Biological Psychology

Cortisol, our stress hormone, exerts widespread influence on neural activity. However, its influence on the aperiodic component of the electroencephalography power spectrum remains to be investigated. Given individual differences in the capacity to cope with stress and adversity, it also remains unclear whether trait resilience moderates this relationship. Hence, the present study examined whether individual differences in trait resilience moderates the association between resting cortisol and aperiodic activity. Participants (N=145) completed various self-report questionnaires (e.g., trait resilience). Electroencephalography was recorded over a 20-minute baseline period, followed by salivary cortisol collection. The results revealed a significant moderating effect of trait resilience in the occipital scalp region. Specifically, higher cortisol concentration was associated with flatter 1/f slopes amongst individuals with low trait resilience, whereas this association was reversed amongst those with high trait resilience. Overall, our findings highlight the role of individual differences in trait resilience in shaping hypothalamic-pituitary-adrenal axis-related neural dynamics.

Cognitive flexibility is an executive function that allows individuals to adjust behaviour in response to changing environmental demands. We assessed volitional switching under uncertainty, without rule-based learning, in the Change Your Mind task. Nineteen patients with obsessive-compulsive disorder (OCD), 19 patients with attention-deficit hyperactivity disorder (ADHD) and matched control participants (20 per group) completed the task whilst undergoing a functional MRI scan. The task was a two-alternative forced choice paradigm where each stimulus was presented twice successively, with spurious feedback following the first presentation. This allowed participants the opportunity to repeat or change their response. Participants with ADHD changed their response more frequently than controls following a previously correct response, associated with reduced accuracy on the second trial. This was accompanied with smaller differences between change and repeat trials in the superior frontal gyrus, paracingulate gyrus and frontal pole compared to controls. Participants with OCD did not differ from healthy controls in their performance but exhibited greater activity on both change and repeat trials in the pre- and postcentral gyri than controls. These results point to distinct neurobehavioural differences in patients with ADHD and OCD underlying what is often termed more broadly inflexible behaviour.

Background Functional neurological disorder (FND), one of the most common neurological conditions, affects women almost twice as frequently as men. The reasons for this are unknown, and there has been minimal research into how physiological and pathological features of women's health interact with symptoms of FND. Methods We conducted an online survey assessing the effect of several aspects of women's health with the severity of symptoms of FND. Results 484 people completed the survey. Among the 223 who had regular or fairly regular menstrual cycles, a strong difference across the menstrual cycle was seen, with symptoms at their best in the follicular phase, worsening in the luteal phase, and worst in the pre-menstrual period and the menses. This effect was not moderated by a proxy measure of pre-menstrual dysphoric disorder (PMDD). Participants who were taking the combined oral contraceptive (COC, n=43) and progesterone-based contraception (n=80) were more likely to report symptom improvement from starting the medication than worsening. When compared to menstruating participants who were not taking the COC, participants taking the COC reported less worsening in their symptoms of FND in the luteal, pre-menstrual, and menstrual phases. Of the 99 women who had passed menopause since developing FND, 76% reported worsening of their FND symptoms after menopause. Discussion This study demonstrates interactions between several aspects of women's health and symptoms of FND. The observed pattern of symptom fluctuation across hormonal states suggests a potential modulatory role of oestrogen, warranting further targeted investigation.

This study examined the extent to which goodness of pronunciation (GoP) scores and phonological posterior probabilities capture perceptual ratings of speech severity in individuals with motor speech disorders (MSD). Speech recordings of the word catastrophe were obtained from 489 participants, including 333 neurologically typical controls and 156 individuals with MSD. GoP scores were derived using traditional acoustic features and self-supervised speech representations, including WavLM and XLS-R, across multiple modeling approaches, while phonological posterior probabilities were extracted using Phonet. Model performance was evaluated using Kendall's rank correlations, regression, and receiver operating characteristic analyses against speech-language pathologists' perceptual ratings of sound distortion and intelligibility. Both GoP and phonological posterior probabilities were significantly associated with perceptual ratings. Self-supervised speech representations substantially outperformed traditional acoustic features, with WavLM-based GoP using k-nearest neighbors achieving the strongest performance. Across correlation, regression, and classification analyses, GoP consistently outperformed phonological posterior probabilities for both sound distortion and intelligibility. Age and gender had minimal influence on model-derived measures or their relationships with perceptual ratings. These findings demonstrate the value of self-supervised GoP as an objective measure of speech impairment while highlighting the complementary role of phonological posterior probabilities in characterizing articulatory aspects of motor speech disorders.

Multimodal imaging approaches that combine structural and functional neuroimaging provide a robust framework for examining neuroplastic adaptations that may not be captured by any single modality. The present study investigated the effects of a four-week expiratory muscle strength training (EMST) program on structural and resting-state functional connectivity in healthy young adults. Five healthy young adult males (aged 19-35 years) completed a standard four-week EMST protocol and underwent pre- and post-training imaging assessments. Structural neuroimaging included T1-weighted and diffusion-weighted MRI, which were analyzed using voxel-based morphometry, surface-based morphometry, and white-matter structural connectivity. Functional neuroimaging consisted of resting-state fMRI to assess training-related changes in functional architecture, network connectivity, and global network measures. Structural MRI analyses revealed no significant changes in gray or white matter volume, cortical morphology, or white-matter structural connectivity following EMST (all FWE- or FDR-corrected p > .05). In contrast, resting-state fMRI demonstrated a significant increase in whole-brain functional connectivity (FDR-corrected p = .036), accompanied by greater network integration, reflected in increased local efficiency and transitivity and reduced modularity. Network-level analyses showed enhanced within- and between-network connectivity in sensorimotor and cognitive circuits. Our findings demonstrate robust functional reorganization following EMST, despite the absence of detectable macrostructural or large-scale white-matter connectivity changes, at least within the timescale and sample characteristics of the current study. These results reflect early-stage neuroplasticity, both globally and within the networks underlying speech and swallowing control and suggest that functional reorganization occurs early in training and likely precedes longer-term structural modifications in these networks.

In noisy environments, visible speech articulations improve listening comprehension. The benefit derives from several sources, including articulatory timing and shape. Recent research has shown that visual cortex encodes a categorical representation of articulatory features and that visual speech can benefit both acoustic and phonetic feature processing separately. The present study advances the hypothesis that the shape of the articulators specifically influences the categorization of auditory speech in terms of its phonetic features. We tested this by linearly modeling electroencephalographic responses to natural, continuous speech (in noise) in terms of the acoustic and articulatory features of the speech. We compared the performance of these models in conditions where the speech was accompanied by a natural video of the speaker with their mouth visible, and a video where their mouth was covered by a dynamic ellipse obscuring articulatory shape but preserving dynamics. The dynamic mask reduced comprehension, neural processing of phonetic features, the associated multisensory benefits, and indices of visual-only linguistic processing over occipital scalp. Our findings support substantial visual involvement in speech comprehension, derived largely from the shape of the articulators. They also corroborate several proposals involving audiovisual speech processing hierarchy and the nature of the information contained in visible speech. HighlightsO_LIVisual speech provides at least two forms of information to enhance acoustic speech processing: redundant temporal dynamics and complementary articulatory information C_LIO_LICovering the mouth with a dynamic mask preserves horizontal and vertical lip movement information, but largely removes articulatory detail C_LIO_LIVisual speech with a mask preserves some general multisensory benefits but removes visual linguistic information and its ability to enhance auditory processing at the level of phonetic features. C_LI

Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional linear frameworks attempting to model synergistic risk. While various loci are identified as contributing to neurocognitive disparities, the emergent phenotypic expression and associated predictive value relative to standard clinical baselines require further investigation. To facilitate dimensional reduction of complex genetic data into identifiable phenotypes, Generalized Low Rank Modeling (GLRM) and K-means clustering are applied to participant data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The utility of these derived archetypes and clusters is assessed, stratifying variance for Mini-Mental State Examination (MMSE) performance. Utilizing generalized additive modeling (GAM) and partial eta squared (p2) effect size, the derived genetic features are compared with other predictors including age, educational attainment, gender, and raw, genetic variant carriage dimensions. Results & Conclusion: In accordance with the hypothesized empirical regularity, age and education persist as primary predictors of MMSE performance. The unsupervised machine learning pipeline successfully identified a composite genetic cluster that emerged as an influential predictor in terms of relative magnitude (p2). Centroid analysis of the GLRM subspace indicated that a particular sub-population (Cluster 2) - defined by a substantial weighting on the EPHA1 target - demonstrated a statistically significant association with MMSE scores, relative to cluster 3. These results suggest that data-driven genetic feature engineering provides an interpretable basis for inference regarding variance in global cognition. By discovering multivariate genetic architecture, this modeling approach captures complexity often missed by individual clinical variable modeling. Such findings implicate the utility of interpretable machine learning for translational dementia research and predictive clinical stratification.

The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

Interception of moving objects requires the nervous system to compensate for sensory delays and uncertainty, yet how behavior is controlled remains debated. Key questions concern whether predictive processes play any role at all and, if so, whether they rely on simple motion extrapolation or incorporate internalized physical priors, such as gravity. Another open question is whether observers adopt a single control strategy or flexibly switch between predictive and reactive control - or between different predictive strategies - depending on task demands. To address these questions, we developed a virtual interception task in which participants intercepted moving targets under systematically varied conditions. We manipulated gravity (1g vs. 0g), visual availability (occluded vs. non-occluded), target velocity, and the initial spatial configuration of the ball and paddle (same vs. opposite side). Results indicate that interception is supported by predictive mechanisms across conditions. Behavioral patterns during occluded 0g trials suggest that participants extrapolate target motion using expectations consistent with gravity. Target velocity, visual occlusion, and task geometry modulated movement strategies, indicating that predictive control is flexibly adapted to task demands. These findings support the view that interception relies on predictive internal models incorporating structured physical priors while revealing flexible, context-dependent adaptations to sensory and task constraints.

Aphasia diagnosis in Indonesia remains challenging due to limited culturally and linguistically appropriate instruments. Widely used tools such as the Boston Diagnostic Aphasia Examination (BDAE) and Western Aphasia Battery (WAB) are not adapted to the Indonesian context, while Tes Afasia untuk Diagnosis, Informasi, dan Rehabilitasi (TADIR) provides screening but lacks diagnostic accuracy. To address this gap, we developed the Instrumen Diagnosis dan Evaluasi Afasia (IDEA) for native Indonesian speakers and evaluated its validity, reliability, and normative cutoff values in cognitively healthy Indonesian adults. Eighty-three cognitively normal adults (screened using MoCA-Ina) with no history of neurological disease were assessed using IDEA, which evaluates six language domains. Items were adapted from existing tools and reviewed by experts. Content validity, internal consistency (Cronbachs alpha), and construct validity (Exploratory Factor Analysis) were analyzed using SPSS v25. A total of 83 participants were included (median age = 55.81 years, 54% secondary education). IDEA demonstrated good feasibility, with an average completion time of 45-60 minutes depending on participant engagement. Content validity was established by unanimous expert consensus. Construct validity showed meritorious sampling adequacy (KMO = .872) and significant sphericity (Bartletts test {chi}^2 (15) = 278.523, p<.001), supporting factor analysis. Internal consistency showed good reliability across six domains (Cronbachs = 0.896). IDEA is a valid and reliable tool for assessing aphasia in Indonesian natives. It is a culturally appropriate assessment tool which offers structured, domain-based evaluation and supports differential diagnosis of both classical and progressive aphasia syndromes. Keywords: Aphasia, Language Assessment, Indonesian, IDEA, Validity

People differ widely in their sensitivity to pain, and this variability is clinically relevant, yet the underlying structural brain mechanisms remain poorly understood. White matter hyperintensities (WMH), a common imaging marker of cerebral small vessel disease, are associated with microstructural abnormalities in white matter tracts and have also been linked to pain related outcomes; however, the mechanisms linking WMH to altered pain perception remain unclear. We investigated whether WMH are linked to pain sensitivity through tract specific microstructural alterations and cortical structural differences. We analysed data from 1,448 participants (mean age 73 years; 53% women) in the population based Rotterdam Study and independently replicated the findings in 1,522 participants (mean age 63 years; 52% women) from the population based Tromso Study. Pain sensitivity was quantified using the cold pressor test. Multimodal magnetic resonance imaging, including T1 weighted, fluid attenuated inversion recovery and diffusion tensor imaging, was used to map WMH to predefined white matter tracts, derive tract specific fractional anisotropy (FA), and estimate cortical measurements. Cox proportional hazards models assessed associations with pain sensitivity, and tract specific mediation analyses evaluated whether white matter microstructure or tract connected cortical regions mediated the relationship between white matter hyperintensities and pain sensitivity. WMH were present in 20 of 27 predefined tracts and were associated with reduced FA in 18 tracts. Higher WMH burden was associated with greater pain sensitivity, particularly in the left anterior thalamic radiation and left superior thalamic radiation, while lower FA in the anterior thalamic radiation, medial lemniscus, superior thalamic radiation and inferior fronto occipital fasciculus was associated with greater pain sensitivity. Mediation analyses showed that white matter microstructural disruption was the principal pathway linking WMH to pain sensitivity, with the strongest indirect effects observed through the inferior fronto occipital fasciculus (44.6% mediated) and anterior thalamic radiation (32.6% mediated). Cortical atrophy in the precentral and postcentral gyri provided a smaller secondary pathway, mediating approximately from 3 to 6% of the association between corticospinal or superior thalamic radiation WMH and pain sensitivity. Replication analyses supported these cortical mediation pathways, and meta analysis strengthened the tract specific associations. Together, the results suggest that vascular white matter injury is associated with pain perception through specific structural pathways, with DTI based markers appearing particularly sensitive to these relationships.

Abstract Background: Medical students carry a disproportionate burden of mental health problems, but large multicentre studies from low- and middle-income countries remain scarce, and most evidence relies on single-institution samples and odds-ratio-based analyses that overstate associations for common outcomes. We provide a comprehensive epidemiological overview of mental health among medical students across an entire Brazilian state, quantifying the burden, its co-occurrence, and its socioeconomic and academic determinants. Methods: Cross-sectional online survey of 1,560 medical students covering all 20 medical schools in operation in Rio Grande do Sul, Brazil (August-December 2023). Validated instruments assessed depressive and anxiety symptoms (PHQ-4), suicidal ideation (PHQ-9 item 9), non-suicidal self-injury, burnout (ESB-eu), quality of life (EUROHIS-QOL-8), spirituality (SSRS), substance use (ASSIST), prescription stimulant misuse, and mistreatment during training. Associations were estimated as adjusted prevalence ratios (aPR) using modified Poisson regression with robust variance, with linear trend tests, prespecified interactions, sensitivity analyses and E-values. Results: Anxiety symptoms affected 64.5% (95% CI 62.1-66.9), depressive symptoms 45.5% (43.1-48.0), burnout 49.1% (46.7-51.6), recent suicidal ideation 20.6% (18.7-22.7), lifetime self-injury 13.1% (11.6- -4.9) and stimulant misuse 7.9%; 57.4% screened positive for >=2 outcomes. Quality of life declined monotonically with cumulative burden. Low family income showed inverse gradients across five outcomes (e.g., depression: PR 0.93 per income level, p = 0.002). Four factors were independently associated with nearly all outcomes: short sleep (<6 h; aPR up to 2.73), minority sexual orientation (aPR up to 2.02), family psychiatric history, and mistreatment during training (reported by 55.6%; aPR up to 1.49). Stimulant misuse tripled from the basic cycle to clerkship (aPR 2.30, 95% CI 1.36-3.88). There was no evidence of multiplicative interaction, indicating that the risk factors acted independently on the outcomes. Estimates were robust across sensitivity analyses. Conclusions: One in two medical students in this state-wide sample screened positive for at least two mental health problems. Socioeconomic vulnerability, sleep deprivation, mistreatment and minority status operate as independent and largely modifiable determinants - actionable targets for medical schools and policymakers.

Background: Female chronic pelvic pain disorders (CPPDs) are highly prevalent and frequently accompanied by sleep disturbance and autonomic nervous system (ANS) dysregulation. Heart rate variability (HRV), a non-invasive index of ANS function, may provide an objective, physiological correlate of sleep health and can be monitored using wearable devices, enabling a continuous, scalable approach. Objectives: This study examined whether wearable-derived daily HRV metrics are associated with self-reported sleep disturbance in women with CPPD(s) compared with healthy controls, using epoch-level data and generalized additive models. Methods: We conducted a retrospective observational study using up to 90 days of data from a mobile health research app. Participants were 128 women with CPPD(s) and 63 demographically matched healthy controls, who completed a daily PROMIS-based 3-item sleep disturbance questionnaire and wore Fitbit devices that provided 5-minute HRV epochs. Primary predictors were high frequency (HF) and low frequency (LF) power and root mean square of successive differences (RMSSD), with group (CPPD vs control), daily pain severity, and menstrual status as covariates. We fit separate generalized additive mixed models (GAMMs) for each HRV metric with a nonlinear smooth term and an HRV x Group interaction. Results: Higher HF and RMSSD were associated with lower sleep disturbance scores, and these associations were stronger in controls than in the CPPD group (HF x group B {approx} -1.59, p < 0.00010; RMSSD x group B {approx} -0.58, p < 0.0001). LF showed a more complex pattern but also differed by group (B {approx} -0.531, p < 0.0001). HRV smooth terms were highly nonlinear, and models explained ~8-9% of deviance in sleep disturbances. Pain severity and menstrual bleeding were strongly associated with worse sleep. Conclusion: These findings indicate small but consistent associations between wearable-derived HRV metrics and daily sleep disturbances in women with CPPD(s) and healthy controls, with weaker associations in CPPD(s). Integrating continuous HRV with symptom tracking could support low-burden and multimodal monitoring of sleep health in chronic pelvic pain, but prospective validation is needed before HRV can be used for diagnostic or treatment response decision making.

Speech perception requires transforming acoustic input into neural representations that support linguistic understanding, yet its underlying computational principles remain unclear. Classical efficient coding theories posit optimal compression of sensory input, whereas alternative accounts propose that neural systems preferentially encode information that supports prediction. A key open question is whether such predictive encoding operates on fixed inputs or on flexible internal representations. We instantiated three hypothesis models of speech processing: (i) optimal compression with deep autoencoders, (ii) predictive reconstruction with predictive autoencoders, and (iii) predictive information representation via latent-space prediction using contrastive learning. We compared resulting speech latent representations to electroencephalographic (EEG) activity during speech listening. Representations learned under the predictive information objective best explained neural latents. Crucially, only representations that selectively compressed predictive information predicted behavioral performance, suggesting that neural speech representations are structured to encode predictive information in latent space rather than to maximize compression or input prediction.

Brain-computer interfaces (BCIs) have achieved impressive performance by decoding motor and articulatory signals associated with speech production. However, considerably less is known about whether higher-level semantic representations can be decoded from human cortical activity. Demonstrating semantic decoding would advance both our understanding of language organization and the development of BCIs that rely on conceptual rather than purely articulatory information. We recorded intracranial neural activity from patients undergoing stereotactic electroencephalography (sEEG) for clinical epilepsy monitoring while they performed language tasks requiring semantic processing. High-gamma power was extracted from local field potentials and used to generate trial-level features for supervised machine-learning classification. Classification performance was evaluated using cross-validation. Semantic category information was decoded significantly above chance, with mean classification accuracy reaching 29.8% across 15 semantic categories (chance = 6.7%). These findings demonstrate that high-gamma activity contains information about conceptual category membership that can be extracted on individual trials. These results provide evidence that semantic information is accessible from intracranial population recordings and support the feasibility of semantic decoding as a complementary direction for future language BCIs. Beyond neuroprosthetic applications, this work contributes to understanding how conceptual knowledge is represented in the distributed human language network.

Chronic kidney disease and heart failure disproportionately burden populations of African ancestry, yet Mendelian randomisation (MR) studies of the causal relationship between kidney function and heart failure subtypes have been conducted exclusively in European ancestry populations. We performed a forward two-sample MR analysis to evaluate the causal effect of genetically predicted estimated glomerular filtration rate (eGFR) on heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) in individuals of African ancestry. Genetic instruments were selected from an African ancestry eGFR genome-wide association study (N = 67,943) at genome-wide significance, with linkage disequilibrium clumping using an African ancestry reference panel. Heart failure subtype summary statistics were obtained from the Million Veteran Program (HFpEF: 5,379 cases / 113,041 controls; HFrEF: 9,104 cases / 109,632 controls). Six independent SNPs (F-statistics 30.5 &#8211 107.3; R&#178 = 0.62%) were retained as instruments. The primary inverse-variance weighted analysis provided no evidence of a causal effect of eGFR on HFpEF (OR 0.92, 95% CI 0.80 &#8211 1.06, p = 0.248) or HFrEF (OR 0.98, 95% CI 0.78 &#8211 1.23, p = 0.878). Sensitivity analyses were directionally consistent. There was no evidence of heterogeneity or directional pleiotropy. Minimum detectable effects at 80% power were OR 1.28 for HFpEF and OR 1.22 for HFrEF. These null findings should be interpreted as inconclusive given current power constraints; larger ancestry-matched studies are needed.

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective. To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods. We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings. Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions. Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications. These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties.

Background: Actigraphy-derived rest-activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data. Objective: In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean-variance dependence. Methods: We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box-Cox or Yeo-Johnson) were applied to the entire study cohort to reduce mean-variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC). Results: Transformations reduced mean-absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%-4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50-0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (+-1 hour), did not improve discrimination between mania and remission. Conclusions: Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

Background. Given uncertainty about whether later-life health at similar ages is improving over time, we examined trends across multiple health domains. Methods. We analysed data from community-dwelling adults aged 50 and older in the English Longitudinal Study of Ageing in 2004/05, 2012/13, and 2023/24 (main survey: N=8389, 8549, and 6090, respectively). Outcomes included self-rated health, limiting long-standing illness, pain, mobility limitations, cardiometabolic and chronic conditions, obesity, inflammation, mental health, quality of life and memory. Weighted pooled modified Poisson and linear regressions compared outcomes over time, overall, and by age group and education, with additional adjustment for sex and wealth. Results. Adjusted estimates showed divergent trends. Fair/poor self-rated health increased from 27% to 34%, and any pain from 37% to 47%, whereas mobility impairments declined from 58% to 52%. Self-reported high cholesterol increased from 19% to 39%, while biomarker-defined high cholesterol declined from 78% to 54%; diabetes increased on both measures. Psychiatric problems increased from 6% to 10%, quality of life declined, and memory improved. However, trends differed by age and education, particularly for limiting long-standing illness, mobility limitations, cholesterol biomarkers, and mental health, indicating that aggregate trends masked unevenly distributed changes. Conclusion. Later-life health in England has not improved uniformly. Gains in functioning, biomarkers, and cognition coexist with rising pain and poorer mental health. Trends were also socially and age patterned, producing increasingly multidimensional and socially patterned health outcomes. Multidomain health monitoring is essential for interpreting population health trends and planning healthy ageing, prevention, long-term care, and work policies.