Explore the vast range of titles available.

Social cognitive function, involved in the perception, processing, and interpretation of social information, has been shown to be crucial for successful communication and interpersonal relationships, thereby significantly impacting mental health, well-being, and quality of life. In this regard, assessment of social cognition, mainly focusing on four key domains, such as theory of mind (ToM), emotional empathy, and social perception and behavior, has been increasingly evaluated in clinical settings, given the potential implications of impairments of these skills for therapeutic decision-making. With regard to neurodegenerative diseases (NDs), most disorders, characterized by variable disease phenotypes and progression, although similar for the unfavorable prognosis, are associated to impairments of social cognitive function, with consequent negative effects on patients’ management. Specifically, in some NDs these deficits may represent core diagnostic criteria, such as for behavioral variant frontotemporal dementia (bvFTD), or may emerge during the disease course as critical aspects, such as for Parkinson’s and Alzheimer’s diseases. On this background, we aimed to revise the most updated evidence on the neurobiological hypotheses derived from network-based approaches, clinical manifestations, and assessment tools of social cognitive dysfunctions in NDs, also prospecting potential benefits on patients’ well-being, quality of life, and outcome derived from potential therapeutic perspectives of these deficits.

The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 on the application of wearable sensor technologies and gait analysis in the early diagnosis of dementia. A targeted search of the Scopus database yielded 126 peer-reviewed studies, which were analyzed using VOSviewer for performance metrics, co-authorship networks, bibliographic coupling, co-citation, and keyword co-occurrence. The findings delineate a multidisciplinary research landscape, with major contributions spanning neurology, geriatrics, biomedical engineering, and computational sciences. Four principal thematic clusters were identified: (1) Cognitive and Clinical Aspects of Dementia, (2) Physical Activity and Mobility in Older Adults, (3) Technological and Analytical Approaches to Gait and Frailty and (4) Aging, Cognitive Decline, and Emerging Technologies. Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows. This review emphasizes the potential of sensor-derived gait metrics to augment early diagnostic protocols and advocates for interdisciplinary collaboration to advance scalable, non-invasive diagnostic solutions for neurodegenerative diseases.

There has recently been a plethora of high-impact neuroscience papers that capitalize on advanced ing These include, but are not limited to, the structural magnetic resonance techniques (MRI) of voxelbased morphometry (VBM), cortical thickness, diffusion tensor imaging (DTI) and tractography, and functional MRI (fMRI) to test hypotheses. The authors report considerable cerebellar reorganization decades after the poliomyelitis infection, which may be interpreted as compensation for anterior horn insult in infancy. [...]to unveil the contribution of cerebellum to the cognitive deficits of patients with multiple sclerosis (MS), Iliadou et al. [...]brain imaging is emerging as a valuable tool for the in vivo study of brain pathology in several psychiatric conditions. The implementation of different research strategies (e.g., CT perfusion, MRI volumetry, DTI tractography, fMRI, MRS), rigorous methodological approaches, and state-ofthe-art techniques is certainly encouraging for the development of precision biomarkers and thus personalized care for the benefit of patients and their families.

The contribution of cerebellar pathology to cognitive and behavioural manifestations is increasingly recognised, but the cerebellar profiles of FTD phenotypes are relatively poorly characterised. A prospective, single-centre imaging study has been undertaken with a high-resolution structural and diffusion tensor protocol to systematically evaluate cerebellar grey and white matter alterations in behavioural-variant FTD(bvFTD), non-fluent variant primary progressive aphasia(nfvPPA), semantic-variant primary progressive aphasia(svPPA), C9orf72-positive ALS-FTD(C9 + ALSFTD) and C9orf72-negative ALS-FTD(C9-ALSFTD). Cerebellar cortical thickness and complementary morphometric analyses were carried out to appraise atrophy patterns controlling for demographic variables. White matter integrity was assessed in a study-specific white matter skeleton, evaluating three diffusivity metrics: fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD). Significant cortical thickness reductions were identified in: lobule VII and crus I in bvFTD; lobule VI VII, crus I and II in nfvPPA; and lobule VII, crus I and II in svPPA; lobule IV, VI, VII and Crus I and II in C9 + ALSFTD. Morphometry revealed volume reductions in lobule V in all groups; in addition to lobule VIII in C9 + ALSFTD; lobule VI, VIII and vermis in C9-ALSFTD; lobule V, VII and vermis in bvFTD; and lobule V, VI, VIII and vermis in nfvPPA. Widespread white matter alterations were demonstrated by significant fractional anisotropy, axial diffusivity and radial diffusivity changes in each FTD phenotype that were more focal in those with C9 + ALSFTD and svPPA. Our findings indicate that FTD subtypes are associated with phenotype-specific cerebellar signatures with the selective involvement of specific lobules instead of global cerebellar atrophy.

Childhood obesity is a major medical and public health issue of global interest, which is influenced by a diverse array of factors and carries significant medical and psychosocial implications. Despite the extensive studies that have been conducted to explore the specific issue, the impact of several factors that influence, generate, worsen, and make chronic the phenomenon needs further exploration. This study aimed to construct a grounded theory that includes and connects the psychological, social, parental, and biological factors affecting childhood obesity. Key psychological factors include mental health issues such as depression and emotional eating, while social factors encompass socioeconomic status and cultural influences. Parental factors involve parenting styles and feeding practices, and biological factors relate to genetic predispositions and prenatal conditions. These factors interact in complex ways, highlighting the multifactorial nature of childhood obesity. The study employed a qualitative grounded theory approach, using research articles to achieve a thorough understanding. Qualitative analysis of the articles was conducted using Atlas.ti 24.0 software. Twenty-five research articles were required to reach theoretical saturation. The analysis resulted in 336 codes that were grouped into seven broad categories and twenty-four different subcategories. Through the construction of the theoretical framework, it was recognized that obesity in minors is a complex and multifactorial issue and that the network of causes and influencing factors covers a broad spectrum ranging from the individual to the family, and subsequently to society at large, which interact with each other.

Changes in the gut microbiome can affect cognitive and psychological functions via the microbiota–gut–brain (MGB) axis. Probiotic supplements are thought to have largely positive effects on mental health when taken in sufficient amounts; however, despite extensive research having been conducted, there is a lack of consistent findings on the effects of probiotics on anxiety and depression and the associated microbiome alterations. The aim of our study is to systematically review the most recent literature of the last 10 years in order to clarify whether probiotics could actually improve depression and anxiety symptoms. Our results indicate that the majority of the most recent literature suggests a beneficial role of probiotics in the treatment of depression and anxiety, despite the existence of a substantial number of less positive findings. Given probiotics’ potential to offer novel, personalized treatment options for mood disorders, further, better targeted research in psychiatric populations is needed to address concerns about the exact mechanisms of probiotics, dosing, timing of treatment, and possible differences in outcomes depending on the severity of anxiety and depression.

Background/Objectives: Chronic stroke survivors often regain walking speed but continue to exhibit heightened gait variability, increasing fall risk. This study investigated the effects of a 12-week moderate-to-high intensity muscle strengthening program on gait parameters and their variability in stroke survivors, without incorporating gait-specific training. Methods: Stroke survivors participated in a twice-weekly, 45–60 min strengthening program using Pilates equipment. Spatiotemporal gait parameters were measured before and after the intervention using 3D motion capture. Walking speed, cadence, step/stride length, step width, and various temporal parameters were analyzed for both paretic and non-paretic limbs, along with their coefficients of variation (CV). Correlation analyses were performed to understand the relationships between parameter changes. Results: Eleven patients (age 61 ± 7.4 years, 9 males) participated in the study. Significant improvements were observed in walking speed for both paretic (0.61 to 0.69 m/s, p = 0.032) and non-paretic limbs (0.62 to 0.69 m/s, p = 0.024). Step length significantly increased in the paretic limb (0.36 to 0.41 m, p = 0.042) with a substantial reduction in variability (CV: 19.91% to 14.99%). Cadence increased significantly in the non-paretic limb (89.24 to 92.01 steps/min, p = 0.024). Correlation analysis revealed distinct adaptation patterns between limbs, with speed improvements strongly associated with stride length in both limbs, but with step length only in the non-paretic limb. Conclusions: A moderate-to-high intensity strengthening program, even without direct gait training, can improve walking speed and reduce movement variability in chronic stroke survivors. The intervention predominantly influenced the spatial parameters, with modest changes in the temporal aspects, suggesting that enhanced force production and control primarily affect step execution while preserving temporal gait patterns.

Background: Claustrophobia is defined as the fear of enclosed spaces, and it is a rather common specific phobia. Although the Claustrophobia Questionnaire (CLQ) is a valid questionnaire to measure claustrophobia, there have been no studies validating this tool in Greek. Thus, our aim was to translate and validate the CLQ in Greek. Methods: We applied the forward–backward translation method to translate the English CLQ into Greek. We conducted confirmatory factor analysis (CFA) to examine the two-factor model of the CLQ. We examined the convergent and divergent validity of the Greek CLQ by using the Fear Survey Schedule-III (FSS-III-CL), the NEO Five-Factor Inventory (NEO-FFI-NL-N), and the Spielberger’s State-Trait Anxiety Inventory (STAI). We examined the convergent validity of the Greek CLQ by calculating Pearson’s correlation coefficient between the CLQ scores and scores on FSS-III-CL, NEO-FFI-NL-N, STAI-S (state anxiety), and STAI-T (trait anxiety). We examined the divergent validity of the Greek CLQ using the Fisher r-to-z transformation. To further evaluate the discriminant validity of the CLQ, we calculated the average variance extracted (AVE) score and the Composite Reliability (CR) score. We calculated the intraclass correlation coefficient (ICC) and Cronbach’s alpha to assess the reliability of the Greek CLQ. Results: Our CFA confirmed the two-factor model of the CLQ since all the model fit indices were very good. Standardized regression weights between the 26 items of the CLQ and the two factors ranged from 0.559 to 0.854. The convergent validity of the Greek CLQ was very good since it correlated strongly with the FSS-III-CL and moderately with the NEO-FFI-NL-N and the STAI. Additionally, the Greek CLQ correlated more highly with the FSS-III-CL than with the NEO-FFI-NL-N and the STAI, indicating very good divergent validity. The AVE for the suffocation factor was 0.573, while for the restriction factor, it was 0.543, which are both higher than the acceptable value of 0.50. Moreover, the CR score for the suffocation factor was 0.949, while for the restriction factor, it was 0.954. The reliability of the Greek CLQ was excellent since the ICC in test–retest study was 0.986 and the Cronbach’s alpha was 0.956. Conclusions: The Greek version of the CLQ is a reliable and valid tool to measure levels of claustrophobia among individuals.

Background: Depression constitutes a major public health issue, being one of the leading causes of the burden of disease worldwide. The risk of depression is determined by both genetic and environmental factors. While genetic factors cannot be altered, the identification of potentially reversible environmental factors is crucial in order to try and limit the prevalence of depression. Aim: A cross-sectional, questionnaire-based study on a sample from the multicultural region of Thrace in northeast Greece was designed to assess the potential association of depression with several sociodemographic characteristics, lifestyle, and health status. The study employed four machine learning (ML) methods to assess depression: logistic regression (LR), support vector machine (SVM), XGBoost, and neural networks (NNs). These models were compared to identify the best-performing approach. Additionally, a genetic algorithm (GA) was utilized for feature selection and SHAP (SHapley Additive exPlanations) for interpreting the contributions of each employed feature. Results: The XGBoost classifier demonstrated the highest performance on the test dataset to predict depression with excellent accuracy (97.83%), with NNs a close second (accuracy, 97.02%). The XGBoost classifier utilized the 15 most significant risk factors identified by the GA algorithm. Additionally, the SHAP analysis revealed that anxiety, education level, alcohol consumption, and body mass index were the most influential predictors of depression. Conclusions: These findings provide valuable insights for the development of personalized public health interventions and clinical strategies, ultimately promoting improved mental well-being for individuals. Future research should expand datasets to enhance model accuracy, enabling early detection and personalized mental healthcare systems for better intervention.

Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors—ones that can be easily examined in clinical settings—into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.