Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy. Issue 16 (13th August 2021) - Wade, Benjamin S. C.; Hellemann, Gerhard; Espinoza, Randall T.; Woods, Roger P.; Joshi, Shantanu H.; Redlich, Ronny; Dannlowski, Udo; Jorgensen, Anders; Abbott, Christopher C.; Oltedal, Leif; Narr, Katherine L.
Abstract: Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS‐6 and HDRS‐17 changes ( p < .05). Pretreatment symptoms, body‐mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes. Abstract : Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. We developed machine learning models to predict symptom change along latent dimensions of depression following treatment with electroconvulsive therapy in a large, multisite cohort using a combination of neuroimaging, clinical, and demographic predictors. Recovery along homogenous latent symptom dimensions was predicted more accurately than change along standard, more heterogeneous total score measures.