Fig. 1: Pipeline for statistical analysis for serum NfL levels and GSP-based features.
Brain imaging data (structural (dMRI) and functional (resting state fMRI)), age, and serum NfL levels were recorded for every subject in the cohorts of 20 healthy subjects and 36 former athletes. For each subject, the dMRI and fMRI were pre-processed to extract the structural connectome (in the form of a 66 × 66 adjacency matrix for 66 cortical brain areas) and BOLD signal (in the form of a time series of length 308 seconds at each brain area considered), respectively. Subject-specific graph filters derived from the eigen-decomposition of the structural connectome were constructed and used to extract different graph frequency components of the BOLD time series for all subjects. The energies (EHI and ELO, evaluated by \({\ell }_{2}\) norm) of the different graph frequency components at different brain areas were investigated (energies of 2 graph frequency components per area for 66 brain areas per subject, i.e., 132 GSP-based predictors) for association with serum NfL levels using univariate feature selection (UFS), and their prediction power evaluated using a rigorous statistical analysis of PLS regression models.
