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

Abstract

Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation 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.

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

“…Briefly, preprocessing of rs-fMRI data included removing nuisance covariates (global signal, mean white matter and cerebrospinal fluid signals), linear and quadratic trends, followed by band-pass filtering (0.02-0.12 Hz). Further details on data preprocessing can be found in (Kaboodvand et al 2019). For the ADHD cohort, we used resting-state fMRI data from subjects diagnosed with ADHD (n = 40, age range: 21-50 yr) provided by the University of California LA Consortium for Neuropsychiatric Phenomics study (Poldrack et al 2016).…”

“…We used a whole-brain, adaptive frequency-based weakly coupled oscillators model (Kaboodvand et al 2019) to assess putative differences of brain dynamics in ADHD compared to a control group. In attempt to find robust parameter sets we used the Monte Carlo simulation technique for parameter optimization of our dynamical model.…”

“…Before we go on to compare amplitude differences in local dynamics, we first wanted to assess whether the simulated data and the empirical BOLD data behave similarly in the frequency domain. To recapitulate, each brain region was modeled as an oscillator with 2 state variables (Z and w) and we extracted the regional BOLD-like activation of each ROI by resampling and low-pass filtering of the real part of the first state variable (Re{Z}) (Kaboodvand et al 2019). Moreover, the first state variable (Z) is a complex value that represents the amplitude dynamics, with the real part serving as the BOLD-like activity and imaginary part as the internal hidden state of the oscillator.…”

“…Second, based on the results obtained in the first step, we aimed to provide insights on how our approach can be used to investigate the whole-brain network effects from locally applied in silico perturbation. Indeed, dynamical systems level analysis of ADHD could potentially have profound implications in finding robust objective targets for interventions and controlling individual responses to brain stimulation via designing goal-directed therapeutic interventions (Deco and Kringelbach 2014;Kaboodvand et al 2020;Kaboodvand et al 2019;Durstewitz et al 2020). To this end, we used an in silico excitation simulation protocol to shift the dynamics of ADHD resting-state networks to that of healthy controls.…”

“…To this end, we used an in silico excitation simulation protocol to shift the dynamics of ADHD resting-state networks to that of healthy controls. This simulation was performed by mimicking a transcranial magnetic stimulation (TMS) effect on different brain regions and assessed the possibility to shift the dynamic regime of the targeted brain region from a stochastic fluctuations' domain into an oscillatory domain by changing the local bifurcation parameter of that region (Kaboodvand et al 2019).…”