ArticleBiological psychiatry global open science2024
Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.
Article in Biological psychiatry global open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Investigation of habenula volume in mood disorders: A meta-analytic study.Psychological medicine · 2026Pooled it
- The disappointment centre of the brain gets exciting: a systematic review of habenula dysfunction in depression.Translational psychiatry · 2024Pooled it
- Article
- Negative correlation between habenular volume and duration of gambling disorder: Modulation by symptom severity and personality traits.Journal of behavioral addictions · 2026Article
- Gray and White matter microstructural alterations in major depressive disorder: a multi-center diffusion imaging study.Translational psychiatry · 2026Article
- Article
- Precise antibody delivery to the brain via nanobubble-actuated focused ultrasound alleviates depression.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Electroconvulsive therapy-specific volume changes in nuclei of the amygdala and their relationship to long-term anxiety improvement in depression.Molecular psychiatry · 2025Observational
- Association between the subclinical level of problematic internet use and habenula volume: a look at mediation effect of neuroticism.General psychiatry · 2025Article
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression ( Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.
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