Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective antidepressant treatment for severe depression. Although recent structural magnetic resonance imaging (MRI) studies have consistently reported ECT-induced hippocampal volume increases, most studies did not find the association of the hippocampal volume changes with clinical improvement. To understand the underlying mechanisms of ECT action, we aimed to identify the longitudinal effects of ECT on hippocampal functional connectivity (FC) and their associations with clinical improvement. METHODS: Resting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. A priori hippocampal seed-based FC analysis and a data-driven multivoxel pattern analysis (MVPA) were conducted to investigate FC changes associated with clinical improvement. The statistical threshold was set at cluster-level false discovery rate-corrected p < 0.05. RESULTS: Depressive symptom improvement after ECT was positively associated with the change in the right hippocampus-ventromedial prefrontal cortex FC, and negatively associated with the right hippocampus-superior frontal gyrus FC. MVPA confirmed the results of hippocampal seed-based analyses and identified the following additional clusters associated with clinical improvement following ECT: the thalamus, the sensorimotor cortex, and the precuneus. CONCLUSIONS: ECT-induced change in the right frontotemporal connectivity and thalamocortical connectivity, and changes in the nodes of the default mode network were associated with clinical improvement. Modulation of these networks may explain the underlying mechanisms by which ECT exert its potent and rapid antidepressant effect.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS: Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS: Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS: Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy.

Electroconvulsive therapy (ECT) is the most effective antidepressant treatment. Biological predictors of clinical outcome to ECT are valuable. We aimed to examine multimodal magnetic resonance imaging (MRI) data that correlates to the efficacy of ECT. Structural and resting-state functional MRI data were acquired from 46 individuals (25 depressed individuals who received ECT, and 21 healthy controls). Whole-brain grey matter volume (GMV) and fractional amplitude of low frequency fluctuations (fALFF) were investigated to identify brain regions associated with post-ECT Hamilton Depression Rating Scale (HAM-D) total scores. GMV and fALFF values were compared with those in healthy controls using analysis of covariance (ANCOVA). Remission was defined by HAM-D ≤7. A multiple regression analysis revealed that pretreatment smaller GMV in the left thalamus was associated with worse response to ECT (i.e. higher post-ECT HAM-D). Pretreatment higher fALFF in the right anterior insula, and lower fALFF in the left thalamus and the cerebellum were associated with worse outcomes. The left thalamus was identified in both GMV and fALFF analyses. Nonremitters showed significantly smaller thalamic GMV compared to remitters and controls. We found that pretreatment thalamic volume and resting-state activity were associated with the efficacy of ECT. Our results highlight the importance of the thalamus as a possible biological predictor and its role in the underlying mechanisms of ECT action.

Dysconnectivity of the Agency Network in Schizophrenia: A Functional Magnetic Resonance Imaging Study.

Background: Self-disturbances in schizophrenia have recently been explained by an abnormality in the sense of agency (SoA). The cerebral structures of SoA in healthy people are considered to mainly include the insula and inferior parietal lobule. In contrast, the functional lesion of aberrant SoA in schizophrenia is not yet fully understood. Considering the recent explanation of establishing SoA from the standpoint of associative learning, the "agency network" may include not only the insula and inferior parietal lobule but also the striatum. We hypothesized that aberrant SoA in schizophrenia is based on a deficit in the "agency network." Methods: Functional magnetic resonance imaging data were acquired while patients with schizophrenia (n = 15) and matched controls (n = 15) performed our adaptation method of agency attribution task on a trial-by-trial basis to assess participants' explicit experience of the temporal causal relationship between an action and an external event with temporal biases. Analysis of functional connectivity was done using the right supramarginal gyrus and the right middle frontal gyrus as seed regions. Results: In healthy controls, analyses revealed increased activation of the right inferior parietal lobule (mainly the supramarginal gyrus), right insula, and right middle frontal gyrus as an activation of the agency condition. We defined activated Brodmann areas shown in the agency condition of healthy controls as the seed region for connectivity analysis. The connectivity analysis revealed lower connectivity between the head of the left caudate nucleus and right supramarginal gyrus in the patients compared to healthy controls. Conclusions: This dysconnectivity of the agency network in schizophrenia may lead to self-disturbance through deficits in associative learning of SoA. These findings may explain why pathological function of the striatum in schizophrenia leads to self-disturbance.