Fractal-driven distortion of resting state functional networks in fMRI: a simulation study

TL;DR

This study uses simulations to demonstrate how fractal properties of cerebral hemodynamics can distort the measurement of functional brain networks in resting state fMRI, especially affecting nodes with low centrality.

Contribution

It introduces a fractal-based model of the resting state hemodynamic response function and shows how fractal behavior impacts network property estimations in fMRI data.

Findings

Fractal behavior causes significant distortion of network properties in simulated rs-fMRI.

Nodes with high centrality are resilient to fractal-induced distortions.

Distortions are more pronounced at nodes with low centrality, especially in low-frequency ranges.

Abstract

Fractals are self-similar and scale-invariant patterns found ubiquitously in nature. A lot of evidences implying fractal properties such as 1/f power spectrums have been also observed in resting state fMRI time series. To explain the fractal behavior in rs-fMRI, we have proposed the fractal-based model of resting state hemodynamic response function (rs-HRF) whose properties can be summarized by a fractal exponent. Here we show, through a simulation studies, that the fractal behavior of cerebral hemodynamics may cause significant distortion of network properties between neuronal activities and BOLD signals. We simulated neuronal population activities based on the stochastic neural field model from the Macaque brain network, and then obtained their corresponding BOLD signals by convolving them with the rs-HRF filter. The precision of centrality estimated in each node was deteriorated overall in three networks based on transfer entropy, mutual information, and Pearson correlation; particularly the distortion of transfer entropy was more sensitive to the standard deviation of fractal exponents. A node with high centrality was resilient to desynchronized fractal dynamics over all frequencies while a node with small centrality exhibited huge distortion of both wavelet correlation and centrality over low frequencies. This theoretical expectation indicates that the difference of fractal exponents between brain regions leads to discrepancy of statistical network properties, especially at nodes with small centrality, between neuronal activities and BOLD signals, and that the traditional definitions of resting state functional connectivity may not effectively reflect the dynamics of spontaneous neuronal activities.