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Related Concept Videos

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Classification accuracy of structural and functional connectomes across different depressive phenotypes.

Hon Wah Yeung1, Aleks Stolicyn1, Xueyi Shen1

  • 1Department of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.

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Summary

Functional brain connectomes better classify major depressive disorder (MDD) phenotypes than structural ones. Stratifying MDD by childhood trauma exposure improved classification accuracy, highlighting sensorimotor and visual subnetworks as potential biomarkers.

Keywords:
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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • Major Depressive Disorder (MDD) diagnosis and neuroimaging associations are inconsistent due to phenotyping variability and disorder heterogeneity.
  • Machine learning applications for MDD diagnosis have faced challenges, potentially linked to inconsistent neuroimaging findings across different patient subgroups.
  • Childhood trauma (CT) is a significant risk factor for MDD, impacting brain development and potentially influencing neurobiological differences.

Purpose of the Study:

  • To evaluate the classification accuracy of structural and functional brain connectomes for various major depressive disorder (MDD) phenotypes.
  • To investigate if stratifying MDD based on childhood trauma (CT) exposure improves diagnostic classification accuracy using neuroimaging data.
  • To identify key predictive neuroimaging features associated with different MDD phenotypes, including those with and without a history of CT.

Main Methods:

  • Logistic ridge regression was applied to classify control and MDD participants (N=14,507) using six different MDD definitions.
  • Brain connectomic data, including six structural and two functional network weightings, were analyzed.
  • Classification accuracy was assessed across different MDD phenotypes, with and without stratification by childhood trauma questionnaire (CTQ) scores.

Main Results:

  • Functional connectomes demonstrated superior performance in classifying MDD phenotypes compared to structural connectomes.
  • The highest classification accuracy (64.8%) was achieved using functional connectomes in currently depressed individuals with a history of CT.
  • Neurobiological differences between MDD patients with and without childhood adversity were indicated by predictive feature overlap analysis.
  • Sensorimotor and visual subnetworks were identified as significant predictors across various MDD phenotypes.

Conclusions:

  • Functional brain connectomes are more effective than structural connectomes for classifying major depressive disorder (MDD) phenotypes.
  • Stratifying MDD patients by childhood trauma exposure can enhance diagnostic classification accuracy.
  • Alterations in sensorimotor and visual brain subnetworks may represent potential neuroimaging biomarkers for MDD.