Non-motor symptoms, including rapid eye movement sleep behaviour disorder (RBD), depression and anxiety, are common and often co-occurring in patients with Parkinson's disease. This study aimed to investigate their potential shared neurobiological substrates by integrating structural, functional and neurochemical imaging data. We analysed data from 638 Parkinson's disease patients from the Parkinson's Progression Markers Initiative (PPMI), with available 3T T1-weighted MRI scans. RBD, depression and anxiety severity were assessed using validated clinical scales (RBD Screening Questionnaire Score, Geriatric Depression Scale and State-Trait Anxiety Inventory). Voxel-based morphometry (VBM) multivariate regression analyses were performed to identify grey matter (GM) volume loss associated with each clinical symptom. All analyses were rigorously controlled for a comprehensive set of potential confounders, including age, sex, education, disease duration, motor severity and cognitive dysfunction, thereby minimizing confounding effects related to other aspects of the disease. Coordinate-based network mapping was then applied using a large normative resting-state functional connectome (N = 1000), to characterize symptom-specific functional networks based on brain areas functionally connected to the VBM-derived clusters. Finally, spatial correlations between these networks and normative neurotransmitter density maps from PET data were assessed. VBM analyses revealed distinct patterns of GM atrophy across the three symptoms (pFWE<0.05), overlapping in the left middle temporal and right middle frontal gyri. The coordinate-based functional network mapping approach demonstrated that the GM atrophy pattern associated with each symptom (pFWE < 10^-6) converged onto brain networks involving several cortical regions and overlapping across symptoms, and with the greatest spatial affinity, among canonical large-scale networks, with the Dorsal and Ventral Attention networks. All three symptom-related networks showed significant alignment with the noradrenaline transporters (NAT) spatial distribution (pFDR < 0.05). Overall, this study proposes a novel conceptual and methodological framework integrating well-established and validated techniques to identify the neuroanatomical bases of specific diseases or symptoms, potentially of interest for future research. Our neuroimaging findings in the large PPMI cohort of early Parkinson's disease patients demonstrate that the brain networks associated with RBD, depression and anxiety non-motor symptoms were largely overlapping, involved the attention networks and were spatially aligned with the noradrenergic system, suggesting that these symptoms may have shared neurobiological substrates.

The correspondence between objective sleep physiology and subjective sleep experience remains poorly understood. Conventional polysomnography (PSG) primarily captures sleep macrostructure and may fail to reflect large-scale neural integration processes underlying perceived sleep quality. This study examined whether sleep-stage-specific brain network integration, quantified via EEG-derived global efficiency (GE), is associated with daytime sleepiness and insomnia severity in healthy university students.

In this cross-sectional observational study, overnight full-night PSG was recorded in 52 healthy male university students. The debiased weighted Phase Lag Index was used to measure functional connectivity to minimize volume conduction effects. GE was computed across five canonical frequency bands during N3 and REM sleep. Subjective sleep outcomes were assessed using the Epworth Sleepiness Scale (ESS) and the Insomnia Severity Index (ISI). Associations between GE, subjective sleep measures, and conventional PSG parameters were evaluated using linear and multiple regression analyses.

Distinct stage-dependent differences in network topology were observed, with lower alpha-band GE during REM sleep and lower delta-band GE during N3 sleep. Higher daytime sleepiness (ESS) was associated with increased GE during REM sleep in the delta, beta, and gamma bands, whereas higher theta-band GE during N3 sleep was associated with lower insomnia severity (ISI). GE-based models accounted for a modest proportion of variance in subjective outcomes (ISI: R² =0.082; ESS: R² =0.190). Conventional PSG macrostructural indices were not significantly associated with subjective sleep measures.

EEG-derived GE showed stage- and frequency-specific associations with subjective sleep outcomes, with limited additional contribution beyond conventional macrostructural PSG measures. These findings provide preliminary evidence that sleep network topology may partially capture aspects of subjective sleep experience not fully reflected by standard sleep architecture indices. EEG-based GE may serve as a potential network-level correlate of subjective sleep experience, suggesting a possible network-level link between sleep physiology and perceived sleep quality in non-clinical populations.

Adolescent suicide is a critical public health challenge. Traditional risk screening relies on self-report measures limited by various biases, while passive EDA monitoring during daily activities, often yields limited predictive validity due to environmental confounding such as motion artifacts, thermoregulatory sweating, and ambient temperature fluctuations. This study proposes an innovative "interview-embedded" framework to capture physiological signatures of suicide ideation (SI) during a standardized clinical probe.

A total of 151 adolescents (102 with active suicide ideation, 49 matched controls) were enrolled. Their electrodermal activity signals were continuously recorded throughout clinical interview (Mini International Neuropsychiatric Interview for Children and Adolescents) and analyzed through advanced machine learning approaches.

XGBoost model achieved superior classification performance (AUC=0.802, sensitivity=0.857, specificity=0.8) compared to CatBoost, and Balanced Random Forest, significantly outperforming resting-state models and clinical symptom baselines (Resting EDA Model: AUC = 0.598, sensitivity = 0.571, specificity = 0.4; Full Clinical Interview Baseline: AUC = 0.926, sensitivity = 0.476, specificity = 1; Symptom-only Clinical Baseline Model: AUC = 0.712, sensitivity = 0.619, specificity = 0.8). Feature importance analysis revealed that dynamic features reflecting physiological reactivity were the most discriminative markers, providing predictive information potentially beyond self-report. EDA features did not significantly correlate with continuous BSS severity scores (all ρ < 0.15, all p > 0.05; regression R² < 0), supporting a threshold rather than dose-dependent physiological response to suicidal ideation. The results of robustness checks and subgroup analyses showed modest but clinically relevant utility of the model, even when accounting for highly comorbid factors such as depression and non-suicidal self-injury.

This study demonstrates that a task-embedded biosensing framework, integrating wearable biosensing into standardized clinical interviews is a feasible and effective approach for adolescent suicide risk detection. Embedding physiological data collection within established clinical workflows offers a scalable solution to improve early suicide risk screening in real-world settings, such as schools and primary care.

Hypertension self-care practices are essential for controlling blood pressure and preventing cardiovascular complications and mortality. However, research on adherence to self-care practices among people with hypertension in Ghana is limited. This study assessed hypertension self-care practices and associated factors among people with hypertension attending follow-up in selected public healthcare facilities in the Ashanti Region, Ghana.

This multicentre cross-sectional study was conducted in three randomly selected public healthcare facilities in the Ashanti Region from 1^st March to 30^th June 2025. A multistage sampling method was employed to recruit 545 people with hypertension on follow-up. Data on hypertension self-care practices, hypertension knowledge, socio-demographic and clinical characteristics, and psychosocial factors were collected using an interviewer-administered questionnaire and supplemented by a review of medical records. IBM-SPSS Statistics was used for data analysis. Descriptive statistics were used to summarize variables, while a binary logistic regression model was performed to identify the factors associated with hypertension self-care practices. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were reported, and statistical significance was set at p<0.05.

Of the 545 people with hypertension recruited, 510 participated in the study, yielding a response rate of 93.6%. Overall, 58.4% (95% CI: 54%-63%) of participants demonstrated good self-care practices. Among the individual practices, smoking cessation had the highest adherence (96.7%, n=493), whereas physical activity had the lowest adherence (22%, n=112). In multivariable analysis, comorbidities (AOR=0.56, 95% CI: 0.36-0.87) and depression (AOR=0.11, 95% CI: 0.05-0.23) were independently associated with lower odds of practising good self-care, whereas good self-efficacy (AOR=12.71, 95% CI: 6.64-24.30) was independently associated with higher odds.

Adherence to hypertension self-care practices was moderate; however, deficiencies were observed in salt reduction and physical activity. Comorbidity, depression and self-efficacy were associated factors of hypertension self-care practices. Interventions should prioritise mental health screening, strengthen self-efficacy through behavioural support, and provide tailored care for individuals with comorbidities to improve sustained self-management.

Cerebral small vessel disease is a major contributor to stroke and cognitive impairment, often associated with reduced cerebral blood flow and cerebrovascular reactivity. In this study, we used MRI to investigate if daily treatment with the phosphodiesterase-5 inhibitor tadalafil over 3 months improved cerebral blood flow and cerebrovascular reactivity in patients with cerebral small vessel disease. Using a randomized, placebo-controlled, parallel-group design, this MRI sub-study was prospectively performed as part of the Effect of Tadalafil in Lacunar Stroke 2 trial, which included a total of 76 participants, of whom 60 were included in the per-protocol analysis. Patients with cerebral small vessel disease and prior stroke or transient ischaemic attack received either tadalafil (20 mg/day) or placebo for 3 months and completed the study. Neurovascular outcomes were assessed at baseline and at the 3-month follow-up using dual-echo pseudo-continuous arterial spin labelling MRI to evaluate reactivity to hypercapnia and visual stimulation. Endpoints included changes in whole-brain baseline cerebral blood flow and cerebrovascular reactivity, visual stimulation response, and neurovascular coupling index. In addition, we investigated arterial transit time measured using diffusion-prepared pseudo-continuous arterial spin labelling and changes in event-related sensory blood-oxygen-level-dependent responses in the primary somatosensory cortex (S1) after stimulation of the index finger. In total, 60 participants (median age: 66.5, 14 females) were included in this per-protocol sub-study analysis (28 on tadalafil and 32 on placebo). Forty-two pseudo-continuous arterial spin labelling datasets (20 on tadalafil and 22 on placebo), 53 blood-oxygen-level-dependent functional MRI datasets (26 on tadalafil and 27 on placebo), and 45 arterial transit time datasets (22 on tadalafil and 23 on placebo) were of sufficient quality to be included in the analysis. Baseline whole-brain cerebral blood flow was 25.4 ± 5.1 ml/100 g/min. Compared to placebo, tadalafil significantly increased whole-brain cerebral blood flow with a mean group difference of 3.33 ± 0.97 ml/100 g/min at follow-up (P = 0.001). None of the other MRI-based outcome metrics differed between groups. Three months of tadalafil treatment significantly enhanced whole-brain cerebral blood flow in patients with cerebral small vessel disease without altering cerebrovascular reactivity, neurovascular coupling, arterial transit time, and event-related blood-oxygen-level-dependent responses. The clinical significance of a tadalafil-induced increase in cerebral blood flow remains to be determined in prospective studies covering longer treatment periods.

Depression is a prevalent psychological issue among chronic kidney disease (CKD) patients. Such symptoms can greatly affect the physical and mental health and life expectancy of middle-aged and older persons with CKD.

The aim of this study is to develop a depression risk prediction model for CKD patients, laying the scientific groundwork for early intervention.

This research utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. The study examined 52 indicators, encompassing socio-demographic variables, behavioral factors, health status, and psychological health parameters. Statistical analysis was performed using SPSS 26.0 and Python. LASSO regression was employed to identify independent predictors of depression risk in patients with CKD. Subsequently, a random forest model was selected to construct the depression risk prediction model based on these predictors, followed by model validation. The model was assessed using accuracy, recall, specificity, F1 score, Area Under Curve (AUC) value, and clinical decision curves. A particle swarm optimization (PSO) algorithm from intelligent optimization algorithms was applied to fine-tune the model's hyperparameters, which were then compared against the unoptimized random forest algorithm.

The final analysis included 734 CKD patients from the CHARLS database. A total of 326(44.4%) CKD patients exhibited depressive symptoms. The findings revealed that gender, household registration, place of residence, self-rated health, arthritis, stomach disease, height, life satisfaction, glycated hemoglobin, educational level, instrumental activities of daily living (IADL), and pain are predictive factors for depression in CKD patients. These factors were used to construct the PSO-random forest model, which demonstrated good consistency and accuracy. The predictive model achieved an AUC value of 0.820.

This study presents a reliable PSO-random forest model for predicting depression risk in CKD patients, leveraging CHARLS data and interpretable features. This tool presents a valuable means of early screening and delivering personalized care, with the potential to significantly improve mental health outcomes within this population.

Preschool children's insufficient sleep is a public health concern in underdeveloped western China, yet its association with parental depressive symptoms remains unclear, particularly whether this relationship varies by socioeconomic context. This study aims to examine this association, explore whether child mental health may partly account for it, and identify vulnerable subgroups.

This cross-sectional study recruited 21,366 parent-child dyads from 189 preschools in western China. Parent-reported children's sleep duration was categorized into three groups: 10-13 h/d (reference), 8-9 h/d, and <8 h/d. Parental depressive symptoms and children's mental health outcomes (total difficulties) were assessed using the Center for Epidemiological Studies Depression Scale (CES-D) and the Strengths and Difficulties Questionnaire (SDQ), respectively. Multivariable logistic regression was used to examine the association between children's sleep duration and parental depressive symptoms, and subgroup analyses were performed to assess effect modification by demographic and socioeconomic factors. Indirect effect analysis was conducted to determine the extent to which children's total difficulties account for this association.

In fully adjusted model, a clear graded relationship was observed that parents of children sleeping 8-9 h/d had 43% higher odds of elevated parental depressive symptoms (OR = 1.43, 95% CI: 1.30-1.56, P < 0.001), while parents of children sleeping <8 h/d had 2.6-fold higher odds (OR = 2.61, 95% CI: 2.13-3.19, P < 0.001) compared with the 10-13 h/d reference group. Subgroup analyses revealed that the strongest associations were found among mortgaged homeowners (OR = 4.21, 95% CI: 2.52-7.04, P < 0.001), urban Hukou registrants (OR = 3.73, 95% CI: 2.62-5.32, P < 0.001), and ever-smokers (OR = 4.50, 95% CI: 2.84-7.12, P < 0.001) for the <8 h/d group. Indirect effect analyses showed that children's total difficulties accounted for 34.3% (95% CI: 25.1%-47.9%) of the association for children sleeping 8-9 h/d, and 39.4% (95% CI: 30.5%-52.9%) for those sleeping <8 h/d.

Shorter sleep duration in preschool children is associated with an increased risk of parental depressive symptoms, and this association is partially accounted for by children's mental health. Future high-quality cohort studies are needed to explore this topic in greater detail.

This study aimed to explore the disproportionality reporting signals of amnesia associated with different statins based on a spontaneous adverse event database, so as to provide preliminary pharmacovigilance evidence for subsequent clinical safety evaluation and further pharmacoepidemiological verification.

This study employed pharmacovigilance analysis methods to monitor and analyze adverse event signals associated with seven commonly used statins. Additionally, subgroups were stratified by gender (male, female) and age (18, 45, and 65 years) to investigate the potential associations between statins and amnesia across different populations. Furthermore, a Weibull distribution analysis was used to assess the onset time of adverse events.

A total of 56,401 statin-related adverse event reports were included, among which 1,108 valid cases were coded as amnesia. Significant positive pharmacovigilance signals were identified for atorvastatin (n = 326, ROR = 2.82, 95% CI: 2.53-3.15, P < 0.001), simvastatin (n = 600, ROR = 4.94, 95% CI: 4.55-5.35, P < 0.001), pravastatin (n = 68, ROR = 4.77, 95% CI: 3.76-6.06, P < 0.001), and lovastatin (n = 26, ROR = 6.96, 95% CI: 4.73-10.24, P < 0.001). Subgroup analyses stratified by gender and age further identified heterogeneous pharmacovigilance signals across distinct populations. Moreover, Weibull distribution analysis also revealed unique distribution patterns among different statin agents.

This study identified significant positive pharmacovigilance signals between certain statins and amnesia. The findings of this study provide preliminary disproportionality signals regarding statin-related amnesia, offering exploratory pharmacovigilance references for subsequent safety evaluation and further pharmacoepidemiological validation.

Mindfulness-based interventions have gained increasing relevance as complementary strategies for addressing attentional, behavioral, and emotional difficulties in individuals with Attention Deficit Hyperactivity Disorder (ADHD). This study aimed to systematically map and analyze the scientific evidence on mindfulness-based interventions in populations with ADHD, focusing on their effects on attention, academic performance, and emotional well-being, as well as on methodological limitations and research gaps.

A scoping review was conducted following the PRISMA-ScR guidelines and the methodological recommendations of the Joanna Briggs Institute. Searches were performed in Scopus and Web of Science for original and review articles published in English or Spanish. The Population-Concept-Context framework guided the eligibility criteria. After duplicate removal and a structured screening of titles, abstracts, and eligible reports, 64 studies were included. Data were synthesized using descriptive, narrative, bibliometric, and thematic mapping approaches.

The evidence indicates that mindfulness-based interventions may improve attentional regulation, executive functioning, academic engagement, emotional control, coping strategies, and psychological well-being among individuals with ADHD or related attentional difficulties. Neurocognitive studies also reported functional changes in brain regions associated with attention and emotional regulation. However, the findings were not consistent across all outcomes, particularly regarding core symptoms of inattention and hyperactivity. Important limitations included small sample sizes, heterogeneous intervention protocols, variable methodological quality, adherence difficulties, and limited longitudinal evidence.

Mindfulness-based interventions represent promising complementary strategies for educational, psychopedagogical, and clinical settings, particularly for supporting attention and emotional self-regulation. Nevertheless, their effectiveness depends on context-sensitive implementation, participant adherence, and appropriate adaptation to the characteristics of ADHD. Future studies should use standardized protocols, larger and more diverse samples, longitudinal designs, and stronger evidence from underrepresented regions, particularly Latin America.

Menopause represents a major neuroendocrine transition characterized by substantial hormonal, metabolic, vascular, and inflammatory changes that may increase vulnerability to cognitive decline and neurodegenerative disease. Declining estrogen levels during the menopausal transition influence multiple neural processes, including synaptic plasticity, cerebral glucose metabolism, mitochondrial function, neuroinflammatory signaling, and cerebrovascular regulation. Women account for nearly two-thirds of individuals diagnosed with Alzheimer's disease, and mounting evidence suggests that menopause may represent a period of heightened neurological vulnerability. Physical exercise has emerged as one of the most promising non-pharmacological strategies for preserving cognitive health and reducing neurodegeneration risk in aging women. Current evidence demonstrates that exercise interventions after menopause are associated with improvements in executive function, memory performance, attention, and global cognition. These benefits appear to result from converging biological mechanisms that include enhanced neurotrophic signaling, increased brain-derived neurotrophic factor (BDNF) expression, improved cerebrovascular function, reduced systemic inflammation, improved insulin sensitivity, enhanced metabolic regulation, and preservation of structural brain integrity. Neuroimaging studies further demonstrate exercise-associated increases in hippocampal volume, cortical thickness, functional connectivity, and cerebral blood flow. Different exercise modalities appear to produce distinct but complementary neurological benefits. Aerobic exercise is strongly associated with improved cerebrovascular function and hippocampal integrity, resistance training demonstrates favorable effects on executive function and white matter preservation, and multimodal interventions combining aerobic, resistance, balance, and cognitively engaging activities appear to produce the broadest cognitive benefits. Emerging evidence further suggests that the timing of exercise initiation relative to menopause may influence outcomes, with earlier interventions potentially conferring greater neuroprotection. This review synthesizes current evidence regarding the effects of exercise on cognitive function in postmenopausal women, with emphasis on biological mechanisms, neuroimaging findings, exercise modality, timing considerations, and implications for neurodegeneration prevention. Understanding these relationships provides a scientific rationale for positioning exercise as a foundational strategy for preserving cognitive health and reducing neurodegenerative disease burden in postmenopausal women.