Paul E. Schwenn

Background Ketamine has emerged as a rapid-acting therapeutic option for post-traumatic stress disorder (PTSD); however, its ability to sustain long-term therapeutic outcomes remains poorly understood. Identifying molecular biomarkers predictive of a sustained response to ketamine may enhance personalised treatment strategies. This study investigates the epigenetic and transcriptomic precursors underpinning ketamine’s long-term therapeutic efficacy in PTSD. Methods This study utilised data from the Oral Ketamine Trial of PTSD (OKTOP), an open-label, dose-ranging clinical study conducted between 2021 and 2024. Baseline differential DNA methylation and gene expression profiles of sustained responders (clinical response >1 month post-ketamine treatment) were compared against non-responders. Epigenomic and transcriptomic analyses were performed to identify differentially regulated genes associated with ketamine response. Results Baseline molecular analyses revealed significant differential methylation and transcriptomic profiles across 112 genes. Key biomarkers included DENND5B (cg02046589), ZFY (cg00272582), PDGFRA (cg21309167), CPT1A (cg10098373), AHRR (cg26076054), RPH3AL (cg17316718), CHI3L1 (cg19081101), UTY (cg04790916), LDHD (cg00004883), TBC1D16 (cg26287152), FAM66A (cg23285059), NME8 (cg02531859), EIF1AY (cg13308744), PCBP3 (cg13695288), PAQR6 (cg03954786), KCNK17 (cg19475903), PLPP2 (cg24452451), ANK1 (cg23668222), LINC00200 (C10ORF139, cg19282259), ALAS2 (cg07471703), ZBP1 (cg06305758), TACSTD2 (cg01821018), and PLEKHH3 (cg24455236). These biomarkers were implicated in pathways related to metabolism, transcriptional regulation, cell signalling, neuronal development, immune response, synaptic plasticity, and cytoskeletal organisation. Non-responders exhibited persistent dysregulation across these pathways, suggesting potential biological barriers to treatment efficacy. Clinically, sustained responders presented with higher baseline PTSD severity and demonstrated a response at lower ketamine doses compared to non-responders. Conclusions This study highlights the potential of methylomic and transcriptomic profiling to identify functional biomarkers predictive of ketamine response in PTSD. The observed molecular distinctions between responders and non-responders suggest a complex interplay between clinical presentation and treatment outcomes. These findings contribute to advancing precision medicine approaches for PTSD by informing biomarker-driven treatment stratification and optimisation of ketamine therapy.

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.

Introduction To better understand the relationships between brain activity, cognitive function and mental health 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 symptoms between such subgroups. Methods Here we implement a 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. EEG data was collected from 59 individuals as part of their baseline assessment 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 core clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. Results We identified 5 core clusters which were associated with distinct subtypes of resting state EEG frequency content. EEG features that were influential in differentiating clusters included Individual Alpha Frequency, relative power in 4 Hz bands up to 16 Hz, and 95% Spectral Edge Frequency. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between these clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. Conclusion In this work we have developed a flexible and scaleable pipeline to identify subgroups of individuals in early adolescence on the basis of resting state EEG activity. These findings provide new clues about neurophysiological subgroups of adolescents in the general population, 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.

We explore the inter-relationship between the phase behavior and photovoltaic performance for two blend systems comprising poly(3-n-hexylthiophene-2,5-diyl) (P3HT) as the electron donating moiety and two newly developed small molecule non-fullerene electron acceptors. Binary non-equilibrium temperature/composition phase diagrams of the two systems are prepared from differential scanning calorimetry (DSC) thermograms of blends of different compositions. The phase behavior is correlated with the optoelectronic performance of corresponding binaries in bulk heterojunction (BHJ) solar cells. The thermal and optoelectronic blend characterization is supported with optical microscopy and specular X-ray diffraction (sXRD) experiments. For both electron-accepting compounds the composition yielding the maximum photocurrent generation in devices was found to be in the hypoeutectic regime, i.e. at compositions that are shifted from the eutectic towards the small molecule rich region in the phase diagrams. We demonstrate that measuring the thermal properties of the blends is useful for rapid component ratio optimization and the evaluation of unexplored materials combinations.

Inorganic semiconductors permeate virtually every sphere of modern human existence. Micro-fabricated memory elements, processors, sensors, circuit elements, lasers, displays, detectors, etc are ubiquitous. However, the dawn of the 21st century has brought with it immense new challenges, and indeed opportunities-some of which require a paradigm shift in the way we think about resource use and disposal, which in turn directly impacts our ongoing relationship with inorganic semiconductors such as silicon and gallium arsenide. Furthermore, advances in fields such as nano-medicine and bioelectronics, and the impending revolution of the 'ubiquitous sensor network', all require new functional materials which are bio-compatible, cheap, have minimal embedded manufacturing energy plus extremely low power consumption, and are mechanically robust and flexible for integration with tissues, building structures, fabrics and all manner of hosts. In this short review article we summarize current progress in creating materials with such properties. We focus primarily on organic and bio-organic electronic and optoelectronic systems derived from or inspired by nature, and outline the complex charge transport and photo-physics which control their behaviour. We also introduce the concept of electrical devices based upon ion or proton flow ('ionics and protonics') and focus particularly on their role as a signal interface with biological systems. Finally, we highlight recent advances in creating working devices, some of which have bio-inspired architectures, and summarize the current issues, challenges and potential solutions. This is a rich new playground for the modern materials physicist.