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: 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.

The impairment of neurocognitive functions occurs in all subtypes of multiple sclerosis, even from the earliest stages of the disease. Commonly reported manifestations of cognitive impairment include deficits in attention, conceptual reasoning, processing efficiency, information processing speed, memory (episodic and working), verbal fluency (language), and executive functions. Multiple sclerosis patients also suffer from social cognition impairment, which affects their social functioning. It is not clear if social cognition impairment occurs independently or secondarily to neurocognitive impairment. Αssociations of variable strengths between neurocognitive and social cognition deficits have been demonstrated and their neural basis is increasingly investigated. The objective of the current paper is to provide an overview of the available-to-date literature on neurocognitive impairment in multiple sclerosis patients by disease subtype and assess its effect on and potential correlation with social cognition performance and impairment. The prompt detection of neurocognitive predictors of social cognition impairment that may be applicable to all multiple sclerosis subtypes and intervention are crucial to prevent further neural and social cognition decline in multiple sclerosis patients.

Stroke survivors are at increased risk of developing depression and cognitive decline. Thus, it is crucial for both clinicians and stroke survivors to be provided with timely and accurate prognostication of post-stroke depression (PSD) and post-stroke dementia (PSDem). Several biomarkers regarding stroke patients’ propensity to develop PSD and PSDem have been implemented so far, leukoaraiosis (LA) being among them. The purpose of the present study was to review all available work published within the last decade dealing with pre-existing LA as a predictor of depression (PSD) and cognitive dysfunction (cognitive impairment or PSDem) in stroke patients. A literature search of two databases (MEDLINE and Scopus) was conducted to identify all relevant studies published between 1 January 2012 and 25 June 2022 that dealt with the clinical utility of preexisting LA as a prognostic indicator of PSD and PSDem/cognitive impairment. Only full-text articles published in the English language were included. Thirty-four articles were traced and are included in the present review. LA burden, serving as a surrogate marker of “brain frailty” among stroke patients, appears to be able to offer significant information about the possibility of developing PSD or cognitive dysfunction. Determining the extent of pre-existing white matter abnormalities can properly guide decision making in acute stroke settings, as a greater degree of such lesioning is usually coupled with neuropsychiatric aftermaths, such as PSD and PSDem.

Introduction: Fatigue is associated with various diseases of different origins extending from immune disorders to cancer. The purpose of this study was to explore the psychometric properties/dimensionality of the Multidimensional Fatigue Inventory (MFI-20) questionnaire in samples of healthy adults and patient subgroups in Greece. Methods: This was a multicenter cross-sectional study that included 80 women with breast cancer, 108 patients with multiple sclerosis (MS), 100 people with thalassemia diagnosis, and 185 healthy adults, aged 18-45 years. All patients were adults. Patients were recruited from a breast surgery clinic, a neurological clinic, and a thalassemia unit, while healthy adults were recruited from the University of West Attica students and personnel. The MFI-20, the modified fatigue impact scale (MFIS), the fatigue severity scale (FSS), and the Hamilton anxiety-depression scale (HANDS) were used. Internal consistency, repeatability, test-retest reliability, construct, and convergent validity were investigated. Results: MFI-20 exhibited excellent reliability properties (internal consistency: Cronbach’s alpha MFI-20 subscales ranged from 0.83 to 0.94; repeatability: Pearson’s r = 0.335 [p < 0.001]). Significant correlations were found between MFI-20 and MFIS: Pearson’s r = 0.870, FSS: Pearson’s r = 0.582 - 0.335, and HANDS: Pearson’s r = 0.734 - 0.442 (all p-values < 0.0001) on all subsamples. However, its dimensionality is questionable depending on the subpopulation tested, and the one-dimension perspective is possible. MS patients exhibited the highest total score (55.26 ± 16.53), while thalassemia patients exhibited the lowest score (45.09 ± 13.54). In all subscales, thalassemia patients differed statistically significantly from the MS patients (p < 0.01), while in the reduced activity subscale, thalassemia patients differed significantly from all other groups (p < 0.01). Conclusions: As strict fatigue subscale classification is questionable, the use of MFI-20 total score is suggested for the assessment of fatigue in clinical populations. As MFI-20 is a very useful research tool for studying fatigue, the use of the total and/or partial scores depends on the clinical population. Total score instead of (or additionally) partial scores is suggested in clinical practice.

Neurological diseases are increasingly diverse and prevalent, presenting significant challenges for their timely and accurate diagnosis. The aim of the present study is to conduct a bibliometric analysis and literature review in the field of neurology to explore advancements in the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL). Using VOSviewer software (version 1.6.20.0) and documents retrieved from the Scopus database, the analysis included 113 articles published between 1 January 2018 and 31 December 2024. Key journals, authors, and research collaborations were identified, highlighting major contributions to the field. Science mapping investigated areas of research focus, such as biomechanical data and gait analysis including AI methodologies for neurological disease diagnosis. Co-occurrence analysis of author keywords allowed for the identification of four major themes: (a) machine learning and gait analysis; (b) sensors and wearable health technologies; (c) cognitive disorders; and (d) neurological disorders and motion recognition technologies. The bibliometric insights demonstrate a growing but relatively limited collaborative interest in this domain, with only a few highly cited authors, documents, and journals driving the research. Meanwhile, the literature review highlights the current methodologies and advancements in this field. This study offers a foundation for future research and provides researchers, clinicians, and occupational therapists with an in-depth understanding of AI’s potentially transformative role in neurology.