Giovanni Restuccia

IntroductionAlzheimer’s disease (AD) is the leading cause of dementia worldwide and is characterized by progressive decline in cognitive functions. Mild Cognitive Impairment (MCI) represents an intermediate stage between normal aging and dementia, with measurable cognitive decline but largely preserved functional independence. However, overlapping clinical features and heterogeneous disease patterns may limit the accuracy of conventional classification approaches. Machine learning (ML) methods can support clinical decision-making by identifying complex neuroanatomical patterns in neuroimaging data and improving the characterization of different stages of cognitive impairment. ObjectivesThis study aimed to compare different supervised ML algorithms for binary and multiclass classification of cognitive impairment using structural MRI-derived volumetric biomarkers. Specifically, we evaluated the performance of models trained using neuroanatomical features alone and models integrating MRI biomarkers with demographic variables. In addition, SHAP-based explainability analysis was applied to identify the features contributing most to model predictions and to improve the interpretability of the classification framework. MethodsThe study included 204 participants, comprising 54 patients with AD, 79 individuals with MCI, and 71 HC. T1-weighted structural MRI scans were processed using automated pipelines to extract cortical and subcortical volumetric measures. Five supervised ML algorithms were evaluated: Logistic Regression, Random Forest, XGBoost, LightGBM, and Support Vector Machine (SVM). All models were implemented within a standardized pipeline including feature scaling, feature selection using SelectKBest with ANOVA F-test, hyperparameter tuning with GridSearchCV, and stratified 5-fold cross-validation. Two experimental settings were compared: models trained only on MRI-derived volumetric features and models trained on a combination of MRI and demographic features. Performance was evaluated using accuracy, F1-score, ROC-AUC, and confusion matrices. Finally, SHAP analysis was applied to the best-performing models to assess the contribution of individual features to classification decisions. ResultsIn the binary classification task distinguishing MCI/AD from HC, all models achieved higher performance than in the multiclass setting. Using only MRI-derived volumetric features, Logistic Regression showed the best performance, reaching an accuracy of 0.70 and a ROC-AUC of 0.76. The inclusion of demographic variables further improved classification performance, with SVM achieving the highest results, corresponding to an accuracy of 0.75 and a ROC-AUC of 0.80. In the multiclass classification task involving AD, MCI, and HC, Random Forest achieved the best performance when using only volumetric biomarkers, with an accuracy of 0.53 and a ROC-AUC of 0.69. When MRI and demographic features were combined, SVM achieved the best performance, reaching an accuracy of 0.56 and a ROC-AUC of 0.72. SHAP analysis identified the amygdala, hippocampus, corpus callosum subdivisions, and age as the most influential features driving model predictions. ConclusionsThe findings support the potential role of structural MRI biomarkers combined with interpretable ML approaches for the classification of cognitive impairment. The integration of demographic variables improved model performance, particularly in binary discrimination tasks, whereas multiclass classification remained more challenging, likely due to the clinical and neuroanatomical overlap between MCI and AD patterns. This framework may represent a promising approach for future clinical decision-support tools aimed at improving diagnostic stratification, although further validation in larger independent cohorts is required.

Background: Fatigue is a highly disabling symptom in multiple sclerosis and is strongly associated with reduced health-related quality of life (HRQoL). However, whether distinct fatigue dimensions show differential associations with specific HRQoL domains remains unclear. This study investigated the relationship between physical, cognitive, and psychosocial fatigue and SF-36 outcomes in people with relapsing-remitting multiple sclerosis (RRMS). Methods: Forty-four people with RRMS were included in this cross-sectional study. Participants were classified as fatigued or non-fatigued according to the Modified Fatigue Impact Scale (MFIS) total score cut-off. Group differences in SF-36 domains were examined using ANCOVAs adjusted for age, sex, disease duration, and disability, with additional sensitivity analyses adjusting for depressive symptoms. Partial Spearman correlations assessed associations between MFIS subscales and SF-36 domains across the whole sample, controlling for demographic and clinical covariates. Results: Eighteen participants were classified as fatigued and 26 as non-fatigued. Fatigued participants showed significantly lower scores across all SF-36 domains. After additional adjustment for depressive symptoms, differences remained significant for Physical Functioning, Role Physical, General Health, Vitality, Social Functioning, and Role Emotional. Physical fatigue was inversely associated with several HRQoL domains, including physical, social, vitality, general health, and mental health-related outcomes. Psychosocial fatigue was associated with poorer Physical Functioning, Role Physical, Bodily Pain, and Social Functioning. Cognitive fatigue was not significantly associated with any SF-36 domain. Conclusions: Physical and psychosocial fatigue appear to be the main fatigue dimensions associated with HRQoL impairment in RRMS. Dimension-specific fatigue assessment may help identify more individualized targets for patient-centered management.

Background: Aquatic therapy is increasingly used in post-stroke rehabilitation, but its effects on balance and gait in the chronic phase remain variably reported. This systematic review aimed to evaluate the effects of aquatic therapy, alone or combined with land-based rehabilitation, on balance and gait in individuals with chronic stroke. Methods: A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted between February and March 2026. Randomized controlled trials enrolling adults with chronic stroke and evaluating aquatic-containing interventions with quantitative balance and/or gait outcomes were included. Owing to clinical and methodological heterogeneity, the primary synthesis was narrative. An exploratory random-effects meta-analysis was additionally performed for post-intervention Berg Balance Scale (BBS) scores. Results: Thirteen randomized controlled trials involving 468 participants were included. Overall, aquatic therapy was associated with more consistent improvements in balance than in gait, while combined aquatic and land-based programs generally showed broader functional gains than land-based rehabilitation alone. In the exploratory meta-analysis, the primary pooled analysis of four studies favored aquatic-containing interventions for post-intervention BBS scores (MD = 3.69, 95% CI 2.69 to 4.69; p < 0.001), with no observed heterogeneity (I² = 0%). Conclusions: Aquatic therapy may be a useful adjunctive rehabilitation strategy for improving balance in chronic stroke, whereas effects on gait appear more variable. These findings should be interpreted cautiously because the quantitative synthesis was exploratory and the overall evidence base remains heterogeneous and limited by small sample sizes and short follow-up.

Background and aims Iron-related alterations are frequently implicated in multiple sclerosis (MS) neurodegeneration, but its relationship with clinical symptoms and cognitive impairment remains unclear. We systematically reviewed evidence linking iron-sensitive susceptibility-based magnetic resonance imaging (MRI) metrics to cognitive outcomes, in order to evaluate their potential role as diagnostic and prognostic biomarkers of cognitive decline in MS. Materials and methods A systematic review was conducted using the PRISMA guidelines. 600 studies were identified by searching PubMed, Scopus, Web of Science, and Embase in October 2025. After screening, 12 studies fulfilled the eligibility criteria and were included. Results Two main patterns emerged: (i) higher basal ganglia susceptibility/iron-related metrics are consistently linked to worse cognitive performance, particularly information processing speed and executive control; and (ii) thalamic findings are more variable in direction and clinical meaning, with susceptibility often capturing a mixed signal that overlaps with thalamic atrophy. Conclusions Iron-sensitive quantitative MRI captures clinically relevant deep gray matter (DGM) pathology associated with cognitive impairment in MS, with basal ganglia measures showing the strongest linkage with cognitive and clinical outcomes. Future studies should include harmonized methodology and a longitudinal component.

Background: Group-based dance interventions (GBDIs) have emerged as a promising approach to rehabilitation for neurological disorders. This pilot study evaluated the feasibility and preliminary effects of a GBDI on motor function, cognition, fatigue, and quality of life in individuals with Relapsing–Remitting Multiple Sclerosis (RRMS). Methods: The intervention consisted of two 60-min ballet sessions per week over 10 weeks, structured as 10 min of warm-up, 40 min of ballet exercises, and 10 min of stretching. Assessments were conducted at baseline (T0) and post-intervention (T1). Concerning motor measures, balance was assessed using the Mini-BESTest; gait performance was evaluated through the 6-min walk test (6MWT), four square step test (FSST), and figure-of-8 walk test (F8WT); upper limb motor functions were assessed using the box and block test (BBT) and 9-hole peg test (9HPT). Regarding cognitive functions, the Rey auditory verbal learning test (RAVLT), symbol digit modalities test (SDMT), and trail making test A and B (TMT-A/B) were administered, while fatigue and quality of life were assessed using the modified fatigue impact scale (MFIS) and the Short Form survey-36 (SF-36), respectively. Results: At T1, participants improved in Mini-BESTest (+17.5%), 6MWT (+7.3%), and BBT dominant hand (+6.9%). Performance also improved on the following cognitive tests: RAVLT Immediate Recall (+5.9%), RAVLT Delayed Recall (+20.3%), SDMT (+47.4%), TMT-A (−21.2%), and (TMT-B −24.5%). Conclusions: The very small sample size (n = 4) and the lack of a control group probably restrict the generalizability of the findings. Consequently, the results obtained by this pilot study should be considered exploratory and hypothesis-generating rather than definitive evidence of a robust benefit. Future studies should confirm these findings by enlarging the intervention cohorts and adopting a randomized controlled design. In this sense, a 10-week GBDI may provide a solid base for a safe and promising dance-based rehabilitation program that could lead to improvements in motor, cognition, and psychosocial spheres in people with RRMS.