Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis.

BACKGROUND: Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction. METHODS: With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions. RESULTS: 15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74-89 %), and a specificity of 86 % (95 % CI 81-90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable. LIMITATIONS: Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy. CONCLUSIONS: Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD. PROSPERO REGISTRATION NUMBER: CRD42021268169.

'Rapid speed of response to ECT in bipolar depression: A chart review.

OBJECTIVE: To validate a faster speed of response to electroconvulsive therapy (ECT) for bipolar depression (BPD) compared to major depressive disorder (MDD) METHOD: Retrospective chart review on an ECT cohort in an academic hospital setting. Speed of response was defined by the number of ECT treatments needed for response or remission. RESULTS: Sixty-four depressed patients were included, of whom 53 (MDD: 40, BPD: 13) could be analyzed. The bipolar group responded faster with a mean difference of 3.3 fewer ECT treatments to meet response criteria (MDD 10.4 vs. BPD 7.1, p = 0.054). When using mixed effects regression models for the response/remitter group (n = 35), a faster response for the bipolar group (AIC 252.83 vs 258.55, χ2 = 11.72, p = 0.008) was shown. Other factors, such as psychotic features or comorbidity, did not influence the speed of response. CONCLUSION: This chart review of an ECT cohort in an naturalistic academic hospital setting shows an evident and clinically relevant faster speed of response in bipolar depression.

Conscious perception and the modulatory role of dopamine: no effect of the dopamine D2 agonist cabergoline on visual masking, the attentional blink, and probabilistic discrimination.

RATIONALE: Conscious perception is thought to depend on global amplification of sensory input. In recent years, striatal dopamine has been proposed to be involved in gating information and conscious access, due to its modulatory influence on thalamocortical connectivity. OBJECTIVES: Since much of the evidence that implicates striatal dopamine is correlational, we conducted a double-blind crossover pharmacological study in which we administered cabergoline-a dopamine D2 agonist-and placebo to 30 healthy participants. Under both conditions, we subjected participants to several well-established experimental conscious-perception paradigms, such as backward masking and the attentional blink task. RESULTS: We found no evidence in support of an effect of cabergoline on conscious perception: key behavioral and event-related potential (ERP) findings associated with each of these tasks were unaffected by cabergoline. CONCLUSIONS: Our results cast doubt on a causal role for dopamine in visual perception. It remains an open possibility that dopamine has causal effects in other tasks, perhaps where perceptual uncertainty is more prominent.

[Reaction on 'Persistant underuse of ECT for persistant depressive disorder?'] / Reactie op 'Persisterend ondergebruik van ECT bij persisterende depressieve stoornis?'.

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[Electroconvulsion therapy for persistent depression in the Netherlands; very low application rate]. / Elektroconvulsietherapie bij persisterende depressie in Nederland; zeer lage toepassingsgraad.

BACKGROUND: Of all depressive disorders, 20% has a persistent course. For persistent depressive patients, electroconvulsive therapy (ect) is recommended for this patient population, since it is the most potent treatment for depression. The Dutch depression guideline advises the use of ect for persistent depressive disorder at approximately 12 months after inadequate efficacy of psychotherapy and/or pharmacological treatment.
AIM: To quantify the use of electroconvulsive therapy in persistent depressive patients in the Netherlands.
METHOD: Quantitative research using the Dutch registration system (diagnosis-treatment-combination; dbc) information system (dis) of the Dutch Healthcare Authority (nza).
RESULTS: Of the patients within the dbc system (in 2014) with the main diagnosis of unipolar depression, 23,597 (26%) were registered for more than two years and could be classified as having a persistent depressive episode. Of these latter patients, only 278 (1.2%) received ect.
CONCLUSION: In the Netherlands, only 1.2% of patients with a persistent depression received ect, whereas this treatment could have been considered for 26% of this group. The low application rate might be caused by professionals' inadequate knowledge about ect and the premature use of the handicap model.