A macaque connectome for large-scale network simulations in TheVirtualBrain

被引:0
|
作者
Kelly Shen
Gleb Bezgin
Michael Schirner
Petra Ritter
Stefan Everling
Anthony R. McIntosh
机构
[1] Rotman Research Institute,Montreal Neurological Institute
[2] Baycrest,Robarts Research Institute
[3] McGill University,Department of Physiology and Pharmacology
[4] Charité – Universitätsmedizin Berlin,Department of Psychology
[5] corporate member of Freie Universität Berlin,undefined
[6] Humboldt-Universität zu Berlin,undefined
[7] and Berlin Institute of Health,undefined
[8] Department of Neurology,undefined
[9] Berlin Institute of Health (BIH),undefined
[10] Bernstein Center for Computational Neuroscience,undefined
[11] University of Western Ontario,undefined
[12] University of Western Ontario,undefined
[13] University of Toronto,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.
引用
收藏
相关论文
共 50 条
  • [1] A macaque connectome for large-scale network simulations in TheVirtualBrain
    Shen, Kelly
    Bezgin, Gleb
    Schirner, Michael
    Ritter, Petra
    Everling, Stefan
    McIntosh, Anthony R.
    [J]. SCIENTIFIC DATA, 2019, 6 (1)
  • [2] GENERATIVE MODELS FOR LARGE-SCALE SIMULATIONS OF CONNECTOME DEVELOPMENT
    Brooks, Skylar J.
    Stamoulis, Catherine
    [J]. 2023 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW, 2023,
  • [3] Large-scale network simulations with GTNets
    Riley, GR
    [J]. PROCEEDINGS OF THE 2003 WINTER SIMULATION CONFERENCE, VOLS 1 AND 2, 2003, : 676 - 684
  • [4] Large-scale topology and the default mode network in the mouse connectome
    Stafford, James M.
    Jarrett, Benjamin R.
    Miranda-Dominguez, Oscar
    Mills, Brian D.
    Cain, Nicholas
    Mihalas, Stefan
    Lahvis, Garet P.
    Lattal, K. Matthew
    Mitchell, Suzanne H.
    David, Stephen V.
    Fryer, John D.
    Nigg, Joel T.
    Fair, Damien A.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (52) : 18745 - 18750
  • [5] Estimating Large-Scale Network Convergence in the Human Functional Connectome
    Bell, Peter T.
    Shine, James M.
    [J]. BRAIN CONNECTIVITY, 2015, 5 (09) : 565 - 574
  • [6] Toward large-scale connectome reconstructions
    Plaza, Stephen M.
    Scheffer, Louis K.
    Chklovskii, Dmitri B.
    [J]. CURRENT OPINION IN NEUROBIOLOGY, 2014, 25 : 201 - 210
  • [7] Conservative synchronization of large-scale network simulations
    Park, A
    Fujimoto, RM
    Perumalla, KS
    [J]. 18TH WORKSHOP ON PARALLEL AND DISTRIBUTED SIMULATION, PROCEEDINGS, 2004, : 153 - 161
  • [8] Modelling on the very large-scale connectome
    Odor, Geza
    Gastner, Michael T.
    Kelling, Jeffrey
    Deco, Gustavo
    [J]. JOURNAL OF PHYSICS-COMPLEXITY, 2021, 2 (04):
  • [9] Mechanisms of distributed working memory in a large-scale network of macaque neocortex
    Mejias, Jorge F.
    Wang, Xiao-Jing
    [J]. ELIFE, 2022, 11
  • [10] A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links
    Chen, Yuhan
    Zhang, Zi-Ke
    He, Yong
    Zhou, Changsong
    [J]. CEREBRAL CORTEX, 2020, 30 (09) : 4771 - 4789