Electroconvulsive therapy and cognitive performance from the Global ECT MRI Research Collaboration.

The Global ECT MRI Research Collaboration (GEMRIC) has collected clinical and neuroimaging data of patients treated with electroconvulsive therapy (ECT) from around the world. Results to date have focused on neuroimaging correlates of antidepressant response. GEMRIC sites have also collected longitudinal cognitive data. Here, we summarize the existing GEMRIC cognitive data and provide recommendations for prospective data collection for future ECT-imaging investigations. We describe the criteria for selection of cognitive measures for mega-analyses: Trail Making Test Parts A (TMT-A) and B (TMT-B), verbal fluency category (VFC), verbal fluency letter (VFL), and percent retention from verbal learning and memory tests. We performed longitudinal data analysis focused on the pre-/post-ECT assessments with healthy comparison (HC) subjects at similar timepoints and assessed associations between demographic and ECT parameters with cognitive changes. The study found an interaction between electrode placement and treatment number for VFC (F(1,107) = 4.14, p = 0.04). Higher treatment was associated with decreased VFC performance with right unilateral electrode placement. Percent retention showed a main effect for group, with post-hoc analysis indicating decreased cognitive performance among the HC group. However, there were no significant effects of group or group interactions observed for TMT-A, TMT-B, or VFL. We assessed the current GEMRIC cognitive data and acknowledge the limitations associated with this data set including the limited number of neuropsychological domains assessed. Aside from the VFC and treatment number relationship, we did not observe ECT-mediated neurocognitive effects in this investigation. We provide prospective cognitive recommendations for future ECT-imaging investigations focused on strong psychometrics and minimal burden to subjects.

rTMS as a Next Step in Antidepressant Nonresponders: A Randomized Comparison With Current Antidepressant Treatment Approaches.

OBJECTIVE: Although repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, little is known about the comparative effectiveness of rTMS and other treatment options, such as antidepressants. In this multicenter randomized controlled trial, rTMS was compared with the next pharmacological treatment step in patients with treatment-resistant depression. METHODS: Patients with unipolar nonpsychotic depression (N=89) with an inadequate response to at least two treatment trials were randomized to treatment with rTMS or to a switch of antidepressants, both in combination with psychotherapy. Treatment duration was 8 weeks and consisted of either 25 high-frequency rTMS sessions to the left dorsolateral prefrontal cortex or a switch of antidepressant medication following the Dutch treatment algorithm. The primary outcome was change in depression severity based on the Hamilton Depression Rating Scale (HAM-D). Secondary outcomes were response and remission rates as well as change in symptom dimensions (anhedonia, anxiety, sleep, rumination, and cognitive reactivity). Finally, expectations regarding treatment were assessed. RESULTS: rTMS resulted in a significantly larger reduction in depressive symptoms than medication, which was also reflected in higher response (37.5% vs. 14.6%) and remission (27.1% vs. 4.9%) rates. A larger decrease in symptoms of anxiety and anhedonia was observed after rTMS compared with a switch in antidepressants, and no difference from the medication group was seen for symptom reductions in rumination, cognitive reactivity, and sleep disorders. Expectations regarding treatment correlated with changes in HAM-D scores. CONCLUSIONS: In a sample of patients with moderately treatment-resistant depression, rTMS was more effective in reducing depressive symptoms than a switch of antidepressant medication. In addition, the findings suggest that the choice of treatment may be guided by specific symptom dimensions.

The gut-microbiome in adult Attention-deficit/hyperactivity disorder - A Meta-analysis.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition that persists into adulthood in the majority of individuals. While the gut-microbiome seems to be relevant for ADHD, the few publications on gut-microbial alterations in ADHD are inconsistent, in the investigated phenotypes, sequencing method/region, preprocessing, statistical approaches, and findings. To identify gut-microbiome alterations in adult ADHD, robust across studies and statistical approaches, we harmonized bioinformatic pipelines and analyses of raw 16S rRNA sequencing data from four adult ADHD case-control studies (NADHD=312, NNoADHD=305). We investigated diversity and differential abundance of selected genera (logistic regression and ANOVA-like Differential Expression tool), corrected for age and sex, and meta-analyzed the study results. Converging results were investigated for association with hyperactive/impulsive and inattentive symptoms across all participants. Beta diversity was associated with ADHD diagnosis but showed significant heterogeneity between cohorts, despite harmonized analyses. Several genera were robustly associated with adult ADHD; e.g., Ruminococcus_torques_group (LogOdds=0.17, pfdr=4.42 × 10-2), which was more abundant in adults with ADHD, and Eubacterium_xylanophilum_group (LogOdds= -0.12, pfdr=6.9 × 10-3), which was less abundant in ADHD. Ruminococcus_torques_group was further associated with hyperactivity/impulsivity symptoms and Eisenbergiella with inattention and hyperactivity/impulsivity (pfdr<0.05). The literature points towards a role of these genera in inflammatory processes. Irreproducible results in the field of gut-microbiota research, due to between study heterogeneity and small sample sizes, stress the need for meta-analytic approaches and large sample sizes. While we robustly identified genera associated with adult ADHD, that might overall be considered beneficial or risk-conferring, functional studies are needed to shed light on these properties.

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.

Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task.

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.

Autistic and non-autistic individuals show the same amygdala activity during emotional face processing.

BACKGROUND: Autistic and non-autistic individuals often differ in how they perceive and show emotions, especially in their ability and inclination to infer other people's feelings from subtle cues like facial expressions. Prominent theories of autism have suggested that these differences stem from alterations in amygdala functioning and that amygdala hypoactivation causes problems with emotion recognition. Thus far, however, empirical investigations of this hypothesis have yielded mixed results and largely relied on relatively small samples. METHODS: In a sample of 72 autistic and 79 non-autistic participants, we conducted a study in which we used the Hariri paradigm to test whether amygdala activation during emotional face processing is altered in autism spectrum disorder, and whether common mental disorders like depression, ADHD or anxiety disorders influence any potential alterations in activation patterns. RESULTS: We found no evidence for differences in amygdala activation, neither when comparing autistic and non-autistic participants, nor when taking into account mental disorders or the overall level of functional impairment. LIMITATIONS: Because we used one basic emotion processing task in a Dutch sample, results might not generalise to other tasks and other populations. CONCLUSIONS: Our results challenge the view that autistic and non-autistic processing of emotional faces in the amygdala is vastly different and call for a more nuanced view of differences between non-autistic and autistic emotion processing.

Structural Changes in Depressed Patients Directly After Treatment With Electroconvulsive Therapy and 3 Months Later.

OBJECTIVES: It is assumed that neuroplasticity plays a central role in the effect of electroconvulsive therapy (ECT) on patients with major depressive disorder. We carried out an explorative study to map out the extent in which gray matter volume changes could be found directly after ECT treatment and after follow-up. METHODS: Initially, 12 patients with treatment-resistant depression were recruited from the Radboud Medical Center. Magnetic resonance imaging scans were conducted at the following 3 time points: before ECT (n = 12), after ECT (n = 10), and at 3-month follow-up (n = 8). Subcortical volume, hippocampal subfield volume, and cortical thickness were analyzed using FreeSurfer. RESULTS: The extensive, generalized changes in gray matter volume are largely transient after treatment with ECT, with the noted exceptions being a sustained increase in volume of the right amygdala and a part of the left cornu ammonis. Post hoc testing revealed no significant correlation with clinical response. DISCUSSION: Our results suggest that the neuroplastic effects of ECT may not be mediators of clinical response and could be transient epiphenomena.

Pre-treatment predictors of cognitive side-effects after treatment with electroconvulsive therapy in patients with depression: A multicenter study.

BACKGROUND: Electroconvulsive therapy (ECT) is a highly effective treatment for major depressive episodes (MDE). However, ECT-induced cognitive side-effects remain a concern. Identification of pre-treatment predictors that contribute to these side-effects remain unclear. We examined cognitive performance and individual cognitive profiles over time (up to six months) following ECT and investigated possible pre-treatment clinical and demographic predictors of cognitive decline shortly after ECT. METHODS: 634 patients with MDE from five sites were included with recruitment periods between 2001 and 2020. Linear mixed models were used to examine how cognitive performance, assessed with an extensive neuropsychological test battery, evolved over time following ECT. Next, possible pre-treatment predictors of cognitive side-effects directly after ECT were examined using linear regression. RESULTS: Directly after ECT, only verbal fluency (animal and letter; p < 0.0001; Cohen's d: -0.25 and -0.29 respectively) and verbal recall (p < 0.0001; Cohen's d: -0.26) significantly declined. However, during three and six months of follow-up, cognitive performance across all domains significantly improved, even outperforming baseline levels. No other pre-treatment factor than a younger age predicted a larger deterioration in cognitive performance shortly after ECT. LIMITATIONS: There was a substantial amount of missing data especially at 6 months follow-up. CONCLUSIONS: Our findings show that verbal fluency and memory retention are temporarily affected immediately after ECT. Younger patients may be more susceptible to experiencing these acute cognitive side-effects, which seems to be mostly due to a more intact cognitive functioning prior to ECT. These findings could contribute to decision-making regarding treatment selection, psychoeducation, and guidance during an ECT course.

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.

Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives.

The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2 = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Electroconvulsive therapy is associated with increased immunoreactivity of neuroplasticity markers in the hippocampus of depressed patients.

Electroconvulsive therapy (ECT) is an effective therapy for depression, but its cellular effects on the human brain remain elusive. In rodents, electroconvulsive shocks increase proliferation and the expression of plasticity markers in the hippocampal dentate gyrus (DG), suggesting increased neurogenesis. Furthermore, MRI studies in depressed patients have demonstrated increases in DG volume after ECT, that were notably paralleled by a decrease in depressive mood scores. Whether ECT also triggers cellular plasticity, inflammation or possibly injury in the human hippocampus, was unknown. We here performed a first explorative, anatomical study on the human post-mortem hippocampus of a unique, well-documented cohort of bipolar or unipolar depressed patients, who had received ECT in the 5 years prior to their death. They were compared to age-matched patients with a depressive disorder who had not received ECT and to matched healthy controls. Upon histopathological examination, no indications were observed for major hippocampal cell loss, overt cytoarchitectural changes or classic neuropathology in these 3 groups, nor were obvious differences present in inflammatory markers for astrocytes or microglia. Whereas the numbers of proliferating cells expressing Ki-67 was not different, we found a significantly higher percentage of cells positive for Doublecortin, a marker commonly used for young neurons and cellular plasticity, in the subgranular zone and CA4 / hilus of the hippocampus of ECT patients. Also, the percentage of positive Stathmin 1 cells was significantly higher in the subgranular zone of ECT patients, indicating neuroplasticity. These first post-mortem observations suggest that ECT has no damaging effects but may rather have induced neuroplasticity in the DG of depressed patients.

Mechanism of Action of ECT in Depression.

Electroconvulsive therapy (ECT) remains the most potent antidepressant treatment available for patients with major depressive disorder (MDD). ECT is highly effective, achieving a response rate of 70-80% and a remission rate of 50-60% even in treatment-resistant patients. The underlying mechanisms of ECT are not fully understood, although several hypotheses have been proposed, including the monoamine hypothesis, anticonvulsive hypothesis, neuroplastic effects, and immunomodulatory properties. In this paper, we provide an overview of magnetic resonance imaging evidence that addresses the neuroplastic changes that occur after ECT at the human systems level and elaborate further on ECTs potent immunomodulatory properties. Despite a growing body of evidence that suggests ECT may normalize many of the structural and functional changes in the brain associated with severe depression, there is a lack of convergence between neurobiological changes and the robust clinical effects observed in depression. This may be due to sample sizes used in ECT studies being generally small and differences in data processing and analysis pipelines. Collaborations that acquire large datasets, such as the GEMRIC consortium, can help translate ECT's clinical efficacy into a better understanding of its mechanisms of action.

Age-related change in cortical thickness in adolescents at clinical high risk for psychosis: a longitudinal study.

Progression to psychosis has been associated with increased cortical thinning in the frontal, temporal and parietal lobes in individuals at clinical high risk for the disorder (CHR-P). The timing and spatial extent of these changes are thought to be influenced by age. However, most evidence so far stems from adult samples. Longitudinal studies are essential to understanding the neuroanatomical changes associated to transition to psychosis during adolescence, and their relationship with age. We conducted a longitudinal, multisite study including adolescents at CHR-P and healthy controls (HC), aged 10-17 years. Structural images were acquired at baseline and at 18-month follow-up. Images were processed with the longitudinal pipeline in FreeSurfer. We used a longitudinal two-stage model to compute the regional cortical thickness (CT) change, and analyze between-group differences controlling for age, sex and scan, and corrected for multiple comparisons. Linear regression was used to study the effect of age at baseline. A total of 103 individuals (49 CHR-P and 54 HC) were included in the analysis. During follow-up, the 13 CHR-P participants who transitioned to psychosis exhibited greater CT decrease over time in the right parietal cortex compared to those who did not transition to psychosis and to HC. Age at baseline correlated with longitudinal changes in CT, with younger individuals showing greater cortical thinning in this region. The emergence of psychosis during early adolescence may have an impact on typical neuromaturational processes. This study provides new insights on the cortical changes taking place prior to illness onset.

Studying additive effects of combining rTMS with cognitive control training: a pilot investigation.

Background: Repetitive transcranial magnetic stimulation (rTMS) on the dorsolateral prefrontal cortex (DLPFC) is an effective treatment for depression that has been proposed to work via the enhancement of cognitive control. Cognitive control training (CCT) can also alleviate depression by relying on DLPFC activation. As the additive effects of rTMS and CCT are unclear, we set out to conduct a within-subject pilot study in healthy controls. Methods: Seventeen participants received two sessions of individualized resting-state connectivity-guided high-frequency rTMS, while randomly performing CCT or a control task. After each session, a negative mood was induced. Results: We found effects on mood and cognitive control after rTMS + CCT as well as rTMS + control, which were indiscriminative between conditions. Based on the statistical evidence for the absence of an additive effect of CCT, we did not perform a full study. Conclusion: Our results demonstrate no differential effects of single sessions combining rTMS and CCT in a healthy population, even with the methodological improvement of individualized neuronavigation. The improvement in cognitive control seen in both conditions could indicate that a simple cognitive task is sufficient when studying additive rTMS effects. Future studies should focus on augmenting the effects of various cognitive tasks and compare the present interventions with rTMS or cognitive tasks alone.

A transdiagnostic view on MDD and ADHD: shared cognitive characteristics?

BACKGROUND: Major Depressive disorder (MDD) and Attention Deficit Hyperactivity Disorder (ADHD) are prevalent mental disorders that often co-occur. There is overlap in symptomatology between MDD and ADHD that complicates diagnostics and treatment selection. Hence, to aid diagnostics of single and comorbid disorders, we aimed to examine the discriminative power of common symptom measures and cognitive dysfunction to differentiate between participants diagnosed with MDD, ADHD, ADHD and comorbid MDD and without a mental disorder. METHODS: Four diagnosed groups were compared: MDD (n = 103), ADHD (n = 78), comorbid MDD + ADHD (n = 29), healthy controls (HC; n = 123). We examined between-group differences and discriminative functions of clinically validated self-report symptom questionnaires, as well as task-based and self-report measures of cognitive dysfunction. RESULTS: Based on the between group comparisons, all patient groups were characterized by clinically relevant levels of ADHD-symptomatology, executive dysfunction, and diminished cognitive performances in the domain of attention; even the MDD-only group. In addition, based on self-reported symptoms of MDD, ADHD, and executive dysfunction, discriminant function analysis classified all HC correctly (100%) and patients diagnosed with ADHD or MDD relatively well (resp. 85% and 82%). Comorbid MDD + ADHD was poorly differentiated from single MDD or ADHD by the commonly used self-report symptom questionnaires for MDD and ADHD (0% correct predictions), which substantially improved by incorporating the questionnaire on executive functioning (42% correct predictions). CONCLUSIONS: In both MDD and ADHD, clinical levels of attentional and executive dysfunction were found, while these clinical groups differed in cognitive flexibility, initiating, inhibition and meta-cognition. Comorbid MDD + ADHD was poorly distinguishable from non-comorbid MDD and ADHD based on self-reported symptoms of depression and ADHD. Addition of subjective executive function in the discrimination models resulted in increased discriminative power. Our findings indicate that executive functioning measure can improve the diagnostic process of ADHD and MDD.

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.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Reviewing the neurobiology of electroconvulsive therapy on a micro- meso- and macro-level.

BACKGROUND: Electroconvulsive therapy (ECT) remains the one of the most effective of biological antidepressant interventions. However, the exact neurobiological mechanisms underlying the efficacy of ECT remain unclear. A gap in the literature is the lack of multimodal research that attempts to integrate findings at different biological levels of analysis METHODS: We searched the PubMed database for relevant studies. We review biological studies of ECT in depression on a micro- (molecular), meso- (structural) and macro- (network) level. RESULTS: ECT impacts both peripheral and central inflammatory processes, triggers neuroplastic mechanisms and modulates large scale neural network connectivity. CONCLUSIONS: Integrating this vast body of existing evidence, we are tempted to speculate that ECT may have neuroplastic effects resulting in the modulation of connectivity between and among specific large-scale networks that are altered in depression. These effects could be mediated by the immunomodulatory properties of the treatment. A better understanding of the complex interactions between the micro-, meso- and macro- level might further specify the mechanisms of action of ECT.