Psychophysiology

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 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

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.

Subcortical structures play a key role in shaping cortical computation through distributed cortico-subcortical networks, yet it remains unclear whether individual differences in subcortical anatomy are reflected in resting-state cortical oscillations. We analysed resting-state magnetoencephalography (MEG) and structural MRI from 533 healthy adults in the Cambridge Centre for Ageing and Neuroscience (CamCAN) cohort to test whether hemispheric asymmetries in subcortical volume predict asymmetries in cortical oscillatory power. Lateralisation indices were calculated for subcortical volumes and for oscillatory power across homologous MEG sensor pairs. Cluster-based permutation testing revealed frequency-specific associations between subcortical anatomy and cortical activity. Globus pallidus asymmetry was positively associated with posterior alpha-band power lateralisation, putamen and caudate asymmetries were associated with beta-band lateralisation, and hippocampal asymmetry was negatively associated with delta-band lateralisation. These findings extend previous task-based observations linking pallidal anatomy with alpha oscillations to the resting state and demonstrate that distinct subcortical structures are associated with specific cortical frequency bands. Our results suggest that resting-state MEG captures functional signatures of cortico-subcortical organisation and provides a non-invasive framework for studying healthy ageing and disorders involving subcortical degeneration.

Background: Bidirectional next-day associations between sleep disturbances and affective symptoms have been shown in previous research, yet the consecutive day effects between these factors remains poorly understood. Methods: We analysed longitudinal ecological momentary assessment (EMA) data obtained from a subsample of young persons in the Brazilian High-Risk Cohort (BHRC) study collected in 2020-2021. Participants reported sleep quality each morning and rated affective symptoms relating to mood, anxiety, and energy four times daily for 28 days. We selected 88 individuals (17.83{+/-}1.74 years, 56 [63.6%] female gender) with at least one instance where individuals were observed three-days in a row. Within-person bidirectional next-day effects between sleep quality and affective symptoms were estimated using mixed-effects regression analysis adjusting. We then applied g-estimation approaches to estimate the effect that lagged sleep quality and consecutive improvements in sleep quality had on affective symptoms. Results: Sleep quality and affective symptoms had bidirectional next-day effects, with sleep quality tending to have greater influence on affective symptoms than the reverse. Improved lagged sleep quality had positive effects on affective symptoms incrementally above the prior night's sleep quality. Also, improvement of sleep quality across consecutive days had incremental and approximately equal effects on affective symptoms. Conclusions: Sleep quality and affective symptoms exhibit a feedback loop, whereby poor sleep quality influences affective symptoms over consecutive days. Breaking these feedback loops, by improving sleep quality across several consecutive nights should improve affective symptoms. This supports interventions that target sustained improvement in sleep and possibly circadian regulation to improve affective symptoms.

Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential differences between its types, BD-I and BD-II, remain unclear. This study used automated facial-expression analysis during naturalistic affective film viewing to examine subtype-specific and context-dependent emotional responding in bipolar depression. Methods: The sample included 135 participants: 69 healthy controls and 66 patients with BD (BD-I, 23; BD-II, 43). Participants viewed nine emotionally evocative film clips spanning negative, positive, neutral, and socially threatening contexts, while their facial expressions were continuously recorded and quantified using computer vision-based facial-expression analysis. Results: Patients with BD-I showed a distinct, context-dependent facial-expression profile, characterized by greater negative responses across multiple contexts than other groups. Specifically, they showed increased sadness during sad, reward, and amusing clips, and elevated anger during sad and neutral clips. In socially threatening contexts, BD-I participants showed a multivalent pattern of elevated anger, fear, and joy, suggesting poorly coordinated or context-incongruent affective expression. In contrast, BD-II participants did not differ significantly from healthy controls on any emotion, despite depressive symptom severity comparable to BD-I participants. Conclusions: These findings suggest that facial-expression patterns in bipolar depression differ across subtypes. BD-I may be characterized by heightened negative reactivity and altered context-appropriate modulation of emotional expression, whereas BD-II may not show comparable alterations in overt facial output. Automated facial-expression analysis during naturalistic stimulation may provide a useful behavioral marker for characterizing subtype-specific affective disturbance in bipolar depression and related psychopathology.

Objective: Working memory (WM) deficits are a co-occurring feature to numerous neuropsychiatric disorders, particularly attention-deficit/hyperactivity disorder (ADHD), and there remain no treatments that directly target WM. The coupling between the phase of theta band activity and amplitude of gamma band activity (i.e., TGC) is an established neural correlate of WM. However, no studies have examined WM-related TGC in ADHD or whether neuromodulation can modulate these oscillatory dynamics in youth. This set of studies examined the effects of intermittent theta burst stimulation (iTBS) to the left dorsolateral prefrontal cortex (DLPFC) and left posterior parietal cortex (PPC) on TGC in youth with ADHD. Methods: In two randomized, double-blind, sham-controlled crossover trials, adolescents with ADHD and clinically significant parent-reported WM symptoms first completed a single-session study comparing DLPFC versus PPC iTBS targeting (n = 47) and then a multi-session clinical trial comparing 10 sessions of active versus sham left DLPFC iTBS (n = 29). Participants completed a computerized visuospatial Sternberg WM task with concurrent electroencephalography (EEG) before and after the single sessions, as well as at baseline, midway through treatment, and approximately 24 hours after the final session within the multi-session trial. Phase-amplitude coupling between theta phase and gamma amplitude was quantified using the Kullback Leibler modulation index at frontoparietal electrodes. Linear mixed-effects models examined treatment effects and associations between change in TGC and WM status (including accuracy, reaction time, and clinical symptoms). Results: Across participants, lower TGC was associated with lower symptoms and better WM performance, including higher accuracy, faster and more consistent RT. Active iTBS increased frontoparietal TGC relative to sham stimulation, with effects observed both acutely after a single session and ~24 hours after multiple sessions. DLPFC-targeted iTBS increased TGC, whereas PPC-iTBS had no measurable effect. Change in TGC was associated with change in WM, such that a decrease in TGC was associated with faster RT and decreased RT variability. Higher baseline TGC was associated with greater improvement in WM. Active iTBS decoupled the TGC-WM association observed during sham iTBS, and greater electric field intensity of iTBS was associated with greater improvement in WM accuracy and greater decrease in TGC. Conclusions: Active iTBS to the left DLPFC modulated WM-related TGC in youth with ADHD. These findings provide preliminary evidence that neuromodulation may improve WW by modifying oscillatory dynamics within frontoparietal networks. Larger clinical trials with higher stimulation doses are needed to determine whether targeting oscillatory coupling represents a potential therapeutic strategy for WM deficits.

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.

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: Accelerated biological aging can be assessed with DNA methylation (DNAm)- based epigenetic clocks. Research suggests that greater DNAm is associated with faster cognitive decline and risk of Alzheimer disease (AD) and other dementias. However, most studies have relied on single-time-point measurements of clocks, rather than evaluating dynamic changes over time. We examined the association between 15-year epigenetic aging trajectories and brain health outcomes in midlife. Methods: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with [&ge;]3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within &plusmn1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, A{beta}42/A{beta}40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*{varepsilon}4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes). Results: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower A{beta}42/A{beta}40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group. Conclusion: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.

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.

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.

Intracranial neurophysiology studies have typically ignored signals from electrodes located in white matter (WM), assuming that their information content is artifactual or related to nearby gray matter (GM). Here, we tested the electrophysiological and functional features of signals recorded from different WM locations. Signals were recorded from 19 patients undergoing intracranial monitoring for drug-resistant epilepsy by means of stereo-electroencephalography (sEEG). Each sEEG electrode was classified into WM or GM based on the surrounding tissue. We obtained recordings from a total of 1,717 sEEG electrode contacts, 36% in WM, while the patients were in awake resting state (5 minutes). For each sEEG electrode, we employed a model-based spectral decomposition to separate periodic and aperiodic components, and we computed signal complexity metrics. For a subset of participants, we computed WM structural information from diffusion-weighted magnetic resonance imaging and we evaluated functional signals during a cognitive control task. Our results show that signals recorded from WM have different spectral features and higher complexity than GM. Complexity correlates positively with fractional anisotropy, and modulations related to behavior during the task were detected in WM. Overall, this indicates that WM signals carry information that may reflect signal propagation across WM fiber tracts.

Executive function (EF) deficits are central features of schizophrenia and strongly influence long-term functional outcomes. Conventional cognitive assessments often lack ecological validity and cultural relevance. This study introduces the Luk Chup Augmented Reality (LCAR) tool a video guided, clay modeling task delivered through wearable AR that integrates culturally familiar activity with realtime neurophysiological monitoring. Thirty individuals diagnosed with schizophrenia (mean age = 38.9, SD. = 7.15 years) completed a series of modeling and memory tasks using LCAR while undergoing quantitative EEG (QEEG). Task duration and theta/beta power were analyzed across procedural and color shape memory phases. Memory phases took significantly longer to complete and were associated with decreased lateral prefrontal theta and increased frontal midline theta activity (Fz, Cz), indicating higher EF demand. A repeated-measures ANOVA revealed significant condition, site, and interaction effects on theta power. The LCAR tool shows promise as a culturally grounded, dual-mode assessment of EF in schizophrenia. It offers a novel integration of performance-based and neurophysiological metrics that may inform future interventions in psychiatric rehabilitation.

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.

The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.

Introduction Auto-Induced Cognitive Trance (AICT) is a non-ordinary state of consciousness (NOSC) that can be accessed by will alone once a standardised self-induction procedure has been learnt. The first research publication on AICT dates back only ten years, meaning that research on this phenomenon is still in its infancy. Previous reports concerning the phenomenology and neurophysiology of AICT revealed similarities with more extensively described NOSCs, as well as unusual features, raising questions about the potential benefits of AICT practice for well-being. Objective This study aimed to gather quantitative descriptive data on features associated with well-being in a large comparative sample of AICT practitioners and non-practitioners. Method This research followed a web-based survey study design which enquired AICT-trained and yet-to-be trained participants to self-report through validated standardised questionnaires on vitality, self-esteem, mental well-being, trait anxiety, life satisfaction, happiness, positive and negative affect, nature-relatedness and connectedness. Data on NOSCs practices, life history events that could have led to spontaneous NOSCs, and demographic data were collected for further inclusion as control variables in statistical models. Results The online questionnaire yielded 607 valid responses, (171 yet-to-be trained participants and 436 AICT-trained participants). AICT practice was found to be associated with increased self-esteem (RSE), overall connectedness (WCS) as well as all subdimensions of connectedness (WCS Self, WCS Others, WCS World). AICT practice Duration exhibited significant effects on global connectedness and all subdimensions of connectedness, self-esteem, trait anxiety (STAIT-5), and positive affect (PANAS+). Conclusions AICT seems to benefit to practitioners well-being shortly after training through increases in self-esteem and in the sense of connectedness. Prolonged AICT practice is associated with added decreased trait anxiety and increased positive affect. Further research is needed to confirm these findings with a sample including AICT-uninterested participants, and to clarify the underlying mechanisms of AICT.

Premature ovarian insufficiency (POI) significantly impacts quality of life, yet the immediate cognitive landscape and lived experience of younger women remain under-researched. In 125 young women (aged 19-48; 66 with idiopathic POI, 59 age-matched controls), we examined self-reported cognitive distress and symptom burden within the POI cohort and compared objective global and domain-specific cognitive performance between groups. Objective accuracy scores were derived from six online tasks (Cognitron) and combined into a robust global measure. Within the POI cohort, there were significant differences in symptom burden domains ({chi}(3) = 61.90, p<0.001), with psychological and sexual symptoms reported at a significantly higher intensity than physical and vasomotor symptoms (all p<0.001). Furthermore, the standardised magnitude of perceived cognitive distress (56.20%) was significantly greater than that of overall symptom burden (42.00%, p<0.001). Case-control comparisons revealed no significant differences in global cognitive performance (p=0.615), yet the POI cohort performed significantly less accurate than controls in verbal analogical reasoning (-0.86 SD, 95% CI: -1.52, -0.20, p = 0.011). The findings highlight an urgent need for comprehensive emotional and psychosexual support in POI care. Additionally, the presence of high cognitive distress alongside localised objective deficits demonstrates that cognitive health monitoring must be proactive in early adulthood, especially given their established long-term risks for later-life cognitive decline and dementia.

The increasing deployment of artificial intelligence (AI) assistive systems across healthcare, education, and organisational domains necessitates a deeper understanding of dispositional factors shaping trust and acceptance. This study investigated the Big Five personality traits as predictors of trust in and acceptance of AI assistive systems among a large adult sample (N = 380) in Makurdi Benue State. Anchored in the Technology Acceptance Model (TAM) developed by Davis (1989), the study examined both direct and indirect pathways linking personality traits to AI acceptance through trust. Participants completed standardised measures of the Big Five Inventory, Trust in AI Scale, and AI Acceptance Scale. Data were analysed using structural equation modelling (SEM) with maximum likelihood estimation. The hypothesised model demonstrated good fit indices (CFI = .84, TLI = .82, RMSEA = .05). Openness to experience ({beta} = .34, p < .001) and agreeableness ({beta} = .27, p < .01) significantly predicted trust in AI systems, which in turn strongly predicted AI acceptance ({beta} = .62, p < .001). Neuroticism negatively predicted trust ({beta} = -.29, p < .001), while conscientiousness showed a modest positive direct effect on acceptance ({beta} = .18, p < .05). Extraversion was not a significant direct predictor but exerted an indirect effect through trust. Mediation analysis confirmed that trust significantly mediated the relationship between personality traits and AI acceptance. The findings underscore the centrality of dispositional traits in shaping technological trust formation and highlight the psychological architecture underlying human AI interaction. These results contribute to social psychological theory and provide empirical guidance for designing personality sensitive AI systems to enhance user adoption and sustained engagement.

INTRODUCTION: Brain age gap (BAG) is the difference between a person's chronological age and the age predicted from the structural appearance of their brain on MRI. A higher BAG indicates an older-appearing brain and provides a global marker of structural brain aging across the Alzheimer's disease continuum. Prior studies suggest that females may show greater Alzheimer's disease-related pathology or faster late-stage neurodegeneration than males. We tested whether sex was associated with baseline BAG or longitudinal BAG change after accounting for APOE {epsilon}4 genetic risk, amyloid positivity, cognitive severity, and disease stage. METHODS: We developed a domain-adaptive deep learning model to estimate BAG from T1-weighted MRIs, training it on 26,512 neurologically healthy UK Biobank data and fine-tuning it on 2,974 amyloid-negative cognitively normal samples from Mayo Clinic Study of Aging and OASIS-3 cohorts. We applied the model to ADNI and used hierarchical mixed-effects models to test whether sex was associated with BAG trajectories after adjusting for Alzheimer's disease risk factors. RESULTS: After adjustment for Alzheimer's disease risk factors, there was no baseline sex differences in BAG. Longitudinally, females showed greater BAG acceleration than males, but this effect was moderated by APOE {epsilon}4 status. APOE {epsilon}4 accelerated brain aging in a dose-dependent manner, independent of amyloid burden. DISCUSSION: Sex differences in BAG across the AD continuum were largely explained by APOE {epsilon}4-related acceleration rather than by an independent effect of sex alone. These findings suggest that females may be more vulnerable to APOE {epsilon}4-associated structural brain aging over time.