Network controllability mediates the relationship between rigid structure and flexible dynamics

Abstract

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

“…Unraveling the mechanisms of different clinical interventions, and designing suitable stimulation protocols are trending topics in computational and clinical neuroscience, which draw upon methods from perturbation analysis (Deco et al 2019;Mana et al 2022;Sanz Perl et al 2022;Vohryzek et al 2022) and control theory (Gu et al 2017;Gu et al 2022;Kamiya et al 2023;Srivastava et al 2020;Yang et al 2021) Notably, optimizing the stimulation locations(s) is usually performed using exhaustive search, which is computationally expensive (Deco et al 2019;Mana et al 2022;Vohryzek et al 2022). Recent work suggests that the nodes' controllability 27 at rest can help to identify the optimal stimulation locations (Yang et al 2021).…”

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

“…A slice-timing correction procedure then followed, correcting for any potential temporal misalignment that may have occurred during the sequential acquisition of the fMRI data; acquisitions with a framewise displacement above 0.9 mm or global BOLD signal changes above 5 standard deviations were flagged as potential outlier scans. The structural scans were segmented into gray matter, white matter, and cerebrospinal fluid tissue classes using SPM (version 12), and both structural and functional scans were subsequently normalized into MNI space (180 × 216 × 180 mm 3 bounding box; functional scans set to 2 mm isotropic; structural scans set to 1 mm isotropic). Lastly, the functional images were smoothed using an 8 mm full-width half-maximum Gaussian kernel, in order to increase the BOLD signal-to-noise ratio.…”

“…To elucidate how the anatomical wiring of the brain sculpts its functional connectivity in support of flexible cognition, recent studies have increasingly focused on the extent to which structure and function are coupled across brain regions. [1][2][3] A brain region's 'structure-function coupling' (SFC) refers to the manner in which its functional and structural connectivity statistically depend upon one another. Here, a structural connection is the white matter projections linking two brain regions, as measured by diffusion magnetic resonance imaging (MRI), whereas a functional connection is the statistical similarity between hemodynamic responses arising from two brain regions, as measured by functional MRI (fMRI).…”

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

“…Network control models have also been recently extended and applied to investigate controllability in drug-resistant focal epilepsy in children (Chari et al, 2022 ), functional dynamics mediated by controllability of the structural connectivity (Gu et al, 2022 ), prediction of aphasia recovery using controllability measures (Wilmskoetter et al, 2022 ), association of controllability measures with normative negative affect variability (McGowan et al, 2021 ), prediction of positive psychosis spectrum symptoms with controllability measures (Parkes et al, 2021 ), role of controllability measures in language tasks (Medaglia et al, 2021 ), altered energy landscape and stability of working memory representations in schizophrenia (Braun et al, 2021 ), structural control energy in psychosis vulnerability (Zoller et al, 2021 ), and dynamics of controllability measures during seizure progression (Scheid et al, 2021 ).…”