Longitudinal associations between resting-state, interregional theta-beta phase-amplitude coupling, psychological distress, and wellbeing in 12-15-year-old adolescents.

Cross-frequency coupling between the phase of slower oscillatory activity and the amplitude of faster oscillatory activity in the brain (phase-amplitude coupling; PAC), is a promising new biological marker for mental health. Prior research has demonstrated that PAC is associated with mental health. However, most research has focused on within-region theta-gamma PAC in adults. Our recent preliminary study found increased theta-beta PAC was associated with increased psychological distress in 12 year olds. It is important to investigate how PAC biomarkers relate to mental health and wellbeing in youth. Thus, in this study, we investigated longitudinal associations between interregional (posterior-anterior cortex) resting-state theta-beta PAC (Modulation Index [MI]), psychological distress and wellbeing in N = 99 adolescents (aged 12-15 years). In the right hemisphere, there was a significant relationship, whereby increased psychological distress was associated with decreased theta-beta PAC and psychological distress increased with increased age. In the left hemisphere, there was a significant relationship, whereby decreased wellbeing was associated with decreased theta-beta PAC and wellbeing scores decreased with increased age. This study presents novel findings demonstrating longitudinal relationships between interregional, resting-state theta-beta PAC and mental health and wellbeing in early adolescents. This EEG marker may facilitate improved early identification of emerging psychopathology.

clusterBMA: Bayesian model averaging for clustering.

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

Electrophysiological phenotypes of suicidality predict prolonged response to oral ketamine treatment.

Oral ketamine has shown to be a rapid-acting antidepressant and a potential treatment option for suicidality, however, repeated doses are often required. Objective markers of prolonged treatment response are needed to help individuals and clinicians make informed treatment decisions. This secondary analysis sought to identify objective electrophysiological predictors of both prolonged response and dose sensitivity to low-dose oral ketamine in people with chronic suicidality. Individuals with a Beck Scale for Suicide Ideation total score (BSS) ≥ 6 (N = 29) completed a six-week ketamine treatment, pre-treatment electroencephalography and follow-up assessment of suicidality (four weeks from the final ketamine dose). Prolonged response was observed in 52% of participants (follow-up BSS reduced by 50% or ≤6); nearly half were prolonged non-responders. There was decisive evidence for a predictive Bayesian linear regression model with follow-up BSS score as the response variable and pre-treatment auditory evoked power bands as predictors (theta, alpha and beta frequencies, BF10 = 17,948, R2 = 0.70). A Bayesian one-way ANOVA indicated strong evidence for a model of positive association between auditory evoked power and ketamine dose sensitivity (theta-alpha BF+0 = 108, effect size δ = 1.3, 95% CI 0.5-2.1; high-beta BF+0 = 7.4, δ = 0.8, 95% CI 0.1-1.6). Given auditory evoked power may index serotonin neurotransmission, these results suggest that a prolonged response to ketamine may, in part, be mediated by pre-treatment serotonergic functioning. In addition, the observed beta power differences may arise from GABAergic functioning. These suicidality phenotypes, identifiable by pre-treatment electrophysiology, may aid diagnosis, treatment selection and prediction of prolonged treatment outcome.

Oral ketamine reduces the experience of stress in people with chronic suicidality.

BACKGROUND: Stress is prevalent in people experiencing suicidality and is a major contributor to the development of mental disorders. Evidence suggests ketamine shows capacity to reverse stress-induced brain changes. Though stress and ketamine have been explored individually for suicidality, this study is the first to examine ketamine treatment for self-reported stress in adults with chronic suicidality, building on pre-clinical evidence of ketamine's capacity to normalize stress-induced responses and contributing to our understanding of oral ketamine in clinical populations. METHODS: Thirty two adult participants (22-72 years; 17 female) with chronic suicidality completed 6 weeks of active treatment, receiving low (0.5 mg/kg - 3.0 mg/kg) doses of oral ketamine once per week, with a 4-week follow-up phase, to assess the effect of ketamine on their perceived stress. Stress was measured via self-report utilizing the Depression Anxiety Stress Scale-21(DASS-21), and analysed at pre-treatment (week 0), post-treatment (week 6) and at follow-up (week 10). RESULTS: Repeated measures ANOVA showed a significant reduction in stress (p<.001) post-treatment and Reliable Change Index calculations confirmed this to be clinically significant. Furthermore, those classified as 'prolonged responders' demonstrated a sustained reduction in stress at follow-up (i.e. after 4 weeks of nil ketamine). LIMITATIONS: Small sample size, open label design, expectancy, secondary analysis CONCLUSIONS: Ketamine showed the capacity to produce a robust and sustained improvement in stress symptoms, in people with chronic suicidality. Future larger, controlled studies examining treatment suitability in a range of stress related disorders are warranted.

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents.

INTRODUCTION: To better understand the relationships between neurophysiology, cognitive function and psychopathology risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health between such subgroups. METHODS: We developed a flexible and scaleable multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. For this preliminary cross-sectional study, EEG data was collected from 59 individuals in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. RESULTS: We identified 5 core clusters associated with distinct subtypes of resting state EEG frequency content. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. CONCLUSION: This method provides the potential to identify neurophysiological subgroups of adolescents in the general population based on resting state EEG, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

Phase-Amplitude Coupling, Mental Health and Cognition: Implications for Adolescence.

Identifying biomarkers of developing mental disorder is crucial to improving early identification and treatment-a key strategy for reducing the burden of mental disorders. Cross-frequency coupling between two different frequencies of neural oscillations is one such promising measure, believed to reflect synchronization between local and global networks in the brain. Specifically, in adults phase-amplitude coupling (PAC) has been shown to be involved in a range of cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Evidence suggests that increased PAC mediates both temporary and lasting improvements in working memory elicited by transcranial direct-current stimulation and reductions in depressive symptoms after transcranial magnetic stimulation. Moreover, research has shown that abnormal patterns of PAC are associated with depression and schizophrenia in adults. PAC is believed to be closely related to cortico-cortico white matter (WM) microstructure, which is well established in the literature as a structural mechanism underlying mental health. Some cognitive findings have been replicated in adolescents and abnormal patterns of PAC have also been linked to ADHD in young people. However, currently most research has focused on cross-sectional adult samples. Whereas initial hypotheses suggested that PAC was a state-based measure due to an early focus on cognitive, task-based research, current evidence suggests that PAC has both state-based and stable components. Future longitudinal research focusing on PAC throughout adolescent development could further our understanding of the relationship between mental health and cognition and facilitate the development of new methods for the identification and treatment of youth mental health.

Low dose oral ketamine treatment in chronic suicidality: An open-label pilot study.

Recently, low-dose ketamine has been proposed as a rapid-acting treatment option for suicidality. The majority of studies to date have utilised intravenous (IV) ketamine, however, this route of administration has limitations. On the other hand, oral ketamine can be administered in a range of settings, which is important in treating suicidality, although studies as to safety and feasibility are lacking. n = 32 adults (aged 22-72 years; 53% female) with chronic suicidal thoughts participated in the Oral Ketamine Trial on Suicidality (OKTOS), an open-label trial of sub-anaesthetic doses of oral ketamine over 6 weeks. Participants commenced with 0.5 mg/kg of ketamine, which was titrated to a maximum 3.0 mg/kg. Follow-up assessments occurred at 4 weeks after the final dose. The primary outcome measure was the Beck Scale for Suicide Ideation (BSS) and secondary measures included scales for suicidality and depressive symptoms, and measures of functioning and well-being. Mean BSS scores significantly reduced from a high level of suicidal ideation at the pre-ketamine (week 0) timepoint to below the clinical threshold at the post-ketamine (week 6) timepoint. The proportion of participants that achieved clinical improvement within the first 6 weeks was 69%, whereas 50% achieved a significant improvement by the follow-up (week 10) timepoint. Six weeks of oral ketamine treatment in participants with chronic suicidality led to significant reduction in suicidal ideation. The response observed in this study is consistent with IV ketamine trials, suggesting that oral administration is a feasible and tolerable alternative treatment for chronic suicidality.