Low-frequency BOLD fluctuations of major resting-state networks in early Parkinson’s disease (PD) were studied and compared with matched normal controls. Empirical Mode Decomposition (EMD) was used to decompose the natural occurring frequency bands of major brain resting-state networks. The novelty of our approach lies in the data-adaptive decomposition of fMRI data using EMD, and identification of resting-state networks based on energy and period (inverse frequency) characteristics of intrinsic mode functions. For most networks studied that showed a large effect size, the frequency content of the associated network time series was found to be significantly reduced in PD.
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