Open AccessWavelet‐based regularity analysis reveals recurrent spatiotemporal behavior in resting‐state fMRI
Author(s) -
Smith Robert X.,
Jann Kay,
Ances Beau,
Wang Danny J.J.
Publication year - 2015
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22865
Subject(s) - wavelet , resting state fmri , pattern recognition (psychology) , artificial intelligence , wavelet transform , default mode network , entropy (arrow of time) , sample entropy , computer science , continuous wavelet transform , neuroimaging , functional connectivity , psychology , discrete wavelet transform , neuroscience , physics , quantum mechanics
One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large‐scale networks. In resting state fMRI (rs‐fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs‐fMRI signal. Here, we develop a wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs‐fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs‐fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet‐based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs‐fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs‐fMRI as well as other biological signals. Hum Brain Mapp 36:3603–3620, 2015 . © 2015 Wiley Periodicals, Inc.