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Objective: Longitudinal neuroimaging studies have demonstrated thatadolescence is the crucial developmental epoch of continued brain growth andchange. A large number of researchers dedicate to uncovering the mechanismsabout brain maturity during adolescence. Motivated by both achievement in graphsignal processing and recent evidence that some brain areas act as hubsconnecting functionally specialized systems, we proposed an approach to detectthese regions from spectral analysis perspective. In particular, as human brainundergoes substantial development throughout adolescence, we addressed thechallenge by evaluating the functional network difference among age groups fromfunctional magnetic resonance imaging (fMRI) observations. Methods: We treatedthese observations as graph signals defined on the parcellated functional brainregions and applied graph Laplacian learning based Fourier Transform (GLFT) totransform the original graph signals into frequency domain. Eigen-analysis wasconducted afterwards to study the behavior of the corresponding brain regions,which enables the characterization of brain maturation. Result: We firstevaluated our method on the synthetic data and further applied the method toresting and task state fMRI imaging data from Philadelphia NeurodevelopmentalCohort (PNC) dataset, comprised of normally developing adolescents from 8 to22. The model provided a highest accuracy of 95.69% in distinguishing differentadolescence stages. Conclusion: We detected 13 hubs from resting state fMRI and16 hubs from task state fMRI that are highly related to brain maturationprocess. Significance: The proposed GLFT method is powerful in extracting thebrain connectivity patterns and identifying hub regions with a high predictionpower

Brain Maturation Study during Adolescence Using Graph Laplacian Learning Based Fourier Transform