301. Multimodal MRI signatures predict ketamine antidepressant response in treatment-resistant depression: a machine-learning analysis of independent clinical trials
P-C Wu, T-P Su, C-T Li, Y-M Bai, M-H Chen
International Journal of Neuropsychopharmacology September 9, 2026 DOI: 10.1093/ijnp/pyag040.042 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Secondary analysis of two independent randomized controlled trials using machine learning Peer reviewed |
|---|---|
| Sample size | 49 |
| Population | Adults with treatment-resistant depression enrolled in two independent randomized controlled trials (training set n=28; independent testing set n=21) |
| Intervention | Ketamine |
| Topics | Depression Esketamine Ketamine |
| Key findings | A machine-learning classifier using pre-treatment multimodal MRI features predicted ketamine antidepressant response with 81.0% accuracy and 91.7% specificity in an independent testing set. The authors propose that this biosignature, involving thalamocortical, visual, and salience network connectivity, could help identify non-responders and support precision stratification in treatment-resistant depression. |
Abstract
Abstract Background While ketamine produces rapid antidepressant effects in treatment-resistant depression (TRD), response heterogeneity necessitates robust biomarkers to optimize clinical decision-making. Aims & Objectives To validate a machine-learning (ML) framework for predicting ketamine antidepressant response from pre-treatment multimodal neuroimaging features across independent datasets.
Method: Data from two independent randomized controlled trials were analyzed (training: Trial 1, n=28; independent testing: Trial 2, n=21). Feature extraction combined resting-state functional connectivity, graph-theoretical metrics, and structural morphometry. A K-nearest neighbors classifier was trained using consensus nested cross-validation with ReliefF and minimum redundancy maximum relevance (mRMR) feature selection, with Gaussian noise–based data augmentation to enhance robustness.
Results: The model generalized well to the independent testing set, achieving 81.0% accuracy and 91.7% specificity. Principal component analysis of the selected features highlighted connectivity patterns involving thalamocortical, visual, and salience networks. Discussion & Conclusions This study identifies a multimodal MRI-based ML biosignature of ketamine antidepressant response with good specificity and external generalizability. Accurate non-responder identification supports precision stratification in TRD.