Abstract

Resting-state brain activity, as observed via functional magnetic resonance imaging (fMRI), displays non-random fluctuations whose functional connectivity (FC) is commonly parsed into spatial patterns of positive and negative correlations (PCs and NCs). Mapping NC patterns for certain key seed regions has shown considerable promise in recent years as a tool for enhancing neuro-navigated targeting and clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) therapies in psychiatry. These successes bring to the fore several major outstanding questions about the neurophysiological origins of fMRI NCs. In this work, we studied candidate mechanisms for the emergence of fMRI NCs using connectome-based brain network modeling. Simulations of fMRI data under manipulation of inhibitory parameters WI and {lambda}, representing local and network-mediated inhibition respectively, were explored, focusing on the impact of inhibition levels on the emergence of NCs. Despite the considerable difference in time scales between GABAergic neuronal inhibition and fMRI FC, a clear relationship was observed, whereby the greater levels of overall inhibition led to significantly greater magnitude and spatial extent of NCs. We show that this effect is due to a leftward shift in the FC correlation distribution, leading to a reduction in the number of PCs and a concomitant increase in the number of NCs. Relatedly, we observed that those connections available for recruitment as NCs were precisely those with the weakest corresponding structural connectivity. Relative to nominally default values for the models used, greater levels of inhibition also improved, quantitatively and qualitatively, single-subject fits of simulated to empirical FC matrices. Our results provide new insights into how individual variability in anatomical connectivity strengths and neuronal inhibition levels may determine individualized expression of NCs in fMRI data. These, in turn, may offer new directions for optimization and personalization of rTMS therapies and other clinical applications of fMRI NC patterns. Author SummaryResting-state brain activity, as detected through functional magnetic resonance imaging (fMRI), demonstrates non-random fluctuations in its covariance structure, often characterized as functional connectivity (FC), which is further divided into spatial patterns of positive and negative correlations (NCs). Mapping patterns of NCs of specific key seed regions have demonstrated significant potential as a method for improving the precision of neuro-navigated targeting and enhancing clinical outcomes in the application of repetitive transcranial magnetic stimulation therapies within the field of psychiatry. In our study, we employed the reduced Wong-Wang neural mass model to investigate the physiological underpinnings of NCs observed in resting-state fMRI (rs-fMRI). Our simulated data partially captures the dynamics of empirical rs-fMRI data, revealing that increased inhibition levels correlate with a higher number of NCs. We also observed differential effects on model stability and NCs with varying levels of excitation and inhibition. These findings shed light on the complex interplay between neural dynamics and rs-fMRI connectivity patterns. Importantly, our work contributes to refining model parameters and offers insights for future validation with empirical clinical data. Understanding the factors influencing NCs in rs-fMRI FC has implications for optimizing therapeutic interventions and advancing our understanding of brain function.