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.
Highlights
Depression is a leading cause of disability worldwide (James et al, 2018)
We identified three symptom dimensions from pretreatment 17-item Hamilton Depression Rating Scale (HDRS-17) items: somatic symptoms, core mood and anhedonia, and insomnia
When patients from Site 1 were excluded, leave-one-site-out cross validation (LOOCV) models were generally improved with R2 scores of 6.2%, 24%, 52%, À10%, and 3% for core mood and anhedonia (CMA), somatic disturbances (SoD), insomnia, Six-item Hamilton Depression Rating Scale (HDRS-6), and Hamilton Depression Rating Scale (HDRS)-17 symptoms, respectively; dropping pretreatment symptoms lowered all R2 scores below zero except for the insomnia dimension (R2 = 1.9%)
Summary
Depression is a leading cause of disability worldwide (James et al, 2018). Despite its prevalence, effective treatment remains challenging. A recent report by Schmitgen et al identified pre- and post-ECT associations between the cortical thickness of the left rostral anterior cingulate, left medial orbitofrontal cortex, and gyrification of the right middle frontal gyrus, and treatment-related reductions in depressive symptoms; pretreatment symptom severity was a key predictor of symptom change (Schmitgen et al, 2020). We build on this work and aim to relate patient demographic, clinical, and patterns of regional ECT-induced volumetric brain changes to changes in latent symptom dimensions using data-driven methods. We hypothesized that prediction of symptom changes would be more accurate along latent symptom dimensions rather than the HDRS-17 total score Characterizing these unique and shared predictors and mechanisms of treatment response will inform development of targeted interventions such as neurostimulation techniques capable of targeting specific neural circuits and directly targeting important dimensions of depression. All participants provided written informed consent as approved by their local ethical committees or Institutional Review Boards (IRBs), and centralized analysis of pooled data was approved by the Regional Ethic Committee South-East in Norway (2018/769)
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