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Electroconvulsive therapy (ECT) remains the benchmark for treatment resistant depression, yet its cognitive adverse effects have a negative impact on treatment. A predictive safety biomarker early in ECT treatment is needed to identify patients at cognitive risk to maximize therapeutic outcomes and minimize adverse effects. We used ictal electroencephalography frequency analysis from suprathreshold treatments to assess the relationships between ECT dose, ictal power across different frequency domains, and cognitive outcomes.Seventeen subjects with treatment resistant depression received right unilateral ECT. Structural magnetic resonance imaging was obtained pre-ECT for electric field modeling to assess ECT dose. Serial assessments with 24-lead electroencephalography captured ictal activity. Clinical and cognitive assessments were performed before and after ECT. The primary cognitive outcome was the change in Delis Kaplan Executive Function Verbal Fluency Letter Fluency.Ictal theta (4-8 Hz) power in the Fp1/Fp2 channels was associated with both whole-brain electric field strength (t(2,12) = 19.5, P = 0.007)/(t(2,10) = 21.85, P = 0.02) and Delis Kaplan Executive Function Verbal Fluency Letter Fluency scores (t(2,12) = -2.05, P = 0.05)/(t(2,10) = -2.20, P = 0.01). Other frequency bands (beta, alpha, delta, and gamma) did not demonstrate this relationship.This pilot data identify ictal theta power as a potential safety biomarker in ECT and is related to the strength of the ECT dose. Ictal theta power could prove to be a convenient and powerful tool for clinicians to identify those patients most susceptible to cognitive impairment early in the treatment series. Additional studies are needed to assess the role of longitudinal changes in ictal theta power throughout the ECT series.

Background Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. Methods In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis–based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. Results Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. Conclusions These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.

Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. IMPACT STATEMENT: This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.

Electroconvulsive therapy (ECT) remains the gold-standard treatment for patients with depressive episodes, but the underlying mechanisms for antidepressant response and procedure-induced cognitive side effects have yet to be elucidated. Such mechanisms may be complex and involve certain ECT parameters and brain regions. Regarding parameters, the electrode placement (right unilateral or bitemporal) determines the geometric shape of the electric field (E-field), and amplitude determines the E-field magnitude in select brain regions (e.g., hippocampus). Here, we aim to determine the relationships between hippocampal E-field strength, hippocampal neuroplasticity, and antidepressant and cognitive outcomes. We used hippocampal E-fields and volumes generated from a randomized clinical trial that compared right unilateral electrode placement with different pulse amplitudes (600, 700, and 800 mA). Hippocampal E-field strength was variable but increased with each amplitude arm. We demonstrated a linear relationship between right hippocampal E-field and right hippocampal neuroplasticity. Right hippocampal neuroplasticity mediated right hippocampal E-field and antidepressant outcomes. In contrast, right hippocampal E-field was directly related to cognitive outcomes as measured by phonemic fluency. We used receiver operating characteristic curves to determine that the maximal right hippocampal E-field associated with cognitive safety was 112.5 V/m. Right hippocampal E-field strength was related to the whole-brain ratio of E-field strength per unit of stimulation current, but this whole-brain ratio was unrelated to antidepressant or cognitive outcomes. We discuss the implications of optimal hippocampal E-field dosing to maximize antidepressant outcomes and cognitive safety with individualized amplitudes.

Background Creutzfeldt-Jakob Disease (CJD) is part of a group of rapidly progressive incurable neurodegenerative disorders. CJD is commonly thought of as a neurological disorder but has a wide variety of clinical phenotypes including an initial presentation with psychiatric symptoms. Methods The authors describe a case of probable CJD with an atypical presentation of major depressive disorder (MDD). The literature surrounding the diagnosis and psychiatric manifestations of CJD is briefly reviewed. The following search terms were used: Creutzfeldt-Jakob disease, depression, catatonia, diagnosis. Approximately 30 sources were reviewed, 6 of which were cases of CJD mimicking depression and catatonia. Results A 61 year-old female presented with abrupt onset of MDD with psychosis and catatonic features, as well as broad cognitive deficits. Pharmacotherapy and electroconvulsive therapy were ineffective, with worsening of symptoms during hospitalization. Neurological workup was notable for basal ganglia and cortical abnormalities on magnetic resonance sequences and the presence of tau and 14-3-3 proteins in cerebrospinal fluid, consistent with probable CJD. Discussion CJD typically presents as a rapidly progressive dementia accompanied by myoclonus, pyramidal or extrapyramidal symptoms, visual/cerebellar dysfunction, or akinetic mutism. Diagnosis is made through clinical features and supporting findings on MRI, EEG, and CSF assays. Psychiatric and behavioral symptoms are now increasingly recognized as part of the clinical presentation. Hallucinations, paranoia, depression, and catatonia are some common manifestations, and often fail to respond to standard therapies.discussion Conclusion This case highlights the importance of maintaining a broad differential and consideration of CJD in late onset intractable psychiatric symptoms.

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed. Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective treatment option for major depressive disorder, so understanding whether its clinical effect relates to structural brain changes is vital for current and future antidepressant research. OBJECTIVE: To determine whether clinical response to ECT is related to structural volumetric changes in the brain as measured by structural magnetic resonance imaging (MRI) and, if so, which regions are related to this clinical effect. We also determine whether a similar model can be used to identify regions associated with electrode placement (unilateral versus bilateral ECT). METHODS: Longitudinal MRI and clinical data (Hamilton Depression Rating Scale) was collected from 10 sites as part of the Global ECT-MRI research collaboration (GEMRIC). From 192 subjects, relative changes in 80 (sub)cortical areas were used as potential features for classifying treatment response. We used recursive feature elimination to extract relevant features, which were subsequently used to train a linear classifier. As a validation, the same was done for electrode placement. We report accuracy as well as the structural coefficients of regions included in the discriminative spatial patterns obtained. RESULTS: A pattern of structural changes in cortical midline, striatal and lateral prefrontal areas discriminates responders from non-responders (75% accuracy, p

BackgroundElectroconvulsive 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, mono-center 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.ObjectiveTo develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting.MethodsMultimodal data (i.e., clinical, sMRI and resting-state fMRI) was collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (HAM-D score ≤7) using a support vector machine classifier.ResultsRemission 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, and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC).ConclusionsThese results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

Substance use disorders (SUD) are diseases of the brain, characterized by aberrant functioning in the neural circuitry of the brain. Resting state functional connectivity (rsFC) can illuminate these functional changes by measuring the temporal coherence of low-frequency fluctuations of the blood oxygenation level-dependent magnetic resonance imaging signal in contiguous or non-contiguous regions of the brain. Because this data is easy to obtain and analyze, and therefore fairly inexpensive, it holds promise for defining biological treatment targets in SUD, which could help maximize the efficacy of existing clinical interventions and develop new ones. In an effort to identify the most likely "treatment targets" obtainable with rsFC we summarize existing research in SUD focused on 1) the relationships between rsFC and functionality within important psychological domains which are believed to underlie relapse vulnerability 2) changes in rsFC from satiety to deprived or abstinent states 3) baseline rsFC correlates of treatment outcome and 4) changes in rsFC induced by treatment interventions which improve clinical outcomes and reduce relapse risk. Converging evidence indicates that likely "treatment target" candidates, emerging consistently in all four sections, are reduced connectivity within executive control network (ECN) and between ECN and salience network (SN). Other potential treatment targets also show promise, but the literature is sparse and more research is needed. Future research directions include data-driven prediction analyses and rsFC analyses with longitudinal datasets that incorporate time since last use into analysis to account for drug withdrawal. Once the most reliable biological markers are identified, they can be used for treatment matching, during preliminary testing of new pharmacological compounds to establish clinical potential ("target engagement") prior to carrying out costly clinical trials, and for generating hypotheses for medication repurposing.

BackgroundElectroconvulsive Therapy (ECT) is one of the most effective treatments for major depressive disorder (DEP). There is recently increasing attention to evaluate ECT’s effect on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of DEP patients with healthy participants, investigate whether dynamic functional network connectivity network (dFNC) estimated from rs-fMRI predicts the ECT outcome, and explore the effect of ECT on brain network states.MethodResting-state fMRI data were collected from 119 patients with depression or DEP (76 females), and 61 Healthy (HC) participants (34 females) with an age mean of 52.25 (N=180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59±6.14 and 11.48±9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each participant. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each individual spends in each state, called occupancy rate or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, number of treatment, and site. Finally, we evaluated the effectiveness of ECT by comparing pre-and post-ECT OCR of DEP and HC participants.ResultsThe main findings include: 1) DEP patients had significantly lower OCR values than the HC group in a state, where connectivity between CCN and DMN was relatively higher than other states (corrected p= 0.015), 2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, predicted the HDRS changes (R=0.23 corrected p=0.03). This means that those DEP patients who spend less time in this state showed more HDRS change, and 3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spend in state 2 (corrected p=0.03). Finally, we found ECT increases the total traveled distance in DEP.ConclusionOur finding suggests that dFNC features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identified a possible underlying mechanism associated with the ECT effect in DEP patients.

Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p, Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. We developed machine learning models to predict symptom change along latent dimensions of depression following treatment with electroconvulsive therapy in a large, multisite cohort using a combination of neuroimaging, clinical, and demographic predictors. Recovery along homogenous latent symptom dimensions was predicted more accurately than change along standard, more heterogeneous total score measures.

Electroconvulsive therapy (ECT) is an established treatment choice for severe, treatment-resistant depression, yet its mechanisms of action remain elusive. Magnetic resonance imaging (MRI) of the human brain before and after treatment has been crucial to aid our comprehension of the ECT neurobiological effects. However, to date, a majority of MRI studies have been underpowered and have used heterogeneous patient samples as well as different methodological approaches, altogether causing mixed results and poor clinical translation. Hence, an association between MRI markers and therapeutic response remains to be established. Recently, the availability of large datasets through a global collaboration has provided the statistical power needed to characterize whole-brain structural and functional brain changes after ECT. In addition, MRI technological developments allow new aspects of brain function and structure to be investigated. Finally, more recent studies have also investigated immediate and long-term effects of ECT, which may aid in the separation of the therapeutically relevant effects from epiphenomena. The goal of this review is to outline MRI studies (T1, diffusion-weighted imaging, proton magnetic resonance spectroscopy) of ECT in depression to advance our understanding of the ECT neurobiological effects. Based on the reviewed literature, we suggest a model whereby the neurobiological effects can be understood within a framework of disruption, neuroplasticity, and rewiring of neural circuits. An improved characterization of the neurobiological effects of ECT may increase our understanding of ECT’s therapeutic effects, ultimately leading to improved patient care. publishedVersion

Introduction Electroconvulsive therapy (ECT) pulse amplitude, which determines the induced electric field magnitude in the brain, is currently set at 800–900 milliamperes (mA) on modern ECT devices without any clinical or scientific rationale. The present study assessed differences in depression and cognitive outcomes for three different pulse amplitudes during an acute ECT series. We hypothesized that the lower amplitudes would maintain the antidepressant efficacy of the standard treatment and reduce the risk of neurocognitive impairment. Methods This double-blind investigation randomized subjects to three treatment arms: 600, 700, and 800 mA (active comparator). Clinical, cognitive, and imaging assessments were conducted pre-, mid- and post-ECT. Subjects had a diagnosis of major depressive disorder, age range between 50 and 80 years, and met clinical indication for ECT. Results The 700 and 800 mA arms had improvement in depression outcomes relative to the 600 mA arm. The amplitude groups showed no differences in the primary cognitive outcome variable, the Hopkins Verbal Learning Test-Revised (HVLT-R) retention raw score. However, secondary cognitive outcomes such as the Delis Kaplan Executive Function System Letter and Category Fluency measures demonstrated cognitive impairment in the 800 mA arm. Discussion The results demonstrated a dissociation of depression (higher amplitudes better) and cognitive (lower amplitudes better) related outcomes. Future work is warranted to elucidate the relationship between amplitude, electric field, neuroplasticity, and clinical outcomes.

Electroconvulsive therapy is regarded as the most effective antidepressant treatment for severe and treatment‐resistant depressive episodes. Despite the efficacy of electroconvulsive therapy, the neurobiological underpinnings and mechanisms underlying electroconvulsive therapy induced antidepressant effects remain unclear. The objective of this investigation was to identify electroconvulsive therapy treatment responsive multimodal biomarkers with the 17‐item Hamilton Depression Rating Scale guided brain structure–function fusion in 118 patients with depressive episodes and 60 healthy controls. Results show that reduced fractional amplitude of low frequency fluctuations in the prefrontal cortex, insula and hippocampus, linked with increased gray matter volume in anterior cingulate, medial temporal cortex, insula, thalamus, caudate and hippocampus represent electroconvulsive therapy responsive covarying functional and structural brain networks. In addition, relative to nonresponders, responder‐specific electroconvulsive therapy related brain networks occur in frontal‐limbic network and are associated with successful therapeutic outcomes. Finally, electroconvulsive therapy responsive brain networks were unrelated to verbal declarative memory. Using a data‐driven, supervised‐learning method, we demonstrated that electroconvulsive therapy produces a remodeling of brain functional and structural covariance that was unique to antidepressant symptom response, but not linked to memory impairment.

Objective The anticonvulsant hypothesis posits that ECT’s mechanism of action is related to enhancement of endogenous anticonvulsant brain mechanisms. Results of prior studies investigating the role of the inhibitory neurotransmitter gamma‐aminobutyric acid (“GABA+”, GABA and coedited macromolecules) in the pathophysiology and treatment of depression remain inconclusive. The aim of our study was to investigate treatment‐responsive changes of GABA+ in subjects with a depressive episode receiving electroconvulsive therapy (ECT). Methods In total, 41 depressed subjects (DEP) and 35 healthy controls (HC) were recruited at two independent sites in Norway and the USA. MEGA‐PRESS was used for investigation of GABA+ in the anterior cingulate cortex. We assessed longitudinal and cross‐sectional differences between DEP and HC, as well as the relationship between GABA+ change and change in depression severity and number of ECTs. We also assessed longitudinal differences in cognitive performance and GABA+ levels. Results Depressive episode did not show a difference in GABA+ relative to HC (t 71 = −0.36, p = .72) or in longitudinal analysis (t 36 = 0.97, p = .34). Remitters and nonremitters did not show longitudinal (t 36 = 1.12, p = .27) or cross‐sectional differences in GABA+. GABA+ levels were not related to changes in antidepressant response (t 35 = 1.12, p = .27) or treatment number (t 36 = 0.05, p = .96). An association between cognitive performance and GABA+ levels was found in DEP that completed cognitive effortful testing (t 18 = 2.4, p = .03). Conclusion Our results failed to support GABA as a marker for depression and abnormal mood state and provide no support for the anticonvulsant hypothesis of ECT. ECT‐induced change in GABA concentrations may be related to change in cognitive function., In depressed subjects treated with ECT (n = 40), gamma‐aminobutyric acid (GABA+) levels did not change over treatment and we found no association between GABA+ levels and antidepressant response. However, change of GABA+ was associated with cognitive performance in a subset of patients (n = 24) who completed cognitive testing.

Highlights • The negative FC networks achieve predictive accuracy of 76.23% for ECT response. • The consensus FCs represent predominately frontal, temporal and subcortical regions. • FCs that changed significantly were concentrated in frontal and limbic networks. • Longitudinal change overlapped with two FCs compared with FC with predictive power., Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.

Background Consultation psychiatrists may be asked by neurological services to identify treatment options for status epilepticus (SE), particularly electroconvulsive therapy (ECT). The successful use of ECT in the treatment of refractory SE has been reported in numerous case studies but remains sparsely utilized despite its apparent efficacy. Methods We present a case of a patient who received ECT for SE, fifty-one days after extensive pharmacologic treatment failed. We then review theories of ECT anticonvulsant effects and suggest approaches to further study and treatment. Results A 51 year-old man developed epilepsia partialis continua refractory to anticonvulsant medications. ECT was implemented through coordination with multiple hospital services. Transient worsening of the epileptic focus, accompanied by a distinct change in seizure pattern, was captured on electroencephalography (EEG) after ECT. Cessation of the status and marked functional improvement eventually followed. Discussion Although multiple case reports detail ECT used in SE, its effectiveness is difficult to ascertain. A novel aspect of our case is individualized electrode placement to target the epileptic focus. ECT may reset the neurotransmitter balance, induce trophic changes, or alter cerebral blood flow and metabolism in ways that promote neuronal stabilization. Earlier use of ECT, optimal electrode placement, and the anticonvulsant mechanisms of effect all require further research. Conclusions ECT for the treatment of SE may be safely and effectively used when pharmacologic treatment fails. Consultation psychiatrists should be aware of the evidence supporting ECT in SE so that they may recommend its use and apply necessary modifications to the treatment protocol.

Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.

Introduction ECT is recognized for its efficacy in the treatment of depressive disorders, however neurocognitive impairment remains a major concern of treatment. Demographic factors, depression severity, and ECT treatment parameters influence neurocognitive outcomes. Studies examining ECT electrode placement and pulse width find that more focal stimulus delivery with right unilateral placement or ultrabrief pulse width can reduce cognitive side effects, however are associated with less efficacy than standard treatments. Pulse amplitude, which determines the induced electric field strength in the brain, is currently set at 800-900?mA on modern ECT devices without any clinical or scientific rationale. Lower pulse amplitudes also produce more focal electric fields and potentially reduce neurocognitive impairment. Recent small studies have demonstrated that low amplitude ECT can generate seizure induction, may improve depression severity, and result in fewer cognitive side effects. There are not yet prospective randomized studies examining the antidepressant and neurocognitive dose-response relationship relative of ECT pulse amplitude. The present study compares three different pulse amplitudes during an ECT series. We hypothesize that the lower amplitudes will maintain the antidepressant efficacy of the standard treatment and reduce the risk of neurocognitive impairment. Methods The present study (ClinicalTrials.gov identifier NCT0299929) prospectively randomized subjects to three treatment arms: 600mA, 700mA, and 800mA (active comparator). Clinical, neuropsychological, and imaging assessments were conducted pre-, mid- (after the sixth ECT treatment) and post-ECT (within one week of finishing the ECT series). Subjects had a diagnosis of major depressive disorder, met the clinical indication for ECT, were between 50 and 80?years of age, and right-handed. Depression severity was tracked using the Hamilton Depression Rating Scale 24-item (HDRS-24). The neuropsychological battery is focused on detecting longitudinal differences in hippocampal-dependent changes in cognitive performance. Our primary outcome measure was the Hopkins Verbal Learning Test-Revised (HVLT-R) raw retention score. All subjects were treated with right unilateral electrode placements with either ultrabrief pulse width (0.3 ms) or brief pulse widths (1.0ms, used for the last 14 subjects following interim data analysis). Treatments were individually titrated to supra-threshold, with subsequent treatments given at 6x seizure threshold. Results For this analysis, we focused on subjects that completed all three time points with RUL electrode placement. 62 patients were randomized to the three different pulse amplitude groups and completed at least the first and mid- visits. Demographics, including age, sex, duration of depressive episode, number of lifetime episodes, and baseline cognitive status were not significantly different between groups. Initial HDRS-24 scores were similar. The lowest amplitude group (600 mA) in our study showed significantly less antidepressant effect compared to the higher amplitudes (700mA and 800mA). The amplitude groups did not have significant differences in our primary cognitive outcome, the HVLT-R retention score. Several of the secondary cognitive outcomes were more sensitive to detecting cognitive impairment than the Hopkins Verbal Learning Test, revealing that subjects receiving 800?mA demonstrated greater impairment effects compared to the lower amplitude. This was most sensitive in the Delis Kaplan Executive Function System (DKEFS)-Verbal Fluency subset, DKEFS-Color Word Interference, and Dot Counting Test. Conclusions This study used a blinded randomized design to examine dose-dependent effects of pulse amplitude on depression response and neurocognitive measures. Despite seizure induction, treatment with the 600mA pulse amplitude did not achieve clinical efficacy in comparison to higher amplitudes. These findings support the concept that seizure is necessary but not sufficient for treatment response. The primary cognitive outcome, the Hopkins Verbal Learning Test-Revised (HVLT-R) raw retention score, did not show significant difference between amplitudes. However, neurocognitive battery outcomes of verbal fluency, color-word interference, and dot counting showed sensitivity in the secondary analyses. Focal electric field exposure through reduction of pulse amplitude may be beneficial in limiting the effects of some aspects of cognitive impairment. Limitations of our study include attrition and missing data from for some subjects who could not complete the full neurocognitive battery. Further work on understanding ECT treatment parameters can optimize and individualize care for patients undergoing ECT, maximizing benefit and reducing cognitive risk. This research was funded by: BRAIN Initiative U01 MH111826