Keywords: fMRI Analysis, Alzheimer's Disease, Resting state fMRI, Empirical Mode Decomposition, Time frequency analysis
Motivation: The time frequency analysis of brain networks in resting state fMRI has largely been based on linear decompositions.
Goal(s): The primary goal of this study is to analyze the temporal dynamics of these networks using an adaptive nonlinear approach devoid of any apriori assumptions or basis functions.
Approach: Empirical Mode Decomposition (EMD), a data driven technique is utilized to investigate the energy period relationship differences in brain networks across cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD).
Results: The AD group operates at a higher frequency with reduced energy in typical resting state networks compared to both CN and MCI.
Impact: The time varying energy and period profiles obtained from EMD could serve as a potential neuromarker for disease progression from MCI to AD, resulting in timely and early clinical intervention.
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