Naturalistic Study on the Effects of Electroconvulsive Therapy (ECT) on Depressive Symptoms.

OBJECTIVE: The effectiveness of ECT under naturalistic conditions has not been well-studied. The current study aimed to 1) characterize a naturalistic sample of ECT patients; and 2) examine the long-term outcomes of ECT on depressive symptoms (Beck Depression Inventory-II; BDI-II) and functional disability symptoms (WHO Disability Assessment Schedule 2.0) in this sample. METHODS: Participants were adults who received ECT for a major depressive episode at an ambulatory ECT clinic between September 2010 and November 2020. Clinical and cognitive assessments were completed at baseline (n = 100), mid-ECT (n = 94), 2-4 weeks post-ECT (n = 64), 6-months post-ECT (n = 34), and 12-months post-ECT (n = 19). RESULTS: At baseline, participants had severe levels of depressive symptoms (BDI-II: M = 41.0, SD = 9.4), and 62.9% screened positive for multiple psychiatric diagnoses on the MINI International Neuropsychiatric Interview. Depressive symptoms (F(4,49.1) = 49.92, P < 0.001) and disability symptoms (F(3,40.72) = 12.30, P < 0.001) improved significantly following ECT, and this was maintained at 12-months follow-up. Improvement in depressive symptoms trended towards significantly predicting reduction in disability symptoms from baseline to post-ECT, (F(1,56) = 3.67, P = 0.061). Although our clinical remission rate of 27% (BDI-II score ≤ 13 and ≥ 50% improvement) and overall response rate of 41.3% (≥50% improvement in BDI-II score) were lower than the rates reported in the extant RCT and community ECT literature, 36% of those treated with ECT were lost to follow-up and did not complete post-ECT rating scales. At baseline, remitters had significantly fewer psychiatric comorbidities, lower BDI-II scores, and lower disability symptoms than non-responders (P < 0.05). CONCLUSIONS: Participants were severely symptomatic and clinically complex. ECT was effective at reducing depressive symptoms and functional disability in this heterogeneous sample. Although a large amount of missing data may have distorted our calculated response/remission rates, it is also likely that clinical heterogeneity and severity contribute to lower-than-expected remission and response rates to ECT.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

OBJECTIVE: The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). METHODS: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. RESULTS: A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. CONCLUSIONS: These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. SIGNIFICANCE: The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.

A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy.

OBJECTIVE: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. METHODS: Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. RESULTS: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. CONCLUSIONS: These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia. SIGNIFICANCE: If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.