Spectral Normative Modeling (SNM) for High-Resolution Brain Abnormality Inference

Brisbane Convention & Exhibition Centre 

Normative models (NMs) in neuroscience aim to characterize interindividual variability in brain phenotypes, establishing reference ranges-or brain charts-against which individual brains can be compared. NMs help identify individual-level abnormalities as potential biomarkers for neurological and psychiatric disorders [1,2,3]. However, conventional NMs face computational challenges in mapping charts at high spatial resolutions. State-of-the-art techniques [4,5] rely on exhaustive model fitting for each voxel or vertex, which is computationally burdensome and limits scalability for large neuroimaging datasets.

The high dimensionality of neuroimaging data complicates the development of high-resolution NMs. Identifying low-dimensional representations of cortical features can help overcome this challenge. Recent works suggest that brain eigenmodes may provide effective low-dimensional basis functions for reconstructing phenotypic variation on the cortical surface [6,7]. Here, we introduce spectral normative modeling (SNM), a novel approach that leverages spatial reconstructions via brain eigenmodes to generate normative brain charts at varying spatial scales and resolutions. By computing normative ranges of eigenmodes and accounting for cross-mode dependencies, SNM offers an efficient solution for high-resolution charting of brain phenotypes.

Alzheimer's disease (AD) is a neurodegenerative disorder marked by the accumulation of amyloid-beta plaques and tau tangles, contributing to neuronal loss and cortical atrophy, leading to progressive structural changes in brain regions critical for memory and cognition, differentiating AD brains from the normative trajectories of healthy aging [8]. We applied SNM to identify these structural deviations at the individual level, demonstrating its utility in characterizing neurological disorders and identifying potential prognostic biomarkers.