Título:
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Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain
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Autor/a:
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Wang, Peng; Kong, Ru; Kong, Xiaolu; Liégeois, Raphaël; Orban, Csaba; Deco, Gustavo; Yeo, Thomas BT
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Abstract:
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We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale
properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to
new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence
of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor
regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized
processing of external stimuli. At the opposing end, default network regions had weak recurrent connections
and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection
strength and subcortical inputs provided complementary information for differentiating the levels of the
hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of
cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminarspecific
neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in
the dynamic resting brain. |
Abstract:
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This work
was supported by Singapore MOE Tier 2 (MOE2014-T2-2-016), NUS Strategic Research (DPRT/944/09/14), NUS SOM Aspiration Fund (R185000271720), Singapore NMRC
(CBRG/0088/2015), NUS YIA, and the Singapore National Research Foundation (NRF)
Fellowship (Class of 2017). Our research also used resources provided by the Center for
Functional Neuroimaging Technologies, P41EB015896, and instruments supported by
1S10RR023401, 1S10RR019307, and 1S10RR023043 from the Athinoula A. Martinos Center
for Biomedical Imaging at the Massachusetts General Hospital. Our computational work for
this article was partially performed on resources of the National Supercomputing Centre,
Singapore (www.nscc.sg). Data were provided by the HCP, WU-Minn Consortium (Principal
Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH institutes
and centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell
Center for Systems Neuroscience at Washington University. |
Derechos:
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Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Tipo de documento:
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Artículo Artículo - Versión publicada |
Editor:
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American Association for the Advancement of Science (AAAS)
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