Functional brain activity constrained by structural connectivity reveals cohort-specific features for serum neurofilament light chain

Abstract

Background Neuro-axonal brain damage releases neurofilament light chain (NfL) proteins, which enter the blood. Serum NfL has recently emerged as a promising biomarker for grading axonal damage, monitoring treatment responses, and prognosis in neurological diseases. Importantly, serum NfL levels also increase with aging, and the interpretation of serum NfL levels in neurological diseases is incomplete due to lack of a reliable model for age-related variation in serum NfL levels in healthy subjects. Methods Graph signal processing (GSP) provides analytical tools, such as graph Fourier transform (GFT), to produce measures from functional dynamics of brain activity constrained by white matter anatomy. Here, we leveraged a set of features using GFT that quantified the coupling between blood oxygen level dependent signals and structural connectome to investigate their associations with serum NfL levels collected from healthy subjects and former athletes with history of concussions. Results Here we show that GSP feature from isthmus cingulate in the right hemisphere (r-iCg) is strongly linked with serum NfL in healthy controls. In contrast, GSP features from temporal lobe and lingual areas in the left hemisphere and posterior cingulate in the right hemisphere are the most associated with serum NfL in former athletes. Additional analysis reveals that the GSP feature from r-iCg is associated with behavioral and structural measures that predict aggressive behavior in healthy controls and former athletes. Conclusions Our results suggest that GSP-derived brain features may be included in models of baseline variance when evaluating NfL as a biomarker of neurological diseases and studying their impact on personality traits.

Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities

“…Numerous methods have been developed to incorporate dMRI and fMRI data together, which utilizes the complementary information provided from both modalities. However, most approaches process the dMRI information separately and sometimes use it to constrain the fMRI information (Crimi et al, 2021 ; Deco et al, 2017 ; Sihag et al, 2022 ). Other fusion approaches use seeds or regions of interests (ROIs) from one or both modalities to associate SC and functional connectivity (FC) (Bonnelle et al, 2012 ; Tarun et al, 2020 ).…”

“…Numerous methods have been developed to incorporate dMRI and fMRI data together, which utilizes the complementary information provided from both modalities. However, most approaches process the dMRI information separately and sometimes use it to constrain the fMRI information (Crimi et al, 2021;Deco et al, 2017;Sihag et al, 2022).…”

Joint cmICA: auto-linking of structural and functional connectivity

“…Numerous methods have been developed to incorporate dMRI and fMRI data together, which utilizes the complementary information provided from both modalities. However, most approaches process the dMRI information separately and sometimes use it to constrain the fMRI information (Crimi, Dodero, Sambataro, Murino, & Sona, 2021; Deco, Kringelbach, Jirsa, & Ritter, 2017; Sihag et al, 2022). Other fusion approaches use seeds or regions of interests (ROIs) from one or both modalities to associate SC and FC (Bonnelle et al, 2012; Tarun, Behjat, Bolton, Abramian, & Van De Ville, 2020).…”