Keywords: Analysis/Processing, fMRI (resting state), Functional Connectivity, Graph Kernel, Brain connectivity, Signal Modeling, Signal Representations
Motivation: Various rs-fMRI studies highlight the need for accurate delineation of different brain functional networks (FNs) to carry out precise therapeutic interventions in the individuals.
Goal(s): To develop a novel zero-shot non-linear graph kernel-assisted approach for enhanced functional brain parcellation at individual and group levels.
Approach: Utilization of Wavelet, Fourier, and Hilbert transformations for feature extraction from BOLD signals, and a propagation attribute graph kernel to capture non-linear temporo-spatial connectivity, using k-means clustering.
Results: The kernel-based approach outperforms static FC matrix parcellations, achieving higher accuracy in network delineation in both individual and group level, as evidenced by Dice and Jaccard scores.
Impact: The study introduced graph kernel-based method for functional brain parcellation, which improved the accuracy of functional network delineation in rs-fMRI data, surpassing traditional static functional connectivity approaches in both individual and group level, as validated by Dice and Jaccard metrics.
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