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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
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Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

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Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes.

Jiao Li1, Zhiliang Long2, Wei Sheng1

  • 1Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.

Biological Psychiatry
|August 12, 2023
PubMed
Summary

Major depressive disorder (MDD) biotypes show distinct brain patterns linked to gene expression. These findings reveal how gene networks influence brain structure in different MDD subtypes.

Keywords:
BiotypeEpicenterMajor depressionNeuromorphic deviationTranscriptomic similarity

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Area of Science:

  • Neuroimaging
  • Genomics
  • Psychiatry

Background:

  • Major depressive disorder (MDD) exhibits significant population heterogeneity, necessitating the identification of distinct biotypes using imaging-derived phenotypes.
  • Neuromorphic heterogeneity in MDD and its relationship with the correlated gene expression (CGE) connectome remain poorly understood.
  • The influence of the CGE connectome on neuromorphic anomalies within MDD biotypes has not been previously investigated.

Purpose of the Study:

  • To investigate the relationship between cortical thickness deviations in MDD biotypes and the CGE connectome.
  • To identify distinct MDD biotypes based on neuroimaging and gene expression patterns.
  • To explore the potential clinical implications of identified MDD biotypes and their associated brain circuits.

Main Methods:

  • Cortical thickness was estimated from 3D T1-weighted MRI scans in two independent cohorts (N=425 and N=217).
  • Transcriptional activity data were sourced from the Allen Human Brain Atlas.
  • A density peak-based clustering algorithm was employed to define MDD biotypes based on regional deviations from healthy controls.

Main Results:

  • Two replicated MDD biotypes were identified across both datasets, characterized by distinct patterns of cortical thickness deviations.
  • Biotype 1 showed widespread cortical thinning, while Biotype 2 exhibited predominantly cortical thickening.
  • Deviations in transcriptionally connected neighbors predicted regional deviations for both biotypes, with distinct CGE-informed epicenters located in cognitive control and social perception circuits, respectively.
  • The likelihood patterns of these epicenters correlated with depression- and anxiety-response maps, suggesting potential links to clinical efficacy.

Conclusions:

  • The study successfully linked the CGE connectome with neuromorphic deviations to identify distinct epicenters within MDD biotypes.
  • These findings provide novel insights into how microscale gene expression patterns inform the neurobiological underpinnings of MDD heterogeneity.
  • The identified biotypes and their associated circuits offer potential targets for understanding and treating distinct clinical presentations of MDD.