Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework.

ABSTRACT: Molecular imaging analyses using positron emission tomography (PET) data often rely on macro-anatomical regions of interest (ROI), which may not align with chemo-architectural boundaries and obscure functional distinctions. While methods such as independent component analysis (ICA) have been useful to address this limitation, the fully data-driven nature can make it challenging to compare results across studies. Here, we introduce the NeuroMark PET approach, utilizing spatially constrained independent component analysis to define overlapping regions that may reflect the brain's molecular architecture. We first generate an ICA template for the PET radiotracer florbetapir (FBP), targeting amyloid-β (Aβ) accumulation in the brain, using blind ICA on large datasets to identify replicable indep

SUBMITTER: Eierud C 

PROVIDER: S-EPMC11870563 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature