The cortical Morphometric Inverse Divergence in major depressive disorder is associated with neurotransmitter and genetic profiles

Major depressive disorder (MDD) is a prevalent psychiatric disorder with significant global impact. Neuroimaging studies have shown cortical thickness and connectivity alterations in MDD, but the underlying mechanisms are still unclear. The Morphometric Inverse Divergence (MIND) network, based on vertex-level morphometric features, offers a new approach to explore cortical morphometric characteristics in MDD. Individual MIND networks were constructed from T1-weighted MRI data for 75 MDD patients and 46 healthy controls (HCs). Group comparisons identified regions with significant morphometric changes. Partial least squares (PLS) regression was used to identify MDD-associated genes, which were then analyzed for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis and protein-protein interaction (PPI) networks. Spatial correlation analyses were also conducted between MIND and neurotransmitter density maps. MDD patients exhibited decreased MIND in regions related to sensation and cognition, with potential epicenters in the left fusiform, postcentral, lingual, and right occipital gyri. MIND alterations in MDD were spatially correlated with genes involved in glutamatergic synapse, calcium signaling pathway, and neuroactive ligand-receptor interaction, and significantly correlated with 5-HT, glutamate, cannabinoid, and opioid receptor/transporter densities. Our study reveals abnormal morphometric similarity patterns in MDD, which are spatially coupled with specific neurotransmitter profiles and associated with a distinct transcriptomic signature. • MIND reveals cortical structural disorganization in MDD. • MIND deficits correlate with serotonin, glutamate, and cannabinoid receptors. • Transcriptional signatures link MIND to synaptic and metabolic pathways. • Hub genes UBA52 and PPARGC1A are associated with MIND changes. • MIND may serve as a neuroimaging endophenotype for MDD.