A Critical Look at Connectomics
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Imaging Genetic Strategies for Predicting the Quality of Sleep Using Depression-Specific Biomarkers
Imaging genetic strategies for predicting the quality of sleep using depression-specific biomarkers Mansu Kim 1, Xiaohui Yao 1, Bo-yong Park 2, Jingwen Yan 3, and Li Shen1,* 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA 2McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Canada 3Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University, USA * Correspondence to [email protected] Overview Joint-connectivity-based sparse CCA Background: Sleep is an essential phenomenon for SNP connectivity Imaging genetics model: The joint-connectivity-based sparse canonical ··· X* samples × maintaining good health and wellbeing [1]. correlation analysis (JCB-SCCA) was applied on preprocessed features n Some studies reported that genetic and p SNPs (Figure 1). JCB-SCCA has an advantage for incorporating connectivity Loading vector u Maximum imaging biomarkers that depression is information and can handle multi-modal neuroimaging datasets. correlation Brain associated with sleep disorder [2]–[4]. connectivity ··· Prior biological knowledge: The average connectivity matrix computed ··· K samples Y* modalitiesn × Imaging genetics: Many studies adopted imaging from the HCP dataset and linkage disequilibrium obtained from 1,000 q voxels genetics methodology to find associations genome project were used as the prior connectivity information of the K = 2 Loading vector V between imaging and genetic biomarkers. In algorithm. The parameters of the algorithm were tuned jointly by nested K = 1 this study, we examine the imaging genetics five-fold cross-validation. Figure 1. JCB-SCCA association in depression and extract biomarkers for predicting the quality of sleep. Experiments and Results Prediction task: We built a prediction model for the Pittsburgh sleep quality index (PSQI) using the identified biomarkers. -
Conservation and Divergence of Related Neuronal Lineages in The
RESEARCH ARTICLE Conservation and divergence of related neuronal lineages in the Drosophila central brain Ying-Jou Lee, Ching-Po Yang, Rosa L Miyares, Yu-Fen Huang, Yisheng He, Qingzhong Ren, Hui-Min Chen, Takashi Kawase, Masayoshi Ito, Hideo Otsuna, Ken Sugino, Yoshi Aso, Kei Ito, Tzumin Lee* Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States Abstract Wiring a complex brain requires many neurons with intricate cell specificity, generated by a limited number of neural stem cells. Drosophila central brain lineages are a predetermined series of neurons, born in a specific order. To understand how lineage identity translates to neuron morphology, we mapped 18 Drosophila central brain lineages. While we found large aggregate differences between lineages, we also discovered shared patterns of morphological diversification. Lineage identity plus Notch-mediated sister fate govern primary neuron trajectories, whereas temporal fate diversifies terminal elaborations. Further, morphological neuron types may arise repeatedly, interspersed with other types. Despite the complexity, related lineages produce similar neuron types in comparable temporal patterns. Different stem cells even yield two identical series of dopaminergic neuron types, but with unrelated sister neurons. Together, these phenomena suggest that straightforward rules drive incredible neuronal complexity, and that large changes in morphology can result from relatively simple fating mechanisms. *For correspondence: Introduction [email protected] In order to understand how the genome encodes behavior, we need to study the developmental mechanisms that build and wire complex centers in the brain. The fruit fly is an ideal model system Competing interests: The to research these mechanisms. The Drosophila field has extensive genetic tools. -
New Trends in Connectomics
FOCUS FEATURE: New Trends in Connectomics Editorial: New Trends in Connectomics 1 2,3,4,5 Olaf Sporns and Danielle S. Bassett 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA 3Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA 4Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA 5Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA ABSTRACT Connectomics is an integral part of network neuroscience. The field has undergone rapid Downloaded from http://direct.mit.edu/netn/article-pdf/02/02/125/1092204/netn_e_00052.pdf by guest on 28 September 2021 expansion over recent years and increasingly involves a blend of experimental and computational approaches to brain connectivity. This Focus Feature on “New Trends in an open access journal Connectomics” aims to track the progress of the field and its many applications across different neurobiological systems and species. The idea that connections among neural elements are crucial for brain function has been central to modern neuroscience almost since its inception. Building on this idea, the emerg- ing field of connectomics adds several new and important components. First, connectomics provides comprehensive maps of neural connections, with the ultimate goal of achieving com- plete coverage of any given nervous system. Second, connectomics delivers insights into the principles that underlie network architecture and uncovers how these principles support net- work function. These dual aims can be accomplished through the confluence of new experi- mental techniques for mapping connections and new network science methods for modeling and analyzing the resulting large connectivity datasets. -
Data Mining in Genomics, Metagenomics and Connectomics
Csaba Kerepesi DATA MINING IN GENOMICS, METAGENOMICS AND CONNECTOMICS PhD Thesis Supervisor: Dr. Vince Grolmusz Department of Computer Science E¨otv¨osLor´andUniversity, Hungary PhD School of Computer Science E¨otv¨osLor´andUniversity, Hungary Dr. Erzs´ebet Csuhaj-Varj´u PhD Program of Information Systems Dr. Andr´as Bencz´ur Budapest, 2017 Acknowledgements I would like to thank my supervisor Dr. Vince Grolmusz for his tireless support with which he started my scientific career. I am very grateful to the PhD School of Computer Science and the Faculty of Informatics, ELTE for their inexhaustible support. I would like to thank my co-authors for their precious work and I also would like to thank all my colleagues, who helped me anything in my re- searches. Finally, I would like to thank my family for their everlasting support. 2 Contents 1 Introduction 6 2 Data Mining in Genomics and Metagenomics 14 2.1 AmphoraNet: The Webserver Implementation of the AM- PHORA2 Metagenomic Workflow Suite . 14 2.1.1 Introduction . 14 2.1.2 Results and Discussion . 15 2.2 Visual Analysis of the Quantitative Composition of Metage- nomic Communities: the AmphoraVizu Webserver . 17 2.2.1 Introduction . 17 2.2.2 Results and Discussion . 19 2.3 Evaluating the Quantitative Capabilities of Metagenomic Analysis Software . 21 2.3.1 Introduction . 21 2.3.2 Results and discussion . 22 2.3.3 Methods . 25 2.3.4 Availability . 29 2.4 The \Giant Virus Finder" Discovers an Abundance of Giant Viruses in the Antarctic Dry Valleys . 30 2.4.1 Introduction . 30 3 2.4.2 Results and discussion . -
Connectomics of Morphogenetically Engineered Neurons As a Predictor of Functional Integration in the Ischemic Brain
http://www.diva-portal.org This is the published version of a paper published in Frontiers in Neurology. Citation for the original published paper (version of record): Sandvig, A., Sandvig, I. (2019) Connectomics of Morphogenetically Engineered Neurons as a Predictor of Functional Integration in the Ischemic Brain Frontiers in Neurology, 10: 630 https://doi.org/10.3389/fneur.2019.00630 Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Permanent link to this version: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161446 REVIEW published: 12 June 2019 doi: 10.3389/fneur.2019.00630 Connectomics of Morphogenetically Engineered Neurons as a Predictor of Functional Integration in the Ischemic Brain Axel Sandvig 1,2,3 and Ioanna Sandvig 1* 1 Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway, 2 Department of Neurology, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway, 3 Department of Pharmacology and Clinical Neurosciences, Division of Neuro, Head, and Neck, Umeå University Hospital, Umeå, Sweden Recent advances in cell reprogramming technologies enable the in vitro generation of theoretically unlimited numbers of cells, including cells of neural lineage and specific neuronal subtypes from human, including patient-specific, somatic cells. Similarly, as demonstrated in recent animal studies, by applying morphogenetic neuroengineering principles in situ, it is possible to reprogram resident brain cells to the desired phenotype. These developments open new exciting possibilities for cell replacement therapy in Edited by: stroke, albeit not without caveats. -
Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury
Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Irimia, Andrei, Micah C. Chambers, Carinna M. Torgerson, Maria Filippou, David A. Hovda, Jeffry R. Alger, Guido Gerig, et al. 2012. Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Frontiers in Neurology 3:10. Published Version doi://10.3389/fneur.2012.00010 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:8462352 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA METHODS ARTICLE published: 06 February 2012 doi: 10.3389/fneur.2012.00010 Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury Andrei Irimia1, Micah C. Chambers 1, Carinna M.Torgerson1, Maria Filippou 2, David A. Hovda2, Jeffry R. Alger 3, Guido Gerig 4, Arthur W.Toga1, Paul M. Vespa2, Ron Kikinis 5 and John D. Van Horn1* 1 Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA 2 Brain Injury Research Center, Departments of Neurology and Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA 3 Department of Radiology, David -
Polygenic Evidence and Overlapped Brain Functional Connectivities For
Sun et al. Translational Psychiatry (2020) 10:252 https://doi.org/10.1038/s41398-020-00941-z Translational Psychiatry ARTICLE Open Access Polygenic evidence and overlapped brain functional connectivities for the association between chronic pain and sleep disturbance Jie Sun 1,2,3,WeiYan2,Xing-NanZhang2, Xiao Lin2,HuiLi2,Yi-MiaoGong2,Xi-MeiZhu2, Yong-Bo Zheng2, Xiang-Yang Guo3,Yun-DongMa2,Zeng-YiLiu2,LinLiu2,Jia-HongGao4, Michael V. Vitiello 5, Su-Hua Chang 2,6, Xiao-Guang Liu 1,7 and Lin Lu2,6 Abstract Chronic pain and sleep disturbance are highly comorbid disorders, which leads to barriers to treatment and significant healthcare costs. Understanding the underlying genetic and neural mechanisms of the interplay between sleep disturbance and chronic pain is likely to lead to better treatment. In this study, we combined 1206 participants with phenotype data, resting-state functional magnetic resonance imaging (rfMRI) data and genotype data from the Human Connectome Project and two large sample size genome-wide association studies (GWASs) summary data from published studies to identify the genetic and neural bases for the association between pain and sleep disturbance. Pittsburgh sleep quality index (PSQI) score was used for sleep disturbance, pain intensity was measured by Pain Intensity Survey. The result showed chronic pain was significantly correlated with sleep disturbance (r = 0.171, p-value < 0.001). Their genetic correlation was rg = 0.598 using linkage disequilibrium (LD) score regression analysis. Polygenic score (PGS) association analysis showed PGS of chronic pain was significantly associated with sleep and vice versa. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Nine shared functional connectivity (FCs) were identified involving prefrontal cortex, temporal cortex, precentral/ postcentral cortex, anterior cingulate cortex, fusiform gyrus and hippocampus. -
Brain Connectivity Meets Reservoir Computing
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.22.427750; this version posted January 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Brain Connectivity meets Reservoir Computing Fabrizio Damicelli1, Claus C. Hilgetag1, 2, Alexandros Goulas1 1 Institute of Computational Neuroscience, University Medical Center Hamburg Eppendorf, Hamburg University, Hamburg, Germany 2 Department of Health Sciences, Boston University, Boston, MA, USA * [email protected] Abstract The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? ANNs’ architectures are carefully engineered and have crucial importance in many recent perfor- mance improvements. On the other hand, BNNs’ exhibit complex emergent connectivity patterns. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain’s ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. -
A Multi-Pass Approach to Large-Scale Connectomics
A Multi-Pass Approach to Large-Scale Connectomics Yaron Meirovitch1;∗;z Alexander Matveev1;∗ Hayk Saribekyan1 David Budden1 David Rolnick1 Gergely Odor1 Seymour Knowles-Barley2 Thouis Raymond Jones2 Hanspeter Pfister2 Jeff William Lichtman3 Nir Shavit1 1Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology 2School of Engineering and Applied Sciences, 3Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University ∗These authors equally contributed to this work zTo whom correspondence should be addressed: [email protected] Abstract The field of connectomics faces unprecedented “big data” challenges. To recon- struct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming electron microscopy data. Existing algorithms provide relatively good accuracy but are unacceptably slow, and would require years to extract connectivity graphs from even a single cubic millimeter of neural tissue. Here we present a viable real-time solution, a multi-pass pipeline optimized for shared-memory multicore systems, capable of processing data at near the terabyte- per-hour pace of multi-beam electron microscopes. The pipeline makes an initial fast-pass over the data, and then makes a second slow-pass to iteratively correct errors in the output of the fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction algorithm and suggest new methods for detecting mor- phological errors. Our fast-pass approach provided many algorithmic challenges, including the design and implementation of novel shallow convolutional neural nets and the parallelization of watershed and object-merging techniques. We use it to reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al. -
Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors
RESEARCH Local connectome phenotypes predict social, health, and cognitive factors 1 2,3 4,5 Michael A. Powell , Javier O. Garcia , Fang-Cheng Yeh , 2,3,6 7 Jean M. Vettel , and Timothy Verstynen 1Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA 2U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA 3Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA 4Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA 5Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA 6Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA 7 an open access journal Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA Keywords: Local connectome, White matter, Individual differences, Behavior prediction, Structural connectivity ABSTRACT The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This Citation: Powell, M. A., Garcia, J. O., fingerprinting approach was applied to a large sample (N = 841) of subjects from the Yeh, F.-C., Vettel, J. M., & Verstynen, T. (2017). Local connectome phenotypes Human Connectome Project, revealing a reliable degree of between-subject correlation in predict social, health, and cognitive factors. -
Proteomic Analysis of Postsynaptic Proteins in Regions of the Human Neocortex
Proteomic analysis of postsynaptic proteins in regions of the human neocortex Marcia Roy1*, Oksana Sorokina2*, Nathan Skene1, Clemence Simonnet1, Francesca Mazzo3, Ruud Zwart3, Emanuele Sher3, Colin Smith1, J Douglas Armstrong2 and Seth GN Grant1. * equal contribution Author Affiliations: 1. Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom 2. School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom 3. Lilly Research Centre, Eli Lilly & Company, Erl Wood Manor, Windlesham, GU20 6PH, United Kingdom 1 Abstract: The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. The compositional signatures in brain regions involved with language, emotion and memory functions were characterized. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions. 2 Introduction: For almost two centuries, scientists have pursued the study of localization of function in the human cerebral neocortex using diverse methods including neuroanatomy, electrophysiology, imaging and gene expression studies. The frontal, parietal, temporal and occipital lobes of the neocortex have been commonly subdivided into Brodmann areas (BA)1 based on cytoarchitectural features, and specific behavioral functions have been ascribed to these regions. -
Connectomics and Molecular Imaging in Neurodegeneration
European Journal of Nuclear Medicine and Molecular Imaging (2019) 46:2819–2830 https://doi.org/10.1007/s00259-019-04394-5 REVIEW ARTICLE Connectomics and molecular imaging in neurodegeneration Gérard N. Bischof1,2 & Michael Ewers3 & Nicolai Franzmeier3 & Michel J. Grothe4 & Merle Hoenig1,5 & Ece Kocagoncu 6 & Julia Neitzel3 & James B Rowe6,7 & Antonio Strafella8,9,10,11,12 & Alexander Drzezga1,5,13 & Thilo van Eimeren1,13,14 & on behalf of the MINC faculty Received: 29 May 2019 /Accepted: 4 June 2019 / Published online: 11 July 2019 # Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Our understanding on human neurodegenerative disease was previously limited to clinical data and inferences about the underlying pathology based on histopathological examination. Animal models and in vitro experiments have provided evidence for a cell-autonomous and a non-cell-autonomous mechanism for the accumulation of neuropathology. Combining modern neuroimaging tools to identify distinct neural networks (connectomics) with target-specific positron emission tomography (PET) tracers is an emerging and vibrant field of research with the potential to examine the contributions of cell-autonomous and non-cell-autonomous mechanisms to the spread of pathology. The evidence pro- vided here suggests that both cell-autonomous and non-cell-autonomous processes relate to the observed in vivo char- acteristics of protein pathology and neurodegeneration across the disease spectrum. We propose a synergistic model of cell-autonomous and non-cell-autonomous accounts that integrates the most critical factors (i.e., protein strain, suscep- tible cell feature and connectome) contributing to the development of neuronal dysfunction and in turn produces the observed clinical phenotypes.