Abstract
Accumulating evidence suggests that the brain state has time-varying transitions, potentially implying that the brain functional networks (BFNs) have spatial variability and power-spectra dynamics over time. Recently, ICA-based BFNs tracking models, i.e., SliTICA, real-time ICA, Quasi-GICA, etc., have been gained wide attention. However, how to distinguish the neurobiological BFNs from those representing noise and artifacts is not trivial in tracking process due to the random order of components generated by ICA. In this study, combining with our previous BFNs tracking model, i.e., Quasi-GICA, we proposed a novel spatial-spectra dynamics-based ranking method for sorting time-varying BFNs, called weighted BFNs ranking, which was based on the dynamical properties in both spatial and spectral domains of each BFN. This proposed weighted BFNs ranking model mainly consisted of two steps: first, the dynamic spatial reproducibility (DSR) and dynamic fraction of amplitude low-frequency fluctuations (DFALFF) for each BFN were calculated; then a weighted coefficients-based ranking strategy for merging the DSR and DFALFF of each BFN was proposed, to make the meaningful dynamic BFNs rank ahead. We showed the effective results by this ranking model on the simulated and real data, suggesting that the meaningful dynamical BFNs with both strong properties of DSR and DFALFF across the tracking process were ranked at the top.
Highlights
Blood oxygen level dependent (BOLD) functional magnetic resonance imaging is powerful modality to discover functional connectivity (FC) among discrete brain regions
The results of the dynamic low-frequency fluctuations for Subject 2 and 3 showed the similar phenomenon, which were not depicted for saving space
At beginning, taking the results of dynamic low-frequency fluctuations (Fig.1) and dynamic spatial reproducibility (Fig.2) of the estimated brain functional networks (BFNs) together, we could find that the BFNs were not always with consistently dynamic properties of spatial reproducibility and low-frequency fluctuations
Summary
Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is powerful modality to discover functional connectivity (FC) among discrete brain regions. Besides the well-known temporal variability of FC [2-4], the brain activation regions show considerable spatial variability and dynamical power spectrum over time [5-6], which has potentials for diagnosing brain diseases [7]. Independent component analysis (ICA), which could extract the brain functional networks (BFNs) from fMRI data at the single subject level or group level under rest or task conditions [1-3, 8-10], has turned out to be a promising tool to decode the brain activity. To our knowledge, most of BFNs ranking methods such as percent variance ranking [2], power spectrum ranking [13], support vector machine (SVM) based ranking [14], multiple ICA estimations based ranking [15-16] and MMC ranking [17], are designed for sorting static BFNs, rather than dynamic BFNs. in this study, a novel spatial-spectra dynamics-based ranking method was proposed to sort the time-varying BFNs, taking advantage of the dynamic features in both spatial and spectral domains of each BFN
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