A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression

Abstract

Electroconvulsive therapy (ECT) is effective even in treatment-resistant patients with major depression. Currently, there are no markers available that can assist in identifying those patients most likely to benefit from ECT. In the present study, we investigated whether resting-state network connectivity can predict treatment outcome for individual patients. We included forty-five patients with severe and treatment-resistant unipolar depression and collected functional magnetic resonance imaging scans before the course of ECT. We extracted resting-state networks and used multivariate pattern analysis to discover networks that predicted recovery from depression. Cross-validation revealed two resting-state networks with significant classification accuracy after correction for multiple comparisons. A network centered in the dorsomedial prefrontal cortex (including the dorsolateral prefrontal cortex, orbitofrontal cortex and posterior cingulate cortex) showed a sensitivity of 84% and specificity of 85%. Another network centered in the anterior cingulate cortex (including the dorsolateral prefrontal cortex, sensorimotor cortex, parahippocampal gyrus and midbrain) showed a sensitivity of 80% and a specificity of 75%. These preliminary results demonstrate that resting-state networks may predict treatment outcome for individual patients and suggest that resting-state networks have the potential to serve as prognostic neuroimaging biomarkers to guide personalized treatment decisions.

Modulation of Intrinsic Brain Activity by Electroconvulsive Therapy in Major Depression

“…Dorsal ACC and mediodorsal thalamus have been associated with ECT-related seizure activity in PET/SPECT research (9; 42; 47; 48), and more recent studies further highlight the relevance of dorsal ACC to ECT. Our own work has indicated that ECT restores white-matter microstructure in tracts adjacent to dorsal ACC (11), and other RSFC studies have reported that baseline RSFC in RSNs overlapping dorsal ACC may help predict clinical response to ECT (13), and that ECT alters functional relationships between an RSN overlapping dorsal ACC and other RSNs. Our current results extend these findings by highlighting the importance of dorsal ACC in relation to the thalamus.…”

“…Some of the brain structures affected by ECT, including the ACC (11–13) and hippocampus (14; 15), are also frequently implicated in the pathophysiology of major depression by other studies. However, not all patients respond to ECT; for example, just over half (55-65%) experience remission when using right-unilateral ECT with optimal parameters (16; 17).…”

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach

“…We hypothesize that highly accurate predictive models will support important clinical decisions such as selection of treatment options, preventive strategies, and prognosis orientations. For instance, several studies recently utilized machine learning techniques to predict treatment response using functional brain scans and neurocognitive data (Hahn et al, 2015; Johnston et al, 2015; van Waarde et al, 2015). Moreover, a recent study reported a prediction model able to estimate the risk of renal failure in patients with BD treated with lithium (Castro et al, 2015).…”

Desynchronization and Plasticity of Striato-frontal Connectivity in Major Depressive Disorder

“…is less focal and yet most effective at reducing symptoms of depression, eliciting generalized seizures associated with more widespread neural changes (Perrin et al 2012;Abbott et al 2013;Tendolkar et al 2013;Zhang et al 2013;Dukart et al 2014;Lyden et al 2014;van Waarde et al 2015;Joshi et al 2015). However, the exact nature of network dysfunction responsible for depressive disorders, and how this dysfunction is best targeted by current neurostimulation therapies, remains poorly understood.…”