Journal
SCIENCE ADVANCES
Volume 5, Issue 1, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aat7854
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Funding
- 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
- Singapore National Research Foundation (NRF) Fellowship
- Center for Functional Neuroimaging Technologies [P41EB015896]
- Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital [1S10RR023401, 1S10RR019307, 1S10RR023043]
- NIH [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [P41EB015896] Funding Source: NIH RePORTER
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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 laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.
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