The sensitivity of the Mini-Mental State Examination to detect objective cognitive side-effects induced by electroconvulsive therapy, results from the Dutch ECT Consortium.

BACKGROUND: Monitoring cognitive side-effects following electroconvulsive therapy (ECT) is crucial for balancing side-effects and clinical effectiveness. Unfortunately, evidence-based guidelines on cognitive testing following ECT are lacking. A frequently used test in global ECT practice is the Mini Mental State Examination (MMSE). We examined the change of the MMSE and its performance in identifying a decline in predefined neuropsychological measures sensitive to ECT-induced cognitive changes: verbal recall and verbal fluency. METHODS: The mean MMSE scores pre- and one week post-ECT were compared using a Wilcoxon signed-rank test. The Reliable Change Index was calculated for all cognitive measures to indicate whether an individual's change score from pre- to post-ECT is considered statistically significant. The sensitivity and specificity of the MMSE were calculated. RESULTS: 426 patients with depression from five sites were included from the Dutch ECT Consortium. The mean MMSE increased significantly from 26.2 (SD=3.9) pre-ECT to 26.8 (SD=3.8) post-ECT (p=0.002). 36 patients (8.5%) showed a significant decline in MMSE score post-ECT. The sensitivity of the MMSE in identifying patients who experienced a significant decline in verbal recall or verbal fluency ranged from 3.6% to 11.1%. The specificity of the MMSE in identifying patients who did not experience a significant decline in verbal recall or verbal fluency ranged from 95.6% to 96.6%. CONCLUSIONS: Given the very low sensitivity of the MMSE, we propose reconsidering the prominence of the MMSE in ECT practice and cognitive monitoring guidelines, advocating for a more comprehensive approach to assess ECT-induced cognitive changes.

Characterization of gray matter volume changes from one week to 6 months after termination of electroconvulsive therapy in depressed patients.

BACKGROUND: Increased gray matter volume (GMV) following electroconvulsive therapy (ECT) has been well-documented, with limited studies reporting a subsequent decrease in GMV afterwards. OBJECTIVE: This study characterized the reversion pattern of GMV after ECT and its association with clinical depression outcome, using multi-site triple time-point data from the Global ECT-MRI Research Collaboration (GEMRIC). METHODS: 86 subjects from the GEMRIC database were included, and GMV in 84 regions-of-interest (ROI) was obtained from automatic segmentation of T1 MRI images at three timepoints: pre-ECT (T0), within one-week post-ECT (T1), and one to six months post-ECT (T2). RM-ANOVAs were used to assess longitudinal changes and LMM analyses explored associations between GMV changes and demographical and clinical characteristics. RESULTS: 63 of the 84 ROIs showed a significant increase-and-decrease pattern (RM-ANOVA, Bonferroni corrected p < 0.00059). Post hoc tests indicated a consistent pattern in each of these 63 ROIs: significant increase from T0 to T1inGMV, followed by significant decrease from T1 to T2 and no difference between T0 and T2, except for both amygdalae, right hippocampus and pars triangularis, which showed the same increase and decrease but GMV at T2 remained higher compared to T0. No consistent relationship was found between GMV change pattern and clinical status. CONCLUSION: The GEMRIC cohort confirmed a rapid increase of GMV after ECT followed by reversion of GMV one to six months thereafter. The lack of association between the GMV change pattern and depression outcome scores implies a transient neurobiological effect of ECT unrelated to clinical improvement.

Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project.

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

How sustainable is resilience? A mixed-methods study on the COVID-19-pandemic as a challenge to resilience resources of older adults who previously recovered from depression.

OBJECTIVES: Despite expanding knowledge about the internal and external resources that contribute to resilience among individuals who have experienced depression, the long-term accessibility and protectiveness of these resources across different stressors is unknown. We investigated whether and how the resilience resources of individuals who previously recovered from late-life depression remained protective during the COVID-19 pandemic. METHODS: We used a sequential explanatory mixed methods design. Quantitative data were derived from two psychiatric case-control cohorts and included twelve repeated measures during the COVID-19 pandemic (n = 465, aged ≥ 60). Qualitative data included two sequential interviews held in 2020 (n = 25) and 2021 (n = 19). We used thematic analysis to determine the protective resources after depression and during the COVID-19 pandemic and linear mixed models to examine the effect of these resources on change in depressive symptoms during the COVID-19 pandemic. RESULTS: While resources of 'Taking agency', 'Need for rest', 'Managing thought processes' and 'Learning from depression' remained accessible and protective during the pandemic, 'Social support' and 'Engaging in activities' did not. 'Negotiating with lockdown measures', 'changing social contact' and 'changing activities' were compensating strategies. Quantitative data confirmed the protectiveness of social contact, social cohesion, sense of mastery, physical activity, staying active and entertained and not following the media. CONCLUSION: Many of the resources that previously helped to recover from depression also helped to maintain good mental health during the COVID-19 pandemic. Where accessibility and protectiveness declined, compensatory strategies or new resources were used. Hence, the sustainability of resilience is enabled through adaptation and compensation processes.

Age-related subtypes of late life depression and mortality: A prospective clinical cohort study.

OBJECTIVES: Late Life Depression (LLD) is associated with increased mortality rates, but it remains unclear which depressed patients are at increased risk. This study examined the mortality risk of previously identified subgroups of depressed older patients based on age-related clinical features (the presence of physical and cognitive frailty). METHODS: A six-year follow-up of a clinical cohort study including 375 depressed older patients and 132 non-depressed persons (NESDO). Depressed patients were diagnosed with the Composite International Diagnostic Interview (CIDI) according to DSM-IV criteria and classified by latent profile analysis on depressive symptom severity, cognitive domains and physical frailty. We estimated the hazard rate of mortality for the four depressed subgroups compared to non-depressed persons by applying Cox-regression analyses. Models were adjusted for age, sex and education as confounders and for explanatory variables per pathway in separate models: somatic burden, lifestyle characteristics, vascular burden or inflammation markers. RESULTS: A total of 61/375 (16.3%) depressed patients and 8/132 (6.1%) non-depressed persons died during the 6-year follow-up. Two of the four subgroups (n = 186/375 (50%) of the depressed sample) had a higher hazard rate (HR) for mortality compared to non-depressed participants, that is, frail-depressed patients (HR = 5.25, [95%-CI: 2.13-13.0]) and pure mild depressed patients (HR = 3.32 [95%-CI: 1.46-7.58]) adjusted for confounders. Adding possible underlying pathways did not explain these associations. CONCLUSIONS: Age-related features (the presence of physical and cognitive frailty) contribute to the increased mortality risk in late-life depression. Future studies in depressed older patients should study the additional value of a clinical geriatric assessment and integrated treatment aimed to at reduce frailty and ameliorate their mortality risk.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Association Between Personality Traits and Functional Limitations in Older Adults with Affective Disorders: A Cross Sectional Study.

OBJECTIVES: Personality traits and affective disorders are both related to functional limitations. It is unknown whether personality traits have an additional effect on functioning in older adults with affective disorders. We studied the association between personality traits and functioning within this group. METHODS: We performed a cross-sectional study of 180 older patients referred to outpatient specialized geriatric mental health care centers with a depressive, anxiety and/or somatic symptom disorder according to DSM-criteria. We studied the association between the Big Five personality traits and functional limitations assessed with the WHO-DAS II, adjusting for potential confounders, including the severity of various affective disorders. RESULTS: The 180 patients (57.1% female, mean age 69.2 years) had an average WHO-DAS II score of 31.3 (SD 15.1). Lower scores on Conscientiousness were associated with more overall functional limitations (p = .001), particularly limitations in self-care (p = .001) and household activities (p = .001). Lower Extraversion scores were associated with more limitations in getting along with others (p = .001). CONCLUSIONS: Personality traits are related to functional limitations independent of the severity of affective disorders in older adults. CLINICAL IMPLICATIONS: Personality traits may be used as predictive factors for functioning in older adults with affective disorders.

The Cortisol Awakening Response as a Biomarker for Cognitive Side-Effects of Electroconvulsive Therapy.

OBJECTIVE: To test whether the cortisol awakening response (CAR) could be a biomarker for cognitive decline during electroconvulsive therapy (ECT). METHODS: We studied 50 older patients with depression who were treated with ECT from the MODECT cohort. We used linear regression analyses to examine the association between CAR and cognitive change, assessed by the change in Mini Mental State Examination scores between baseline and 1 week after ECT course. CAR was assessed by the area under the curve of cortisol levels, according to Pruessner's-formula. Associations were adjusted for putative confounders, based on previous literature and availability. RESULTS: We found no significant associations between the CAR and cognitive change during the ECT course in (un)adjusted models. CONCLUSION: Our results indicate that the CAR is not usable as a biomarker for ECT-induced cognitive decline during ECT course. Further research in cohorts with larger samples is needed.

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE: We investigated whether there are consistent changes in effective resting-state connectivity. METHODS: This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS: Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS: A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.

Pharmacological prevention of postictal agitation after electroconvulsive therapy-A systematic review and meta-analysis.

Background: Postictal agitation (PIA) after electroconvulsive therapy (ECT) is a serious clinical problem estimated to occur in 7-36% of patients and recur in 19-54% of patients. PIA has the potential to cause dangerous situations for the patient and staff members aside from the financial impact. To date, it is unclear which pharmacological interventions should be used in the management of PIA. This study aimed to systematically review the (preventative) pharmacological treatment options for PIA after ECT. Method: A systematic search was done in PubMed, EMBASE, PsycINFO, and Web of Science from inception until 10 November 2022. We included randomized trials with any pharmacological intervention or comparison and a predefined outcome measure on PIA. Studies that solely included patients with neurodegenerative disorders or stroke were excluded. Data quality was assessed with the RoB2 and GRADE. Meta-analysis was performed if possible. This study was registered on PROSPERO under CRD42021262323. Results: We screened 2,204 articles and included 14 studies. Dexmedetomidine was investigated in 10 studies. Alfentanil, lignocaine, esmolol, midazolam, propofol, ketamine, haloperidol, and diazepam were each studied in only one study. Meta-analysis revealed an OR of 0.45 (0.32-0.63), a moderate effect size, in favor of dexmedetomidine than placebo to prevent PIA with very low heterogeneity (I2 = 0%). The certainty of the evidence was moderate. The other interventions studied were all found to have low certainty of evidence. Conclusion: For clinical practice, we believe that our results indicate that dexmedetomidine should be considered for the prevention of PIA in patients that have previously experienced PIA.