Thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy

Subanesthetic Ketamine Suppresses Locus Coeruleus-Mediated Alertness Effects: A 7T fMRI Study

BACKGROUND

The NMDA antagonist S-ketamine is gaining increasing use as a rapid-acting antidepressant, although its exact mechanisms of action are still unknown. In this study, we investigated ketamine in respect to its properties toward central noradrenergic mechanisms and how they influence alertness behavior.

METHODS

We investigated the influence of S-ketamine on the locus coeruleus (LC) brain network in a placebo-controlled, cross-over, 7T functional, pharmacological MRI study in 35 healthy male participants (25.1 ± 4.2 years) in conjunction with the attention network task to measure LC-related alertness behavioral changes.

RESULTS

We could show that acute disruption of the LC alertness network to the thalamus by ketamine is related to a behavioral alertness reduction.

CONCLUSION

The results shed new light on the neural correlates of ketamine beyond the glutamatergic system and underpin a new concept of how it may unfold its antidepressant effects.

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.

Parsing the Network Mechanisms of Electroconvulsive Therapy

Electroconvulsive therapy (ECT) is one of the oldest and most effective forms of neurostimulation, wherein electrical current is used to elicit brief, generalized seizures under general anesthesia. When electrodes are positioned to target frontotemporal cortex, ECT is arguably the most effective treatment for severe major depression, with response rates and times superior to other available antidepressant therapies. Neuroimaging research has been pivotal in improving the field's mechanistic understanding of ECT, with a growing number of magnetic resonance imaging studies demonstrating hippocampal plasticity after ECT, in line with evidence of upregulated neurotrophic processes in the hippocampus in animal models. However, the precise roles of the hippocampus and other brain regions in antidepressant response to ECT remain unclear. Seizure physiology may also play a role in antidepressant response to ECT, as indicated by early positron emission tomography, single-photon emission computed tomography, and electroencephalography research and corroborated by recent magnetic resonance imaging studies. In this review, we discuss the evidence supporting neuroplasticity in the hippocampus and other brain regions during and after ECT, and their associations with antidepressant response. We also offer a mechanistic, circuit-level model that proposes that core mechanisms of antidepressant response to ECT involve thalamocortical and cerebellar networks that are active during seizure generalization and termination over repeated ECT sessions, and their interactions with corticolimbic circuits that are dysfunctional prior to treatment and targeted with the electrical stimulus.

Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features

Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions (p < 0.01, r² = 0.11) in GPRs trained with GMD. We did not find significant correlations between the true percentage of HAMD score decreases and predictions in GMV (p = 0.16, r² = 0.00), ALFF (p = 0.125, r² = 0.00), and fALFF (p = 0.485, r² = 0.10). Our results suggest that GMD of the thalamus has good potential as an aid in individualized treatment response predictions of MDD patients.

Association between startle reactivity to uncertain threats and structural brain volume

Sensitivity to uncertain threat (U-threat) is a clinically important individual difference factor in multiple psychopathologies. Recent studies have implicated a specific frontolimbic circuit as a key network involved in the anticipation of aversive stimuli. In particular, the insula, thalamus, and dorsal anterior cingulate cortex (dACC) have recently been found to be robustly activated by anticipation of U-threat. However, no study to date has examined the association between U-threat reactivity and structural brain volume. In the present study, we utilized a pooled sample of 186 young adult volunteers who completed a structural MRI scan and the well-validated No-Predictable-Unpredictable (NPU) threat of electric shock task. Startle eyeblink potentiation was collected during the NPU task as an objective index of aversive reactivity. ROI-based analyses revealed that increased startle reactivity to U-threat was associated with reduced gray matter volume in the right insula and bilateral thalamus, but not the dACC. These results add to a growing literature implicating the insula and thalamus as core nodes involved in individual differences in U-threat reactivity.

Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective antidepressant treatment for severe depression. Although recent structural magnetic resonance imaging (MRI) studies have consistently reported ECT-induced hippocampal volume increases, most studies did not find the association of the hippocampal volume changes with clinical improvement. To understand the underlying mechanisms of ECT action, we aimed to identify the longitudinal effects of ECT on hippocampal functional connectivity (FC) and their associations with clinical improvement.

METHODS

Resting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. A priori hippocampal seed-based FC analysis and a data-driven multivoxel pattern analysis (MVPA) were conducted to investigate FC changes associated with clinical improvement. The statistical threshold was set at cluster-level false discovery rate-corrected p < 0.05.

RESULTS

Depressive symptom improvement after ECT was positively associated with the change in the right hippocampus-ventromedial prefrontal cortex FC, and negatively associated with the right hippocampus-superior frontal gyrus FC. MVPA confirmed the results of hippocampal seed-based analyses and identified the following additional clusters associated with clinical improvement following ECT: the thalamus, the sensorimotor cortex, and the precuneus.

CONCLUSIONS

ECT-induced change in the right frontotemporal connectivity and thalamocortical connectivity, and changes in the nodes of the default mode network were associated with clinical improvement. Modulation of these networks may explain the underlying mechanisms by which ECT exert its potent and rapid antidepressant effect.

Association of electroconvulsive therapy-induced structural plasticity with clinical remission

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective treatment for severe depression. Recent neuroimaging studies have consistently reported that ECT induces volume increases in widely distributed brain regions. However, it still remains unclear about ECT-induced volume changes associated with clinical improvement.

METHODS

Longitudinal assessments of structural magnetic resonance imaging were conducted in 48 participants. Twenty-seven elderly melancholic depressed individuals (mean 67.5 ± 8.1 years old; 19 female) were scanned before (TP1) and after (TP2) ECT. Twenty-one healthy controls were also scanned twice. Whole-brain gray matter volume (GMV) was analyzed via group (remitters, nonremitters, and controls) by time (TP1 and TP2) analysis of covariance to identify ECT-related GMV changes and GMV changes specific to remitters. Within-subject and between-subjects correlation analyses were conducted to investigate the associations between clinical improvement and GMV changes. Depressive symptoms were evaluated using the 17-item Hamilton Depression Rating Scale (HAM-D), and remission was defined as HAM-D total score ≤ 7.

RESULTS

Bilateral ECT increased GMV in multiple brain regions bilaterally regardless of clinical improvement. Remitters showed a larger GMV increase in the right-lateralized frontolimbic brain regions compared to nonremitters and healthy controls. GMV changes in the right hippocampus/amygdala and right middle frontal gyrus showed correlations with clinical improvement in within-/between-subjects correlation analyses.

CONCLUSIONS

ECT-induced GMV increase in the right frontolimbic regions was associated with clinical remission.

Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

BACKGROUND

Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).

METHODS

Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.

RESULTS

The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.

CONCLUSIONS

The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

A multimetric systematic review of fMRI findings in patients with MDD receiving ECT

BACKGROUND

Electroconvulsive therapy (ECT) is considered the most effective treatment for major depressive disorder (MDD). In recent years, the pursuit of the neurobiological mechanisms of ECT action has generated a significant amount of functional magnetic resonance imaging (fMRI) research.

OBJECTIVE

In this systematic review, we integrated all fMRI research in patients with MDD receiving ECT and, importantly, evaluated the level of convergence and replicability across multiple fMRI metrics.

RESULTS

While according to most studies changes in patients with MDD after ECT appear to be widely distributed across the brain, our multimetric review revealed specific changes involving functional connectivity increases in the superior and middle frontal gyri as the most replicated and across-modality convergent findings. Although this modulation of prefrontal connectivity was associated to ECT outcome, we also identified fMRI measurements of the subgenual anterior cingulate cortex as the fMRI signals most significantly linked to clinical response.

CONCLUSION

We identified specific prefrontal and cingulate territories which activity and connectivity with other brain regions is modulated by ECT, critically accounting for its mechanism of action.

Inflammatory cytokines derived from peripheral blood contribute to the modified electroconvulsive therapy-induced cognitive deficits in major depressive disorder

Little is known about the pathophysiology of memory deficits in patients with major depressive disorder (MDD) treated with modified electroconvulsive therapy (MECT). This study examined the profiles of cytokines, the memory function, and their association in MECT-treated MDD patients. Forty first-episode, drug-free MDD patients and 40 healthy controls were recruited. MECT was started with antidepressant treatment at a stable initial dose. The Wechsler Memory Scale (WMS) and Hamilton Rating Scale for Depression 17 (HRSD-17) were used to assess the cognitive function. MDD patients were divided into the memory impairment group (WMS < 50) and the non-memory impairment group (WMS ≥ 50) based on the total WMS scores after MECT. The levels of NOD-like receptor 3 (NLRP3) inflammasome, interleukin-18 (IL-18) and nuclear factor kappa-B (NF-κB) in the serum were measured. MDD patients showed significantly higher levels of NLRP3 inflammasome, IL-18 and NF-κB than that in the controls prior to MECT, and the levels also significantly increased after MECT. In MDD patients, the serum levels of these inflammatory cytokines were negatively associated with the total WMS scores and likely contributed to the scores independently. The receiver operating characteristic curve showed that the serum levels of these inflammatory cytokines may predict the cognitive impairment risk in MDD patients receiving MECT. Abnormal levels of NLRP3 inflammasome, IL-18 and NF-κB reflecting the disturbed balance of pro-inflammatory and anti-inflammatory mechanisms likely contribute to the MECT-induced cognitive deficits in MDD patients.

Do increases in deep grey matter volumes after electroconvulsive therapy persist in patients with major depression? A longitudinal MRI-study

BACKGROUND

Previous MRI studies reported deep grey matter volume increases after electroconvulsive therapy (ECT) in patients with major depressive disorder (MDD). However, the clinical correlates of these changes are still unclear. It remains debated whether such volume changes are transient, and if they correlate with affective changes over time. We here investigated if ECT induces deep grey matter volume increases in MDD-patients; and, if so, whether volume changes persist over more than 9 months and whether they are related to the clinical outcome.

METHODS

We examined 16 MDD-patients with 3Tesla MRI before (baseline) and after an ECT-series and followed 12 of them up for 10-36 months. Patients' data were compared to 16 healthy controls. Affective scales were used to investigate the relationship between therapy-outcome and MRI changes.

RESULTS

At baseline, MDD-patients had lower values in global brain volume, white matter and peripheral grey matter compared to healthy controls, but we observed no significant differences in deep grey matter volumes. After ECT, the differences in peripheral grey matter disappeared, and patients demonstrated significant volume increases in the right hippocampus and both thalami, followed by subsequent decreases after 10-36 months, especially in ECT-responders. Controls did not show significant changes over time.

LIMITATIONS

Beside the relatively small, yet carefully characterized cohort, we address the variability in time between the third scanning session and the baseline.

CONCLUSIONS

ECT-induced deep grey matter volume increases are transient. Our results suggest that the thalamus might be a key region for the understanding of the mechanisms of ECT action.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach

OBJECTIVE

To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.

METHODS

Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation.

RESULTS

Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder.

CONCLUSIONS

Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.