Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) are several common neurodegenerative diseases (NDs). At present, is the lack of effective diagnosis, progression, prognosis and therapeutic biomarkers. it is a urgent demand to search the relevant confident biomarkers. This review systematically analysed the potential biomarkers of blood, cerebrospinal fluid, neuroimaing and emerging non-invasive indicators, and synthesises current evidences on the biomarkers of AD, PD and ALS about diagnosis, progression, prognosis and therapeutic, especially diagnosis biomarkers. In this review, we focus on discussing relevant diagnosis, progression, prognosis and therapeutic biomarkers for AD, PD and ALS in recent years, and prospecting the possible future directions of relevant biomarkers.

IntroductionSexual dysfunctions are prevalent public health issues with understudied neurobiological correlates. This study explores structural brain differences in individuals with DSM-5 sexual dysfunctions vs. matched controls.MethodsThis cross-sectional study employed voxel-based morphometry (VBM) to analyse structural brain scans from the Hamburg City Health Study. Participants with erectile disorder (ED; n = 20), premature ejaculation (PE; n = 20), genito-pelvic pain/penetration disorder (GPPPD; n = 8), and female sexual interest/arousal disorder (FSIAD; n = 32) were compared with matched controls (n = 40,40,24,32, respectively). Whole-brain VBM analyses used SPM12 and CAT12. Statistical parametric maps were thresholded at uncorrected voxel-level (p < .001) and false discovery rate (FDR) correction (p_FDR < .05).ResultsED showed clusters in right putamen with reduced rGMV and left postcentral gyrus with higher rGMV. PE displayed lower rGMV in cerebellar lobe VI/Crus I and left superior/medial temporal gyrus. GPPPD exhibited higher rGMV in right middle frontal gyrus. FSIAD showed no differences. No clusters survived FDR correction, underscoring the exploratory and hypothesis-generating nature of the reported findings.ConclusionsThis study identifies potential associations between sexual dysfunctions and structural brain differences in regions related to sexual function, arousal, inhibition, motor coordination, and pain processing. Results require cautious interpretation due to small sample sizes, but provide hypothesis-generating evidence for future research .

The aim of our study was to investigate neurobiological markers and gender difference for diagnosing anxious major depressive disorder (aMDD) through a meta-analysis. We systematically searched multiple databases for whole-brain neuroimaging studies comparing anxious major depression disorder (aMDD), pure MDD, and healthy controls, with publication dates through December 2024. We extracted brain coordinates and their corresponding peaks, for further analysis using the Seed-based d Mapping software package. A total of 11 studies were included in this study, encompassing 829 aMDD patients, 681 MDD patients, and 865 healthy controls. The meta-analysis revealed that aMDD patients exhibited increased functional alteration in the left middle temporal gyrus compared to individuals diagnosed with MDD. In the comparison between individuals with aMDD and healthy controls, the meta-analysis revealed increased functional alteration in the anterior commissure and decreased functional alteration the right middle frontal gyrus. Furthermore, the meta-regression analysis revealed heightened neural activity of the left middle cingulate gyrus and right anterior thalamic regions, as well as weakened neural activity of the left rolandic operculum in females with aMDD compared to the control group. We identified specific functional alterations in brain regions that may serve as potential neurobiological markers for aMDD and associated differences.

Background: Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) with naturalistic stimuli has advanced our understanding of the neural mechanisms underlying mental disorders. Naturalistic paradigms use dynamic, multimodal stimuli that capture complex emotional and cognitive processes more effectively than traditional experimental designs. Objective: This review synthesizes research from 2014 to 2024 exploring neural mechanisms of anxiety, depression, and schizophrenia within naturalistic paradigms. Methods: Recent EEG–fMRI studies employing naturalistic tasks were examined to identify common and disorder-specific neural alterations across affective and cognitive networks. Results: In anxiety, hyperactivity in the amygdala, prefrontal cortex, anterior cingulate cortex, and insula, together with changes in the dorsal attention, default mode, and frontoparietal networks, reflects excessive fear responses and impaired regulation. Depression is characterized by disruptions in default mode and frontoparietal connectivity and altered amygdala-prefrontal interactions, indicating maladaptive introspection and cognitive control. Schizophrenia shows abnormalities in motor and language processing, with aberrant activity in frontal, parietal, and temporal regions including the insula and temporoparietal junction. Conclusion: These findings highlight the shared involvement of the amygdala, prefrontal cortex, anterior cingulate cortex, and insula across disorders and demonstrate the potential of naturalistic paradigms for advancing personalized diagnostics and interventions in mental health.

IntroductionResearch suggests that affective temperaments influence adherence to pharmacotherapy; however, this has not been investigated in infertility treatment. Our prospective longitudinal study assessed the impact of affective temperaments on medication adherence during infertility treatments.Methods179 women presenting at an Assisted Reproduction Centre completed the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego (TEMPS-A) questionnaire before treatment, and the Morisky Medication Adherence Scale (MMAS) six months later. Univariate linear regression assessed whether affective temperaments predict medication adherence; multivariate and interaction models examined the influence of sociodemographic and medical variables on this relationship, and potential moderating effects of age and education.ResultsHigher cyclothymic, depressive, irritable, and anxious affective temperament scores predicted significantly poorer adherence to pharmacotherapeutic recommendations (β = −0.122, p < 0.001, β = −0.178, p < 0.001, β = −0.114, p = 0.002, β = −0.071, p = 0.08; respectively). These results remained significant in multivariate models including sociodemographic and medical factors, which did not influence adherence. Increasing age intensified the negative impact of anxious temperament on medication adherence (β = −0.015, p = 0.024).ConclusionsAffective temperaments impact adherence to pharmacotherapeutic recommendations among women experiencing infertility, possibly influencing treatment outcomes. Screening for affective temperaments can identify patients at risk of medication non-adherence. Applying patient-tailored psychological interventions to aid adherence could increase the chances of successful pregnancies.

BackgroundNon suicidal self injury (NSSI) is a public health concern, and its prevalence has increased significantly following the COVID-19 pandemic. Despite its rising incidence, the neurobiological mechanisms underlying NSSI behaviour in adolescents remain poorly understood.MethodsA sample of 89 adolescents (46 NSSI positive, 43 NSSI negative) aged 15.39 ± 1.77 years was recruited from clinical settings. NSSI behaviour and psychological resilience were evaluated. Resting-state functional magnetic resonance imaging (Rs-fMRI) was conducted to examine brain connectivity patterns. Data analysis incorporated descriptive and inferential statistics, as well as support vector machine algorithms, to identify the neural correlates of NSSI and resilience.ResultsThe NSSI positive group had significantly lower resilience scores (M = 23.41, SD = 7.95). Connectivity between the sensorimotor and limbic networks was negatively associated with NSSI (r = −0.222, p < 0.05), while connectivity between the sensorimotor and subcortical networks showed a positive association (r = 0.201, p < 0.05). Stronger connectivity between dorsal attention and default mode networks indirectly reduced NSSI by enhancing psychological resilience, highlighting resilience as a critical protective factor.ConclusionThese findings underscore the importance of targeting specific brain connectivity patterns and enhancing psychological resilience as crucial components of neurobiologically informed interventions.

The International Society for Bipolar Disorders (ISBD) Chronobiology and Chronotherapy Task Force conducted a comprehensive review to deliver concise evidence-based recommendations on the use of bright light therapy (BLT) for bipolar disorder (BD). Adjunctive BLT is likely an efficacious acute treatment for bipolar depression as implicated by higher quality evidence. The position of maintenance BLT for relapse prevention awaits further investigation. Protocols of effective BLT in BD are similar to parameters indicated for treatment of seasonal and non-seasonal major depressive disorder. Anti-manic prophylaxis (especially for BD-I) and clinical monitoring are recommended with initiation of and ongoing light treatment. Administer BLT daily, preferably in the morning or at mid-day. If mornings are prohibitive, then mid-day exposure, implemented to avoid excessively early wake times, is an acceptable alternative. Informed by the literature, target 30 min/day of BLT exposure. Patients wary of emergent hypomania or partial responders, can initiate 15 min/day and increase by 15 min each week to full response (or 30-60 min/day by the fourth week). Consider patient centred outcome assessments to evaluate mood response, safety and side effects. Clinical improvement is typically observed within 1-2 weeks, with response/remission expected by 4-6 weeks. Integration of BLT with other chronotherapeutic strategies may enhance long-term efficacy.

Insomnia, a widespread sleep disorder, affects a significant portion of the global population. This study is the first in Asia to evaluate the efficacy of electrical vestibular stimulation (VeNS) as a treatment for insomnia in Hong Kong adults, addressing a gap in non-pharmacological interventions. A double-blind, randomized, sham-controlled trial was conducted with 101 adults exhibiting insomnia symptoms. Participants were randomized into active VeNS or sham groups (1:1 ratio) and underwent twenty 30-minute VeNS sessions over four weeks. Psychological outcomes, including insomnia severity, sleep quality, and quality of life were assessed at baseline (T1), post-intervention (T2). Follow-up assessments were conducted at one- (T3) and three-month (T4) to evaluate the sustainability of VeNS effects. Of 83 participants (40 VeNS and 43 sham-VeNS), the VeNS group showed significant reductions in insomnia severity at T2 (p = 0.03, d = -0.47) and T4 (p = 0.02, d = -0.32), alongside improved quality of life (i.e., role-physical) at T2. VeNS is a novel, non-invasive and safe neuromodulation device that may serve as an adjunct treatment for primary insomnia. The present findings provide a foundation for future multisite comparison studies to further evaluate VeNS efficacy. ClinicalTrials.gov Identifier: NCT04452981.

Depression, including Bipolar and Unipolar types, is a widespread mental health issue. Conventional diagnostic methods rely on subjective assessments, leading to possible underreporting and bias. Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression using behavioral and demographic data. This study proposes a hybrid AI framework combining structured demographic features with synthetic actigraph time-series data. Demographic data is modeled using an XGBoost ensemble, while temporal data is analyzed through a deep convolutional neural network (CNN). The training pipeline includes stratified k-fold cross-validation, hyperparameter tuning, and statistical testing. Model explainability is enhanced using SHAP (XGBoost) and Grad-CAM (CNN). The hybrid model demonstrated strong classification performance across metrics like accuracy, sensitivity, and specificity. Integrating temporal and static features improved prediction of Bipolar and Unipolar Depression. Interpretability tools revealed key features and time patterns influencing predictions. This work introduces a robust and interpretable framework for depression classification using synthetic multimodal data. While not clinically validated, the model serves as a methodological foundation for future research with real-world datasets.

Understanding the decision-making mechanisms underlying trust is essential, particularly for individuals with mental disorders who often experience difficulties in forming interpersonal trust. In this study, we aimed to explore biomarkers associated with trust-based decision-making through quantitative analysis. However, quantifying internal decision-making processes is challenging, as they are not directly observable. To address this, we developed a machine learning method based on a Bayesian hierarchical model to quantitatively infer latent decision-making parameters from behavioural data collected during a trust game. Applying this method to data from patients with major depressive disorder (MDD) and healthy controls (HCs), we estimated individualised model parameters that regulate trust-related decisions. The model successfully predicted participants' behaviours in the task. Although no significant group-level differences were observed in the estimated parameters between the MDD and HC groups, we uncovered hidden links between trust-related decision-making processes and specific blood biomarkers. Notably, metabolites such as 5-aminolevulinic acid, acetylcarnitine, and 2-aminobutyric acid were significantly associated with individual differences in trusting behaviour. These findings provide valuable insight into the biological basis of trust-based decision-making. They also offer a novel framework for integrating behavioural modelling with biomarker discovery, potentially informing the development of targeted interventions to enhance social functioning and overall well-being.