EigenBrain maps cortical molecular architecture from functional, geometric, and structural eigenmodes

Abstract

Neurotransmitter receptor and transporter distributions shape neural communication across the cortex and underpin mechanistic interpretations of brain function. Magnetic resonance imaging (MRI)-derived eigenmodes, low-dimensional spatial patterns that capture the principal axes of cortical organization, are increasingly used to reconstruct brain maps and offer a potential route to approximating molecular topographies in large cohorts where direct measurements are unavailable. Yet whether the type of MRI data used to derive these eigenmodes determines which receptor and transporter distributions can be approximated, and which molecular systems are accessible through this approach at all, remains unknown. Using a spatial reconstruction framework applied to 23 molecular systems spanning neurotransmitter, metabolic, and pathological markers, we show that eight receptor and transporter maps, spanning serotonin, dopamine, histamine, cholinergic, and opioid markers, can be selectively approximated from functional eigenmodes but not structural or geometric ones. This selectivity also reveals that functional brain organization encodes a specific subset of neuromodulatory topographies that structural and geometric organization does not. To illustrate translational potential in clinical cohorts where molecular imaging is unavailable, we applied individual-level eigenmode decompositions of MRI data projected onto the eight validated molecular systems and detected cross-sectional differences in autism and longitudinal changes in schizophrenia. Together, these findings reveal a difference in how geometric, structural, and functional brain organization relate to molecular architecture, offering a method for approximating validated molecular profiles in population neuroimaging cohorts that lack direct molecular imaging.