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Seminars & Lectures

* TITLE

Fractal-driven distortion of connectivity and information flow in resting state fMRI time series

* SPEAKERS

Name	Affiliation	E-mail
Wonsang You	Leibniz Institute for Neurobiology	you(at)lin-magdeburg.de

* HOST(Applicant)

Name	Affiliation	E-mail
Pan-Jun Kim	APCTP	pjkim(at)apctp.org

* DATE / TIME

2012-06-18, 10:30

* 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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