Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex

Abstract

Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulated that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employed atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings were consistent across processing pipelines: 1) increased myelination and lower EI-ratio associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 2) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurred when traversing from granular to agranular cortical regions. Collectively, our work delivers a novel framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks

“…Second, most current studies model a single, globally-uniform structure-function relationship across the brain. Recent reports, however, suggest that structure-function coupling is regionally heterogeneous, with stronger correspondence between structural and functional connectivity in uni-modal cortex, and weaker correspondence in transmodal cortex [12, 108, 151, 157, 163], potentially reflecting underlying molecular and cytoarchitectural gradients [12, 13, 48, 138, 151]. Altogether, a more detailed biological understanding of structure-function relationships – one that takes into account both neurophysiological activity and regional heterogeneity – is necessary [138].…”

Pharmaco-resistant temporal lobe epilepsy gradually perturbs the cortex-wide excitation-inhibition balance

“…We calculated the region-wise Hurst exponent value, a metric that is mathematically related to the 1/f exponent of neural signals 21, 56 and used as an index of the E/I ratio. As previously described, 5, 57 resting-state fMRI time series were modeled as multivariate fractionally integrated processes, and the Hurst exponent was estimated via the univariate maximum likelihood method and discrete wavelet transform. 5 As such, an elevated E/I ratio would manifest in a lower Hurst exponent value.…”

“… (a) Mean regional patterns of the Hurst exponent of resting-state fMRI time series in healthy controls and TLE patients: the lower the Hurst exponent, the higher the excitation/inhibition ratio. 5,57 (b) Top: statistical map of TLE-control difference in regional Hurst exponent, effect size as Cohen’s d . Significant regions, corrected for multiple comparisons using the false discovery rate procedure ( P FDR < 0.05), are surrounded by solid white outlines.…”

Multi-modal and multi-model interrogation of large-scale functional brain networks

“…Recent endeavours have shown the use of including transcriptomically derived differences in the excitability of local populations in the representation of static (Demirtaş et al, 2019) and dynamic (G. Deco et al, 2021) features of large-scale brain activity. In addition, results suggest that the variations in structure-function coupling across the cortical hierarchy are shaped by heterogeneities in local E-I balance and myelination levels (Fotiadis et al, 2022), or in cortico-subcortical interactions in terms of neuroreceptors density maps (Beliveau et al, 2017), temporal time-scales (Baldassano et al, 2017), gene expression (Hawrylycz et al, 2012), myelin content (in terms of T1/T2-weighted MRI signal) (Glasser & Van Essen, 2011) and functional connectivity (Kong et al, 2021) - offering further explanations as to why empirical FC exhibits characteristics that cannot be explained solely by SC. Therefore, we believe that it is essential for further modelling studies to make use of multilevel datasets (Arnatkeviciūtė et al, 2019; Royer et al, 2022) to constrain models with directed connectivity that define cortical hierarchies (G. Deco et al, 2021).…”