Wavelet-based regularity analysis reveals recurrent spatiotemporal behavior in resting-state fMRI.

ABSTRACT: 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 transf

SUBMITTER: Smith RX 

PROVIDER: S-EPMC4635674 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature