Abstract #4135
Wavelet Based Multiscale Entropy Analysis of Resting-State FMRI
Robert X. Smith 1 , Kay Jann 2 , Beau Ances 3 , and Danny J.J. Wang 4,5
1
Neurology, UCLA, Los Angeles, CA, United
States,
2
UCLA,
CA, United States,
3
Neurology,
Washington University School of Medicine, St. Louis, MO,
United States,
4
Neurology,
UCLA, CA, United States,
5
Radiology,
UCLA, CA, United States
Our aim is the quantification of the complex neural
fluctuations seen in resting state fMRI to provide a
measure of mental health and cognitive function. We
present here a wavelet based multiresolution entropy
calculation that employs noise estimation measures to
determine the complexity of the underlying neural
behavior. In the presence of nonstationary data, wavelet
analysis holds a significant advantage over Fourier
analysis. We develop a pseudo-complexity measure using
the stationary wavelet transform (SWT) of the original
rs-fMRI time series to investigate the intrinsic
irregularity of the energy density fluctuations at
multiple temporal scales. We apply our measure to a
cohort of 26 cognitively normal (clinical dementia
rating scale (CDR) = 0) and 26 mild cognitively impaired
(CDR = 0.5) individuals from the Healthy Aging and
Senile Dementia program project. We report a reduced
entropy seen in various resting state networks including
default mode regions for CDR=0.5 individuals.
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