Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates

TL;DR

This study enhances whole-brain models by incorporating frequency dynamics of oscillations, enabling prediction of regional vulnerability and hazardousness through simulated lesions and divergence mapping.

Contribution

It introduces a novel frequency-based modeling approach that captures dynamic oscillation features and assesses regional vulnerability via in silico perturbations.

Findings

Model reproduces key features of brain dynamics.

Lesion simulations reveal regional vulnerability patterns.

Divergence mapping quantifies hazardousness of brain regions.

Abstract

Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in co-activation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space