ArticleProceedings. IEEE International Symposium on Biomedical Imaging2017
DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.Molecular psychiatry · 2025Pooled it
- Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.Translational psychiatry · 2021Pooled it
- Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion.Psychological medicine · 2022Article
- Depressive Symptom Dimensions in Treatment-Resistant Major Depression and Their Modulation With Electroconvulsive Therapy.The journal of ECT · 2020Article
- Mapping abnormal subcortical neurodevelopment in a cohort of Thai children with HIV.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.