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Abstract Mental health disorders are associated with decreased quality of life, economic productivity loss, and increased mortality. The association between stressful experiences and psychopathology is well documented. However, studies are needed to understand the impact of timing of stressful events, types of traumatic experiences, and of family resilience on internalizing and externalizing symptoms in early childhood. The present study used a longitudinal design towards this end. Parents (N = 456) completed study measures at infancy, 2 years, 3 years, 5 years, and 7 years. At 3 years, greater stressful events during the prenatal period, 1-2 years, and 2-3 years (B = 0.833–0.369, p = 0.028–0.046) predicted internalizing symptoms for female participants only. For externalizing symptoms at 3 years, every time point assessed was significantly associated with more symptoms across both sexes (B = 1.071–0.414, p

This temporally rich, longitudinal study of early adolescents (N = 88, 277 datasets, 12–13 years) investigated the relationship between bilateral subcortical grey matter volume (GMV) in the hippocampus, amygdala, accumbens-area, caudate, putamen and pallidum with self-reported mental wellbeing at four timepoints, across 12 months. Generalised Estimating Equations (GEE) revealed (1) higher ‘total wellbeing’ was associated with smaller left caudate and larger left accumbens-area; (2) higher eudaimonic wellbeing was associated with smaller left caudate and larger right caudate; and (3) higher hedonic wellbeing was associated with larger left accumbens-area. Further analyses and plots highlighted different associations between GMV and wellbeing for adolescents who consistently experienced ‘moderate-to-flourishing’ wellbeing (n = 63, 201 datasets), compared with those who experienced ‘languishing’ wellbeing at any timepoint (n = 25, 76 datasets). These findings demonstrate several associations between subcortical GMV and measures of wellbeing, at short intervals in early adolescence. Taken together, sub-types of wellbeing appear uniquely associated with specific subcortical regions; and there may be a distinct neurobiological and wellbeing profile for adolescents who experience poorer wellbeing over the course of their first year(s) of secondary school. This study implicates the bilateral caudate and left accumbens-area as important targets for future research into the mental wellbeing of adolescents.

IntroductionNutrition, sleep and physical activity are termed the “big three” health enhancing behaviors (HEB) associated with psychological distress and wellbeing. This study sought to understand differential associations between an expanded group of HEB (nutrition, sleep, physical activity, mindfulness, social connectedness) and psychological distress/wellbeing in early adolescents.MethodsCorrelational and regression analyses were conducted in N=103 (51% females) adolescents (12.6 ± 0.3 years of age) recruited from the Longitudinal Adolescent Brain Study.ResultsHigher scores on sleep, social connectedness and mindfulness scales were significantly associated with lower psychological distress scores. While higher scores on social connectedness and mindfulness scales were significantly associated with higher wellbeing scores. When adjusting for sex, nutrition, sleep, social connectedness and mindfulness accounted for a significant proportion of variance in the psychological distress model whereas physical activity and social connectedness accounted for a significant proportion of the variance in the wellbeing model.DiscussionsOverall findings make a strong case for expansion of the “big three” HEB to include mindfulness and social connectedness, especially given social connectedness emerged as the strongest predictor of both psychological distress and wellbeing. In addition, this research suggests that early adolescent nutrition, sleep quality, and mindfulness should be prioritized in efforts to reduce risk of difficulties, and physical activity prioritized as a protective factor for wellbeing in this population. Findings have implications for interventions, emphasizing the importance of addressing HEB factors comprehensively and tailoring strategies to the unique needs of early adolescents to foster positive mental health outcomes.

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.

We provided the dataset of brain connectome matrices, their similarities measures to self and others longitudinally, and Kessler's psychological distress scales (K10) including the response to each question. The dataset can be used to replicate the results of the manuscript titled “A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence”. The functional connectome (whole-brain and 13 networks) matrices were calculated from the resting-state functional MRIs (rs-fMRIs). We collected rs-fMRI and Kessler's psychological distress scale (K10) in 77 adolescents longitudinally up to 9 times from 12 years of age every four months. After removal of data with excessive motion, 262 functional connectome matrices were provided with this paper. The 300 regions of interest (ROIs) were defined using the Greene lab brain atlas. The functional connectome matrices were calculated as correlations between time series from any pair of ROIs extracted from pre-processed fMRIs. This dataset could be potentially used to 1. Understand developmental changes in the functional brain connectivity, 2. As a normal control database of functional connectome matrices, 3. Develop and validate connectome and network-related analysing methods.

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.

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.

Each human brain has a unique functional synchronisation pattern (functional connectome) analogous to a fingerprint that underpins brain functions and related behaviours. Here we examine functional connectome (whole-brain and 13 networks) maturation by measuring its uniqueness in adolescents who underwent brain scans longitudinally from 12 years of age every four months. The uniqueness of a functional connectome is defined as its ratio of self-similarity (from the same subject at a different time point) to the maximal similarity-to-others (from a given subject and any others at a different time point). We found that the unique whole brain connectome exists in 12 years old adolescents, with 92% individuals having a whole brain uniqueness value greater than one. The cingulo-opercular network (CON; a long-acting ‘brain control network’ configuring information processing) demonstrated marginal uniqueness in early adolescence with 56% of individuals showing uniqueness greater than one (i.e., more similar to her/his own CON four months later than those from any other subjects) and this increased longitudinally. Notably, the low uniqueness of the CON correlates (β = -18.6, FDR-Q