Combined treatment with escitalopram and memantine increases gray matter volume and cortical thickness compared to escitalopram and placebo in a pilot study of geriatric depression

Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission

BACKGROUND AND HYPOTHESIS

There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network.

STUDY DESIGN

We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions.

STUDY RESULTS

We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission.

CONCLUSIONS

Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.

Comparative Efficacy of Augmenting Escitalopram with Modified Electroconvulsive Therapy or High-Frequency Repetitive Transcranial Magnetic Stimulation on Depressive Symptoms, Quality of Life, and Cognitive Function in Treatment-Resistant Depression

Treatment-resistant depression (TRD) poses significant therapeutic challenges despite available interventions. Escitalopram (ESC) is a highly selective antidepressant. This study aimed to compare ESC alone and ESC combined with modified electroconvulsive therapy (MECT) or high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) in TRD patients. Ninety participants were randomized into ESC alone, ESC + MECT, and ESC + HF-rTMS groups. Notable differences were observed in Hamilton Depression Rating Scale (HDRS-17) scores at 12 weeks among ESC (14.37), ESC + MECT (10.27), and ESC + HF-rTMS (10.77) groups (P = 0.006). In terms of overall quality of life (QoL) evaluated using the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) at 12 weeks, the ESC, ESC + MECT, and ESC + HF-rTMS groups scored 2, 3, and 3.5, respectively. ESC + MECT/HF-rTMS groups showed reduced depressive symptoms compared to the ESC group, accompanied by higher overall QoL scores and increased satisfaction with health. Patients receiving ESC + MECT demonstrated no significant alterations in short-term memory and orientation, as measured by the Montreal Cognitive Assessment (MoCA), before and after treatment. Moreover, a decline in language was observed compared to baseline (12 weeks: median 2, IQR 2-3; baseline: median 1, IQR 1-3; P = 0.022). The positive impact of ESC with HF-rTMS on cognitive function was evidenced by improvements in all domines MoCA.Combining ESC with MECT or HF-rTMS exhibited enhanced effectiveness in alleviating depressive symptoms and enhancing QoL compared to ESC monotherapy. Specifically, the ESC + HF-rTMS combination displayed potential as a comprehensive treatment strategy for TRD, addressing both emotional and cognitive aspects.

Grey matter volume predicts improvement in geriatric depression in response to Tai Chi compared to Health Education

OBJECTIVES

Geriatric depression (GD) is associated with cognitive impairment and brain atrophy. Tai-Chi-Chih (TCC) is a promising adjunct treatment to antidepressants. We previously found beneficial effects of TCC on resting state connectivity in GD. We now tested the effect of TCC on gray matter volume (GMV) change and the association between baseline GMV and clinical outcome.

PARTICIPANTS

Forty-nine participants with GD (>=60 y) underwent antidepressant treatment (38 women).

INTERVENTION

Participants completed 3 months of TCC (N = 26) or health and wellness education control (HEW; N = 23).

MEASUREMENTS

Depression and anxiety symptoms and MRI scans were acquired at baseline and 3-month follow-up. General linear models (GLMs) tested group-by-time interactions on clinical scores. Freesurfer 6.0 was used to process T1-weighted images and to perform voxel-wise whole-brain GLMs of group on symmetrized percent GMV change, and on the baseline GMV and symptom change association, controlling for baseline symptom severity. Age and sex served as covariates in all models.

RESULTS

There were no group differences in baseline demographics or clinical scores, symptom change from baseline to follow-up, or treatment-related GMV change. However, whole-brain analysis revealed that lower baseline GMV in several clusters in the TCC, but not the HEW group, was associated with larger improvements in anxiety. This was similar for right precuneus GMV and depressive symptoms.

CONCLUSIONS

While we observed no effect on GMV due to the interventions, baseline regional GMV predicted symptom improvements with TCC but not HEW. Longer trials are needed to investigate the long-term effects of TCC on clinical symptoms and neuroplasticity.

Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review

Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of "top-down" executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.

Application of Real-World Data and the REWARD Framework to Detect Unknown Benefits of Memantine and Identify Potential Disease Targets for New NMDA Receptor Antagonists

BACKGROUND

Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-D-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine.

METHODS

Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan^® Commercial Database (CCAE), the IBM MarketScan^® Multi-State Medicaid Database (MDCD), the IBM MarketScan^® Medicare Supplemental Database (MDCR), and the Optum^© De-Identified Clinformatics^® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases.

RESULTS

Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders.

CONCLUSION

The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study.

Machine Learning Prediction of Treatment Outcome in Late-Life Depression

Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.