Examining brain maturation during adolescence using graph Laplacian learning based Fourier transform

Longitudinal neuroimaging studies have demonstrated that adolescence is a crucial developmental period of continued brain growth and change. Motivated by both achievements in graph signal processing and recent evidence that some brain areas act as hubs connecting functionally specialized systems, we propose an approach to detect these regions from a spectral analysis perspective. In particular, as the human brain undergoes substantial development throughout adolescence, we evaluate functional network difference among age groups from functional magnetic resonance imaging (fMRI) measurements.

We treated these measurements as graph signals defined on the parcellated functional brain regions and proposed a graph Laplacian learning based Fourier transform (GLFT) to transform the original graph signals into the frequency domain. Eigen-analysis was conducted afterwards to study the behaviors of the corresponding brain regions, which enabled the characterization of brain maturation.

We first evaluated our method on the synthetic data and then applied it to resting state and task fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22 years of age. The method provided an accuracy of 94.9% in distinguishing different adolescent stages and we detected 13 hubs from resting state fMRI and 16 hubs from task fMRI related to brain maturation.

The proposed GLFT demonstrated its superiority over conventional graph Fourier transform and alternative graph Fourier transform with high predictive power.

The method provides a powerful approach for extracting brain connectivity patterns and identifying hub regions.