ArticleBrain and behavior2026
Radiomic Signature of the Substantia Nigra on Neuromelanin-Sensitive MRI Distinguishes Bipolar II Disorder From Unipolar Depression.
Article in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeEarly differentiation between bipolar disorder type II (BD-II) and unipolar depression (UD) is critical yet challenging owing to overlapping depressive symptoms. This study aimed to develop and validate a radiomic signature based on substantia nigra (SN) neuromelanin-sensitive magnetic resonance imaging (NM-MRI) for distinguishing BD-II, UD, and healthy controls (HCs). MATERIALS AND
methodsThis secondary analysis enrolled 46 drug-naïve BD-II patients, 38 drug-naïve UD patients, and 42 HCs. A total of 2854 radiomic features were extracted from manually segmented SN regions. A multi-stage feature selection pipeline-including inter-observer reproducibility (ICC > 0.75), false discovery rate-corrected ANOVA, correlation analysis (|r| < 0.8), and least absolute shrinkage and selection operator (LASSO) regression-was used to identify a robust signature. Diagnostic performance of linear support vector machine (SVM) and multinomial logistic regression (LR) was evaluated via leave-one-out cross-validation (LOOCV).
resultsThe multi-stage selection yielded a parsimonious 7-feature radiomic signature from 2854 initial candidates. For three-class discrimination (BD-II vs. UD vs. HC), SVM outperformed multinomial LR with higher overall accuracy (69.0% vs. 62.7%) and superior discriminative power. SVM's macro-average AUC was 0.849 (95% CI: 0.797-0.884) and its micro-average AUC was 0.840 (95% CI: 0.797-0.884), compared to LR's macro-average AUC 0.812 (95% CI: 0.770-0.861) and micro-average AUC 0.815 (95% CI: 0.770-0.861). Notably, SVM showed marked advantage in the clinically critical BD-II versus UD classification: AUC 0.812 (95% CI: 0.720-0.905) versus LR's 0.756 (95% CI: 0.625-0.860), along with higher specificity (80.6% vs. 67.7%). The SVM model also effectively distinguished patients from HCs (UD vs. HC: AUC 0.869, 95% CI: 0.784-0.954; BD-II vs. HC: AUC 0.860, 95% CI: 0.781-0.939).
conclusionThis radiomic signature preliminarily demonstrates potential in distinguishing BD-II, UD, and HCs via radiomic analysis of SN NM-MRI. The SVM model, based on the compact radiomic signature, holds promise as an objective tool for addressing BD-II/UD diagnostic challenges, potentially supporting early intervention.
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