How Do Neurotransmitter Pathways Contribute to Neuroimaging Phenotypes?

Abstract

Neuroimaging could accurately reflect human behaviour in health and disease, but the mechanism by which image-derived phenotypes correspond to neurotransmitter systems remains uncertain. Prior studies have explored spatial correlations between neuroimaging phenotypes and positron emission tomography radiotracers. However, the influence of neurotransmitters goes beyond the receptors/transporters, influencing a wider array of intracellular components as pivotal parts of neurotransmitter pathways. Here, we used unsupervised learning to understand how the brain maps of healthy function (i.e., magnetoencephalography frequency-specific power) and abnormal structure (i.e., disorder-specific cortical thickness) are closely anchored to underlying neurotransmitter pathways assessed by gene expression data. To do this, we used large-scale datasets of the Human Connectome Project (HCP), Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and Allen Human Brain Atlas (AHBA). We considered spatial and random gene null models to mitigate false positives. We replicate our analyses using different gene stability thresholds. This analytic approach paves the way for personalised medicine and advanced biomarkers.