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Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter

Tongpeng Chu1, Xiaopeng Si2, Haizhu Xie3

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.

Biological Psychiatry
|September 1, 2024
PubMed
Summary

Major depressive disorder (MDD) shows altered brain connectivity. These changes in structural-functional connectivity (SC-FC) coupling are linked to neurotransmitters and gene expression, offering new therapeutic targets.

Keywords:
Biological mechanismsDefault Mode NetworkFrontoparietal NetworkMajor Depressive DisorderPredictionRegional

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • Major depressive disorder (MDD) is associated with widespread abnormalities in structural-functional connectivity (SC-FC) coupling.
  • Previous research has not fully explored the regional variability and hierarchical distribution of these SC-FC coupling abnormalities.
  • The underlying biological mechanisms of regional SC-FC coupling patterns in MDD remain poorly understood.

Purpose of the Study:

  • To investigate intergroup differences in regional SC-FC coupling between patients with MDD and healthy controls.
  • To evaluate the diagnostic and predictive potential of SC-FC coupling as biomarkers for MDD.
  • To explore the relationship between regional SC-FC coupling alterations, neurotransmitter distributions, and gene expression in MDD.

Main Methods:

  • Recruited 182 MDD patients and 157 healthy controls for SC-FC coupling analysis.
  • Employed machine learning models (XGBoost, SVM, Random Forest) for biomarker assessment.
  • Examined correlations between SC-FC coupling, neurotransmitters, and gene expression patterns.

Main Results:

  • MDD patients exhibited increased SC-FC coupling in the default mode network and decreased coupling in the frontoparietal network.
  • Machine learning models demonstrated strong predictive performance for MDD diagnosis (AUCs ranging from 0.832 to 0.853).
  • Alterations in SC-FC coupling correlated with neurotransmitter distributions and gene expression related to neuronal function and immune signaling.

Conclusions:

  • This study provides novel insights into the neurobiological underpinnings of MDD.
  • Identified SC-FC coupling patterns as potential biomarkers for MDD.
  • Findings suggest new avenues for developing targeted therapeutic interventions for MDD.