Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Abstract

Background Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. Methods Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. Results Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82–0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70–0.73 AUC). Conclusions These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Alterations of dynamic and static brain functional activities and integration in stroke patients

“…Meanwhile, cognitive deficit negatively impacts the rehabilitation therapy and recovery of stroke patients. Magnetic resonance imaging (MRI) is becoming the most popular neuroimaging technique owing to its high-resolution imaging, non-invasive, as well as its value in identifying imaging biomarkers of neurological and mental diseases ( Feng et al, 2015 ; Tozzi et al, 2020 ; Lee et al, 2022 ; Bruin et al, 2023 ). Based on blood-oxygen-level dependent functional MRI (BOLD-fMRI), the signal strength of spontaneous brain activity within the frequency range of 0.01–0.01 Hz was measured as an amplitude of low-frequency fluctuation (ALFF), which has been used to explore intrinsic neural activity changes in stroke patients ( Zhu et al, 2015 ; Hu et al, 2021 ; Quan et al, 2022 ).…”