A short-interval longitudinal study of associations between psychological distress and hippocampal grey matter in early adolescence.

This study of Australian adolescents (N = 88, 12-13-years-old) investigated the relationship between hippocampal grey matter volume (GMV) and self-reported psychological distress (K10) at four timepoints, across 12 months. Participants were divided into two groups; those who had K10 scores between 10 and 15 for all four timepoints were categorised as "low distress" (i.e., control group; n = 38), while participants who had K10 scores of 16 or higher at least once over the year were categorised as "moderate-high distress" (n = 50). Associations were tested by GEE fitting of GMV and K10 measures at the same time point, and in the preceding and subsequent timepoints. Analyses revealed smaller preceding left GMV and larger preceding right GMV were associated with higher subsequent K10 scores in the "moderate-high distress" group. This was not observed in the control group. In contrast, the control group showed significant co-occurring associations (i.e., at the same TP) between GMV and K10 scores. The "moderate-high distress" group experienced greater variability in distress. These results suggest that GMV development in early adolescence is differently associated with psychological distress for those who experience "moderate-high distress" at some point over the year, compared to controls. These findings offer a novel way to utilise short-interval, multiple time-point longitudinal data to explore changes in volume and experience of psychological distress in early adolescents. The results suggest hippocampal volume in early adolescence may be linked to fluctuations in psychological distress.

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.

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.

Structural connectivity and its association with social connectedness in early adolescence.

Adolescence is a critical period of social and neural development. Brain regions which process social information develop throughout adolescence as young people learn to navigate social environments. Studies investigating brain structural connectivity (indexed by white matter (WM) integrity), and social connectedness in adolescents have been limited until recently, with literature stemming mostly from adult samples, broad age ranges within adolescence or based on social network characteristics as opposed to social connectedness. This cross-sectional study of 12-year-olds (N = 73) explored the relationship between social connectedness (SCS) and structural connectivity in early adolescence, to gauge how this snapshot of WM development is associated with social behaviour. Whole brain voxel-wise diffusion tensor imaging was undertaken to determine correlations between SCS and fractional anisotropy (FA), radial (RD) and axial (AD) diffusivity of clusters within WM tracts. Significant negative relationships between FA and SCS scores were found in clusters within 11 WM tracts, with significant positive correlations between SCS and both RD and AD across clusters within 13 and 8 clusters, respectively. Clusters within the genu of the corpus callosum (CCgn) showed strong correlations for all three metrics, and regression models that included gender, age, and psychological distress, revealed SCS to be the only significant predictor of CCgn FA, RD and AD values. Overall, these findings suggest that those with lower social connectedness had a WM profile suggestive of reduced axonal density and/or coherence. Longitudinal research is needed to track such WM profiles during adolescent development and determine the associations with mental health and well-being outcomes.