI Alexander WOODWARD
DS-9 KANPHOS Platform: A database for neural phosphoproteomics with quality control Junichiro YOSHIMOTO
DS-10 Mouse Behavioral Phenotype Database Satoko HATTORI
DS-11 Consistent data organization made easy: A versatile file format for neuroscience Christian J. KELLNER
DS-12 Brain Knowledge Engine Hongyin ZHU
DS-13 SciCrunch: A Cooperative And Collaborative Data And Resource Discovery Portal For Scientific Communities Jeffrey S. GRETHE
DS-14 KnowledgeSpace: a community-based encyclopedia for neuroscience Mathew ABRAMS
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Keynote Lecture Abstracts
12 AINI2016 ＆ INCF Nodes Workshop
KL-1 Databasing brain gene expression information
Teiichi FURUICHI Dept. of Biological Science, Fac. of Science and Technol., Tokyo University of Science [email protected]
Genetic mechanisms underlying brain development and function are attributable to temporal and spatial control of the expression of thousands of specific genes. To systematize the entire expression of transcripts (“transcriptome”) during cerebellar development, as a model of brain development, we generated the Cerebellar Development Transcriptome Database, supported by the INCF Japan Node. The first web version was released in 2005. This database contains large amounts of information in a searchable format. It includes experimental data on spatio-temporal gene expression profiles during development and relevant bioinformatic and literature information with direct links to the original or relevant source website. Abnormalities in gene expression patterns are thought to affect normal brain development and animal behaviors. Our integrated database provides a foundation for analyzing numerous brain-expressed genes based on their spatio-temporal expression patterns, as well as Gene Ontology and annotation. Therefore, we could use this database for mining genes of interest, for example, disease-related genes including those potentially associated with autism-spectrum disorders. In 2015, we pursued a new direction and started to enhance our database for visualizing the transcriptome at various stages and states of the mammalian brain: the database is currently called BrainTx. In parallel, we are attempting to develop Neural Gene Ontology. This will provide a framework where the gene information system in the new database would be beneficially organized for integrating interdisciplinary information, from molecular and cellular to cognitive and behavioral neuroscience.
KL-2 The interface between data and knowledge: Multi-scale data integration in neuroscience
Maryann E. MARTONE Department of Neurosciences University of California, San Diego [email protected]
In the past few years, data and data science has exploded into the academic and public consciousness, as “Big data” and “Data science” have taken hold. Most recently in the United States, the US National Institutes of Health has launched its BD2K program: Big Data to Knowledge. However, clearly this relationship is not unidirectional. That is, to achieve Big Data to Knowledge, you have to bring Knowledge to Big Data. In this presentation, I will present an overview of the Neuroscience Information Framework and its supporting platform, SciCrunch. NIF has been surveying and tracking the biomedical resource landscape, with an emphasis on but not restricted to, neuroscience since 2008. Through a monitoring and tracking system, we see the fluidity of the resource ecosystem, with resources coming and going and “flowing” from one resource or location to the next. NIF/SciCrunch provides an ideal platform in which to explore the resource ecosystem as it evolves over time, and to take advantage of the connectedness among research tools and data and differing points of view about the same data. An integral part of this ecosystem is not just the data and databases, but the knowledge and knowledge-structures that allow us to compare what we think we know, embodied in community ontologies, human curation and domain expertise, to data on a large scale. With new types of web-based annotation tools, e.g., Hypothes.is, this crucial interface between data and knowledge can be added as a light-weight linking layer over more static research objects. The interface between data and knowledge allows us to identify uncertainties, potential sources of bias and potential data-knowledge mismatches in the current biomedical data space. Together with the INCF and Human Brain Project, we are bringing knowledge and data together within a new entity-based platform, the Neuroscience Knowledge Space, which will provide a data-driven, testing ground for the conceptual framework that currently undergirds neuroscience.
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KL-3
Potential Contribution of AI to Neuro-informatics
Junichi TSUJII Artificial Intelligence Research Center, AIST [email protected]
We have witnessed significant methodological changes in big sciences. They have been brought by advances of computer science and information technology, first by computer simulation followed by data-sharing through common ontologies. Data-oriented methods including machine-learning and AI are new additions. My talk addresses how AI can contribute to big sciences such as brain science.
KL-4
Benefits of comprehensive data management for efficient data access, reproducibility, and data sharing
Thomas WACHTLER Ludwig-Maximilians-Universität München, Germany [email protected]
Advances in technology and methodology have dramatically increased the volume and complexity of data recorded in neuroscience. At the same time, scientific progress has become more and more dependent on collaborative efforts, exchange of data, and re-analysis of previously recorded data. Ensuring that data stays accessible and can be shared and re-used has become increasingly important as well as challenging. Neuroinformatics provides methods and tools to deal with these challenges. Key requisites for reproducible and shareable data are the collection and integration of metadata with the data and the consistent machine-readable representation of data and metadata in common formats. Integrating comprehensive annotation and organization of data in the laboratory data workflow has the immediate benefit of making data access and analysis efficient and reproducible, and at the same time eliminates the need of additional data preparation for collaborative data sharing or publication.
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Oral Session I Whole Brain Imaging for Approaches to Functions and Anatomy Abstracts
Session Organizers
Yuko Okamura-OHO RIKEN Center for Advanced Photonics/Brain Research Network (BReNt)
Hideki KASHIOKA National Institute of Information and Communications Technology (NICT)
Invited Speakers
Hiroshi MATSUDA Integrative Brain Imaging Center, National Center of Neurology and Psychiatry
Pedro A. VALDÉS-SOSA Joint China-Cuba Lab Neurotech, Cuban Neuroscience Center
Jong Chul YE KAIST
Okito YAMASHITA Neural Information Analysis Laboratories, ATR
Hiromasa TAKEMURA Center for Information and Neural Networks (CiNet)
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OSI-1
MRI morphometry in Alzheimer’s disease
Hiroshi MATSUDA Integrative Brain Imaging Center, National Center of Neurology and Psychiatry [email protected]
MRI based evaluation of brain atrophy is regarded as a valid method to stage the disease and to assess progression in Alzheimer’s disease (AD). Over the last decade software packages such as FreeSurfer provide measurements of cortical and subcortical gray matter features based on MRI data. Moreover a recent advance in MRI analysis, automatic segmentation hippocampal subfields has made it possible to automatically measure hippocampal subfields and adjacent cortical subregions. For longitudinal morphometry to assess progression of brain atrophy using structural MRI, popular methods include boundary shift integral (BSI), tensor-based morphometry, and FreeSurfer-longitudinal. BSI uses linear registration to align the baseline and repeat images and then track the shift of the brain boundary location and has been shown to provide accurate measurements of brain atrophy that are sensitive biomarkers of disease progression. However these software tools have not become routinely used either, since the execution time of the volumetric workflow required long time. At present, voxel based morphometry (VBM) is easily applicable to the routine clinical procedure with a short execution time. The importance of the VBM approach is that it is not biased to one particular structure and is able to assess anatomical differences throughout the brain. Stand-alone VBM software running on Windows, Voxel-based specific regional analysis system for AD, has been widely used in the clinical diagnosis of AD in Japan. On the other hand, graph theoretical analysis offers a diverse range of quantitative measures for characterizing the topology of structural network for the whole brain without a priori hypothesis. AD patients showed altered small-world architecture in the structural cortical networks, implying a less optimal topological organization in AD.
OSI-2 Data and Model Driven EEG/fMRI fusion with an emphasis on Brain Connectivity
Pedro A. VALDÉS-SOSA (1),(2) (1) Joint China-Cuba Lab Neurotech, (2)Cuban Neuroscience Center [email protected]
Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or “atoms”. We introduce Markov-Penrose diagrams —an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Nonlinear Partial Least Squares and Coupled Tensor Fusion. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings show the potential of the methods and suggest their use in other scientific domains.
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OSI-3
NIRS-SPM: statistical parameter mapping for near infrared spectroscopy
Jong Chul YE KAIST [email protected]
NIRS-SPM is a software package for statistical analysis of near-infrared spectroscopy (NIRS) signals, developed at the Bio Imaging Signal Processing (BISP) lab at KAIST in Korea. Based on the general linear model (GLM), and Sun's tube formula / Lipschitz-Killing curvature (LKC) based expected Euler characteristics, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total- hemoglobin, but also allows for super-resolution activation localization. In NIRS-SPM, wavelet-MDL detrending algorithm effectively removes an unknown global trend due to breathing, cardiac, vaso-motion, or other experimental errors. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals and uncorrelated noise components as distinct scales. NIRS-SPM provides Sun's tube formula and Lipschitz-Killing curvature based expected Euler characteristics for p-value correction. In the case of Sun's tube formula correction, p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. We also provide the expected Euler characteristic approach based on Lipschitz-Killing curvature to control the family-wise error rate. In addition, we have developed a method to estimate cerebral metabolic rate of oxygen (CMRO2) without hypercapnia by using simultaneous measurements of NIRS and fMRI (Tak et al., 2010). Using the optimization framework, many assumed parameters such as baseline hemoglobin concentration and hypercapnia can be readily estimated, which promise more accurate estimation of cerebral blood flow (CBF) and CMRO2.
OSI-4 Reverse Engineering Macro-scale Human Brain Dynamics
Okito YAMASHITA (1) (1) Department of Computational Brain Imaging, Neural Information Analysis Laboratories, ATR [email protected]
Recent development of resting-state fMRI (rsfMRI) experiment and diffusion MRI (dMRI) measurement allows for investigation of macro-scale functional and anatomical networks of human brain. Further understanding of brain functions requires investigation of macro-scale network dynamics. Some computational studies investigating macro-scale network dynamics demonstrated that the neural mass model constrained by anatomical connectivity measured with dMRI reproduced functional connectivity patterns measured with rsfMRI. On the other hands, data-driven methods such as the dynamical causal modeling and multivariate time series modeling provided empirical evidence of macro-scale network dynamics by identifying parameters of network dynamics from experimental data. However these methods have limitation to pre-select a small number of regions and dynamics identification of a large network is still challenging. We propose a network dynamics identification method of whole-cortex consisting of two thousand nodes and more than one million connections. To overcome problems due to a huge degree of freedom, we integrated multiple experimental measurements: magnetoencephalography (MEG), fMRI, dMRI and T1-MRI. We construct a linear network dynamics model of neural current sources whose network architecture is constrained by anatomical connectivity obtained from dMRI, which dramatically reduce the degree of freedom. The network dynamics parameters are identified from neural current sources that are obtained from MEG with help of fMRI (which also reduce the effective degree of freedom). To show feasibility of our method, we will demonstrate stimulus-evoked network dynamics during face perception and word comprehension. 17 AINI2016 ＆ INCF Nodes Workshop
OSI-5 Computational Neuroanatomy of the human visual white matter
Hiromasa TAKEMURA (1)(2)(3) (1) Center for Information and Neural Networks (CiNet), NICT and Osaka Univ., (2) JSPS, (3) Graduate School of Frontier Biosci., Osaka Univ. [email protected]
In classical studies, neuroanatomical data had been provided as manual drawings which qualitatively described the shape and connectivities of the brain structures. Modern mehods, such as diffusion MRI (dMRI), now provide three-dimenstional digital anatomy dataset from living human brains. The progress of dMRI opens an avenue for computational human neuroanatomy, which models the trajectory of white matter pathways as well as the biological properties of brain tissues using computational methods. The dMRI measurement from living brains also provides opportunities to study white matter in relation to health, developments, and behavior in large populations. This talk will introduce how we incorporate the advantage of dMRI and computational neuroanatomy to understand the questions in the system neuroscience. I will start by describing the principles of the computational methods (tractography) for identifying the trajectory of white matter pathways from dMRI data. I will then provide an overview of my recent studies on white matter pathways in the visual system. I will describe studies for identifying major white matter pathways in visual cortex, and comparing the characteristics of the pathways between humans and macaques. I will particularly focus on the pathway named Vertical Occipital Fasciculus (VOF), which has been largely ignored in the literature, but critically important to understand the human visual processing. Finally, I will describe studies examining the properties of visual white matter pathways in relation to retinal disease and behavioral performances.
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Oral Session II Multi-dimensional Theoretical Neuroscience Abstracts
Session Organizers
Yoshiyuki ASAI Okinawa Institute of Science and Technology Graduate University (OIST)
Ryohei KANZAKI The University of Tokyo
Hidetoshi IKENO University of Hyogo
Invited Speakers
Jun IGARASHI RIKEN
Tadashi YAMAZAKI The University of Electro-Communications
Shigeru SHINOMOTO Kyoto University
Yoshihiko NAKAMURA The University of Tokyo
Stephen John EGLEN University of Cambridge UK
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OSII-1 A large-scale simulation of the basal ganglia-thalamocortical circuit on K computer for understanding Parkinson's motor symptoms
Jun IGARASHI(1)(2), Osamu SHONO(2)(4), Jan MOREN(2), Junichiro YOSHIMOTO(2)(3), Kenji DOYA(2) (1) ACCC RIKEN, (2) NCU OIST, (3) GSIS NAIST (4) HRIJ [email protected]
Parkinson's disease (PD) is caused by a loss of dopamine supply to basal ganglia. PD patients show some motor symptoms. Basal ganglia, thalamus, and motor cortex exhibit various oscillatory activities in PD. How the neural oscillations occur and how the neural oscillations are involved in PD motor symptoms are still unclear. To address these, we have been developing a model of basal ganglia-thalamocortical circuit. First, we performed simulations of basal ganglia to see how dopamine concentration would affect to the oscillatory activity. For low dopamine concentration, abnormally synchronized neural oscillation which had a peak of power at about ~14 Hz appeared in globus pallidus external segment (GPe) and subthalamic nucleus (STN) neurons. The results suggest that firing property of STN neurons and decrease in spontaneous firings of GPe neurons may be critical in the occurrence of the oscillation. We next performed simulations of thalamocortical circuit to examine how the oscillatory activity would be associated with PD motor symptoms. For strong inhibitory currents, spatially distant thalamocortical cells showed anti-phase synchronization of ~5Hz Ca 2+ burst firings. Output neurons projecting peripheral in motor cortex showed anti-phase synchronization by the thalamic output. The result suggests that the anti-phase synchronization might underlie in the alternative excitation of muscles in tremor. Finally, we tested a large-scale simulation of basal ganglia-thalamocortical circuits using K computer. The maximum size was ~ 3 million of neurons corresponding to the rat brain. The large-scale model may help to understand the interaction among brain regions.
OSII-2 Toward building an artificial cerebellum
Tadashi YAMAZAKI (1),(2) (1) IE, UEC, (2) NIJC, RIKEN BSI [email protected]
Mathematical and computational modeling are two different approaches in the field of theoretical neuroscience. Mathematical modeling is a top-down, hypothesis-driven approach, which simplifies all details of experimental data and extracts only the minimal components which are essential to realize a function. In this approach, a model is composed of a small set of mathematical equations with a minimal number of parameters. On the other hand, computational modeling is a bottom-up, data-driven approach, which accumulates all details of experimental data and build a replica of the biological brain which reproduces the brain dynamics. In this approach, a model is composed of millions of differential equations describing diffusion of membrane voltage in neurons, kinetics of ion channels, synaptic dynamics and so on. These approaches are complementary. In the top-down approach, a researcher decides which experimental data are included or discarded for a model subjectively. To justify the subjectivity, an elaborated model built by the bottom-up approach is necessary. On the other hand, in the bottom-up approach, due to a number of free parameters, a model is possible to exhibit any types of dynamics. To justify how the model should behave, we need a simplified model built by the top-down approach. In this talk, I would like to present how these two approaches work together. Specifically, I would like to introduce our studies on modeling post-training memory consolidation in the cerebellum both in the top-down approach and bottom-up approach.
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OSII-3 Criticality in event cascades spreading through inhomogeneous networks
Shigeru SHINOMOTO(1) and Tomokatsu ONAGA(1) (1) Department of Physics, Kyoto University Criticality in event cascades spreading through inhomogeneous networks [email protected]
There is a commonality among contagious diseases, tweets, crimes, nuclear reactions, and neuronal firings that past events facilitate the future occurrence of events. The spread of events exhibiting catastrophic chain reactions has been extensively studied; however, their subthreshold states for the case of the weaker infectivity are not fully understood. Here we reveal that these systems are possessed by nonstationary cascades of event-occurrences already in the subthreshold regime, and they exhibit a transition in which nonstationary cascades appear from the stationary state when the infectivity is much weaker than the epidemic threshold. Cascades of event-occurrences can be a nuisance in some contexts, when the peak-demand causes vaccine shortages, heavy traffic on communication lines, frequent crimes, or large fluctuations in nuclear reactions, but may be beneficial in other contexts, such that spontaneous activity in neural networks may be used to generate motion or store memory. Thus, it is important to comprehend the stationary-nonstationary transition and consider controlling a system by manipulating connections of individuals. The critical infectivity depends greatly on the network structure in which individuals are connected. We demonstrate that we can predict whether cascades may emerge in a network, given information about the interactions between individuals. We also suggest a systematic method of controlling systems to incite or impede cascade bursting by manipulating the interactions between individuals. References T. Onaga and S. Shinomoto, Physical Review E (2014) 89:042817 T. Onaga and S. Shinomoto, arXiv:1603.05859 (2016)
OSII-4 Supercomputing interaction of spinal neuron pools and the wholebody musculoskeletal system
Yoshihiko NAKAMURA The University of Tokyo [email protected]
Computer simulation of the human wholebody neuromuscular system is a grand challenge of supercomputing. The system includes the central and peripheral nerve systems, and the wholebody musculoskeletal system. As a member of the team in K-Computer project for predictive medicine, we have worked on the modeling of neuron pools of motor neurons in the spine and sensory neurons in the spinal nerves as well as of the wholebody skeletal muscles. The neurons were modeled by spiking neurons model using the leaky integral-and-fire circuits. The muscles were modeled by visco-elastic continuum bodies using the FEM. This talk will introduce the hypotheses and algorithms we have based on and a very preliminary result of computation.
21 AINI2016 ＆ INCF Nodes Workshop
OSII-5 On a role for competitions in computational neuroscience
Stephen EGLEN (1), Catherine CUTTS (1), JJ Johannes HJORTH (1), David C STERRATT (2), David J WILLSHAW (2) (1) Cambridge UK (2) Edinburgh UK [email protected]
"Competition" is commonly discussed in the context of e.g. "competition for limited resources" in developmental neuroscience. However, another common usage is that of a contest among participants to see who performs the best. Competitions have advanced fields such as protein structure prediction (http://predictioncenter.org/). I will describe two recent projects where we have informally used a competition framework. First, I will show how we have compared four models of retinotopic map formation. We find that old models are nearly as good as the latest models. Second, I will show a blind comparison of 35 methods for calculating correlations between spike trains. We suggest four methods optimal for calculating correlations between sparse spike trains, such as those observed in spontaneous retinal waves. I believe such competitions are useful to avoid the "self-assessment trap" (Norel et al., 2011).
22 AINI2016 ＆ INCF Nodes Workshop
Oral Session III Asian and Pacific Neuroinformatics I Abstracts
Invited Speakers
Ahmad Fadzil Mohd HANI Universiti Teknologi PETRONAS
James GEE University of Pennsylvania
Yi ZENG Institute of Automation, Chinese Academy of Sciences
Pedro A. VALDÉS-SOSA The Joint China-Cuba Lab for Frontier Research in Translational Neurotechnology
23 AINI2016 ＆ INCF Nodes Workshop
OSIII-1
Towards Clinical Neuroinformatics in Malaysia
Ahmad Fadzil Mohd HANI Universiti Teknologi PETRONAS [email protected]
Neuroinformatics has potential to inform doctors on a patient’s state of mental health, predisposition to brain disease and efficacy of treatment. By exposing doctors to information computed via brain analytics in everyday clinical situations, clinical neuroinformatics will advance because many more doctors will want to be involved in creating new applications and use cases. Malaysia Node’s initial effort is to develop a cloud-hosted service to provide brain analytics to clinicians. The service runs open source and freely available computing pipelines to compute morphometric and diffusion tensor measures from MRI brain images. Workflows are also being developed to acquire streaming datasets from EEG, fNIRS, wearable sensors and textual report input for patient registries. Finally, machine learning algorithms will extract relationships and groupings from the various datasets. Use-cases will be developed for managing workplace stress, grading addiction, traumatic brain injury and epilepsy. To succeed, it is critical that clinicians actively engage in defining the workflows and use them. To motivate clinicians towards novel applications of neuroinformatics, it is vital to demonstrate value early in the collaboration by creating easy-to-use methodology and solving urgent, unmet needs for low complexity and low cost data acquisition and hosting.
OSIII-2 Next-Generation Waxholm Space for Rodent+ Neuroinformatics
James GEE University of Pennsylvania [email protected]
Data collections of every variety now abound and continue to grow in the neurosciences but their lack of accessibility, especially in an integrative way, to the average investigator presents a persistent and vexing challenge to the field and ultimately limits the potential impact of these resources. To address this undesirable state of the science, the INCF established the Digital Atlasing Program to formulate standards and practices for atlas-based data sharing, and to instantiate these, for the rodent brain initially, in infrastructure, systems and methods capable of serving the scientific goals of the community. A central outcome has been the development and prototypical implementation of Waxholm Space (WHS), a standard coordinate system for the adult C57BL/6 mouse brain, and its underlying digital atlasing infrastructure that allows researchers to query any data resource registered to WHS as an atlas hub. As with its counterpart in human brain studies, the Talairach coordinate system, an essential requirement is the ability to normalize user data into standardized space; the inverse transformation allows standardized data to be accessed in user's native space. The next generation of WHS will develop, evaluate and deploy this critical functionality under community support. Specifically, an improved multi-modality atlas defining the canonical coordinate system of WHS is proposed for the mouse and rat, expanding both the range of experimental studies possible and the types of analyses that may be applied to these in the field. This first-of-its-kind analytic capability will be enabled by a suite of tools that accommodate a variety of user backgrounds and needs. The successful completion of this project will be a major step toward realizing the full potential of Waxholm Space in facilitating rodent neuroscience research.
24 AINI2016 ＆ INCF Nodes Workshop
OSIII-3 Brain-inspired Intelligence and Its Applications in Intelligent Robotics
Yi ZENG(1)(2) (1)Institute of Automation, Chinese Academy of Sciences, China, (2) Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China [email protected]
Creating an intelligent system that can perform any cognitive tasks that the human can is the dream of Artificial Intelligence research since its birth [1, 2]. Until now, still only human brain can achieve this level of generality. Brain-inspired Intelligence tries to get inspirations from the brain at multiple scales to tackle the grand challenge of Human-level Artificial Intelligence with general cognitive abilities [3]. Inspirations of the brain can be useful to future intelligent models and systems from several perspectives. From the structure perspective, the morphology of different types of neurons, the layered architecture of the neocortex, feed forward and feed backward connections of brain regions, as well as motifs of brain building blocks at multiple scales brings new insights for designing brain-inspired artificial neural networks. From the mechanism perspective, models of spike information encoding and decoding, functions of different types of neurons, synaptic models, plasticity models, coordination mechanisms at different scales (neuron, micro-circuits, brain region, etc.) bring potential operational principles to design efficient computational models and algorithms for general Artificial Intelligence. From the behavior perspective, observations and analysis on how different cognitive functions coordinate with each other will bring inspirations and evaluation standards for brain-inspired intelligent systems to compare with human cognitive performance. In this talk, I will introduce the Brain-inspired cognitive computation model that is designed based on these inspirations. I will provide several applications of this model which demonstrate our explorations of Brain-inspired Intelligent Robotics with a cognitive brain. These applications include an unmanned aerial vehicle (UAV) with a cognitive brain that can automatically avoid static and moving obstacles, a humanoid robot with a cognitive brain that is with the abilities of multi-sensory concept learning, and sensory-motor binding, etc.
References: [1] Allen Newell, John Clifford Shaw, Herbert A. Simon. Report on a general problem-solving program. Proceedings of the International Conference on Information Processing. 256-264, 1959. [2] John McCarthy. From Here to Human-level AI. Artificial Intelligence 171: 1174-1182, 2007. [3] Yi Zeng, Chenglin Liu, Tieniu Tan. Retrospect and Outlook of Brain-inspired Intelligence Research [in Chinese]. The Chinese Journal of Computers, 39(1): 212-222, 2016.
OSIII-4
The Joint China Cuba Neuroinformatics Collaboratory
Pedro A. VALDÉS-SOSA (1,2) (1)Joint China-Cuba Lab UESTC, (2)Cuban Neuroscience Center [email protected]
A brief presentation is made about the collaborative efforts between the Cuban Neuroscience Center, Havana, Cuba and the Key Lab of Neuroinformation at the University of Electronic Sciences and Technology of China. The focus of the work is on frontiers research in translational neurotechnology. Experiences with using neuroinformatics tools for aid primary health care are explained as part of strategies to cope with the Global Burden of Brain Disorders.
25 AINI2016 ＆ INCF Nodes Workshop
Oral Session IV Asian and Pacific Neuroinformatics II Abstracts Invited speakers
Ben David FULCHER Monash University
Soo-Young LEE Korea Advanced Institute of Science and Technology (KAIST)
Yoko YAMAGUCHI RIKEN BSI
26 AINI2016 ＆ INCF Nodes Workshop
OSIV-1 Brain connectivity and dynamics: highly comparative time-series analysis of neuroscience data, and gene expression patterns of brain connectivity
Ben D. FULCHER, Alex FORNITO Monash Institute of Cognitive and Clinical Neuroscience, Monash University [email protected]
In this presentation I will first overview the structure and main areas of expertise of the Australia INCF Node. I will then discuss two projects on brain connectivity and dynamics in more detail. The first is a general tool for highly comparative time-series analysis for neuroscience data [1], hctsa (https://github.com/benfulcher/hctsa), which provides a systematic platform for computing structural properties from time series. We demonstrate the broad utility of this flexible methodological approach to applications in neuroscience, from classifying BOLD signals of different patient groups, and learning low-dimensional spaces that represent informative structure in EEG datasets. I will also summarize our ongoing work on brain connectivity and gene expression [2]. We have developed methods for processing large-scale transcriptional datasets that have allowed us to extract interpretable transcriptional signatures of: (i) brain connectivity in general (which we show is driven by genes regulating neuronal, synaptic, and axonal structure and function) and (ii) different topological classes of links in the connectome (where we show that connections involving hubs show the highest transcriptional coupling, driven by genes regulating the oxidative synthesis and metabolism of ATP). References 1. Fulcher, B. D., Little, M. A. & Jones, N. S. Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 20130048 (2013). 2. Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl. Acad. Sci. USA 113, 1435 (2016).
OSIV-2 Investigating neural correlates of human trust in machine using a theory-of-mind game
Suh-Yeon DONG, Bo-Kyeong KIM, Kyeongho LEE and Soo-Young LEE School of EE and BSRC, KAIST [email protected]
Human trust in machines is important for the safe and efficient performance of human-machine systems. Multi-dimensional frameworks have been developed to account for the nature of human trust (Barber 1983, Bisantz 2000). For example, Barber (1983) defines trust in terms three dimensions: persistence, technical competence, and fiduciary responsibility. However, models of trust between people might not be suitable for human trust in machines. In the present paper, we review a novel model of trust composed of three characteristics: willingness to cooperate, technical competence, and human-likeness. In order to test the model, we recorded event-related potentials while healthy subjects (N = 31) played a sequential-move 2x2 matrix game (Hedden and Zhang, 2002) with a computerized agent. In summary: (i) the computer opponent’s high technical competence elicited large increases in activity in the left parietal regions; (ii) the computer opponent’s non-cooperative decisions elicited large increases in activity in the frontal/central regions; (iii) the computer opponent’s human-likeness enhanced the differences in (i) and (ii), respectively; and (iv) these activities correlating with these trust dimensions are implicated in the ToM network (Frith and Frith, 2000). According to these results, the proposed trust model successfully articulates important aspects of human-machine interaction. And, in order to ensure safe and efficient human-machine systems, it may be useful to monitor electrophysiological signals originating in brain regions belonging to the ToM network of human operators, modulating machine performance accordingly.
27 AINI2016 ＆ INCF Nodes Workshop
OSIV-3 Integration of Neuroinformatics Platforms in INCF Japan Node
Yoko YAMAGUCHI NIJC, RIKEN BSI [email protected]
INCF Japan Node (J-Node) has 14 neuroinformatics platforms in individual neuroscience fields. They provide web databases in individual neuroscience domains and neuroinformatics infrastructures. It is challenging to manage these databases in an integrative manner. These databases are comprehensively managed by using a common web database management system XooNIps, while their data integration for further advanced use requires additional development of common infrastructures. In 2015, we started to organize collaborative development of neuroinformatics tools as J-Node Hackathon. Until now, we developed histological image data registration tool, 3D web viewer for brain gene expression data, 3D web viewer for nifty data. Source code of those tools are to be released from J-Node Software Center. In addition, some of J-node platforms were registered to RIKEN metadatabase as RDF databases together with utilization of ontology. These developments are promoting integration data and knowledge across web-databases in J-Node. In addition, we hope the sea activities should serve for an infrastructure of further integration of data and knowledge among INCF Nodes and also across fields and countries. Such integration may help association of neuroinformatics communities as well.
28 AINI2016 ＆ INCF Nodes Workshop
Oral Session V Utilization of Ontology for Data Sharing and Mining Abstracts
Session Organizers
Akiko AIZAWA National Institute of Informatics
Hayato OHWADA Tokyo University of Science
Teiichi FURUICHI Tokyo University of Science
Invited Speakers
Kim Jin-DONG Database Center for Life Science (DBCLS)
Yoshinobu KANO Shizuoka University
Hideaki TAKEDA National Institute of Informatics
Yi ZENG Institute of Automation, Chinese Academy of Sciences
29 AINI2016 ＆ INCF Nodes Workshop
OSV-1
Toward a map of whole scientific literature to navigate open science Kim Jin-DONG Database Center for Life Science (DBCLS) [email protected]
As text mining is more and more recognized as an important component of data-driven open science, there are now a number of large projects to develop annotations to a large scale of scientific literature, e.g. the whole PubMed, PMC, or others. However there is a bottleneck of data exchange and data storage of literature annotation. PubAnnotation is developed to address the problem. PA is a repository of literature annotation, which currently has it focus on literature of life sciences. It is not just a collection of annotations, but it adds new value to the annotation data sets deposited to PA, by aligning all the annotations to the same texts: thanks to the alignment, all the annotations in PA become comparable to each other. With all the annotations collected and aligned in PA, it is becoming the largest map of scientific literature, which will navigate scientists in the ever growing space of scientific literature.
OSV-2 Text Mining for Neuroscience Papers
Yoshinobu KANO Faculty of Informatics, Shizuoka University [email protected]
More papers are getting published and available for researchers. However, it is virtually impossible for humans to read all of the available papers. Text mining could help researchers to automatically obtain necessary information from such huge amount of papers. While this is true for the neuroscience domain as well, we need to tune our text mining system to each specific domain in order to fit with demands of the corresponding domain's researchers. I will introduce our current research plan on our text mining project for neuroscience papers, where we mainly aim to extract human brain related information. We also explain our background why we started this work.
30 AINI2016 ＆ INCF Nodes Workshop
OSV-3
Semantic Web technologies to enhance scientific research
Hideaki TAKEDA National Institute of Informatics [email protected]
There needs the framework for research data sharing to enable researchers to share their data in their research activities smoothly. The main requisites are i.e., accessibility, persistency, and understandability. Data should be accessible syntactically and semantically. Data is expected to be persistent. Data should be understandable not only by the researchers in the same domain or community but also by other researchers. The framework for research data sharing should have some layers to realize the requisites. The first layer is the data itself and then the second is the data format. The third and the fourth are metadata and metadata schemes. They are needed to make data understandable and accessible semantically. The fifth is the identifier. It is needed to realize data accessible syntactically and persistent. Semantic Web is enabling technology for metadata and metadata schemes. In particular, Linked Data and Ontology are now becoming de facto standard when publishing research data. The key contributions of Semantic Web for research data publishing are interoperability and consistency. We picked up two research examples to show how we can realize better interoperability and consistency. URI and RDF schema enables interoperability beyond domains. But a good design is needed to mint URI as identifier. For example, scientific names for taxon look suitable for identifier. But they can change as times goes by so that scientific names themselves are not suitable as identifier. In our project, we design ontology for change in knowledge then we can express scientific names with time constraint and history. Then they can work as identifier, i.e., URI to represent taxon. The other example is how to represent consistency among entities. Here logic, in particular, description logics can help to describe it. In our project, terminology is design with Description Logics. Here a concept which is more specific to the other concept is defined by adding more properties or changing property values to more specific ones. Then logical inference can infer any relationship among concepts logically and furthermore can suggest a good position when a new concept is given.
31 AINI2016 ＆ INCF Nodes Workshop
OSV-4 Linked Brain Data: a Web-scale Knowledge Platform for Brain and Neuroscience
Yi ZENG(1)(2) (1) Institute of Automation, Chinese Academy of Sciences, China, (2) Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China [email protected]
Various experimental data and millions of literature related to the brain are distributed on the Web in various formats, and it is very challenging for researchers to manually integrate them together and acquire synthesized knowledge from them to get comprehensive understanding of the brain at multiple scales. Linked Brain Data (LBD, http://www.linked-brain-data.org) is designed to be a platform that integrate, link and analyze worldwide brain data and knowledge across various sources. It focuses on providing comprehensive understanding of the brain at the knowledge level through automatic data and knowledge acquisition, integration, synthesis, semantic understanding, and reasoning. LBD data sources include but are not limited to PubMed, Allen Brain Atlas, Wikipedia, NeuroLex, NeuroMorpho, etc. Here we summarize major services of LBD. * Multi-scale Brain Structures present the organization of brain building blocks at multiple scales, and for various animals (including mouse, non-human primate and human brain, etc.). The hierarchy is either partially extracted from other data sources (e.g. Allen Brain Atlas), and with additional extensions through automatic structure extraction from scientific literatures (various types of neurons and neurotransmitters for a specific brain region, etc.) and alignment, or built from the bottom up fully automatically. Definitions of different brain building blocks are automatically extracted from Wikipedia and linked to them. * Brain Association Graph provides associative relations among various cognitive functions, brain diseases, and brain building blocks at multiple scales (i.e. brain region, neuron, protein, gene, and neurotransmitter scale). The relations are extracted from all the PubMed articles. Each binary association is assigned with an appearance time and two types of probabilistic values to analyze their relationship in different perspectives. For example, the positive related relation between Hippocampus and Short-term memory (STM) appeared 186 times in different literatures. P(STM | Hippocampus)=0.024 means that only 2.4% of the probability for the research is on the relationship between STM and hippocampus, compared to all the other cognitive functions that are related to Hippocampus. P(Hippocampus | STM)=0.292 indicates that 29.2% of the probability for the research is on the relationship between hippocampus and STM, compared to all the other related brain regions that are related to STM. It represents the importance of hippocampus to STM. * Brain Knowledge Engine focuses on providing comprehensive understanding of brain related concepts from various perspectives. It shows how the specific concept is related to other concepts in LBD, and the relationships are obtained based on which species. In addition, it presents images, experimental data related to this concept from the Web. All the data and relations are also automatically extracted from online literatures and other related Web sites. Linked Brain Data also provides semantic search and reasoning endpoint so that the domain knowledge in LBD not only can be used within and outside the LBD online services, but also can automatically generate new knowledge based on existing LBD domain knowledge. LBD is a never ending learning and analysis system. It obtains new knowledge, reorganizes the knowledge base, and updates various calculations as the original sources have updates. Currently, LBD covers 110 cognitive functions, 422 brain diseases, and 42,931 concepts related to the brain at multiple scales. And the semantic triples in LBD which represent various domain knowledge of the brain at multiple scales have reached 10 million.
32 AINI2016 ＆ INCF Nodes Workshop
Oral Session VI International Neuroinformatics Abstracts
Invited Speakers
Maryann MARTONE University of California, San Diego
Jeffrey S. GRETHE University of California, San Diego
Thomas WACHTLER Ludwig-Maximilians-Universität München
Stephen EGLEN Cambridge University
33 AINI2016 ＆ INCF Nodes Workshop
OSVI-1 The Neuroscience Knowledge Space: A shared global platform for integrative neuroscience
Maryann E. MARTONE (1), Linda LANYON (2), Jeff GRETHE (1), Sean HILL (3), Tom GILLESPIE (1), Willy WONG (1) (1)University of California, San Diego, (2)INCF, (3)École Polytechnique Fédérale de Lausanne (EPFL) [email protected]
In the discovery and advancement of scientific knowledge, the iterative cycle of hypothesis and experiment is well established. When carried out over thousands of labs and millions of articles, however, the method poses a challenge for assessing the state of knowledge in modern neuroscience. How much evidence is there to support the definition of a concept, such as a neuron type or a connection pathway? With the amount of data and literature increasing daily, we rely on automated agents to find relevant information. The ready availability of search engines technically means that the fragmentation of knowledge should be decreasing, but yet finding data relevant to a specific concept remains difficult. Establishing a common vocabulary for neuroscience is essential to evaluating the extent to which a concept is supported by evidence. In our data driven world, some might claim that this common vocabulary is not necessary, but humans communicate through concepts and these concepts provide a very compact and understandable way for us to compare human knowledge to data. Machine-processable vocabulary allows us to integrate datasets linked with common concepts, but perhaps of most importance, it allows us to compare assertions about data to the data themselves. Through this process,, we can evaluate the degree to which the data supports the definition of a single, well developed concept. To address these aspects of organizing and disseminating neuroscience data and knowledge we propose the KnowledgeSpace (https://knowledge-space.org/). KnowledgeSpace aims to provide a unique, global interface between current brain research concepts and the data, models and literature that support or weaken their definition. In this presentation, I will discuss the background, concept, implementation, governance and roadmap of the KnowledgeSpace.
OSVI-2 Center-TBI and International TBI Projects
Jeffrey GRETHE, Anita BANDROWSKI, Michael CHIU, Tom GILLESPIE, James GO, Yueling LI, Burak OZYURT, Maryann MARTONE University of California, San Diego [email protected]
CENTER-TBI is a large European project that aims to improve the care for patients with Traumatic Brain Injury (TBI). CENTER-TBI brings the newest technologies and many of the world's leading TBI experts together in a much needed effort to tackle the silent epidemic of TBI. International and multidisciplinary collaboration are key elements to the project in which past dogmas will be left behind and innovative approaches undertaken. INCF is coordinating the establishment of an informatics platform for acquisition, storage and analysis of CDE-based clinical data. The goal is to develop a next generation open standards-based platform to support advanced large-scale analytics and model building. Such a platform also provides a model for future clinical studies on brain diseases and disorders. This common open international platform will be a resource for future TBI studies. CENTER-TBI forms part of the larger global initiative, InTBIR: International Initiative for Traumatic Brain Injury Research with projects currently ongoing in Europe, the US and Canada. One such project is the NIH NINDS-funded, multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study which is generating a rich and diverse precision medicine dataset.
34 AINI2016 ＆ INCF Nodes Workshop
OSVI-3 German Neuroinformatics Node: Neuroinformatics for efficient data management in neurophysiology
Thomas WACHTLER, Christian KELLNER, Michael SONNTAG, Adrian STOEWER, Andrey SOBOLEV, Jan GREWE Ludwig-Maximilians-Universität München, Germany [email protected]
Scientific progress depends increasingly on collaborative efforts that involve exchange of data and re-analysis of previously recorded data. The German Neuroinformatics Node (G-Node, www.g-node.org) aims to support solutions for efficiently managing, accessing, and sharing of electrophysiological data and metadata, so that the researcher can focus on the scientific questions rather than on problems of data management. Methods and tools include solutions for data access from different formats [1], metadata collection and management [2-4], and integrated management of data and metadata [5], which supports efficient data processing workflows in the lab as well as data sharing. The Node fosters collaborations across the INCF community [3,6-9]. G-Node data sharing services enable researchers to flexibly access and share their data. In addition, G-Node supports scientists in making resources and data available, by hosting databases, services, and published data. Short courses offered on a regular basis contribute to training in neuroinformatics.
[1] Kellner et al (2012) doi: 10.3389/conf.fninf.2014.08.00042 [2] Grewe et al (2011) doi: 10.3389/fninf.2011.00016 [3] Le Franc et al (2014) doi: 10.3389/conf.fninf.2014.18.00053 [4] Zehl et al (2014) doi: 10.3389/conf.fninf.2014.18.00029 [5] Stoewer et al (2015) doi: 10.3389/conf.fnins.2015.91.00050 [6] Rautenberg et al (2014) doi: 10.3389/fninf.2014.00055 [7] Jezek et al (2013) doi: 10.1109/VLHCC.2013.6645264 [8] Garcia et al (2014) doi: 10.3389/fninf.2014.00010 [9] Bakker et al (2012) doi: 10.3389/fninf.2012.00030
OSVI-4 Recent developments in the UK node
Stephen EGLEN (1), Leslie SMITH (2) (1) Cambridge UK (2) Stirling UK [email protected]
In this talk I will summarise recent developments in the UK in the last year. The biggest, and most disappointing, news is that our 2015 application for continued funding from the UK's Medical Research Council was rejected. This means that the UK is no longer contributing to the INCF, nor do we have funds to support a UK network. We are currently exploring alternative routes to support a network, and I will report on any outcomes from a meeting due to be held in early May. In other news, I will report on a recent UK-led initiative to encourage code and data sharing, and end with a reminder about the annual congress in Reading, UK.
35 AINI2016 ＆ INCF Nodes Workshop
Poster Session Abstracts
36 AINI2016 ＆ INCF Nodes Workshop
PS-1 Visiome Platform
Shunji SATOH(1), Yoshimi KAMIYAMA(2), Shin'ya NISHIDA(3), Shigeki NAKAUCHI(4), Izumi OHZAWA(5), Takao SATO(6), Akiyoshi KITAOKA(6), Hiroshi ASHIDA(7), Hayaru SHOUNO(1), Kazushi MARUYA(3), Kenchi HOSOKAWA(3), Manabu TANIFUJI(8) , Satoshi SHIOIRI(9), Yoko YAMAGUCHI(8) and Shiro USUI(4)(8) (1) The University of Electro-Communications, (2) Aichi Prefectural University, (3) NTT Communication Science Laboratories, (4) Toyohashi University of Technology, (5) Osaka University, (6) Ritsumeikan University, (7) Kyoto University, (8) RIKEN Brain Science Institute, (9) Tohoku University [email protected]
Vision science is an interdisciplinary research field. Visiome Platform has been developed as a web-based database system with a variety of digital research resources including experimental data, mathematical model, illusory stimulus and analytical tools. Educational resources are also provided. By collaborating with Simulation Platform (http://sim.neuroinf.jp), users can execute computer programs of some mathematical models without requirement for installing extra software or libraries. Archive files may be written in any formats and may include explanatory figures, program sources, readme files, and other related files. Users can also browse the platform contents via branch sites (A catalog of illusions, Visitope, Visiome Channel), which introduce user friendly view of items such as basic images and original artworks of visual illusions with high resolution.
Visiome Platform provides a data and model sharing framework, and thus, users can improve the reproducibility of simulations. The items in Visiome Platform are useful not only for reproducing the published results, but also for advancing and expanding the research in vision science.
PS-2 BCI Developmental Tools Available for Online EEG Data Acquisition and Time-Frequency Analyses by Using the Wavelet Lifting Scheme
Balbir SINGH (1), Mayu ICHIKI (1), Guangyi AI (1), Hiroaki WAGATSUMA (1)(2)(3) (1) LSSE, KYUTECH, (2) RIKEN BSI, (3) AIRC, AIST [email protected]
The necessity of the real-time processing has grown in BCIs recently. A Lifting scheme method for the reconstruction of wavelet non-linear filters has been used to reveal frequency characteristics of multiple components in EEGs to present the neuronal activity on the context of what the brain computes necessary information in a specific task [1]. Therefore, real-time analyses are crucial not only for building a BCI system but also contribution to a discovery in neuroscience especially on neuro-feedback phenomena. EEG observations are physically the non-invasive method; however it is invasive in a sense of the brain circuit modulates (or learns) with respect to the intension of the subject who makes efforts to fit for the request in the ongoing task. Such an adaptive property of the brain is expected to apply neuro-rehabilitation, which aims to accelerate the brain circuit reconstruction effectively in the physically non-invasive methods including the Transcranial Magnetic Stimulation (TMS). We present here the way to construct the non-linear filter for EEG online data analyses using the hardware compatible method [2][3].
References: 1. Asensio-Cubero et al. (2014) Wavelet Lifting over Information-Based EEG Graphs for Motor Imagery Data Classification. Physiol. Comput. Sys., Springer, 3-19. 2. Shirvaikar et al. (2008) VHDL Implementation of Wavelet Packet Transforms Using SIMULINK tools. Real-Time Image Processing, Proc. the SPIE, 6811, article id. 68110T, 10. 3. Anitha.K et al. (2012) Modified Lifting Based DWT/IDWT Architecture for OFDM on Virtex-5 FPGA. Global J. Res. in Eng., Electrical and Electronics Eng., 12(8).
37 AINI2016 ＆ INCF Nodes Workshop
PS-3 A Simultaneous Recording of EEGs and the Eye-Tracking System based on Multitask Operation Design Compatible with Driving Distractions
Mayu ICHIKI (1), Guangy AI(1), Jonathan Shi Khai OOI (2), Siti Anom AHMAD (2), Hiroaki WAGATSUMA (1)(3)(4) (1) LSSE, KYUTECH, (2) Faculty of Engineering, UPM (3) RIKEN BSI, (4) AIRC, AIST [email protected]
For investigating of driver’s cognitive workload, the definition of criterions to evaluate how much the awareness level in cognition remains intact in the driver is crucial. Strayer et al. [1][2] introduced multiple criterions including psychological or subjective workload measures known as the NASA Task Load Index (NASA TLX), physical reaction time derived from a peripheral light detection task (DRT) [3], and neuro-physiological measures associated with EEG activity using ERPs time-locked to the peripheral light detection task, in the form of P300. The method seems to be applicable for the real driving situation; however there is a concern that the classical oddball paradigm, which is basically used in P300 tasks, overestimates EEG response against target stimulus [4]. We have explored about the suitable task design of multitask operations that is compatible with natural driving distractions, which requires to validate the real driving situation.
References: 1. Strayer et al. (2013): Measuring cognitive distraction in the automobile. AAA Foundation for Traffic Safety (AAA FTS). 2. Strayer et al. (2014): Measuring Cognitive Distraction in the Automobile II: Assessing In-Vehicle Voice-Based Interactive Technologies. AAA FTS. 3. Bruyas et al. (2014) Sensitivity of Detection Response task (DRT) to the Driving Demand and Task Difficulty. Proc 7th int'l Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 64–70. 4. Birngruber et al. (2015) Introducing a control condition in the classic oddball paradigm: Oddballs are overestimated in duration not only because of their oddness. Atten Percept Psychophys, 77(5), 1737-1749.
PS-4 Linking INCF Japan Node Platforms with Application Networks based on Garuda Platform
Yoshiyuki ASAI(1), Samik GHOSH(2), Nikolaos TSORMAN(2), Hiroaki WAGATSUMA(3),(4),(5) (1)Integrated Open Systems Unit, Okinawa Institute of Science and Technology, (2) The Systems Biology Institute, (3)Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, (4)RIKEN BSI, (5)Artificial Intelligence Research Center, AIST [email protected]
INCF Japan Node (J-Node) has been promoting neuroinformatics platforms, one of which is the Dynamic Brain Platform (DBPF) storing mathematical models of physiological functions, commentaries and other information. To effectively utilize such data, a DBPF client application complying with Garuda alliance (called a Garuda gadget) has been developed. Garuda (http://garuda-alliance.org/) is a platform providing a set of protocols for applications to connect to each other, and to discover and navigate through inter-operable applications. The DBPF client can access to the DBPF database via XooNIps API (http://xoonips.osdn.jp) which is a base technology building DBPF site, and seamlessly bridge the data through the Garuda platform to other Garuda gadgets, such as a model building application PhysioDesigner and a simulator Flint (both are available at http://www.physiodesigner.org). In the INCF J-Node hackathon held in April 2016 at Tokyo, Japan, Garuda gadgets for other platforms and database sites such as Invertebrate Brain PF: Silkmoth Brain and Neurons database, Neuroimaging PF, Simulation PF, ViBrism PF, J-Node cross-search site (http://www.neuroinf.jp/modules/xsearch/), and RIKEN Metadatabase (http://metadb.riken.jp) were developed. Unlike the DBPF client gadget which equips full functions to communicate with other gadgets, these newly developed gadgets are only for data search, but are already standing a good starting point to be discovered and linked by other gadgets on Garuda platform. 38 AINI2016 ＆ INCF Nodes Workshop
PS-5 Ontology-Based Search Server (ObSS) Extends Availability of Neuroinformatics Data by Listing Relevant Metadata if it is Unfamiliarly Indexed
Yoshiyuki ASAI(1), Kyota KAMIYOSHI(1), Yoshiyuki KIDO(2), Taishin NOMURA(3), Hiroaki WAGATSUMA(4)(5)(6) (1) Integrated Open Systems Unit, OIST, (2)Cybermedia Center, Osaka Univ, (3)Department of Mechanical Science and Bioengineering, Osaka Univ, (4)Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, (5)RIKEN BSI, (6)Artificial Intelligence Research Center, AIST [email protected]
PHML (Physiological Hierarchy Markup Language)[1] was proposed to enhance model sharing in integrative physiology, and offers integrative model development environment and databases[2]. Designing of the common metadata format is one of hot topics, and ontology is an effective solution for it. We have discussed on the framework "In-Silico Modeling Ontology (ISMO)"[3] to apply J-Node platforms (PF) for cross communications of different PFs, and Ontology-Based Search Server (ObSS)[4] was successfully demonstrated. In the present study, we extends the concept to the general neuroinformatics contents in PFs, which enhances cooperation among J-Node platform projects, and the availability for a user who is unfamiliar to the target PF data by listing relevant ontology keywords. References: 1. PHML, http://www.physiodesigner.org/phml/ 2. J-Node DynamicBrain Platform, https://dynamicbrain.neuroinf.jp/, Physiome.jp PH-Database, http://physiome.jp/phdb/index.html 3. Y. Kido, et. al. (2009) Discovery of relation between modeling data using in-silico modeling ontology and meta-ontology. XXXVI International Congress of Physiological Sciences. 4. H. Wagatsuma, et. al. (2015) Infrastructure for Neuroscience Ontologies Bridging Between Experimental Data in 3D Atlas, Computational Modeling and Analytical Tools: A basement Approach of DynamicBrain Platform (DB-PF) toward Inter-Platform Coordination in the J-Node. Front. Neurosci. Neuroinformatics 2015.
PS-6 ERP Topography of the effects of Music Therapy for children with severe neurological disorders
Maria L BRINGAS(1,2), Mrielle BESSON(3), Pedro A ROJAS(2), Pedro A. VALDÉS-SOSA (1,2) (1) Joint China-Cuba Lab Neurotech, (2)Cuban Neuroscience Center (3)CNRS Marseille France [email protected]
This study was a two-armed parallel group design aimed at testing real world effectiveness of a music therapy (MT) intervention for children with severe neurological disorders. The control group received only the standard neurorestoration program and the experimental group received an additional MT “Auditory Attention plus Communication (ACC) protocol” just before the usual occupational and speech therapy. Multivariate Item Response Theory (MIRT) identified a neuropsychological status-latent variable manifested in all children and which exhibited highly significant changes only in the experimental group. Changes in brain plasticity also occurred in the experimental group, as evidenced using a Mismatch Event Related paradigm which revealed significant post intervention positive responses in the latency range between 308 and 400 ms in frontal regions. LORETA EEG source analysis identified prefrontal and midcingulate regions as differentially activated by the MT in the experimental group. Taken together, our results showing improved attention and communication as well as changes in brain plasticity in children with severe neurological impairments, highlight/comfort the importance of MT for the rehabilitation of patients across a wide range of dysfunctions.
39 AINI2016 ＆ INCF Nodes Workshop
PS-7 On the neural circuits mediating sense to action - Insight from physiology and numerical modeling
Andreas KARDAMAKIS, Sten GRILLNER Karolinska Institute, Sweden [email protected]
Goal-directed behavior is usually executed based on information provided by sensory signals. This type of action relies on the function of interneurons mediating signal detection via sensory-receptor input, and muscle contraction via motor neuron innervation, which are organized and connected up to form microcircuits within specific regions of the vertebrate nervous system. The interaction of basic networks located within the basal ganglia, cortex, superior colliculus and brainstem, which are flexibly controlled and operate with a considerable degree of automony, are able to modulate the oscillating circuits within the spinal cord to guide behavior suitable to the perceived situation. Our overarching aim is to uncover the simplifying physical rules that give rise to the seemingly complex neural processes that govern sensorimotor control during goal-directed behavior. By using biophysically detailed, full-scale computational models of the motor pattern generators in the brainstem and spinal cord, we demonstrate the general network principles that generate locomotor and eye-head reorientation movements. These motor systems are the basic building blocks fundamental for navigation. We are currently incorporating electrophysiological and anatomical findings to the model with emphasis on the roles of the a) superior colliculus in event detection and multisensory integration, b) striatum and basal ganglia output nuclei in action selection, c) substantia nigra pars compacta in dopaminergic modulation and d) cortex -mammalian homologue of pallium- in executive control. By fully integrating these structures at the cellular, synaptic, circuit and behavioural level within our existing model, our goal is to discover the deterministic rules underlying the emergent neural processes that govern meaningful motor behavior.
PS-8 Community Detection and Mean Field Approximation for Dimension Reduction of Spiking Network Models
Carlos Enrique GUTIERREZ(1), Junichiro YOSHIMOTO(1)(2), Kenji DOYA(1) (1) OIST, (2) NAIST [email protected]
Advances in serial section electron microscopy and optical imaging are making cellular connectomic data available. Spiking network models (SNM) based on connectomic data can help us understand how dynamic neuron-to-neuron interactions realize representations in local circuits. However, when we try to understand the whole brain function, SNM can be impractical because of their huge computational requirements and parameters to be inferred. We propose a framework for network model reduction by combining community detection and mean field (MF) approximation. First, we identify densely connected subpopulations by an unsupervised community detection algorithm [Clauset et al., 2004]. Second, we formulate the mean activity of each subpopulation by MF assuming it as a homogeneous excitatory-inhibitory randomly connected population. Finally, we connect those subpopulation MF models by the mean connection weights between them. We ran simulations of the original SNM and the subpopulation-level SNM by NEST [Gewaltig & Diesmann, 2007] and compared mean firing rates under different inputs. We first used artificially generated network of 58 neurons with 3 subpopulations. We found a good approximation with a mean absolute error (MAE) of 2.3 Hz., which increases with the input. We then took a macroscopic connection matrix of 66 brain areas [Deco et al. 2012] as a surrogate example of realistic connectomic data. We observed a similar MAE and an improved computation response time of 55% for low stimuli, rising to 70% as input grows. The proposed approach enables efficient simulation of a large-scale network model constrained by neuron-level connectomics data and can be used as a predictive tool for investigating the effects of different changes on neuronal properties and input stimuli. 40 AINI2016 ＆ INCF Nodes Workshop
PS-9 A Strategy to infer hidden sources behind multiple neural recording data for neural circuit inference
Ildefons MAGRANS DE ABRIL(1), Junichiro YOSHIMOTO(1)(2), Kenji DOYA(1) (1) Okinawa Institute of Science and Technology Graduate University, (2) Nara Advanced Institute of Science and Technology [email protected]
A promising setup to address the neuronal circuit inference problem in the presence of hidden inputs is to use a generalized linear model with a latent process. The expectation maximization algorithm can be used to estimate the parameters of the latent process by maximizing the likelihood objective function. Because the parameter space for a model with a latent process tend to be quite complex, we propose a solution to speed up and to improve the quality of the overall EM algorithm by creating a good starting estimate for the latent process parameters. Specifically, we propose two pre-processing steps: 1) to identify neurons that receive an external input. The main idea of this first step is that a net contribution from an unobserved input should be noticed as an abnormally large input bias in the estimated GLM. 2) To unveil how observed neuron pairs (i,j), identified in the first step, share the common unobserved inputs, we propose a sub-sampling strategy of the spike train Yi. This strategy removes spikes possibly generated due to a common unobserved input, which can be detected as neuron i and j increasing their firing rates simultaneously. Then, we can check the sharing hypothesis by fitting a GLM for neuron i using the sub-sampled spike train Yi' and analyzing the change in the estimated input bias. The figure below describes in more detail the sub-sampling strategy. Experimental results with simulated networks show that we can correctly recover the hidden incoming connections.
PS-10 Analysis on the mouse gait patterns by using a neuro-musculoskeletal model
Satoshi OOTA(1), Yosuke IKEGAMI(2), Nobunori KAKUSHO(3), Koh AYUSAWA(4), Akihiko MURAI(4), Atsushi YOSHIKI(1), Yuko Okamura-OHO(5), Hideo YOKOTA(5), Yoshihiko NAKAMURA(1, 2) (1)RIKEN BRC, (2)University of Tokyo, (3)Japan Neutron Optics Inc., (4)National Institute of Advanced Industrial Science and Technology, (5)RIKEN CAP [email protected]
Our overall goal is to elucidate the evolutionary and biomechanical relationships between human and experimental animals by modeling their neuro-musculoskeletal systems. Through the evolutionary relationships between bipedalism and quadrupedalism, we try to elucidate fundamental mechanism of the locomotor functions of vertebrates, which has been optimized during the long history of life. In this workshop, we are focusing on the laboratory mouse hind limb, by which simple inverse dynamics is capable. We have acquired the mouse skeletal geometry data by X-ray scanning. We developed a neuro-musculoskeletal model of the mouse hind limb, composed of 10 bone segments with totally 10 joints and six muscle tendons. The bone segments and the muscle tendons are loosely associated to an existing human neuro-musculoskeletal model. We performed an inverse kinematics analysis with this model and motion data of normal and mutant mice. Since many of aberrant mouse gait patterns are caused by abnormal motor commands from the central nervous system, it is significant to elaborate upon how the locomotion functions are disturbed by specific muscle tendon activities, which can be associated with the abnormal motor commands. This disturbance-based approach will contribute to understand the animal’s durable locomotor functions. The another point of this study is that, by quantitatively describe the phenotypes, our framework will directly contribute to the brain mapping study with the 3-dimensional gene expression patterns. 41 AINI2016 ＆ INCF Nodes Workshop
PS-11 A novel approach for repetitive sequential activity detection
Keita H. WATANABE(1)(2), Tatsuya HAGA(2), Tomoki FUKAI(1)(2) (1) Graduate School of Frontier Science, the University of Tokyo, (2) RIKEN Brain Science Institute [email protected]
Recent technological advances enable us to record the activity of hundreds and thousands of neurons simultaneously in awake animals. However, mathematical methods available for analyzing large-scale neural data are still limited. Accumulating evidence suggests that sequences of neural firing play a role in various behavioral tasks such as spatial navigation, sensory discrimination and decision-making [1][2]. In this study, we propose a novel method to detect the repetition of similar but noisy spatiotemporal patterns of firing in a large neural ensemble. Novel methods for sequence detection has to be developed since the conventional analysis methods, such as template matching, are neither efficient nor robust when applied to large-scale data in the presence of biological noise, which may induce strong fluctuations in the temporal order of neuronal firing, and eliminate some spikes from or add additional spikes to repeated sequences. To overcome the difficulties, our method extended a concept of edit similarity to determine similarity between two different temporal segments of neuronal activities. That is determined by the minimum number of operations (insertion, deletion, substitution) required to transform one text to another. We will show example utilizations of sequential activity detection from actual single unit recordings in rat hippocampus during liner maze task and showed the existence of repetitions of sequential activities.
PS-13 Efficient numerical simulation of neuron models with spatial structure on graphics processing units
Tsukasa TSUYUKI (1), Yuki YAMAMOTO (2), Tadashi YAMAZAKI (1)(3) (1) Univ Electro-Comm, (2) Tokyo Med Dent Univ, (3) NIJC, RIKEN BSI [email protected]
Spatial neuron models are neuron models with morphological structure such as soma, dendrites and axons. These models are composed of equipotential segments called "compartments" connected with axial resistances. Current flow in each compartment is described by a partial differential equation (PDE) called a cable equation. A conventional way to solve PDEs on a computer is to discretize the equations both in space and time, and derive a set of linear equations. We solve the equations for each time step and obtain the results. Because the calculation is not tedious, usually we use some dedicated software such as GENESIS and NEURON. Although they work well on multicore computers, they do not support accelerators such as graphics processing units (GPUs). Moreover, numerical methods that they use to solve linear equations would not be so efficient for large-scale networks. To address these, we have been developing our own software for numerical simulation of spatial neuron models on GPUs. We implemented a conjugate gradient (CG) method to solve linear equations efficiently on a GPU. The CG method is known much faster and more efficient than the conventional gaussian elimination, when the coefficient matrix is huge and sparse. We represented the sparse coefficient matrix in a compressed row storage (CRS) format. We also implemented parallel calculation of ion channels on a GPU. Our software successfully simulate a Purkinje cell model developed by De Schutter and Bower (1994), which is composed of 1,600 compartments with 10 ion channels and 13 gate variables for each compartment. Compared with a single-threaded CPU version, the GPU version achieved 3 times faster calculation for isolated 15 Purkinje cells.
42 AINI2016 ＆ INCF Nodes Workshop
PS-14 Honeybee neurons responsive to the pulsed vibration produced by waggle dance
Ai, H.(1), KAI, K.(1), WATANABE, H.(1), Itoh, T.(1), KUMARASWAMY, A.(2), RAUTENBERG, P.(2), WACHTLER, T.(2) and IKENO, H.(3) (1) Dep. Earth Sys. Sci., Fukuoka Univ., Japan, (2) Dep. Biol. II, Ludwig-Maximilians-Univ. München, Germany, (3) Sch. Human Sci. and Env., Univ. Hyogo, Japan. [email protected]
Honeybees communicate with hive-mates about the vector information to the profitable flower by waggle dance. The waggle dance consists of waggle phase and return phase. The duration of the waggle phase increases as the distance increases, therefore the distance is suggested to be encoded in the duration of the waggle phase. During the waggle phase the dancer beats their wings briefly at the timings when the tail end passes the Run axis. Such intermittent wingbeats cause the vibratory pulses. The pulse rate is constant with c.a. 30 Hz. The hive-mates detect the airborne vibration by Johnston’s organ (JO) on the antennae. In the present study we analyzed vibration-sensitive interneurons by morphology and response patterns to the vibration to the JO and investigated the GABA immunoreactivity of three critical interneurons. We have analyzed 188 vibration-sensitive interneurons arborizing in the primary JO center. The local interneuron, DL-Int-1 is responsive to continuous pulse vibration with a tonic inhibition during the pulse train. As DL-Int-1 has GABA immunoreactivity, it is suggested an inhibitory neuron. The output neuron, DL-Int-2 is responsive to continuous pulse vibration with a tonic excitation during the pulse train. The bilateral DL-dSEG-LP could follow pulse vibration with phase-locked spikes or EPSPs. DL-Int-2 and bilateral DL-dSEG-LP are not positive to the GABA immunoreactivity, suggesting these are excitatory neurons. Based on these results, we will discuss about the putative neural circuits of the honeybee to decipher the distance information from the pulse vibration caused by the waggle dance.
PS-15 The Invertebrate Brain Platform (IVB-PF) -updates of IVBPF in 2016-
Daisuke MIYAMOTO(1), Tomoki KAZAWA(1), Hidetoshi IKENO(1), Yoko YAMAGUCHI(2), Ryohei KANZAKI(1) (1) The University of Tokyo, (2) University of Hyogo, (3) RIKEN BSI [email protected]
Collecting and organizing information of brains and neurons with many species give us a lot of insight of brain. Especially, invertebrates would be an excellent model system because its brain generally evolves a variety of sophisticated properties such as multimodal sensory integration, learning and memory generated by a moderate number of neurons. For the purpose, our platform the IVBPF (the Invertebrate Brain Platform) is collecting and providing access to physiological and morphological data of individual neurons of representative invertebrate species (i.e., silkmoth, honeybee, cricket, cockroach, ant, crayfish and two species of files). Recently, we started expanding our target to vertebrates by collaborating JSCPB (the Japanese Society for Comparative Physiology and Biochemistry). We will update our platform to the CNSPF (the Comparative Neuroscience Platform) to offer wider understanding of brains by an evolutionary point of view. In our poster, we will provide the example of data and use cases of the platform. (The IVBPF and the CNSPF are organized by J-Node of INCF in collaboration with the Japanese Society for Comparative Physiology and Biochemistry, JSCPB.)
43 AINI2016 ＆ INCF Nodes Workshop
PS-16 The Cerebellar Platform and its recent innovation
Shinji KAKEI (1), Tadashi YAMAZAKI (2), Yutaka HIRATA (3), Soichi NAGAO (4), Takeru HONDA (1), Yoko YAMAGUCHI (4) (1) Tokyo Metropolitan Institute of Medical Science, (2) UEC, (3) Chubu Univ, (4) NIJC RIKEN BSI [email protected]
The cerebellum constitutes only 10% of the total volume of our brain, but it contains more than 50% of its neurons. Its structure comprises a series of highly regular, repeating units, each of which contains the same basic microcircuit. The similarity of the architecture and physiology across the cerebellum strongly suggests that different regions of the cerebellum perform identical or similar computational operations. Therefore, it is of primary importance to understand the common computational principle of the cerebellum. The Cerebellar Platform (PF) is a digital research archive to promote various disciplines of the cerebellar research that challenges to solve the enigma. The PF contains mini-reviews of the cerebellum, experimental data for the modeling, source codes of models of the cerebellum, and more. You may download and use its contents freely. You may also upload your own contents on the PF with a simple procedure. We are currently developing an SNS-like interface to improve communication in the cerebellar community. Recently, we are extending the PF to cover translational medicine. The aim of this extension is to reform the neurological method for evaluation of cerebellar disorders. The classical method for examination of cerebellar patients is subjective and poorly quantitative and recordable. Therefore, it is not suitable to use it for development of cutting-edge technologies in regenerative medicine to cure cerebellar ataxia. Based on our recent research, we are developing a cloud system to make a quantitative evaluation of motor functions of cerebellar patients on the cerebellar PF. We are going to discuss issues of to achieve our goal.
PS-17 Development of an on-line simulation environment for computational neuroscience
Hidetoshi IKENO(1), Tadashi YAMAZAKI(2), Takayuki KANNON(3), Yoshihiro OKUMURA(4), Yoshimi KAMIYAMA(5), Akito ISHIHARA(6), Keiichiro INAGAKI(7), Yutaka HIRATA(7), Shunji SATOH(2), Hiroaki WAGATSUMA(8), Yoshiyuki ASAI(9), Yoko YAMAGUCHI(4), Robert McDOUGAL(10), Rixin WANG(10), Luis MARENCO(10), Thomas MORSE(10), Gordon SHEPHERD(10), Shiro USUI(11) (1) University of Hyogo, (2) The University of Electoro-Communications, (3) Kanazawa University, (4) RIKEN, (5) Aichi Prefectural University, (6) Chukyo University, (7) Chubu University, (8) Kyushu Institute of Technology, (9) Okinawa Institute of Science and Technology, (10) Yale University, (11) Toyohashi University of Technology [email protected]
Many computational models have been provided on the Internet. However, they usually require a time-intensive setup of the appropriate computer environment for using these software resources. We have been providing a virtual machine (VM) environment for neuroscience research called Simulation Platform to use computational models from the Internet without any barrier. By accessing the site through a web browser, the VM-based Linux is started, then a script is executed for downloading the model files and running them on the VM automatically. We have been developing online simulation for ModelDB contents. A collaboration between two teams has produced a method for setting and updating the direct links to Sim-PF contents from ModelDB.
44 AINI2016 ＆ INCF Nodes Workshop
PS-18 Cloud computing for clinical brain analytics
Eric Tatt Wei HO (1), Muhd Juwaini ABD RAHMAN (1) Ahmad Fadzil MOHD HANI (1) (1) CISIR, UTP [email protected]
The volume, size and fiber connection of brain regions can be measured by software post-processing on images of the brain from a variety of MRI imaging modalities. Such measurements if made available to doctors have the potential to enhance clinical diagnosis and monitoring by introducing quantitative and objective information of the patient's brain. We describe preliminary efforts to make such a service available to clinicians using a cloud computing model. It is crucial to build both a local database of reference images as well as software services to compare a patient's brain images to population statistics obtained from the local database.
PS-19 Brain decoding can be improved by diffusion imaging based registration
Takuya FUCHIGAMI(1), Yumi SHIKAUCHI(1), Ken NAKAE(1) Manabu SHIKAUCHI(2) , Shin ISHII(1) (1) Graduate School of Informatics, Kyoto University, (2) ATR, Cognitive Mechanisms Laboratories [email protected]
Brain decoding is to decode brain states from measurable brain activities, for example, functional magnetic resonance imaging (fMRI), and is expected to put into practical use in brain machine interface etc. To build a reliable brain decoder, we need to collect brain activities for each subject, which is often time-consuming and can be a burden for the subject. One basic idea to reduce this burden relies on a subject-transfer decoder, which can predict the brain states of a target subject by fully utilizing other subject's brain activities. When building such a subject-transfer decoder, we would need to transform individual brain activities into a common space using brain image registration. A T1-weighted image has widely been used for brain image registration in many fMRI studies. A T1-weighted image, however, cannot capture the network structure in the white matter, which, we assume, essentially determines the locality of many neural functions. In this study, we utilize a diffusion tensor image (DTI) to improve subject-transfer decoding. DTI can delineate the direction of axons in the white matter, which could reflect the network structure of the brain. Therefore, we expect that the brain activities of a target subject are well transferred to those of another subject by co-registering their DTIs. We show a comparison of the accuracy of subject-transfer decoding between co-registrations in terms of DTI and T1. We found a subject-transfer decoder based on DTI registration achieved higher decoding accuracy than that based on T1 registration. This result suggests that neural functions can be better registered by using tract information (in terms of DTI) rather than by structure information (in terms of T1).
45 AINI2016 ＆ INCF Nodes Workshop
PS-20 Lending Global Fiber-Tracking to the Tracing Of Dense Neurites in Fluorescence Micrographs
Henrik SKIBBE, Shin ISHII Kyoto University [email protected]
The reconstruction of neurons from images is still a challenging task in brain science. We address the problem of tracing neurites in densely labeled samples of neurons in 3D micrographs. The overlapping, often incomplete neurites allow for many ambiguous interpretations. A big challenge in dense images is the differentiation between crossings and bifurcations. We use a model based on connected particles to model the neurites. In order to explore the huge space of interpretations, we use a Monte-Carlo method known from DTI fiber tracking. We mimic a high angular resolution DTI signal by using higher order tensors to model the local image appearance. We demonstrate that, compared to measurements based on second order tensors, like the Hessian matrix, higher orders are essential to resolve crossings. We will show some promising results.
PS-22 Recent activities of Neuroimaging Platform
Hideki KASHIOKA NICT [email protected]
In this paper, we show recent Neuroimaging platform (NIMG-PF) activities. NIMG-PF opened to the public in 2009 to accumulate and to share articles regarding non-invasive neuroimaging approaches. At present, 2045 articles (1442 for normal and 603 for patients) are accessible with a search program “Search with Brain Atlas” through our web page (https://nimg.neuroinf.jp/). In current version of the program, users can select the data sets for normal and/or patient subjects depending on the purpose. We accumulated bibliographic data from pubMED by searching articles with non-invasive neuroimaging approaches. We assigned indexes of the categories to all of the articles. In this platform, brain functions are divided into 17 categories with tree structure in first step such as Vision, Audition, Somatosensory, Gustation, Olfaction, Sensory integration, Motor, Sensory-motor integration, Learning and memory, Language, Executive function, Emotion, Awareness/Consciousness, Rhythm/Sleep, Development/Aging, Social cognition and Resting State Network. We compared the distribution characteristics of the index tree categories between pubMED and our data by simple keyword matching. We will discuss about the characteristics of the contents of the bibliographic data in NIMG-PF and how systematically articles should be accumulated from broad brain research fields. As another unique activity to support the beginners as brain researchers, we present the activity to accumulate and to upload neuroimaging data for analysis skill training. Up to now, we have uploaded two fMRI datasets, one with basic experimental paradigm and the other with advanced technique, with analysis training manual. In addition to that, we are almost ready to upload MEG data for basic visual and auditory stimuli.
46 AINI2016 ＆ INCF Nodes Workshop
PS-23 Mathematical modeling and dynamical analysis using structural and functional connectivity
Hiromichi TSUKADA(1), Hiroaki HAMADA(1), Ken NAKAE(2), Shin ISHII(2), Junichi HATA(3)(4), Hideyuki OKANO(3)(4) and Kenji DOYA(1) (1) OIST, (2) Kyoto University, (3) RIKEN BSI, (4) Keio University School of Medicine [email protected]
How to best integrate functional connectivity and structural connectivity data being available from MRI and other measurements is an open question. One approach for understanding structure–function relationship is to run a dynamic model based on the connectivity derived from diffusion MRI data and compare that with the brain activity data from functional MRI [1]. The relationship between anatomical and functional connectivity can be explored by selecting the form and manipulating the parameters of the mathematical model. Here we test whether a dynamic mean field (DMF) model [2] could reproduce the functional connectivity data from structural connectivity data of mouse and marmoset. The structural connectivity matrix is estimated by global tracking method [3] from diffusion MRI data. We constructed a DMF model consisting of 434 anatomical areas with the structural connection matrix derived from the diffusion MRI data of a marmoset and ran simulations. We observed how the firing rate of the most active area changed with the parameters of coupling strength and found regions that exhibited mono-stable and multi-stable dynamics. By applying the hemodynamic response model, we can calculate synthetic functional connectivity matrix to compare with measured functional connectivity. Acknowledgements: This research was supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and Development, AMED. References: 1. Deco, G. et al., J. Neurosci., 33, 11239–11252 (2013). 2. Wong, K.F. & Wang, X.J., J. Neurosci., 26, 1314–1328 (2006). 3. Reisert, M. et al., Neuroimage., 54, 955–962 (2011).
PS-24 Parallelization of the registration method for ISH images into the 3D standard brain atlas
Daisuke MIYAMOTO(1), Hidetoshi IKENO(2), Yuko Okamura-OHO(3), Yoshihiro OKUMURA(4), Nobuyuki MUNEMURA(5), Akira SATO(5), Yoko YAMAGUCHI(4), Ryohei KANZAKI(1), Teiichi FURUICHI(5) (1) The University of Tokyo, (2) University of Hyogo, (3) RIKEN Center for Advanced Photonics, (4) RIKEN BSI, (5) Tokyo University of Science [email protected]
Image registration technics would be very useful to comparing different kind of data, but in many cases, they need a lot of calculation cost. We had developed a method to fit a 2D in situ hybridization (ISH) images to the 3D anatomical standard space: Waxholm mouse brain space (WHS) to estimate special gene expression patterns. However, applying our method to all 3,000 ISH images provided by BrainTx (http://www.cdtdb.neuroinf.jp/) database, it needs enormous calculation time (two month or more) with a usual computer. To solve this problem, we expanded our method to parallelization by using IPython cluster with MPI (Message Passing Interface) and calculated with PLATO cluster (provided by NIJC/RIKEN BSI). This method gave us 60 times faster than single computer execution and we could complete registration of all of the data. This largescale registration method allows us to compare with other modality data like transcriptome tomography from ViBrism (http://vibrism.neuroinf.jp/).
47 AINI2016 ＆ INCF Nodes Workshop
PS-25 RIKEN MetaDatabase: a life-science database integration and publication platform based on the Semantic Web
Norio KOBAYASHI(1)(2), Yoko YAMAGUCHI(3), Hiroshi MASUYA(2)(1) (1) Advanced Center for Computing and Communication (ACCC), RIKEN, (2) BioResource Center (BRC), RIKEN, (3) Brain Science Institute (BSI), RIKEN [email protected]
RIKEN is a comprehensive research institution having specialised life-science centres such as the Brain Science Institute and the BioResource Center, which produce various large-scale datasets as their research results and undertakings. We developed "RIKEN MetaDatabase", a light-weight database platform to support database publication from centres and data integration across centres using Resource Description Framework (RDF), which is a standardized framework of the Semantic Web. The platform launched on April 2015 and currently carries 19 standardised ontologies and 155 metadata databases from various centres in RIKEN, and those are successfully integrated and published so far in a global standardised manner. In the neuroinformatics, it has been desired to realise unified handling of large and heterogeneous experimental datasets, including images and related metadata of MRI and electron microscope of mammalian brains generated in different institutes with different experimental conditions. To solve this problem, metadata integration approach is expected. We have been developing a neuroinforatics integration database named "NIJC Meta Database Trial" as a clone of RIKEN MetaDatabase. The core result is a proposal of RDF based metadata schema that describes meta-information of experimental imaging devices, experimental conditions and sample information including bio-resources and anatomical regions to be imaged. The schema employs existing vocabularies and ontologies adapted by other RIKEN centres to integrate metadata. In this presentation, we discuss the detail of the data schema and a future prospective for the global standardisation of neuroinformatics data.
PS-26 Hypotheses of proximity-rule generation during visual search tasks
Akihiro MASAOKA(1), Yuta MAEDA(1), Takeshi KOHAMA(2) (1)Graduate School of Biology-Oriented Science and Technology, Kindai University, (2)The faculty of Biology-Oriented Science and Technology, Kindai University [email protected]
Visual system obtain a lot of information from the outside world by moving eyes, and process them to understand the surrounding environment. Therefore, to analyze the feature of eye movements leads to understanding the visual information processing. It is well known that the line of sight tends to shift from current gazing position to nearby object (Motter & Belky, 1998). This feature of eye movements is called the proximity-rule (PR). Some lately proposed mathematical models of eye movement system can replicate the scan paths during visual search tasks. One of these models, the neuron network among the visual area 4 (V4), the inferior temporal cortex (IT), and the lateral intraparietal cortex (LIP) is mathematically described (Lanyon & Denham, 2004). However, it is not sufficiently known the mechanisms to reproduce the PR while searching a visual target. In this study, we developed a mathematical model and proposed two hypotheses about PR production mechanism. Our first hypothesis is that deterioration of visual resolution with the increase in eccentricity of fixation position modulates the representation of information in higher-order brain area. The second hypothesis is that biased-signaling in dorsal pathway is activated between the LIP and the frontal eye field (FEF) for effective visual search by the prefrontal cortex (PF). We simulated with our proposed mathematical model and verified these hypotheses. The simulation results show that either of the hypotheses is able to replicate the characteristics of PR, which implies that both hypotheses are valid.
48 AINI2016 ＆ INCF Nodes Workshop
PS-27 Theory of STDP in Dynamic Boltzmann Machines for Learning Temporal Patterns
Sakyasingha DASGUPTA, Takayuki OSOGAMI IBM Research - Tokyo [email protected]
Stochastic neural networks such as a Boltzmann machine (BM), can be trained with a Hebbian-like rule enabling it to store static patterns and make an associative recall upon cue presentation. Learning in the original BM and its extensions, are all based on an approximate Hebbian rule using Markov chain Monte Carlo sampling methods. This type of learning rule typically does not guarantee convergence and cannot deal with dynamic patterns in the manner of living creatures. Furthermore, although the original BM was motivated from a biological perspective, the sampling based learning rule is biologically implausible. Hebbian plasticity in the form of correlations between the timing of pre- and post-synaptic spikes - termed as spike-timing dependent plasticity (STDP) is mainly responsible for learning in biological systems. Inspired by this, we recently proposed [1] a structured BM, referred to as a dynamic Boltzmann machine (DyBM) (Fig. 1), as a stochastic model of multidimensional temporal sequences. This proposed structure is motivated from learning in biological systems, such that the synaptic weights are strengthened or weakened, depending on the timing of spikes (STDP). We show that the learning rule for updating the parameters of the DyBM in the direction of maximizing the loglikelihood of a given temporal sequence can be interpreted as STDP with long-term potentiation and long-term depression. It guarantees convergence and can be performed in a distributed matter with limited memory. We show that unfolding a DyBM in time allows it to have a very deep structure, with infinitely many layers of units, but allows exact inference when its parameters have a proposed structure. We show recent results on learning high-dimensional temporal patterns constructed using the MNIST dataset and the role of the STDP-based learning rule.
49 AINI2016 ＆ INCF Nodes Workshop
PS-28 Transcriptional signatures of connectome topology in the mouse brain
Ben D. FULCHER, Alex FORNITO Monash Institute of Cognitive and Clinical Neuroscience, Monash University [email protected]
In this work, we show that connections involving hubs of the mouse connectome are central and costly aspects of network organization that carry a distinctive genetic signature. We analyzed the neuronal connectivity of 213 brain regions and the transcriptional coupling (or gene coexpression), across 17,642 genes, of each pair of regions. We first correct for a decaying exponential dependence of transcriptional coupling with distance, allowing us to disentangle the strong spatial correlations in gene expression across the brain. Spatially-corrected gene coexpression is (i) higher in pairs of connected brain regions of unconnected pairs, and (ii) highest for pairs of connected hubs, intermediate for links between hubs and non-hubs, and lowest for connected pairs of non-hubs. To determine which biological processes drive the relationships between transcriptional coupling and brain connectivity, we quantified the contribution of each gene to a given topological network structure, as a gene coexpression contribution (GCC) score. The presence of a structural connection was found to be driven by genes regulating neuronal, synaptic, and axonal structure and function. In contrast, the high transcriptional coupling associated with hub connectivity was driven by genes regulating the oxidative synthesis and metabolism of adenosine triphosphate (ATP). We therefore report a relationship between molecular function and large-scale topology of neuronal connectivity, showing that highly active brain hubs display a tight coordination of gene expression that is intimately related to their metabolic requirements. Reference: Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl. Acad. Sci. USA 113, 1435 (2016).
PS-29 Brain Transcriptome Database (BrainTx)
Akira SATO(1), Nobuyuki MUNEMURA(1), Yoshitake SANO(1), Yo SHINODA(2), Tetsushi SADAKATA(3), Noriyuki MORITA(4), Michisuke YUZAKI(5), Takafumi INOUE(6), Mitsuhiro HASHIMOTO(7), Hayato OHWADA(1), Hirozumi NISHIBE(8), Yoko YAMAGUCHI(8), Teiichi FURUICHI(1) (1) Tokyo Univ of Sci, (2) Tokyo Univ of Pharm and Life Sci, (3) Gunma Univ, (4) Yasuda Women's Univ, (5) Keio Univ, (6) Waseda Univ, (7) Fukushima Med Univ, (8) RIKEN BSI [email protected]
The Brain Transcriptome Database (BrainTx) (http://www.cdtdb.neuroinf.jp) is a neuroinformatics database that contains gene expression information related to various stages and states of the mammalian brain and aims to create an integrated platform for visualizing and analyzing the these gene information data. BrainTx is systematized by spatio-temporal gene expression data obtained by time-series microarray analyses, in situ hybridization cellular mRNA mapping (brain regional and cellular expression patterns) and tissue distribution patterns microarray analyses, etc. We aim to systematize our original data and the publicly accessible big data on the brain transcriptome in different stages and states, including time and space, conditions, environment, age, and diseases. BrainTx not only provides the easy-to-use interface to cross-search for expression data, but also includes brain image viewers, graph analysis tools, gene ontology search function, etc. All registered genes have hyperlinks to websites of many relevant bioinformatics regarding genome, proteins, pathways, neuroscience and literatures, so that BrainTx also acts as a portal to these bioinformatics websites. These neuroinformations help to understand the genetic blueprint underpinning the brain and nervous system and the neural processes. BrainTx has already been demonstrated to be valuable for neuroscience research (for example, identification of several brain genes important for neural circuit development). 50 AINI2016 ＆ INCF Nodes Workshop
PS-30 Finding unknown disease-related genes by comparing Random Forest Results to Secondary Data in Medical Science Study
Takahiro KOIWA, Kazutaka NISHIWAKI, Katshutosi KANAMORI, and Hayato OHWADA Tokyo University of Science [email protected]
Finding a disease-related gene is important in drug discovery, for instance Alzheimer disease. In this disease, there are a lot of genes and doing experiment to find a disease-related gene is high cost. Therefore, machine learning on gene expression microarray data is suitable method to address this problem. However, sometimes the results are not fully correct. The present study aims to find a disease related-gene using random forest and compare the results with previous studies from medical sciences. In doing so, genes are determined to be related-gene by two methods are likely to be correct. In the Random Forest, we use the microarray data from GEO datasets. We feature a common gene in each of data, and combine them into data set. A gene that have high importance is chosen. The Random forest was found 51 genes. In contrast, from the previous study in medical science, the study found 6167 genes. The study used the square of the impact factor to rank the genes. The impact factor is gained from the average number of citations received per paper published in that journal during the two preceding years. Based on the results from Random Forest and then compare them into previous studies, in this study we found 31 genes are the same. There are 4 genes that related to GENE database. And the remaining 27 genes are related potential genes or unknown genes found in this study. Those high-ranking among them is considered to be very likely related. A gene that have low ranking should be considered. Genes in which the first method has been extracted. In conclusion, comparing random forest results to another related study in medical science is powerful method to find the unknown related-gene in Alzheimer disease.
PS-31 Finding a Disease-Related non-coding RNA Using Random Forest
Hiromu IDE, Masakazu UMEZAWA, Hayato OHWADA Tokyo University of Science [email protected]
A non-coding RNA (ncRNA) is a type of RNA molecule which is not coded into a protein. Long non-coding RNAs (lncRNA) are a large and diverse class of transcribed RNA molecules with a length of more than 200 nucleotides. lncRNAs are important regulators of gene expression, and the have a wide range of fuctions in cellular and developmental processes. However, to date, a small number of well-studied lncRNAs have given us important clues about their relevance to particular disease. This study aims to find disease-related lncRNAs from microarray data. More specifically, this study focus on acute lung injury (ALI) diseases. Random Forest is used to rank the importance of variables in a regression or classification problem and it is widely used in the field of bioinformatics. We utilize microarray data including mRNA and lncRNA that contain transcriptomic characterization of mice’s brain inflammation during acute lung injury (brain) and these samples of brain were obtained at 6 time points: 1h, 1h30, 3h, 4h, 18h, 24h, after injection of oleic acid or physiological serum. In the data, the number of samples are much less than the number of genes, therefore, selection on some good genes is accomplished. Then, we divide the data into 3 groups differed by their elapsed time after injection: Group1 contains the early elapsed time (1h, 1.5h, 3h, 4h), Group2 contains the late elapsed time (18h, 24h) and Group3 contains a full-length elapsed time (1h–24h). From its result, we investigated a lncRNA that are related to the acute lung injury disease. The final results indicate that lncRNA has high importance of variables in Group1 and Group2. From this study, we have proposed new knowledge for biologists and medical scientist and this work has cover a work on lncRNAs that focused on finding a disease-related lncRNA for acute lung injury (ALI) disease. 51 AINI2016 ＆ INCF Nodes Workshop
PS-32 The report from Hackathon working group in the J-node: INCF Japan Node Hackathon 2016 April
Tomoki KAZAWA(1), Yoshihiro OKUMURA(2). Yoko MORII(3). Daisuke MIYAMOTO(1). Yoshiyuki ASAI(3), Hidetoshi IKENO(4), Akira SATO(5) And Yoko YAMAGUCHI(2) (1)Research Center for Advanced Science and Technology, The University of Tokyo, (2)Neuroinformatics Japan Center, RIKEN Brain Science Institute, Saitama, (3)Integrated Open Systems Unit, Okinawa Institute of Science and Technology, Okinawa, (4)University of Hyogo, Himeji, Japan, (5)Tokyo University of Science, Noda [email protected]
Our activities of INCF Japan Node about the developments of software tools that handle our database platforms and more neuroscience data in the hackathons began the last year. The INCF Japan Node sponsored the Brain Atlas Ideathon/Hackathon (BAH2015) in 2015 as an opportunity to share information on neuroscience databases, the 3-D brain atlas, and software development. This year’s J-Node Hackathon will cover a slightly broader topic: mainly focused 3D image and ontology on neuroscience but include wide themes about neuroinfomatics software/data. In our hackathon in April (http://www.neuroinf.jp/hackathon2016/april.html), there are more than 20 participants and seven teams were organized as shown below. Team 1: parallelized registration of ISH images using Vibrism-DB and BrainTX resources Team 2: design of meta data of BRAIN/MIND on RIKEN Meta Database Team 3: re-organization of metadata in IVB-PF on a MediaWiki Team 4: visualizations of 3D data on the WWW Team 5: software center like GitHub Team 6: GARUDA Team 7: visualization of ontology search on WWW from NIMG-PF data We will more details about the hackathons and their deliverables in the presentation.
PS-33 INCF Japan Node Software Center
Takayuki KANNON(1)(2), Yoshihiro OKUMURA(2), Yoko YAMAGUCHI(2) (1) Kanazawa University, (2) RIKEN BSI [email protected]
INCF Japan Node Software Center (JNSC) is the web-based system for supporting promotion and development of neuroinformatics tools developed by Japan Node. JNSC was launched in 2008, and had been operated experimentally. Here we report that JNSC has been renewed the whole system toward popular utility for neuroinformatics tools promotion and development. JNSC consists of two separate web sites related each other, one is the catalog database of software collection, another is code repository for supporting collaborative development of software (figure). Catalog database serves as a center for neuroinformatics tools developed not only at Japan Node but also in related fields/communities. Comprehensive data collection and classification shall be designed for the sake of efficient publicity and convenient use of the tools. Code repository provides the series of tools required to develop the software and to organize the project community, such as source code management, document management (wiki system), mailing list, message forum, bug tracker, task management, file release and web site hosting. The renewed software center is now available for your trial use at https://software.neuroinf.jp
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PS-34 Development of Research Portal for Multi-scale Heterogeneous Brain Data of Brain/MINDS Project
Yoko MORII(1), Alexander WOODWARD(1), Yoshihiro OKUMURA(1), Masahide MAEDA(1), Takeshi TAKEUCHI(1), Hideki OKA(1), Tsutomu HASHIKAWA(1), Masahiko MORITA(2), Takehiro TAWARA(2), Masaomi NISHIMURA(2), Hideo YOKOTA(2), Yoko YAMAGUCHI(1) (1)Neuroinformatics Japan Center, RIKEN BSI (2)Image Processing Research Team, RIKEN Center for Advanced Photonics [email protected]
Brain/MINDS project was started for the study of human diseases in the common marmoset brain research in 2014, supported by AMED. RIKEN has mission to construct database and data integration as a core institute. We started to develop Brain/MINDS Research Platform for collaboration among researchers, and Data Portal for data sharing in public. The research platform has a large scale shared storage and provides data sharing and analysis environments with several applications such as BiCC. To share and handle the incoming huge data in the research platform, we equipped the Research Portal. The main functionality of the Research Portal is to manage the data as a meta database with a user friendly interface. Data is registered as a pair of a data set and metadata in a Brain/MINDS standard format. The format was developed toward the easy traceability of data provenance, while some part is under development for additional data types. The Research Portal is to start the service in spring 2016. 1. Brain/MINDS data portal URL: http://www.bminds.brain.riken.jp 2. Brain/MINDS research portal URL: http://research.bminds.brain.riken.jp(Limited) 3. Brain/MINDS project URL: http://brainminds.jp/ Acknowledgement: This research is partially supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED.
PS-35 Data Analysis Pipeline Environment on the Brain/MINDS Research Platform
Masahide MAEDA, Alexander WOODWARD, Hideki OKA, Takeshi, Yoko MORII, Yoshihiro OKUMURA, Yoko YAMAGUCHI NIJC RIKEN BSI [email protected]
The Brain/MINDS research platform has been constructed for neuroscientific data storage, sharing and analysis amongst researchers. This platform combines a high performance computer cluster (HPC) with a large scale storage system (LSSS) over a high speed network. Data is stored on the LSSS and users can access this data for analysis. If users could directly analyze their data from this platform it would obviate the need for transferring a huge amount of data over limited bandwidth networks in order to conduct the analysis on their local environments. To implement such a solution, we firstly deployed a virtual machine (VM) to provide an operating system to the HPC so GUI based software can be used, for example: 3D Slicer, ImageJ, and the Jupyter Notebook through remote desktop function. Users can comfortably perform their analysis tasks on huge datasets since the VM mounts the LSSS over a high-speed network and has hardware accelerated desktop rendering. By using the Jupyter Notebook web application, we can edit source code, execute commands, display analysis results and then share these notebooks with other users. Furthermore, we have developed a Python package for creating HPC data analysis pipelines called SPyKGD which uses Open Grid Scheduler/Grid Engine. Using this one can easily submit jobs to the cluster computer and display the results via the high speed network.
Acknowledgement: This research is supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED. 53 AINI2016 ＆ INCF Nodes Workshop
PS-36 Big Data Transfer Tool on Brain/MINDS Research Portal
Takeshi TAKEUCHI(1), Yoko MORII(1), Yoshihiro OKUMURA(1), Yoko YAMAGUCHI(1) (1) NIJC RIKEN BSI [email protected]
In "Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project", we have constructed Research Platform on the Brain/MINDS server, SAKI. Research platform manages big data storage and data sharing. The size of the experimental data to be shared on the research platform is estimated around several PB. Data transfer between the server and clients using HTTP and TCP can cause serious inconvenience due to long time delay and related troubles. In order to solve the above problem in the research platform, we developed "Research Portal", a web based system, as a friendly user interface, where we introduced a UDP based file transfer tool, named SilverBullet. When sending files, SilverBullet divides them into small segments, and transmits the segments multiplexly to the far side over UDP. Then the received segments at the far side are reassembled and restored as the original files. As SilverBullet is a Java based application, the mechanism of installing/launching it on the client side is needed. To realize these requirements, we developed an intermediate application wrapping SilverBullet on the server side. When a user starts to download a file or directory on a browser, "Research Portal" forwards the request to the intermediate application, it generates/returns JNLP (Java Network Launching Protocol) file containing the target resource information. Then "Research Portal" passes the JNLP file to the browser, it installs/launches the SilverBullet for client according to JNLP specification. In conclusion, we have achieved to integrate "Research Portal" and SilverBullet seamlessly encapsulizing complex procedures with high transfer performance. References: 1. Brain/MINDS research portal URL: http://reseach.bminds.brain.riken.jp 2. Brain/MINDS project URL: http://brainminds.jp/ Acknowledgement: This research is supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED.
PS-37 Brain Science Dictionary
Tomoe FURUYA RIKEN BSI [email protected]
Brain Science Dictionary is an online dictionary for students and researchers in neuroscience field, which is a Wikipedia style dictionary but written only by invited authors with peer reviewing process. Items are explained in Japanese. The articles can be accessed by anybody without any fee. The dictionary is managed by the Editorial Committee headed by Dr. Yasunori Hayashi, RIKEN Brain Science Institute as a part of the Japan Node of International Neuroinformatics Coordination Facility. Our effort is totally non-profit, researcher-initiated, public awareness activity. We are aiming to publish total 1000 words in the wide field of neuroscience and cognitive science. Now about 495 words are finished and 90 under revision/review. We count daily access of about 9000, mostly from Japan. In Feb. 2015, Brain Science Dictionary is approved as an official activity of Japan Neuroscience Society.
54 AINI2016 ＆ INCF Nodes Workshop
PS-38
Developing large scale simulation of silkmoth brain using super computer
1Tomoki KAZAWA, 1Daisuke MIYAMOTO3 2Akihiko GOTO, 2Tetsuya FUKUDA 2Heewon PARK and 1Ryohei Kanzaki Research Center for Advanced Science and Technology, The University of Tokyo, Japan; 2Graduate School of Information Science and Technology, The University of Tokyo, Japan; E-mail: [email protected]
The complexity of human brain blocks the understanding of brain from each neuroscientist who shares the same goal but have its original sub-discipline. It appears to be nearly impossible work to build integrative simple models from vast hierarchical data in the real world. However current computing power in the supercomputers exponentially increasing year by year according to the Moor’s law may enable the simulations of insect brain by biophysically detailed multicomponent Hodgkin-Huxley type model. Insect has a fairy intelligent brain that has the abilities of high adaptability to natural circumstances, integrations of multi-sensory modalities, associate leanings and etc. We have been attempting to build whole brain simulation from olfactory input to odor searching behavior decision using biophysically detailed Hodgkin-Huxley type multi-compartment model in the silkmoth in strong computational resource, K computer and archived real-time speed in 10000 neuronal circuit simulation using hybrid parallelization and cell-split method between cores. The CMA-ES algorism, a solver based on parallelized evolutional algorism, was developed to harmonize experimental data and simulation. Standard brain mapping of each neuron enable the estimations of connections between neurons. An antennal lobe model was build and normalized olfactory response of antennal lobe projection neuron observed in our experiment were reproduced in our simulation. We constructed the recurrent neural circuit that can recognize visual patterns using STDP and mono-aminergic rewards.
55 AINI2016 ＆ INCF Nodes Workshop
Demonstration Session Abstracts
56 AINI2016 ＆ INCF Nodes Workshop
DS-1 HI-brain: a software platform for numerical simulation of the complex vision system by connecting and reusing existing models
Kazuki URABE(1), Daiki NAKAMURA(1), Shunji SATOH(1) (1)The University of Electro-Communications [email protected]
The visual system of our brain is a typically complex system composed of a lot of cortical areas or neurons executing various image processing. In order to simulate such a complex system, connecting a lot of existing computational models, e.g. a retinal model and a V1/MT/MST model, is a possible way if those models are written by a common computer language and by a common I/O format for data exchange among models. Moreover, we should replace a part of complex models by a revised version when we found new physiological experimental results. However, connecting various models and replacing a part of connected models are technically difficult problems because existing models are developed by different computer languages and different I/O formats. To alleviate the above mentioned problem, we have developed a software platform: HI-brain. Our HI-brain bases on RT-middleware, which is a standard specification for robotic software. Considering the complexity of robot system, we can expect that our HI-brain based on RT-middleware will be suitable for developing complex models of vision. HI-brain provides useful functions and libraries to connecting diverse vision models. OpenCV-RTC is one of those libraries, and it enables us to use OpenCV functions (de facto standard, the most popular library for image processing) as basic components for developing new models. We can download some models from Visiome Platform. In this Demonstration, we show some examples of model simulation on our platform; (1) parvocellular and magnocellular layer of retinal ganglion cells and a V1 model, (2) object detector connected with lower order vision processing, and (3) parallel/distributed computing using "Raspberry Pi."
DS-2 A Workflow to Build and Validate the BCI System Architecture Using the SSVEP Stimulator and Robotic Care Devices Archived in the Neuroinformatics Platform
Kiyohisa NATSUME(1), Guangyi AI(1), Balbir SINGH(1), Hiroaki WAGATSUMA(1)(2)(3) (1)LSSE, KYUTECH, (2) RIKEN BSI, (3) AIRC, AIST [email protected]
The importance of BCIs is increasing nowadays for providing devices to help disable people. With respect to invasive BCIs, non-invasive BMIs are expected to develop as the less stressful method to like robotic prosthetic tools; however effective signal processing methods becomes an inevitable issue [1]. J-Node Dynamic Brain Platform (DB-PF) [2] have offered tool sets to aid researchers for building their own BCI Systems and validate the performance of the architectures by providing programming source codes running on commonly used software and hardware. The workflow requires three components consist of a stimulator to the subject, an online signal processor and robotic devices to control a target. For instance, Microsoft Foundation Framework (MFC) based SSVEP stimulators, MATLAB/Simulink based wavelet analyzers reconstructed by the lifting scheme fits for the hardware implementation and Aldebaran NAO’s MATLAB API compatible codes help BCI developers from novice to professional levels. It is hard to figure out the whole BCI system design even for experts in a specific research area, and therefore the neuroinformatics platform deeply contributes to an acceleration of ongoing research projects relevant to BCIs and an establishment of fair and reasonable benchmarks to validate those developing software and hardware related to the issue. References: 1. W. Li, M. Li, J. Zhao (2015) Control of humanoid robot via motion-onset visual evoked potentials. Front Syst Neurosci. 8:247. doi: 10.3389/fnsys.2014.00247. 2. SSVEP Stimulus Ver 3.0 (Windows 7; multi-core CPU based), J-Node Dynamic Brain Platform, https://dynamicbrain.neuroinf.jp/modules/xoonips/detail.php?item_id=843
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DS-3 An Explorative Framework to Accelerate Collaborative Data Analysis of Medical Time Series Supported by the J-Node Platform Data Conversion Service
Hiroaki WAGATSUMA (1)(2)(3), Yoshiyuki ASAI (4), Masao MATSUMASHI(5), Tatsuya MIMA(6) (1) LSSE, KYUTECH, (2) RIKEN BSI, (3) AIRC, AIST, (4) IOSU, OIST, (5) HBRC, Kyoto Univ., (6) GSCEFS, Ritsumeikan Univ. [email protected]
Data-driven and model-based analyses are currently highlighted toward the integrative understanding of human nature, i.e. non-linear oscillation phenomena in neuroscience, Neuro-oscillology [1]. Medical time series from patients accumulated by clinical researchers suggest us further potentials of complex mathematics for elucidations of underlying dynamics [2]. A good instance of the way to treat medical data systematically is a completion style analysis [3]. Neuroimformatics professionals have long enhanced collaborative studies between experimental and theoretical researchers in neuroscience. J-Node Dynamic Brain Platform (DB-PF) [4] launched a special task force to accelerate collaborative researches between clinical researchers to possess a large scale medical time series such as EEGs in the livelong day and computational neuroscientists, or general data scientists, and then started to consolidate a workflow from the protection of personally identifiable information of patients to data-conversion services of a specialized medical data format into compatible formats available for the scientists. References: 1. Grant-in-Aid for Scientific Research on Innovative Areas: Non-linear Neuro-oscillology: Towards Integrative Understanding of Human Nature, http://www.nips.ac.jp/oscillology/ 2. Matsubayashi et al. (2015) Source-level analysis of brain oscillatory activity using hilbert transformation. Clinical Neurophysiology, 126(6), e56. 3. American Epilepsy Society Seizure Prediction Challenge (2014), https://www.kaggle.com/c/seizure-prediction 4. J-Node DB-PF, https://dynamicbrain.neuroinf.jp/
DS-4 Synchronized Motion Generation Derived from Implemented Neurodynamics based on Coupling Nonlinear Oscillators Toward Perspectives of Suitable Human-Robot Interactions
Motoharu HAGIO (1), Marine JULIERON (1)(2) Mayu ICHIKI (1), Hiroaki WAGATSUMA (1)(3)(4) (1) LSSE, KYUTECH, (2) ENSEIRB-MATMECA, Bordeaux INP (3) RIKEN BSI, (4) AIRC, AIST [email protected]
Non-linear phenomena as spontaneous oscillation and synchronization in the neuronal systems, can be reconstructed by model-based approaches with non-linear oscillators, which has a significant potential to elucidate functional differentiations and self-organization of the information processing [1][2], known as Neuro-oscillology [3]. Robotic devices are frequently discussed in BCI applications [4][5], while it is also useful for neuroscience-based designs to explore potential human-robot interfaces. We have developed the brain-inspired robotics [6] and extended it to the humanoid robot framework accompanied with the online non-linear system simulation including millisecond-order communications with microcontrollers (chipKit Max32) and TCP/IP protocol (IEEE 802.11). References: 1. Matsubayashi et al. (2015) Source-level analysis of brain oscillatory activity using hilbert transformation. Clinical Neurophysiology, 126(6), e56. 2. Maezawa et al. (2016) Cortico-muscular synchronization by proprioceptive afferents from the tongue muscles during isometric tongue protrusion. Neuroimage, 128, 284-292. 3. Non-linear Neuro-oscillology: Towards Integrative Understanding of Human Nature, http://www.nips.ac.jp/oscillology/ 4. McFarland et al. (2008) Brain-Computer Interface Operation of Robotic and Prosthetic Devices. Computer, 41, 52-56. 5. Li et al. (2015) Control of humanoid robot via motion-onset visual evoked potentials. Front Syst Neurosci. 8:247. doi: 10.3389/fnsys.2014.00247. 6. Wagatsuma et al. (2011) Problems of Temporal Granularity in Robot control: Levels of Adaptation and a Necessity of Self-Confidence, Proc. of IJCNN, 2670-2675.
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DS-5 Analysis of motor function of arm movements on Kinect v2
Takeru Honda(1)(2), Hirotaka YOSHIDA(3), Jongho LEE(1), Shinji KAKEI(1) (1) Tokyo Metropolitan Institute of Medical Science (2)BSI RIKEN (3)TUAT [email protected]
Quantitative estimation of motor functions of patients with neurological disorders is vital for evidence-based treatments. Previous investigations of movement kinematics have also focused on it. Due to redundancy of the musculoskeletal system, muscle activities contain more information than the movement kinematics. We have developed a linear model for the wrist joint to approximate the causal relationship between muscle activities and movement kinematics in terms of the wrist joint torque (Lee et al., 2015). We have used a canonical correlation analysis to verify the causality between the muscle activities and the movement kinematics in the model. Our previous study has shown that the ratio of the weights for position- and velocity-related torque components characterized the contents of the muscle activities in terms of the movement kinematics. Here we extended to measurements of the whole body movements and proposed a new method to evaluate the motor functions at the level of movement kinematics by using the kinect v2 developed by Microsoft. In our task, the participants were requested to follow to a target presented on a display with a pointer which position reflects his/her index finger. We analyzed the error (|(the finger-pointer position) - (the target position)|) on the display were measured in real-time. In the horizontal direction, we found large errors in the healthy subjects. It is very easy for participants to use our system anywhere there are a kinect v2 and a windows computer connected with internet. In future, a task program and data sets will be obtained or provided by using a database system through internet. This system may connect patients with medical doctors on-line and may ubiquitously provide a practical test of motor control.
DS-6 ViBrism DB Platform
Yuko OKAMURA-OHO(1)(2), Masahiko MORITA(2), Masaomi NISHIMURA(2), Takahiro TAWARA(2), Shuhei WEMLER(3), Kazuro SHIMOKAWA(4), Hideo YOKOTA(2) (1) BReNt, (2) RIKEN RAP, (3) Wemler Software, (4) ToMMo, Tohoku Univ. [email protected]
We introduce renewed ViBrism DB (http://vibrism.neuroinf.jp/) as a platform of NIJC activity, aiming at an increase of information about brain architecture based on molecular distribution of many genes on the three dimensional (3D) anatomical context. Comprehensive gene expression densities in the mouse brain were measured with microtomy-based technology, which we invented, Transcriptome Tomography1, and mapped on the 3D MRI space compatible to the INCF mouse standard brain coordinate (WHS)2. Now 172,023 maps of overall gene expression in the four developmental stages to the adult after the birth are searchable by gene IDs, and the results are shown as 2D/3D maps on the MRI images. Also, the measured densities at each stage are usable directly for gene-by-gene correlation analysis of co-expression using Pearson correlation coefficient as a similarity measure3, and then co-expression search results can be browsed as network tables and graphs associated with the 2D maps. These frameworks enable to comprehensively assess expression patterns underlying brain structures and functions. ViBrism DB is a unique database for anatomy/function association based on gene expression. References: 1. Okamura-Oho, Y. et al. PLoS One 7, e45373 (2012). 2. Johnson, G. A. et al. Neuroimage 53, 365–72 (2010). 3. Okamura-Oho, Y. et al. Sci. Rep. 4, 6969 (2014). Acknowledgement: This work was supported by members in RIKEN Neuroinformatics Japan Center (NIJC) and in the INCF Task Forces of the on Digital Brain Atlasing. Funding: JSPS KAKENHI Grant Number 25560428, 26280110, 268032 and 15HP8038, and JST Acceleration Utilization of University IP Program to Y.O., and the NIJC funding support to the ViBrism DB Committee.
59 AINI2016 ＆ INCF Nodes Workshop
DS-7 Marmoset Atlas with Web 3D Viewer
Alexander WOODWARD(1), Keiji MATSUDA(2), Carlos GUTIERREZ(3), Tomoyuki KUBOTA(4), Takao OISHI(5), Yoko YAMAGUCHI(1) (1) Neuroinformatics Japan Center, RIKEN BSI, Saitama, Japan, (2) Systems Neuroscience Group, Human Information Research Institute, AIST, Tsukuba, Japan, (3) Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa, Japan, (4) Research Center for Advanced Science and Technology, The University of Tokyo, Japan, (5) Systems Neuroscience Section, Primate Research Institute, Kyoto University, Japan [email protected]
For the 2016 J-Node Hackathon we extended the functionality of a web 3D brain viewer developed by Matsuda et al. [1]. The viewer has a client-server architecture implemented with Apache HTTP Server and PHP. The server-side renders 3D data and sends 2D image data to a web-browser user interface. Since heavy calculations are performed on the server the viewer can support a wide number of platforms. Users can interactively view data from any angle and slice the data to view internal structures. To extend the viewer we firstly added functionality for uploading new brain data to a database. Users can then view or delete individual datasets through a web-interface. Secondly, we added support for viewing a 3D marmoset atlas consisting of MRI T1, T2, Nissl stained volume data and brain region delineations. The user can click on any region and see the anatomical name. Additionally, a tree-view of the brain region hierarchy was added. Users can search through the tree structure and click on a region of interest which gets highlighted in the 3D viewer.
References: 1. Matsuda, K., Oishi, T., Higo, N. & Hayashi,M. Web-based MRI Brain image database system. Society for Neuroscience 2008, Washington DC, USA. Acknowledgement: This research is partially supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED.
DS-8 Computational Methods For Accurate 3D Digital Marmoset Brain Atlas Creation From Histology and MRI
Alexander WOODWARD, Tsutomu HASHIKAWA, Masahide MAEDA, Yoko YAMAGUCHI Neuroinformatics Japan Center, RIKEN BSI [email protected]
A 3D digital brain atlas is an important tool for anatomical reference and in registration to data from new specimens as a guide for studies such as tracer injection, fMRI, anatomical region delineation, and connectivity analysis. We will demonstrate the latest version of a 3D Multi-modal Marmoset Brain Atlas whose annotation was originally created by T. Hashikawa [1]. The atlas contains coregistered MRI T1 and T2 volumes, and Nissl stained images with region delineation. In order to construct the atlas, Nissl stained images were stacked and aligned to the MRI T2 data using 2D to 3D registration and the ANTs multidimensional image registration tools [2]. A challenge was how to remove high-frequency distortion between slices and to precisely align the different data modalities. The latest version is composed of 170 Nissl images along the superior-inferior axis and high-frequency distortion between these images was removed using an approach based on Gauss-Seidel iteration [3]. Finally, to improve the registration accuracy between MRI and histology, landmarks were included into the registration workflow to help the optimization reach a strong solution of the objective function for image matching. References: 1. T. Hashikawa, R. Nakatomi, A. Iriki, Current models of the marmoset brain, in Neuroscience Research, vol. 93, pp. 116-127, April 2015. 2. ANTs Advanced Normalization Tools: http://stnava.github.io/ANTs/ 3. S. Gaffling, V. Daum, S. Steidl, A. Maier, H. Köstler and J. Hornegger, A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction, in IEEE Transactions on Medical Imaging, vol. 34, no. 2, pp. 514-530, Feb. 2015. Acknowledgement: This research is supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED. 60 AINI2016 ＆ INCF Nodes Workshop
DS-9 KANPHOS Platform: A database for neural phosphoproteomics with quality control
Junichiro YOSHIMOTO(1)(2), Takayuki KANNON(3), Mutsuki AMANO(4), Tomoki NISHIOKA(4), Shiro USUI(5), Kozo KAIBUCHI(4) (1) NAIST, (2) OIST, (3) Kanazawa University, (4) Nagoya University Graduate School of Medicine, (5) NIJC RIKEN BSI [email protected]
Protein phosphorylation is involved in the regulation of a wide variety of physiological processes in the nervous system. It is predicted that the number of protein kinases and phosphorylated sites in human proteins amounts to approximately 500 and 200,000, respectively. However, little is known about which sites are phosphorylated by a specific kinase and which extracellular stimuli regulate the protein phosphorylation via intracellular signaling cascades. To uncover the basic issue, we developed a new methodology for comprehensive screening of the target phosphorylation sites of a given kinase using mass spectrometry, and succeeded in identifying hundreds of phosphorylation sites of representative kinases (Amano et al., 2015; Nishioka et al., 2015). The KANPHOS platform presented here are providing a summary of the identified phosphorylation sites. As of April 2016, about 8,300 pairs of protein kinases and phosphorylated sites identified by our method have been registered in the database. All data are controlled for quality via review and curation by specialists. Each protein item is linked with external databases such as Uniprot KB, HGNC DB, Allen Brain Atlas etc., enabling us to easily predict unknown functions of the protein phosphorylation. As an advanced option, the platform can provide a list of pathways in which the set of substrates phosphorylated by a specific condition is overrepresented more than expected, via communication with Reactome (http://www.reactome.org). For an example of this function, we demonstrate an estimation of pathways in striatal medium spiny neurons modulated by extracellular dopaminergic stimulation (Nagai, et al., 2016).
DS-10 Mouse Behavioral Phenotype Database
Satoko HATTORI(1), Hirotaka SHOJI(1), Keizo TAKAO(2)(3), Tsuyoshi MIYAKAWA(1)(3) (1)Fujita Health University, (2)University of Toyama, (3)National Institute for Physiological Sciences [email protected]
The mouse is a powerful research tool because of its advantages as an animal model. Since about 99% of mouse genes have homologs in human, a large-scale project has been undertaken to encompass knockout of every gene in mice. Approximately 80% of all genes are expressed in the brain, and we have been identifying the genes that have significant impact on the brain functions efficiently by examining the final output level of gene function in the brain, that is, behavior. The influence of a given gene on a specific behavior is determined by conducting behavioral analysis of genetically engineered mice. In brain-behavior phentoyping, phenotype data should be obtained systematically with reasonably standardized methods, and the data obtained in such projects should be included in a public database. Here, we introduce the database “Mouse Phenotype Database” (http://www.mouse-phenotype.org/), which is managed by Comprehensive brain science network (CBSN) platform (PF). Mutant mice and their wild-type littermates were subjected to comprehensive behavioral test battery, which covers sensori-motor functions, emotion, learning and memory, attention, etc. We have been constructing a relational database consisting of such data. So far, raw data of 164 indices in 19 tests from 180 strains are stored in a FileMaker file. Raw data for each strain that was published in articles are available in the Mouse Phenotype Database. Additionally, by analyzing the combined data of a large number of control mice in the database, we can evaluate the effects of factors, such as age and sex, on behaviors. Our database also serves softwares for behavioral analyses. The utilization of our database may contribute to progress in understanding gene-brain-behavior relationship. 61 AINI2016 ＆ INCF Nodes Workshop
DS-11 Consistent data organization made easy: A versatile file format for neuroscience
Christian J KELLNER(1), Adrian STOEWER(1) Michael SONNTAG(1) Andrey SOBOLEV(1) Jan BENDA(2) Thomas WACHTLER(1) Jan GREWE(2) (1) Department Biologie II, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany (2) Institut für Neurobiologie, Eberhard-Karls-Universität Tübingen, 72076 Tübingen, Germany [email protected]
Managing neuroscience data requires the integration of information from multiple sources. Background information, or metadata, about the experiment is necessary to interpret the resulting data correctly. Storing such information consistently is an essential part of experimental research and depends crucially on available file formats. Many existing formats provide only limited support for storing metadata along with the data. Here we present the NIX project [1], consisting of an open format and software tools to store and organize data and metadata. The format can store recorded or derived data as well as all the meta-information, including relationships between data items. Units and dimension information is stored alongside the data, to make quick interpretation possible. The format further allows specifying regions of interest, such as areas in an image or events in a recorded signal. Efficient use of NIX features is facilitated by software libraries for different languages, including C++, Python, Matlab, and Java. Installable packages exist for different platforms, together with documentation, examples, and tutorials. Additionally the NixView program [2] can be used to explore, plot and export the stored data in a user friendly and convenient way. The NIX project thus supports comprehensive annotation and efficient organization of neuroscience data, and the variety of libraries makes it easy to integrate access to data and metadata in the lab data collection and analysis workflow.
[1] https://github.com/G-Node/nix [2] http://bendalab.github.io/NixView/
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DS-12 Brain Knowledge Engine
Hongyin ZHU(1), Yi ZENG(1)(2), Dongsheng WANG(1) (1) Institute of Automation, Chinese Academy of Sciences, China (2) Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences [email protected]
Brain Knowledge Engine (BKE) is an automatic knowledge extraction, integration, analysis and service engine for understanding the Brain ( http://www.linked-brain-data.org/bke ). It is currently part of Linked Brain Data. The domain knowledge of the brain is automatically extracted and integrated from various sources containing PubMed, Allen Brain Atlas [1], Neuroscience Information Framework [2], NeuroLex [3], NeuroMorpho [4], Wikipedia, etc. BKE integrates massive brain knowledge and displays them with a user-friendly interface. It mainly contains the relations among brain building blocks, cognitive functions, brain diseases, etc. All the entities related to the target entity can be displayed in different categories respectively. For example, if the query term is “hippocampus”, users would get the knowledge including the relations on region and disease, region and cognitive functions, neurons and regions, type of neurons in the specific region, and URIs that are related to this query. BKE provides users a relatively complete set of results from multiple perspectives. Facts about the brain are always species specific. Knowledge extracted from different species cannot be simply mixed together, and whether it is true for other species needs biological experiment validations. Hence, BKE is designed to provide species meta-data for each knowledge triples extracted from various sources. Species labels can help users to compare the similarities and differences between different species easily. BKE has a service which can automatically identify the type of neurons in the corresponding brain regions. Namely, it discovers contain relationships among brain regions and different types of neurons. Keyword match to find these relations turns out to be not efficient since it only identify a small number of relations of this kind. Hence, machine learning techniques need to be applied for improvements. However, the labeled training data for type of neurons in the specific brain region is not enough. In order to solve this problem, we manually annotate some training corpus and adopt a supervised learning strategy. During the corpus annotation stage, we summarized some elaborately design rules which can be adopted as features. This rule set consists of two subsets which are the positive set and negative set. The sentences, which are in accordance with the positive rule set, have a higher probability to describe the content of a specific brain region containing the type of neurons. On the contrary, the sentences following the rules of the negative rule set are considered as having little or nothing to do with region containing neurons. We also use the dependency parser [5] to automatically extract the syntax features of every sentence, including the distance of two entities, the length of the dependency path, the distance of every entity to the root node, etc. Then, we adopt the neural network to learn a model to classify the natural language sentences. 85% correct classification rate using 10-fold cross validation is achieved. Compared with the KNN, SVM and logistic regression, the neural network gets the best generalization performance. BKE can integrate the newly extracted data which are with different formats compared to previous data very easily. In addition, it also integrates the figures that are highly relevant to the target entity. The figures are automatically extracted from Wikipedia and other websites. The integration of brain science related knowledge and the user-friendly interface can help users get relatively complete structured knowledge in seconds.
63 AINI2016 ＆ INCF Nodes Workshop
DS-13 SciCrunch: A Cooperative And Collaborative Data And Resource Discovery Portal For Scientific Communities
Jeffrey GRETHE, Anita BANDROWSKI, Michael CHIU, Tom GILLESPIE, James GO, Yueling LI, Burak OZYURT, Maryann MARTONE University of California, San Diego [email protected]
SciCrunch was designed to allow communities of researchers to create focused portals that provide access to resources, databases, information and tools of relevance to their research areas. SciCrunch is one of the largest aggregations of scientific data and tools available on the Web. One can think of SciCrunch as a “PubMed” for tools and data. Just as you can search across all the biomedical literature through PubMed, regardless of journal, SciCrunch lets you search across hundreds of databases and millions of data records from a single interface. SciCrunch was designed to break down the traditional types of portal silos created by different communities, so that communities can take advantage of work done by others and share their expertise as well. When a community brings in a data source, it becomes available to other communities, thus ensuring that valuable resources are shared by other communities who might need them. At the same time, individual communities can customize the way that these resources are presented to their constituents, to ensure that their user base is served. SciCrunch currently supports a diverse collection of communities, each with their own data needs: Neuroscience Information Framework (NIF) – is a biological search engine that allows students, educators, and researchers to navigate data resources relevant to neuroscience; NIDDK Information Network (dkNET) – serves the needs of basic and clinical investigators by providing seamless access to large pools of data relevant to the mission of The National Institute of Diabetes, Digestive and Kidney Disease (NIDDK); Research Identification Initiative (RII) – aims to promote research resource identification, discovery, and reuse.
DS-14
KnowledgeSpace: a community-based encyclopedia for neuroscience
Tom GILLESPIE(1), Willy WONG(1), Catherine ZWAHLEN(2), Silvia JIMENEZ(2), Jeff GRETHE(1)(3） (1)University of California, San Diego, USA 2École Polytechnique Fédérale de Lausanne, Blue Brain Project, Switzerland 3Neuroscience Information Framework (NIF) [email protected]
KnowledgeSpace (KS) is a community encyclopedia that links brain research concepts with data, models and literature from around the world. It is an open project and welcomes participation and contributions from members of the global research community. KS is the result of recommendations from a community workshop held by the INCF Program on Ontologies of Neural Structures in 2012. KS builds on a vocabulary service, populated with an integrated set of neuroscience ontologies with initial content coming from the Neuroscience Lexicon (NeuroLex), and the Brain Architecture Management System (BAMS). It links to an expanding set of data sources through the Neuroscience Information Framework (NIF) federated search infrastructure. The current KnowledgeSpace integrated data sources are: Neurolex.org, Allen Brain Institute cell-types, Blue Brain Project, Neuromorpho.org, Neuroelectro.org, Cell Image Library, NIF Integrated Connectivity, Channelpedia.net, Ion Channel Genealogy, ModelDB, OpenSourceBrain, GenSat, and Pubmed. The beta prototype of KS will be presented.
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AINI2016 ＆ INCF Nodes Workshop
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Program & Abstracts
4th INCF Japan Node International Workshop Advances in Neuroinformatics 2016 and 14th INCF Nodes Workshop
May 28-29, 2016 Saitama, JAPAN
Edited by Teiichi FURUICHI, Yoko YAMAGUCHI and Linda LANYON
Published and distributed by Neuroinformatics Japan Center, RIKEN Brain Science Institute 2-1, Hirosawa, Wako Saitama 351-0198 JAPAN INCF Japan Node Portal http://www.neuroinf.jp Neuroinformatics Japan Center Portal https://nijc.brain.riken.jp
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