Sign in
NeuroscienceNeuron
Peer-reviewed journal article

Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics

Murat DemirtaşiD, Joshua B. BurtiD, Markus HelmeriD, Jie Lisa JiiD, Brendan D. AdkinsoniD, Matthew F. GlasseriD, David C. Van EsseniD, Stamatios N. SotiropoulosiD, Alan AnticeviciD, John D. MurrayiD

Abstract

The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from MRI-derived T1w/T2w mapping, and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to fMRI-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized high-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers
Citations
361
FWCI
23.24
Field percentile
top 1%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISKong '21Chaudhuri '15Barnes '26Li '22Liu '26Shafiei '20
78% semantic matchNeuroscience
Xiaolu Kong, Ru Kong, Csaba Orbán+8 more·bioRxiv·Mar 15, 2021· 7 citations