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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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