Lia Mills

Background: Adolescent suicide is a leading cause of death in Australia, arising from multiple determinants. Psychological distress, lifestyle behaviours and socioeconomic factors are associated with adolescent suicidality. Existing research has predominantly employed cross-sectional designs, limiting our understanding of how these factors interact over time. Longitudinal and data-driven approaches are needed to help identify the factors associated with the emergence of suicidality throughout adolescence. Method: Participants aged 12–17 years completed longitudinal measures of suicidal ideation, psychological distress, sleep quality, mindfulness, physical activity, eating habits, and social connectedness. Subgroups were determined via hierarchical cluster analysis, based on average scores across later timepoints (9–15). ANOVA and pairwise effect size calculations were used to compare clusters across variables, and their preceding developmental trajectories were examined using generalised additive mixed models (across earlier timepoints; 1–8). Clusters were also compared on self-reported wellbeing, long-term suicidality, and socioeconomic status. Result: Three clusters characterised by low-, moderate-, and high-severity of suicidal ideation and psychological distress, and poorer sleep, social connectedness, physical activity, mindfulness, and eating habits were identified. Across earlier timepoints, the high-severity group showed consistently elevated suicidality and deteriorating wellbeing and lifestyle scores. Conclusion: Youth with high levels of suicidality had greater psychological distress, lower wellbeing, lower socioeconomic status, and poorer lifestyle behaviours. This subgroup was also found to have poorer scores on wellbeing and lifestyle factors in their early adolescence. Findings highlight the importance of early, preventative interventions targeting both mental health and lifestyle factors to reduce suicidality in adolescents.

Background: Predicted brain-age derived from structural neuroimaging is being increasingly explored as a marker of brain maturation and biological age, with mental ill-health linked to advanced biological ageing in adults. Longitudinal evidence in adolescents remains limited, and the relationship between suicidality, psychological distress, wellbeing and brain-age gap (BrainAGE; predicted brain-age minus chronological age) is poorly understood. Methods: Data were drawn from 135 adolescents (54.8% female; 12-16.9 years; n=688 observations) from the Longitudinal Adolescent Brain Study. Latent profile analysis (LPA) used person-level means and standard deviations of distress (K10), wellbeing (COMPAS-W) and suicidal ideation (SIDAS). BrainAGE was estimated using the CentileBrain Global-BrainAGE pipeline from FreeSurfer-derived morphometric features. Associations between cluster membership and BrainAGE were examined using linear regression and linear mixed-effects models, adjusted for chronological age and sex. Results: Three profiles emerged: low distress (N=110; 50% female), moderate distress with greater suicidal ideation (N=14; 64% female) and moderate distress with lower suicidal ideation (N=11; 91% female). The moderate distress with lower suicidal ideation cluster showed significantly higher BrainAGE relative to the low distress cluster (B=1.87, SE=0.76, p=.015). Whereas, the moderate distress with greater suicidal ideation cluster did not differ to the low distress cluster. Chronological age was positively associated with BrainAGE (B=0.67, SE=0.07, p<.001). Conclusions: Distinct adolescent mental health profiles may be associated with BrainAGE, depending on the levels and stability of distress and suicidality. However, the longitudinal associations were less robust across small extensions of the developmental range studied, warranting some cautious interpretation and need for replication.

Objectives: To determine whether a biopsychosocial model of suicidality, specifically sleep, nutrition, physical exercise, mindfulness, social connectedness, lower socioeconomic status (SES) and sex are uniquely associated with increased suicidal ideation, longitudinally over adolescence. Design: Longitudinal, prospective cohort study. Setting: A structured self-report questionnaire was collected as part of the Longitudinal Adolescent Brain Study at the University of the Sunshine Coast’s Thompson Institute (Queensland, Australia) from July 2018 to January 2024. Participants: 159 Australian adolescents (n=91 female; 68 male) aged 12 to 17 years. Outcome measures: Self-reported suicidal ideation was measured longitudinally. Data were also collected on self-reported lifestyle factors (sleep, nutrition, physical exercise, mindfulness and social connectedness), psychological distress, SES and sex. All measures were collected at 4-monthly intervals for each participant for up to 5 years (maximum of 15 time points). Results: Significant relationships were identified between increased suicidal ideation and poor sleep (OR 2.6, 95% CI 1.4 to 4.6, p=0.002), socioeconomic disadvantage (SES quintile 1: OR 6.3, 95% CI, 1.8 to 21.8, p=0.004; SES quintile 2: OR 8.7, 95% CI 1.4 to 56.2, p=0.022), psychological distress (OR 5.7, 95% CI 2.1 to 15.6, p≤0.001) and eating habits (β −0.08, 95% CI −0.2 to −0.0). Conclusions: Poor sleep, socioeconomic disadvantage, psychological distress and eating habits were all found to be significantly associated with increased adolescent suicidal ideation over time. These biopsychosocial factors should be considered in targeted interventions and policies for reducing adolescent suicidality. Further research should employ multilevel modelling to examine factor interactions and rigorously evaluate interventions targeting lifestyle factors and socioeconomic inequalities through randomised controlled trials and quasi-experimental designs.

Background: Adolescence represents a sensitive developmental period characterised by an increased incidence of emerging mental health symptoms and formal diagnostic onset. These conditions can remain a significant burden throughout life. The Longitudinal Adolescent Brain Study (LABS) commenced in 2018 to track the onset and trajectory of mental health symptoms among participants aged 12–17 years. This research is aimed at identifying the clusters of emerging symptoms transdiagnostically in adolescents and examining how these clusters vary by age and change over time, providing insights into the pluripotentiality of disorder development. Methods: LABS participants (12–17 years, n = 166) completed the Mini International Neuropsychiatric Interview (MINI Kid) approximately every 4 months, up to 15 timepoints. The high dimensional dataset underwent a dimensionality reduction step (uniform manifold approximation and projection [UMAP]), followed by Bayesian model averaging of k ‐means, Gaussian mixture model and hierarchical clustering to identify distinct symptom clusters. Symptom clusters were described in terms of the original neuropsychiatric interview responses using separate XGBoost classifier models. Symptom cluster dynamics were analysed using Markov chain transition probability matrices and longitudinal analysis. To explore the relationship between symptom clusters and psychological distress and well‐being, correlational analyses were conducted using scores from the Kessler Psychological Distress Scale (K10) and the COMPAS‐W Wellbeing Scale. Results: Six symptom‐based clusters (states) were identified: attention, anxiety, depression, manic episode—heritability, anhedonia, and well. Depression and anxiety clusters had the greatest pluripotentiality. Analysis of psychological distress and well‐being scores demonstrated an inverse relationship between the states: those with greater psychological distress had more symptoms; conversely, those with greater well‐being had fewer symptoms. Conclusions: Mapping clusters of mental health symptoms and their pluripotential and transitory trajectories in adolescents enables more effective targeting of preventive interventions. This approach moves beyond categorical classifications, addressing comorbidity of emerging symptoms to mitigate the progression of early symptoms into enduring psychiatric disorders.

The impact of cyberbullying on mental health is a significant concern among adolescents, yet there is limited research on the neurophysiological markers of cyberbullying. This study aims to address this by exploring whether resting state electroencephalography (EEG) power, among traditional frequency bands (delta, theta, alpha, beta), predicts cyberbullying experiences over time. Participants (N=167 with n=904 datapoints; aged 12.0-17.9 years) completed EEG and other assessments at 4 monthly-intervals for five years. Results revealed several associations between EEG power across brain regions and various cyberbullying roles. Key findings include a decrease in EEG power across all frequency bands over time across the entire sample, aligning with typical developmental patterns. However, in early adolescence, cyberbully-victims exhibited lower delta power compared to other groups, which may suggest heightened emotional reactivity. Conversely, later in adolescence there were decreases in delta power among cyberbullies, potentially reflective an adaptive stress response. Longitudinally, cyberbully-victims retained more alpha power over time (i.e. into later adolescence) in frontal and central regions, suggesting greater cognitive effort in processing emotional experiences. Conversely, cyberbullies showed a relative steeper decline in alpha power (into later adolescence) in frontal regions, possibly linked to impulsivity and higher levels of general aggression. Longitudinal analyses highlight the importance of early interventions to target cognitive and emotional processes that may be implicated in cyberbullying in order to reduce the impact of cyberbullying and protect the mental health of adolescents. Future research should involve larger, more diverse samples to improve our knowledge of complex relationships in this research area.

Background: ‘Brain fingerprinting’ research posits that individual uniqueness can be identified by structural and functional features that may also be linked to mental health outcomes. Global structural features of the brain can be succinctly and directly captured from magnetic resonance imaging (MRI) via the eigenmodes of the cortical surface - known as geometric eigenmodes . This research investigates how the uniqueness of geometric eigenmodes changes across adolescence and their longitudinal relation to mental health and wellbeing. Methods: The current study utilised n=613 MRI, self-report and demographic datasets from N=116 community-recruited adolescents enrolled in the Longitudinal Adolescent Brain Study (LABS), between the ages of 12-17 years. MPRAGE scans at each participant’s visit were used to derive 225 left-hemisphere geometric eigenmodes. Eigenmodes were clustered into 14 eigengroups and developmental trajectories of their uniqueness and longitudinal associations with mental wellbeing and psychological distress were examined. Results: All eigengroups become significantly more unique longitudinally, and higher mode (shorter wavelength) eigengroups were more unique than lower mode groups in adolescence. Less uniqueness in ‘eigengroup 6’ was significantly associated with higher psychological distress and lower mental wellbeing at concurrent and future timepoints. Conclusion: Geometric eigengroup brain fingerprinting offers a novel way to examine neurodevelopment. This study provides evidence that eigengroups have distinct trajectories from adolescence to adulthood, consistent with other imaging studies demonstrating increasing uniqueness in this period. Importantly, they are associated with mental health state and thus may represent neurobiological markers for mental illness onset, building on previous LABS research demonstrating that the functional uniqueness of the ‘cognitive control network’ predicts psychological distress four months later.

Background: Adolescence represents a sensitive developmental period characterised by an increased incidence of emerging mental health symptoms and formal diagnostic onset. These conditions can remain a significant burden throughout life. The Longitudinal Adolescent Brain Study (LABS) commenced in 2018 to track the onset and trajectory of mental health symptoms among general population participants. This research aims to identify polysymptomatic clusters of emerging mental health symptoms in adolescents and examine how these clusters vary by age and change over time, providing insights into the pluripotentiality of disorder development. Methods: LABS participants aged 12-17 years ( n =166) completed the Mini International Neuropsychiatric Interview (MINI Kid) approximately every 4 months, with up to 15 timepoints. Due to this high dimensional dataset, the data was first processed using a dimensionality reduction step (uniform manifold approximation and projection; UMAP). Following this, the data was clustered using Bayesian model averaging of k-means, gaussian mixture model and hierarchical clustering to identify distinct symptom clusters. Symptom clusters were described in terms of the original neuropsychiatric interview responses using separate XGBoost classifier models. Symptom cluster dynamics were analysed using Markov chain transition probability matrices and longitudinal analysis. To explore the relationship between symptom clusters and psychological distress and wellbeing, correlational analyses were conducted using scores from the Kessler Psychological Distress Scale (K10) and the COMPAS-W Wellbeing Scale. Outcomes: Six symptom-based clusters (states) were identified: attention, anxiety, depression, manic episode - heritability, anhedonia, and well. Depression and anxiety clusters had the greatest pluripotentiality. Analysis of psychological distress and wellbeing demonstrated an inverse relationship between the states: those with greater psychological distress had more symptoms, conversely those with greater wellbeing had fewer symptoms. Interpretations: Mapping polysymptomatic clusters of mental health symptoms and their pluripotential and transitory trajectories in adolescents enables more effective targeting of preventive interventions. This approach moves beyond categorical classifications to mitigate the progression of early symptoms into enduring psychiatric disorders.

Cyberbullying is an increasingly problematic psychosocial health risk, particularly in youth. Electroencephalography (EEG) is commonly utilized to investigate the potential effects of social behaviors on brain activity. Hence, the current paper provides a systematic review of EEG-related studies that have addressed cyberbullying-like behaviors. Initial searches from 4 databases returned 1150 unique articles, which were screened according to PRISMA guidelines. The 29 articles remaining after full text screening investigated online social exclusion, a method of cyberbullying. Across these studies, there was evidence of links between social exclusion and abnormalities in a range of event related potential (ERP) and EEG measures representative of deviance detection (“N2” ERP), response to detection (“P3” ERP), emotional attention (“late slow wave” ERP) and emotional regulation (“frontal theta” EEG). Meta-analysis demonstrated increased P3 and late slow wave amplitudes in response to social exclusion, as well as increases in frontal-medial theta power, particularly in child and adolescent samples. However, many studies had small sample sizes, and lacked longitudinal insight into the effects of recurrent ostracism on brain function. Future research should explore the effects of a broader range of cyberbullying behaviors on psychophysiology longitudinally, particularly in vulnerable populations such as adolescents.

Cyberbullying is recognized as a problematic behavior that is often first identified during adolescence, a period which has increased susceptibility to developing mental health disorders. Due to the ever-growing nature of social media and technology, cyberbullying behaviors are becoming increasingly problematic for the adolescent demographic. Research has shown that impulsivity, social connectedness, and gender may influence cyberbullying behaviors. To our knowledge, the current study was the first to investigate the direct relationship between these variables and cyberbullying involvement in early adolescents. The sample consisted of participants of the same age and school grade, in order to ensure that data were comparable regarding stage of social development. The sample was 12-year-old participants in grade 7 (N = 69), recruited from the Sunshine Coast, Australia. It was hypothesized that higher impulsivity, lower social connectedness, and female gender would predict higher cyberbullying perpetration and victimization. The results did not support this hypothesis; however, a significant positive relationship between cyberbullying perpetration and victimization was revealed, as well as between impulsivity and social connectedness. Findings from this study help to inform preventative interventions to protect vulnerable individuals from serious mental health disorders, as well as support the need for further research in this area.