Sensory-motor cortices shape functional connectivity dynamics in the human brain

Xiaolu Kong,B T Thomas Yeo,Avram Holmes,Kevin Anderson,Csaba Orban,Martijn Van Den Heuvel,John D Murray,Peng Wang,Ru Kong,Shaoshi Zhang,Gustavo Deco

doi:10.1038/s41467-021-26704-y

Abstract

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

Highlights

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity
Using data from the Human Connectome Project (HCP), we demonstrated that parametric mean-field model (pMFM) achieved markedly more realistic static functional connectivity (FC) and FC dynamics in new outof-sample participants
Optimization of the parametric mean-field model. 1052 participants from the HCP S1200 release were randomly divided into training (N = 351), validation (N = 350), and test (N = 351) sets

Summary

Introduction

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. Recent studies have shown that additional important insights can be gained from studying momentto-moment variation in inter-regional synchrony, i.e., timevarying dynamic functional connectivity[12,13,14,15,16] It is currently unclear how spatial heterogeneity in local circuit properties contributes to both time-averaged and time-varying properties of large-scale brain dynamics. Most previous large-scale circuit models assumed that local circuit properties (e.g., local synaptic strength, etc.) are identical across brain regions, which is not biologically plausible Recent studies in both humans and macaques[23,24,25] have demonstrated that allowing local circuit properties to vary along the brain’s hierarchical axis yielded significantly more realistic static functional connectivity (FC). We investigated the spatial relationship among FC dynamics, fMRI signal amplitude, and gene expression patterns from the Allen Human Brain Atlas (AHBA)

Methods

Results

Conclusion