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(R,S)-ketamine (ketamine) and its enantiomer (S)-ketamine (esketamine) can produce rapid and substantial antidepressant effects. However, individual response to ketamine/esketamine is variable, and there are no well-accepted methods to differentiate persons who are more likely to benefit. Numerous potential peripheral biomarkers have been reported, but their current utility is unclear. We conducted a systematic review/meta-analysis examining the association between baseline levels and longitudinal changes in blood-based biomarkers, and response to ketamine/esketamine. Of the 5611 citations identified, 56 manuscripts were included (N = 2801 participants), and 26 were compatible with meta-analytical calculations. Random-effect models were used, and effect sizes were reported as standardized mean differences (SMD). Our assessments revealed that more than 460 individual biomarkers were examined. Frequently studied groups included neurotrophic factors (n = 15), levels of ketamine and ketamine metabolites (n = 13), and inflammatory markers (n = 12). There were no consistent associations between baseline levels of blood-based biomarkers, and response to ketamine. However, in a longitudinal analysis, ketamine responders had statistically significant increases in brain-derived neurotrophic factor (BDNF) when compared to pre-treatment levels (SMD [95% CI] = 0.26 [0.03, 0.48], p = 0.02), whereas non-responders showed no significant changes in BDNF levels (SMD [95% CI] = 0.05 [-0.19, 0.28], p = 0.70). There was no consistent evidence to support any additional longitudinal biomarkers. Findings were inconclusive for esketamine due to the small number of studies (n = 2). Despite a diverse and substantial literature, there is limited evidence that blood-based biomarkers are associated with response to ketamine, and no current evidence of clinical utility., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Coffee is the most widely consumed source of caffeine worldwide, partly due to the psychoactive effects of this methylxanthine. Interestingly, the effects of its chronic consumption on the brain's intrinsic functional networks are still largely unknown. This study provides the first extended characterization of the effects of chronic coffee consumption on human brain networks. Subjects were recruited and divided into two groups: habitual coffee drinkers (CD) and non-coffee drinkers (NCD). Resting-state functional magnetic resonance imaging (fMRI) was acquired in these volunteers who were also assessed regarding stress, anxiety, and depression scores. In the neuroimaging evaluation, the CD group showed decreased functional connectivity in the somatosensory and limbic networks during resting state as assessed with independent component analysis. The CD group also showed decreased functional connectivity in a network comprising subcortical and posterior brain regions associated with somatosensory, motor, and emotional processing as assessed with network-based statistics; moreover, CD displayed longer lifetime of a functional network involving subcortical regions, the visual network and the cerebellum. Importantly, all these differences were dependent on the frequency of caffeine consumption, and were reproduced after NCD drank coffee. CD showed higher stress levels than NCD, and although no other group effects were observed in this psychological assessment, increased frequency of caffeine consumption was also associated with increased anxiety in males. In conclusion, higher consumption of coffee and caffeinated products has an impact in brain functional connectivity at rest with implications in emotionality, alertness, and readiness to action., (© 2021. The Author(s).)

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates., (© 2020. The Author(s).)

Dysfunction in a wide array of systems-including the immune, monoaminergic, and glutamatergic systems-is implicated in the pathophysiology of depression. One potential intersection point for these three systems is the kynurenine (KYN) pathway. This study explored the impact of the prototypic glutamatergic modulator ketamine on the endogenous KYN pathway in individuals with bipolar depression (BD), as well as the relationship between response to ketamine and depression-related behavioral and peripheral inflammatory markers. Thirty-nine participants with treatment-resistant BD (23 F, ages 18-65) received a single ketamine infusion (0.5 mg/kg) over 40 min. KYN pathway analytes-including plasma concentrations of indoleamine 2,3-dioxygenase (IDO), KYN, kynurenic acid (KynA), and quinolinic acid (QA)-were assessed at baseline (pre-infusion), 230 min, day 1, and day 3 post-ketamine. General linear models with restricted maximum likelihood estimation and robust sandwich variance estimators were implemented. A repeated effect of time was used to model the covariance of the residuals with an unstructured matrix. After controlling for age, sex, and body mass index (BMI), post-ketamine IDO levels were significantly lower than baseline at all three time points. Conversely, ketamine treatment significantly increased KYN and KynA levels at days 1 and 3 versus baseline. No change in QA levels was observed post-ketamine. A lower post-ketamine ratio of QA/KYN was observed at day 1. In addition, baseline levels of proinflammatory cytokines and behavioral measures predicted KYN pathway changes post ketamine. The results suggest that, in addition to having rapid and sustained antidepressant effects in BD participants, ketamine also impacts key components of the KYN pathway., (© 2019. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.