Fractal-driven distortion of default-mode functional networks in resting state fMRI

Wonsang You, Special Lab Non-Invasive Brain Imaging

Leibniz Institute for Neurobiology

June 20, 2012 5:00 PM

302- 309

 

Abstract:

 

Understanding the complex dynamics of default-mode brain network is one of essential topics in neuroscience. One of simplest ways to explore a brain network is to measure either functional connectivity or information flow from non-invasive imaging signals such as EEG and fMRI. One of noticeable features in resting state fMRI time series is that they tend to exhibit long memory or fractal properties such as 1/f power spectrum over low frequencies. Based on such a phenomenon, neuroscientists have modeled resting state fMRI time series as an increment process of fractional Brownian motion. However, the relationship of Hurst exponent with functional connectivity or information flow has not been clear. In this talk, a fractal-based model of resting state fMRI time series is introduced; we propose the resting state hemodynamic response function (rs-HRF) whose properties can be summarized by a fractal exponent. This model allows us to theoretically understand the impacts of fractal dynamics on connectivity and information flow. The model also suggests that an rs-fMRI time series can be well modeled as a fractionally integrated process than a fractional Gaussian noise. We simulated neuronal population activities based on the stochastic neural field model, and then generated their corresponding BOLD signals through long memory filters. We measured the dissimilarity of wavelet correlations and information transfer between neuronal activities and BOLD signals. Our results suggest that the difference of fractal exponents between brain regions cause significant discrepancy of network properties in a complex brain network between neuronal activities and BOLD signals. We also propose the nonfractal connectivity, as a novel concept of resting state functional connectivity, which is defined as the correlation of nonfractal components of a multivariate time series. The nonfractal connectivity may provide us a better information on correlation structure of spontaneous neuronal activities from resting state neuroimaging signals. In conclusion, the fractal-based model of resting state fMRI time series may give us insight into the physical influences of fractal behavior on complex functional networks of the brain.

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Last update: June 20, 2012