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.publishedVersio

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.publishedVersio

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

Cortical gray matter reduction precedes transition to psychosis in individuals at clinical high-risk for psychosis: A voxel-based meta-analysis

Gray matter and cortical thickness reductions have been documented in individuals at clinical high-risk for psychosis and may be more pronounced in those who transition to psychosis. However, these findings rely on small samples and are inconsistent across studies. In this review and meta-analysis we aimed to investigate neuroanatomical correlates of clinical high-risk for psychosis and potential predictors of transition, using a novel metaanalytic method (Seed-based d Mapping with Permutation of Subject Images) and cortical mask, combining data from surface-based and voxel-based morphometry studies. Individuals at clinical high-risk for psychosis who later transitioned to psychosis were compared to those who did not and to controls, and included three statistical maps. Overall, individuals at clinical high-risk for psychosis did not differ from controls, however, within the clinical high-risk for psychosis group, transition to psychosis was associated with less cortical gray matter in the right temporal lobe (Hedges' g = −0.377), anterior cingulate and paracingulate (Hedges' g = −0.391). These findings have the potential to help refine prognostic and etiopathological research in early psychos

Interrogating Associations Between Polygenic Liabilities and Electroconvulsive Therapy Effectiveness

Background: Electroconvulsive therapy (ECT) is the most effective treatment for severe major depressive episodes (MDEs). Nonetheless, firmly established associations between ECT outcomes and biological variables are currently lacking. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response in psychiatry are seldom reported. Here, we examined whether PRSs for major depressive disorder, schizophrenia (SCZ), cross-disorder, and pharmacological antidepressant response are associated with ECT effectiveness. Methods: A total of 288 patients with MDE from 3 countries were included. The main outcome was a change in the 17-item Hamilton Depression Rating Scale scores from before to after ECT treatment. Secondary outcomes were response and remission. Regression analyses with PRSs as independent variables and several covariates were performed. Explained variance (R 2) at the optimal p-value threshold is reported. Results: In the 266 subjects passing quality control, the PRS-SCZ was positively associated with a larger Hamilton Depression Rating Scale decrease in linear regression (optimal p-value threshold = .05, R 2 = 6.94%, p < .0001), which was consistent across countries: Ireland (R 2 = 8.18%, p = .0013), Belgium (R 2 = 6.83%, p = .016), and the Netherlands (R 2 = 7.92%, p = .0077). The PRS-SCZ was also positively associated with remission (R 2 = 4.63%, p = .0018). Sensitivity and subgroup analyses, including in MDE without psychotic features (R 2 = 4.42%, p = .0024) and unipolar MDE only (R 2 = 9.08%, p < .0001), confirmed the results. The other PRSs were not associated with a change in the Hamilton Depression Rating Scale score at the predefined Bonferroni-corrected significance threshold. Conclusions: A linear association between PRS-SCZ and ECT outcome was uncovered. Although it is too early to adopt PRSs in ECT clinical decision making, these findings strengthen the positioning of PRS-SCZ as relevant to treatment response in psychiatry

Volume of the human hippocampus and clinical response following electroconvulsive therapy

BACKGROUND: Hippocampal enlargements are commonly reported after electroconvulsive therapy (ECT). To clarify mechanisms, we examined if ECT-induced hippocampal volume change relates to dose (number of ECT sessions and electrode placement) and acts as a biomarker of clinical outcome. METHODS: Longitudinal neuroimaging and clinical data from 10 independent sites participating in the Global ECT-Magnetic Resonance Imaging Research Collaboration (GEMRIC) were obtained for mega-analysis. Hippocampal volumes were extracted from structural magnetic resonance images, acquired before and after patients (n = 281) experiencing a major depressive episode completed an ECT treatment series using right unilateral and bilateral stimulation. Untreated nondepressed control subjects (n = 95) were scanned twice. RESULTS: The linear component of hippocampal volume change was 0.28% (SE 0.08) per ECT session (p < .001). Volume change varied by electrode placement in the left hippocampus (bilateral, 3.3 +/- 2.2%, d = 1.5; right unilateral, 1.6 +/- 2.1%, d = 0.8; p < .0001) but not the right hippocampus (bilateral, 3.0 +/- 1.7%, d = 1.8; right unilateral, 2.7 +/- 2.0%, d = 1.4; p = .36). Volume change for electrode placement per ECT session varied similarly by hemisphere. Individuals with greater treatment-related volume increases had poorer outcomes (Montgomery-Asberg Depression Rating Scale change -1.0 [SE 0.35], per 1% volume increase, p = .005), although the effects were not significant after controlling for ECT number (slope -0.69 [SE 0.38], p = .069). CONCLUSIONS: The number of ECT sessions and electrode placement impacts the extent and laterality of hippocampal enlargement, but volume change is not positively associated with clinical outcome. The results suggest that the high efficacy of ECT is not explained by hippocampal enlargement, which alone might not serve as a viable biomarker for treatment outcome

Measuring the Neutrino Cross Section Using 8 years of Upgoing Muon Neutrinos Observed with IceCube

The IceCube Neutrino Observatory detects neutrinos at energies orders of magnitude higher than those available to current accelerators. Above 40 TeV, neutrinos traveling through the Earth will be absorbed as they interact via charged current interactions with nuclei, creating a deficit of Earth-crossing neutrinos detected at IceCube. The previous published results showed the cross section to be consistent with Standard Model predictions for 1 year of IceCube data. We present a new analysis that uses 8 years of IceCube data to fit the ν$_{μ}$ absorption in the Earth, with statistics an order of magnitude better than previous analyses, and with an improved treatment of systematic uncertainties. It will measure the cross section in three energy bins that span the range 1 TeV to 100 PeV. We will present Monte Carlo studies that demonstrate its sensitivity