Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics

By Murat Demirtaş, J. Burt, Markus Helmer, Jie Lisa Ji, B. Adkinson, Matthew F. Glasser, D. V. Van Essen, Stamatios N. Sotiropoulos, Alan Anticevic, John D. Murray. bioRxiv, 2018

353 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/64a7e0de9ae08a33a3662d3cd9b1b65f5702e83c

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In Cognitive Neuroscience

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Citations in the corpus

Parameterizing neural power spectra into periodic and aperiodic components (2020, background): “4a); and (3) there is a gradient across the cortex of the aperiodic exponent 36 .”
Linking Structure and Function in Macroscale Brain Networks. (2020, neutral): “A complementary study found that biophysical models could be fitted to FC significantly better if they were informed by hierarchical heterogeneity estimated from T1w/T2w ratios [80].”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “Complementary work by Demirtas et al. (2019) used MRI to map anatomical hierarchy as indexed by cortical myelination, and found that incorporating hierarchy information into a biophysical model of neural dynamics signiﬁcantly improved the ﬁt to human rfMRI data.”
neuromaps: structural and functional interpretation of brain maps (2022, background): “Given the proliferation of such datasets in recent years, a large body of work has arisen focused on investigating similarities across brain maps [7, 18, 24, 31, 67, 80, 84].”