Background: No clinically useful biomarkers currently predict antidepressant response to ketamine in treatment-resistant major depressive disorder (MDD) or bipolar disorder (BD). This study investigated whether baseline thyroid-stimulating hormone (TSH) levels are associated with ketamine's antidepressant, anti-anhedonic, and anti-suicidal effects. Methods: This was a secondary exploratory analysis of data drawn from four NIH-sponsored, randomized, placebo-controlled, double-blind crossover studies of ketamine in inpatients with current MDD (n = 39) or bipolar depression (n = 44). Baseline TSH levels were measured before the first intravenous saline or ketamine infusion (0.5 mg/kg). A two-week washout preceded the second infusion. Depression and anhedonia were measured with the Montgomery-& Aring;sberg Depression Rating Scale (MADRS) and the Snaith-Hamilton Pleasure Scale, respectively. Suicidal ideation (SI) was measured as a weighted average of the Beck Depression Inventory and the MADRS suicide items. Linear mixed models were used to evaluate baseline TSH levels as a moderator of response to ketamine (versus placebo), controlling for thyroid disease, lithium use, and depression severity at 230 min and days 1, 2, and 7 post-infusion. Results: In individuals with BD, higher baseline TSH levels were associated with greater decreases in the severity of depressive (drug*TSH, p < 0.0007) and anhedonia (drug*TSH, p < 0.0001) symptoms post-ketamine. TSH levels were not associated with changes in depression, anhedonia, or SI severity in MDD post-ketamine. Conclusion: Baseline TSH levels were associated with ketamine's antidepressant and anti-anhedonic effects in BD but not MDD. These findings suggest that TSH may serve as a predictive biomarker of response and highlight a potential thyroid-glutamate influence in ketamine's mechanism of action.

Previous studies have suggested that alterations in white matter (WM) microstructure are implicated in suicidal thoughts and behaviours (STBs). However, findings of diffusion tensor imaging (DTI) studies have been inconsistent. In this large-scale mega-analysis conducted by the ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium, we examined WM alterations associated with STBs. Data processing was standardised across sites, and resulting WM microstructure measures (fractional anisotropy, axial diffusivity, mean diffusivity and radial diffusivity) for 25 WM tracts were pooled across 40 cohorts. We compared these measures among individuals with a psychiatric diagnosis and lifetime history of suicide attempt (n=652; mean age=35.4±14.7; female=71.8%), individuals with a psychiatric diagnosis but no STB (i.e., clinical controls; n=1871; mean age=34±14.8; female=59.8%), and individuals with no mental disorder diagnosis and no STB (i.e., healthy controls; n=642; mean age=29.6±13.1; female=62.9%). We also compared these measures among individuals with recent suicidal ideation (n=714; mean age=36.3±15.3; female=66.1%), clinical controls (n=1184; mean age=36.8±15.6; female=63.1%), and healthy controls (n=1240; mean age= 31.6±15.5; female=61.0%). We found subtle but statistically significant effects, such as lower fractional anisotropy associated with a history of suicide attempt, over and above the effect of psychiatric diagnoses. These effects were strongest in the corona radiata, thalamic radiation, fornix/stria terminalis, corpus callosum and superior longitudinal fasciculus. Effect sizes were small (Cohen's d < 0.25). Recent suicidal ideation was not associated with alterations in WM microstructure. This large-scale coordinated mega-analysis revealed subtle regional and global alterations in WM microstructure in individuals with a history of suicide attempt. Longitudinal studies are needed to confirm whether these alterations are a risk factor for suicidal behaviour.

This systematic review and network meta-analysis (NMA) sought to compare different antidepressant treatments for treatment-resistant depression (TRD) in order to facilitate evidence-based choices. A literature search of PubMed, Cochrane Library, and Embase from inception until April 13th, 2023 identified randomized, controlled trials (RCTs) of adults with depression who had not responded to at least two antidepressant trials; all RCTs had ≥10 participants per study arm, and participants with bipolar or psychotic depression were excluded. The Cochrane Risk of Bias Tool-2 was used to assess study quality. Response rate was the primary outcome measure. Odds ratios (ORs) using a random effects NMA are reported. From 8234 records, 69 RCTs were included in this analysis, encompassing 10,285 participants (5662F/4623M) and 25 separate treatments. Six of the 25 treatments demonstrated a higher response rate versus placebo or sham treatment: electroconvulsive therapy (ECT), minocycline, theta-burst stimulation (TBS), repetitive transcranial magnetic stimulation (rTMS), ketamine, and aripiprazole. ORs ranged from 1.9 (95%CI=[1.25; 2.91]) for aripiprazole to 12.86 (95%CI=[4.07; 40.63]) for ECT. Moderate heterogeneity of the model was observed (I2=47.3% (95%CI [26.8-62%]). Of the included studies, 12.5% were rated as having high risk of bias, 28.13% as having low risk, and 59.38% as showing some concerns. Several effective treatments for TRD showed robust treatment effects across outcomes (ECT, TBS, rTMS, and ketamine), and others showed promising results for some, but not all, outcomes (minocycline, aripiprazole). These findings may help guide evidence-based treatment choices for TRD. Study Registration: PROSPERO (#CRD42023420584). © 2024. The Author(s).

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

BackgroundAn urgent need exists to identify neural correlates associated with differing levels of suicide risk and develop novel, rapid-acting therapeutics to modulate activity within these neural networks.MethodsElectrophysiological correlates of suicide were evaluated using magnetoencephalography (MEG) in 75 adults with differing levels of suicide risk. During MEG scanning, participants completed a modified Life-Death Implicit Association Task. MEG data were source-localized in the gamma (30-58 Hz) frequency, a proxy measure of excitation-inhibition balance. Dynamic causal modeling was used to evaluate differences in connectivity estimates between risk groups. A proof-of-concept, open-label, pilot study of five high risk participants examined changes in gamma power after administration of ketamine (0.5 mg/kg), an NMDAR antagonist with rapid anti-suicide ideation effects.ResultsImplicit self-associations with death were stronger in the highest suicide risk group relative to all other groups, which did not differ from each other. Higher gamma power for self-death compared to self-life associations was found in the orbitofrontal cortex for the highest risk group and the insula and posterior cingulate cortex for the lowest risk group. Connectivity estimates between these regions differentiated the highest risk group from the full sample. Implicit associations with death were not affected by ketamine, but enhanced gamma power was found for self-death associations in the left insula post-ketamine compared to baseline.ConclusionsDifferential implicit cognitive processing of life and death appears to be linked to suicide risk, highlighting the need for objective measures of suicidal states. Pharmacotherapies that modulate gamma activity, particularly in the insula, may help mitigate risk.Clinicaltrials.gov identifier: NCT02543983, NCT00397111.ConclusionsDifferential implicit cognitive processing of life and death appears to be linked to suicide risk, highlighting the need for objective measures of suicidal states. Pharmacotherapies that modulate gamma activity, particularly in the insula, may help mitigate risk.Clinicaltrials.gov identifier: NCT02543983, NCT00397111.

Well-established animal models of depression have described a proximal relationship between stress and central nervous system (CNS) inflammation - a relationship mirrored in the peripheral inflammatory biomarkers of individuals with depression. Evidence also suggests that stress-induced proin-flammatory states can contribute to the neurobiology of treatment-resistant depression. Interestingly, ketamine, a rapid-acting antidepressant, can partially exert its therapeu-tic effects via anti-inflammatory actions on the hypothala-mic-pituitary adrenal (HPA) axis, the kynurenine pathway or by cytokine suppression. Further investigations into the relationship between ketamine, inflammation and stress could provide insight into ketamine's unique therapeutic mechanisms and stimulate efforts to develop rapid-acting, anti-inflammatory-based antidepressants.