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Sep 2026

Cognitive heterogeneity in major depressive disorder: Neuroimaging, transcriptomic, and neurotransmitter profiles.

BACKGROUND Major depressive disorder (MDD) is often accompanied by cognitive impairment; however, the cognitive heterogeneity of MDD and its neurobiological context remain poorly understood. METHODS A total of 198 participants with MDD and 275 HCs underwent multi-domain cognitive assessments and multi-modal MRI acquisition. A semi-supervised approach was applied to identify cognitive dimensions of MDD, and individual-level structural-enriched functional networks (SFNs) were constructed. Network-based statistics were applied to characterize network-level associations between structural-functional coupling deviation and cognitive dimensions. Furthermore, the correlations between the spatial pattern of SFN deviation and meta-analytic neurocognitive terms, cortical transcriptome, and neurotransmitter density distribution maps were detected. RESULTS In the MDD group, 103 individuals were assigned to Cluster 1, presenting widespread cognitive impairments, whereas 95 were assigned to Cluster 2, presenting cognitive preservations. An abnormally enhanced SFN subnetwork (PPerm = 0.042) was identified, which showed significant spatial correlation with meta-analytic neurocognitive maps (r = 0.181, P < 0.001). The SFN deviation pattern was spatially associated with 2458 genes enriched primarily in neuronal and synaptic function, and these genes also showed enrichment for pathways annotated to neurodegenerative diseases (PFDR < 0.05). In addition, SFN deviation was spatially associated with three neurotransmitter maps, including N-methyl-D-aspartate receptor, cannabinoid type-1 receptor, and norepinephrine transporter (PFDR = 0.028). CONCLUSIONS The study provides a data-driven characterization of cognitive heterogeneity in MDD, identifying two cognitive dimensions spanning from relative preservation to widespread impairment. By integrating structural-functional coupling deviations with transcriptomic and neurotransmitter maps, these findings provide preliminary biological context for interpreting cognitive heterogeneity in MDD.

Yu-Shun Yan, Jia'ao Yu, Liang Zuo et al. · 0 citations
Review Aug 2026

Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder.

Electroconvulsive therapy (ECT) is an effective treatment for major depressive disorder (MDD), yet its underlying mechanisms remain unclear. This study investigated the antidepressant effects of ECT through a multimodal neuroimage meta-analysis combined with functional, genetic, and neurotransmitter assessments. Resting-state functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) data were analyzed using seed-based d mapping with permutation of subject images (SDM-PSI) to identify changes in spontaneous brain activity and gray matter volume (GMV) before and after ECT. Further analysis of regions with altered activation and GMV was conducted using Neurosynth, postmortem gene expression data, and receptor/transporter distribution maps to explore molecular underpinnings. The whole-brain multimodal meta-analysis included 291 patients from resting-state fMRI studies and 302 patients from VBM studies. The results showed convergent increases in spontaneous activity and GMV in the left angular gyrus (AG) following ECT. Functional annotation linked the left AG to memory, attention, and perceptual processing. Gene expression analysis identified TFAP2B and OTX2 as the most highly expressed genes in this region. Notably, ECT-associated changes in spontaneous brain activity and GMV were positively correlated with 5-HT1a receptor and dopamine transporter distribution. These findings suggest the left AG is a key region mediating ECT's effects. Neurotransmitter analysis further indicates that ECT may exert its antidepressant action by modulating neurotransmitter systems, offering insights into the neural and molecular basis of its therapeutic efficacy in MDD.

Ruifeng Shi, Yi-Kai Dou, Ying He et al. · 0 citations

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