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.Comment: The 3rd Biennial Conference on Resting State Brain Connectivit