Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

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

Increased Glutamate Plus Glutamine in the Right Middle Cingulate in Early Schizophrenia but Not in Bipolar Psychosis: A Whole Brain 1H-MRS Study

Proton magnetic resonance spectroscopy ( 1 H-MRS) studies have examined glutamatergic abnormalities in schizophrenia and bipolar-I disorders, mostly in single voxels. Though the critical nodes remain unknown, schizophrenia and bipolar-I involve brain networks with broad abnormalities. To provide insight on the biochemical differences that may underlie these networks, the combined glutamine and glutamate signal (Glx) and other metabolites were examined in patients in early psychosis with whole brain 1 H-MRS imaging ( 1 H-MRSI). Data were acquired in young schizophrenia subjects ( N = 48), bipolar-I subjects ( N = 21) and healthy controls ( N = 51). Group contrasts for Glx, as well as for N-acetyl aspartate, choline, myo-inositol and creatine, from all voxels that met spectral quality criteria were analyzed in standardized brain space, followed by cluster-corrected level alpha-value (CCLAV ≤ 0.05) analysis. Schizophrenia subjects had higher Glx in the right middle cingulate gyrus (19 voxels, CCLAV = 0.05) than bipolar-I subjects. Healthy controls had intermediate Glx values, though not significant. Schizophrenia subjects also had higher N-acetyl aspartate (three clusters, left occipital, left frontal, right frontal), choline (two clusters, left and right frontal) and myo-inositol (one cluster, left frontal) than bipolar-I, with healthy controls having intermediate values. These increases were likely accounted for by antipsychotic medication effects in the schizophrenia subgroup for N-acetyl aspartate and choline. Likewise, creatine was increased in two clusters in treated vs. antipsychotic-naïve schizophrenia, supporting a medication effect. Conversely, the increments in Glx in right cingulate were not driven by antipsychotic medication exposure. We conclude that increments in Glx in the cingulate may be critical to the pathophysiology of schizophrenia and are consistent with the NMDA hypo-function model. This model however may be more specific to schizophrenia than to psychosis in general. Postmortem and neuromodulation schizophrenia studies focusing on right cingulate, may provide critical mechanistic and therapeutic advancements, respectively

Electroconvulsive therapy treatment responsive multimodal brain networks.

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

Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder

Abstract Background Although electroconvulsive therapy (ECT) is an effective treatment for depression, ECT cognitive impairment remains a major concern. The neurobiological underpinnings and mechanisms underlying ECT antidepressant and cognitive impairment effects remain unknown. This investigation aims to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks and assesses whether they are associated with the ECT-induced electric field (E-field) with an optimal pulse amplitude estimation. Methods A single site clinical trial focused on amplitude (600, 700, and 800 mA) included longitudinal multimodal imaging and clinical and cognitive assessments completed before and immediately after the ECT series (n = 54) for late-life depression. Another two independent validation cohorts (n = 84, n = 260) were included. Symptom and cognition were used as references to supervise fMRI and sMRI fusion to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks. Correlations between ECT-induced E-field within these two networks and clinical and cognitive outcomes were calculated. An optimal pulse amplitude was estimated based on E-field within antidepressant-response and cognitive-impairment networks. Results Decreased function in the superior orbitofrontal cortex and caudate accompanied with increased volume in medial temporal cortex showed covarying functional and structural alterations in both antidepressant-response and cognitive-impairment networks. Volume increases in the hippocampal complex and thalamus were antidepressant-response specific, and functional decreases in the amygdala and hippocampal complex were cognitive-impairment specific, which were validated in two independent datasets. The E-field within these two networks showed an inverse relationship with HDRS reduction and cognitive impairment. The optimal E-filed range as [92.7–113.9] V/m was estimated to maximize antidepressant outcomes without compromising cognitive safety. Conclusions The large degree of overlap between antidepressant-response and cognitive-impairment networks challenges parameter development focused on precise E-field dosing with new electrode placements. The determination of the optimal individualized ECT amplitude within the antidepressant and cognitive networks may improve the treatment benefit–risk ratio. Trial registration ClinicalTrials.gov Identifier: NCT02999269