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2015 Brainhack Proceedings Abstracts

GIGASCIENCE 2016年第1期5卷 1-26页

作者： [Anonymous] Computational Neuroimaging Lab Center for Biomedical Imaging and Neuromodulation Nathan Kline Institute for Psychiatric Research Orangeburg USA Center for the Developing Brain Child Mind Institute New York USA Centre de Recherche Institut Universitaire de Gériatrie de Montréal Montréal Canada Department of computer science and operational research University of Montreal Montreal Canada Max Planck Research Group for Neuroanatomy & Connectivity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany SRI International Menlo Park USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford USA Functional Imaging Unit Center for Diagnostic Radiology Unviersity Medicine Greifswald Greifswald Germany Université de Montréal Montréal Canada University of Massachusetts Medical School Worcester USA University of California Berkeley USA Institut Pasteur Paris France University of California San Diego La Jolla USA University of Washington Seattle USA Poldrack Lab Department of Psychology Stanford University Stanford USA Montréal Neurological Institute McGill University and Institute of Pscychology Montréal Canada Douglas Mental Health Institute Montréal Canada Otto-von-Guericke University Magdeburg Germany Montreal Neurological Institute McGill University Montreal Canada University of Hawaii John A. Burns School of Medicine Honolulu USA Oregon Health & Science University Portland USA University of Greifswald Greifswald Germany Duke Institute for Brain Sciences Durham USA Department of Psychology and Neuroscience University of Colorado Boulder USA Institute of Cognitive Neuroscience University College London London United Kingdom Section on Functional Imaging Methods Laboratory of Brain and Cognition National Institute of Mental Health Bethedsa USA City University of New York-Hunter College New York USA Monash Institute of Cognitive and Clinical Neurosciences Monash University Melbourne Australia McGill Centre fo

I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johns

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Human Connectome Project Minimal Preprocessing Pipelines to Nipype

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GigaScience 2016年第SUPPL_1期5卷

作者： Earl, Eric Demeter, Damion V. Mills, Kate Mihai, Glad Ruzic, Luka Ketz, Nick Reineberg, Andrew Reddan, Marianne C. Goddings, Anne-Lise Gonzalez-Castillo, Javier Gorgolewski, Krzysztof J. Oregon Health & Science University Portland OR USA University of Greifswald Greifswald Germany Duke Institute for Brain Sciences Durham NC USA Department of Psychology and Neuroscience University of Colorado Boulder CO USA Institute of Cognitive Neuroscience University College London London United Kingdom Section on Functional Imaging Methods Laboratory of Brain and Cognition National Institute of Mental Health Bethedsa MD USA Department of Psychology Stanford University Stanford CA USA

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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functional MRI: A confluence of fortunate circumstances

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NeuroImage 2012年第2期61卷 A3-A11页

作者： Bandettini, Peter A. Section on Functional Imaging Methods Functional MRI Core Facility National Institute of Mental Health 10 Center Dr. Rm 1D80 Bethesda 20892 MD United States

functional MRI has existed for about twenty years and by almost all measures has been incredibly successful. What are the reasons behind this success? In this review, eight extremely fortunate circumstances came together to produce BOLD based fMRI as we know it today. They are as follows: 1. The MRI signal, 2. The MRI relaxation rates, 3. The oxygen-dependent magnetic susceptibility of blood, 4. Neuronal–hemodynamic coupling, 5. The spatial scale of brain activation, 6. The prevalence of scanners able to perform echo planar imaging (EPI), 7. The parallel development of computing power, and 8. The very large group of neuroscientists who, pre-1991, were perfectly poised, willing, and able to exploit the capability of fMRI. These circumstances are discussed in detail. The desired goal of this review is primarily to convey the field of fMRI from the perspective of what was critically important before, during and after its inception and how things might have been if these circumstances would have been different. While there are many instances where circumstances could have been better, it is clear that they worked out extremely well, as the field of fMRI, a major aspect of functional neuroimaging today, is thriving. © 2012

关键词： Blood susceptibility BOLD Brain imaging fMRI functional MRI MRI Neurovascular coupling

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Measuring the consistency of global functional connectivity using kernel regression methods

Measuring the consistency of global functional connectivity ...

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International Workshop on Pattern Recognition in Neuroimaging

作者： Chu, Carlton Handwerker, Daniel A. Bandettini, Peter A. Ashburner, John Section on Functional Imaging Methods LBC National Institute of Mental Health NIH Bethesda United States Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology London United Kingdom

ISBN: (纸本)9780769543994

This paper describes a novel approach to estimate the consistency of global functional connectivity. We apply kernel regression methods, kernel ridge regression (KRR) and support vector regression (SVR), to predict the time-series from a target voxel using voxels in the rest of the brain as features. A correlation coefficient, obtained by cross-validation, was used to define the consistency of global functional connectivity of each target voxel. This procedure was applied to all the voxels in the brain, and a map of correlation coefficients, which measures the accuracy of predictions, over the whole brain was generated. The method was applied to two separate 10 min resting runs of four subjects. The most accurately predicted regions were mostly in the grey matter. This efficient method can detect regions with low global connectivity and also allows visualization of changes in functional connectivity between tasks. © 2011 IEEE.

关键词： Forecasting

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Classification of neurodegenerative diseases using Gaussian process classification with automatic feature determination

Classification of neurodegenerative diseases using Gaussian ...

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Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging

作者： Chu, Carlton Bandettini, Peter Ashburner, John Marquand, Andre Kloeppel, Stefan Section on Functional Imaging Methods LBC National Institute of Mental Health Bethesda United States Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology London United Kingdom Department of Clinical Neuroscience Institute of Psychiatry King's College London London United Kingdom Freiburg Brain Imaging Department of Psychiatry and Psychotherapy University Clinic Freiburg Freiburg Germany

ISBN: (纸本)9780769541334

There has been a growing interest in applying pattern classification methods to discriminate patients with neurodegenerative diseases from normal controls using structural MRI data. Despite an impressive array of publications, most applications are framed as discrimination problems with categorical class decisions. For many clinical applications, probabilistic estimation would be more useful, especially when predictions from other models and measurements are combined. The Gaussian Process (GP) model is a Bayesian learning algorithm and is closely linked with classical variance component estimation. The Bayesian formulation allows the model to automatically determine model hyper-parameters by means of maximizing the "marginal likelihood" or the "evidence of the data". We utilized such a formulation by using marginal likelihood maximization to weight the importance of different grey matter (GM) regions automatically in the training processes, without additional cross validation. The algorithms were applied to two sets of patients with Huntington's disease and normal controls. Classification accuracy was improved from 70% to 73% with automatic feature selection for the first dataset, and was improved from 58% to 69 % for the second dataset. © 2010 IEEE.

关键词： Gaussian distribution

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Classification of Neurodegenerative Diseases Using Gaussian Process Classification with Automatic Feature Determination

Classification of Neurodegenerative Diseases Using Gaussian ...

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Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD)

作者： Carlton Chu Peter Bandettini John Ashburner Andre Marquand Stefan Kloeppel Section on Functional Imaging Methods LBC National Institute of Mental Health Bethesda USA Centre for Neuroimaging Sciences Department of Clinical Neuroscience Institute of Psychiatry King''s College London UK Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London London UK Freiburg Brain Imaging Department of Psychiatry and Psychotherapy University Clinic Freiburg Freiburg im Breisgau Germany

There has been a growing interest in applying pattern classification methods to discriminate patients with neurodegenerative diseases from normal controls using structural MRI data. Despite an impressive array of publications, most applications are framed as discrimination problems with categorical class decisions. For many clinical applications, probabilistic estimation would be more useful, especially when predictions from other models and measurements are combined. The Gaussian Process (GP) model is a Bayesian learning algorithm and is closely linked with classical variance component estimation. The Bayesian formulation allows the model to automatically determine model hyper-parameters by means of maximizing the “marginal likelihood” or the “evidence of the data”. We utilized such a formulation by using marginal likelihood maximization to weight the importance of different grey matter (GM) regions automatically in the training processes, without additional cross validation. The algorithms were applied to two sets of patients with Huntington's disease and normal controls. Classification accuracy was improved from 70% to 73% with automatic feature selection for the first dataset, and was improved from 58% to 69 % for the second dataset.

关键词： Covariance matrix Support vector machines Accuracy Gaussian processes Probabilistic logic Brain models

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The neuroscientific exploitation of high-resolution functional magnetic resonance imaging

The neuroscientific exploitation of high-resolution function...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Nikolaus Kriegeskorte Peter Bandettini Section on Functional Imaging Methods Laboratory of Brain and Cognition National Institute of Mental Health USA

High-resolution functional magnetic resonance imaging (hi-res fMRI) promises to bridge the gap between the macro- and the microview of brain function afforded by conventional neuroimaging and invasive cell recording, respectively. Hi-res fMRI (nominal voxel sizes les(2 mm) 3 ) is robustly achievable in human studies today using widely available clinical 3-Tesla scanners. However, the neuroscientific exploitation of the greater spatial detail poses three challenges: (1) Fine-scale neuronal activity patterns are inaccurately depicted in the hemodynamic images obtained. (2) Single small voxels yield very noisy measurements. (3) For groups of subjects, the interindividual correspondency mapping is unknown at the fine scale of millimeters. Here we argue that these challenges can be met by abstracting from the regional fine-scale activity patterns themselves and instead asking how well they distinguish the experimental conditions

关键词： Magnetic resonance imaging Neuroimaging Humans Information analysis Decoding Pattern analysis Bridges Robustness Hemodynamics Cities and towns

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