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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
763

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Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a

Benjamin S C Wade1,2, Jing Sui3,4,5, Gerhard Hellemann6

  • 1Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA.

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Brain imaging after electroconvulsive therapy (ECT) can predict depression relapse. Structural neuroimaging measures, particularly ratios of connected brain regions, show promise for personalized relapse prevention strategies.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • High rates of depression relapse after treatment necessitate predictive biomarkers.
  • Electroconvulsive therapy (ECT) is effective but has a high relapse rate (>50%), making it a valuable model for studying relapse predictors.
  • Previous research linked ECT-induced brain morphometry changes to clinical response, but long-term relapse prediction remains underexplored.

Purpose of the Study:

  • To identify predictors of depression relapse within six months following electroconvulsive therapy (ECT).
  • To investigate the utility of structural neuroimaging measures, including intra- and inter-hemispheric ratios, for predicting ECT relapse.
  • To assess the effectiveness of machine learning models in predicting relapse risk using pre- and post-ECT imaging and clinical data.

Main Methods:

  • Structural magnetic resonance imaging (MRI) data were collected from 42 ECT-responsive patients at two sites (UCLA and UNM) before and after ECT.
  • Random forest models were employed to predict relapse using imaging features (asymmetries, cortical thickness ratios) and clinical variables.
  • Analyses included within-site and pooled-cohort predictions, evaluating feature importance for relapse risk.

Main Results:

  • Relapse was accurately predicted (balanced accuracies of 71-78%) using both within-site and pooled data.
  • Key predictors of relapse included cingulate isthmus asymmetry, pallidal asymmetry, and ratios of cortical thickness in specific brain regions.
  • Pooling data and using post-treatment measures yielded the best predictive performance, though age-disparate cohort models showed limited generalizability.

Conclusions:

  • Post-treatment structural neuroimaging measures, particularly ratios of interconnected brain regions, are significant predictors of depression relapse risk after ECT.
  • These findings suggest that structural imaging may aid in developing personalized preventative strategies for depression.
  • Further research is warranted to validate these imaging biomarkers for clinical application in relapse prevention.