# Unraveling Hierarchical Brain Dysfunction in Major Depressive Disorder: A Multimodal Imaging and Transcriptomic Approach

**Authors:** Chen Xiayan, Dai Haowei, Niu Lijing, Chen Zini, Xiaoyue Li, Zeng Yuanyuan, Zhu Qingzi, Lin Kangguang, Zhang Ruibin

PMC · DOI: 10.1002/hbm.70277 · Human Brain Mapping · 2025-07-01

## TL;DR

This study uses brain imaging and genetic data to show how brain hierarchy is disrupted in depression, linking these changes to neurotransmitters and gene activity.

## Contribution

The study integrates multimodal imaging and transcriptomic data to reveal hierarchical brain dysfunction and its molecular correlates in MDD.

## Key findings

- MDD showed increased SDI in somatosensory cortex and decreased SDI in prefrontal, parietal, and orbitofrontal cortices.
- SDI alterations correlated with neurotransmitters like 5-HT1a, 5-HT2a, and GABAa, and were enriched in kinase binding genes.
- An SVM model achieved 76.7% accuracy in classifying MDD using SDI features.

## Abstract

Major depressive disorder (MDD) is characterized by deficits in sensory processing and higher‐order executive functions, reflecting dysfunction in the hierarchical organization of the brain. However, current methods for investigating brain hierarchy in MDD have not fully integrated multimodal data, and the underlying biological mechanisms remain poorly understood. We acquired diffusion tensor imaging and functional magnetic resonance imaging (fMRI) data from 100 participants with MDD and 77 healthy controls (HCs). The structural‐decoupling index (SDI) was employed to quantify the hierarchical organization in MDD and HC. We identified intergroup differences in the hierarchical brain organization and explored the molecular mechanism related to significantly different brain regions by investigating genetic factors and their relationship with neurotransmitter receptors/transporters. Finally, 10‐fold cross‐validation was used to develop a support vector machine (SVM) classification model. Dysfunctional hierarchical organization in MDD was characterized by increased SDI in the bilateral somatosensory cortex, while decreased SDI was observed in the bilateral visual, prefrontal, and parietal cortices, as well as the left orbitofrontal cortex and temporal pole. Moreover, SDI alterations showed negative correlations with neurotransmitters, including 5‐HT1a, 5‐HT2a, D1, GABAa, SERT, and mGluR5. The SDI alteration‐related genes were enriched in kinase binding. After 10‐fold cross‐validation, the SVM exhibited a mean accuracy of 0.767 (area under the curve = 0.972). Our research employed multimodal data to investigate hierarchical brain dysfunction in MDD and established its associations with neurotransmitters and transcriptome profiles. This approach may improve the understanding of the neural, biological, and molecular genetic underpinning of SDI in MDD.

Dysfunctional hierarchy in major depressive disorder (MDD) features decreased SDI in regions linked to high‐order cognitive functions, like the prefrontal, parietal, and orbitofrontal cortices. Conversely, areas related to low‐level sensory‐motor functions, the somatosensory cortex, showed increased SDI in MDD. And these abnormalities are correlated with neurotransmitter levels and gene profiles.

## Linked entities

- **Chemicals:** D1 (PubChem CID 31250)
- **Diseases:** Major depressive disorder (MONDO:0002009), MDD (MONDO:0012048)

## Full-text entities

- **Genes:** HTR2A (5-hydroxytryptamine receptor 2A) [NCBI Gene 3356] {aka 5-HT2A, HTR2}, GRM5 (glutamate metabotropic receptor 5) [NCBI Gene 2915] {aka GPRC1E, MGLUR5, PPP1R86, mGlu5}, SLC6A4 (solute carrier family 6 member 4) [NCBI Gene 6532] {aka 5-HTT, 5-HTTLPR, 5HTT, HTT, OCD1, SERT}, HTR1A (5-hydroxytryptamine receptor 1A) [NCBI Gene 3350] {aka 5-HT-1A, 5-HT1A, 5HT1a, ADRB2RL1, ADRBRL1, G-21}
- **Diseases:** Brain Dysfunction (MESH:D001927), MDD (MESH:D003865)

## Full text

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## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12210147/full.md

## References

69 references — full list in the complete paper: https://tomesphere.com/paper/PMC12210147/full.md

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Source: https://tomesphere.com/paper/PMC12210147