Background This Review integrates evidence from neuroscience, psychology, and clinical research to outline a multisensory, food-based neurosensory framework as a promising non-pharmacological approach to neurological rehabilitation. Moving beyond a traditional focus on flavour perception, we discuss how food-evoked multisensory stimulation, integrating visual, olfactory, gustatory, auditory, and somatosensory cues, can engage neural circuits within the orbitofrontal cortex, hippocampus, amygdala, and related limbic networks. Scope and approach We examine how carefully designed culinary experiences, including colour–aroma congruency, sensory-rich presentation, and culturally meaningful flavours, may promote experience-dependent neuroplastic changes in individuals with neurological vulnerability. Although current evidence derives primarily from preclinical models of environmental enrichment, mechanistic studies, and small-scale pilot interventions, further clinical trials are required to confirm this potential.This review integrates existing evidence, identified through a systematic search of PubMed, Scopus, and Web of Science, according to clearly defined inclusion criteria, into a coherent neurosensory framework applicable to brain health and disease. Key findings and conclusion While the evidence base remains largely preliminary and predominantly derived from narrative synthesis, pilot studies, and mechanistic reasoning rather than randomised controlled trials, the convergence of findings across neuroscience, sensory science, and clinical rehabilitation supports the plausibility of food-based multisensory enrichment as a complementary non-pharmacological approach. By reframing food-related experience as an active, structured form of sensory-cognitive engagement, this review identifies promising directions for translational research. However, clinical recommendations will require rigorous experimental validation, integration of cultural and personal factors into intervention design, development of standardised protocols, and disease-specific adaptations.

The Enhanced Virtual Exposure (EVE) module of the ARCADIAVR platform is a virtual reality system designed to support graded food cue exposure in cognitive-behavioural programs for bulimia nervosa (BN). Grounded in associative learning models of binge eating and exposure therapy principles, EVE aims to facilitate inhibitory learning through repeated, clinician-guided exposure to food-related cues within immersive and controllable virtual environments. This paper describes the clinical rationale, hardware/software architecture, immersive environments, biosensor integration, clinician dashboard, and personalization logic of EVE. The system was developed in Unity using C# and implemented for the Meta Quest 3 headset. It includes 30 three-dimensional food stimuli and four realistic eating-related contexts: kitchen, restaurant, dining room, and bedroom. Real-time physiological data, including heart rate and respiration, are acquired through a ComfTech wearable sensor and transmitted to a clinician dashboard, allowing therapists to monitor user responses and guide exposure progression. This study does not assess therapeutic efficacy in BN patients. Instead, it evaluates the technical readiness, usability, user experience, and tolerability of EVE in a non-clinical sample. A preliminary usability assessment involving 118 healthy volunteers indicated that the system was functional, comfortable, realistic, and engaging, with no severe cybersickness symptoms or early session termination reported. Clinical efficacy will be evaluated in forthcoming randomized controlled trials.

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation.

Facial Expression Analysis (FEA) is a computational approach, crucial for understanding human emotions and mental states, with applications in healthcare, social robotics, and driver-state monitoring. Traditional FEA techniques often rely on Action Units (AUs) and the Facial Action Coding System (FACS), which provide a structured framework but may miss subtle micro-expressions and can become ambiguous in complex or rapidly changing emotional states. Blendshape Features (BFs), originally developed for computer graphics, offer a continuous, high-resolution alternative. This study offers two key contributions: (i) a systematic review of AUs and BFs in FEA research, with a focus on their roles in human-centered applications, and (ii) an expert-validated mapping procedure that links BFs to AUs, integrating the descriptive precision of BFs and the interpretive structure of AUs. The mapping was developed through an independent annotation and consensus process involving ten licensed clinical psychologists and psychotherapists with expertise in analyzing nonverbal behavior. Overall, 88% of mappings reached unanimous agreement among the experts, while 98% were supported by a majority (i.e., 6/10). To our knowledge, this represents one of the first publicly documented efforts toward a standardized mapping between MediaPipe's 52 blendshape coefficients and AUs. This resource provides a milestone for advancing FEA toward greater expressivity, scalability, and psychological interpretability, with direct implications for behavioral science, clinical diagnostics, human-computer interaction, and mental health assessment.

Autism is characterized by differences in communication and social interaction, where nonverbal behaviors such as gestures provide valuable indicators for understanding social communication development. Traditional gesture assessment methods heavily rely on manual video review, which is labor-intensive, error-prone, and lacks scalability. Artificial Intelligence (AI) is a powerful solution to enhance the accuracy of autism diagnosis and therapies by enabling continuous, objective analysis of children nonverbal behaviors, such as gestures, thereby improving the assessment of their social and communication skills. This work leverages computer vision for gesture recognition in autistic children diagnosis, proposing AI4ASC, a hierarchical, multi (four)-level AI framework for the automated analysis and monitoring of gestures in autistic children. It focuses on pointing gesture, one of the early indicators of social communication development, to identify behaviors in children that, according to clinical evidence, behave differently to neurotypical ones. Experimental results on real-world clinical videos show that AI4ASC achieves high performance across all system levels: child detection with 88.6% mAP, hand detection with 95.7% of precision, and pointing gesture classification with 97.4% of overall precision. The final clustering level consolidates detected gestures with precision and quantifies them for clinician-guided interpretation. A user-friendly graphical interface and a client–server architecture enable therapist interaction and validation, supporting the integration of AI4ASC into clinical workflows for behavioral assessment.

Conversational technologies are increasingly investigated as supportive tools for autistic individuals; however, existing approaches remain largely application-driven and conceptually fragmented, with limited integration between conversational design and the cognitive foundations of daily living skills. To date, no unified framework explicitly conceptualizes chatbot-based interaction as a structured environment supporting functional autonomy in autism. This Perspective article introduces the Neuroinclusive Conversational Framework (NCF), a theoretical model that reframes chatbots as cognitive scaffolds designed to support everyday functioning in addition to social simulation. Drawing on cognitive psychology, executive functioning research, mediated learning theory, and neuroinclusive design principles, the NCF conceptualizes conversational interaction as a structured learning environment capable of supporting planning, sequencing, task initiation, and self-monitoring within daily routines. The framework is articulated along three interrelated dimensions-cognitive-functional, structural-adaptive, and contextual-ecological-each addressing mechanisms relevant to cognitive accessibility, adaptability, and ecological transfer. By synthesizing evidence across diverse conversational technologies and identifying recurring design principles such as predictability, low social pressure, and adaptive structure, the NCF provides a coherent lens for interpreting existing systems and guiding future design. The article further discusses emerging directions, including biofeedback-informed adaptivity, perceptually accessible interface design, and context-sensitive interaction, emphasizing the importance of ecological validity and integration with human guidance. Overall, the NCF offers a theoretical foundation to support systematic research and development of conversational technologies aimed at strengthening daily living skills and functional autonomy in autistic individuals.

Technology-based interventions for Autism Spectrum Disorder (ASD) are frequently justified on the grounds that digital tools “increase engagement” and “enhance motivation.” However, across domains such as robot-assisted therapy, immersive environments (virtual and augmented reality), and ICT-based educational applications, outcomes labeled as engagement are often derived from observable indicators including gaze, time-on-task, interaction duration, task adherence, or reduced off-task behavior. While informative, these measures may primarily index sustained attention and, when considered in isolation, do not provide sufficient evidence to support inferences about intentional involvement or intrinsic motivation. In this Perspective paper, we argue that part of the literature implicitly equates increased on-task behavior with increased engagement, despite engagement and motivation being inferential constructs that require clearer operationalization. We first clarify conceptual distinctions between engagement, motivation, and sustained attention, highlighting how overlapping behavioral indicators can lead to interpretative ambiguity. We then summarize a recurring evidence pattern showing that technology-related outcomes are most consistently captured through markers of attentional stability during task performance. Finally, we propose an alternative interpretation: technology may function as a context that supports sustained attention in ASD by leveraging predictable structure, sensory coherence, repetition, and immediate feedback, and in some cases by aligning with restricted interests while the indicators most commonly reported are insufficient to determine whether motivation or deeper forms of engagement have increased. We conclude that improving conceptual precision and measurement practices is essential to interpret intervention outcomes accurately and to identify which technological components modulate attention, motivation, and active participation in autistic individuals.

IntroductionImmersive and wearable virtual reality (VR) is an emerging technology with growing potential to support assessment and intervention ifor autistic people. The methodological heterogeneity of existing studies limits the interpretation and generalization of current evidence.MethodsA systematic review with a narrative synthesis was conducted in accordance with the PRISMA guidelines. Electronic searches were performed in PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar, identifying studies published between 2015 and August 2025. Twenty-two studies investigating wearable and immersive VR interventions in children and adults with ASD met the eligibility criteria.ResultsThe included studies demonstrated that wearable VR interventions may improve social communication, joint attention, emotional regulation, daily living skills, executive functioning, and user engagement. Innovative technologies, including eye-tracking and artificial intelligence-based systems, also enabled objective assessment of gaze behaviour, social interaction, and physiological responses. Nevertheless, the evidence was characterized by considerable methodological heterogeneity, predominantly small sample sizes, limited use of randomized controlled designs, and scarce long-term follow-up, reducing the generalizability of the findings.DiscussionWearable VR represents a promising tool for personalized assessment and intervention in ASD. Based on the current evidence, we propose a structured pre-intervention assessment integrating sensory, cognitive, emotional, and VR tolerance profiles to support individualized intervention planning. Future research should prioritize standardized outcome measures, rigorous study designs, and longitudinal investigations to strengthen the clinical translation of VR-based interventions in autism.

PurposeThis study aimed to explore the potential application of NAO in guiding patients through rehabilitative exercises using external audiovisual stimuli, focusing on temporospatial control in terms of range of motion (ROM), execution time and movement smoothness.MethodsThis is a preliminary analysis involving ten healthy volunteers and two patients with shoulder musculoskeletal disorders. The protocol was developed in two phases (III and IV) with different ROM limits and including flexion-extension (FE), external-rotation (ER) and internal-rotation (IR) exercises, performed at two speeds and both with and without NAO assistance. Simultaneously, upper limb kinematics were assessed using a stereophotogrammetric system as a reference. Performance was evaluated by mean absolute error (MAE) for ROM and execution time, with smoothness assessed through Log Dimensionless Jerk analysis.ResultsIn phase III, results for volunteers showed ROM differences in FE and ER, while IR was unaffected by NAO presence. In phase IV, NAO assistance resulted in reduced MAE across nearly all exercises. Patients who only performed phase III exercises at lower speed stayed within ROM limits for all movements performed with NAO, except for ER. For all the participants, results showed a significant reduction in the time MAE when using NAO. Patients exhibit greater smoothness during FE performed with NAO.ConclusionsNAO showed potential in aiding patients with shoulder musculoskeletal disorders to replicate rehabilitation exercises, guiding both ROM and timing while influencing movement smoothness. NAO imitation could lead to improved rehabilitation outcomes and enhanced motor learning of motor skills, fostering greater adherence to prescribed therapy.Level of EvidenceLevel V, diagnostic.

We conducted a study in an ecological setting to evaluate the heart rate variability (HRV) of expert communicators during a live national primetime video interview. The study involved 32 expert science communicators, all with mid- to long-term experience in public speaking and outreach work, who were evaluated by an external jury to assess their communication skills. Prior to the experiment, participants completed an online survey to gather socio-demographic data, work-related information, and psychological profiles. The six indices of communication abilities assessed by jury were: Interest, Agreement, Engagement, Authoritativeness learning, and Clarity. HRV acquisitions were divided into three phases: baseline pre-interview, during the interview, and another baseline recording after the interview. Science communicators were characterized by high levels of self-esteem and prosociality, which were positively correlated with communication indices and inversely correlated with age. Evaluation of physiological responses showed that the total power and low-frequency components of HRV were significantly higher in the post-interview phase compared to both the interview and pre-interview phases. However, when we divided the entire group according to high and low Authoritativeness and Clarity indices, significant interactive effects were detected. Indeed, for the low Authoritativeness and Clarity subgroups, significant differences among all phases were observed, with total power decreasing from the pre-interview to the interview phase and increasing in the post-interview phase. This indicates a clear pattern of stress response and recovery. In contrast, the high Authoritativeness and Clarity subgroup showed less variation across phases, suggesting better stress regulation or less perceived stress during the interview. We provided the psychophysiological basis of science communication expertise that can affect the control of stress regulation during public speaking.

Background: Body representation is a complex process involving sensory, motor, and cognitive information. Frequently, it is disrupted after a stroke, impairing rehabilitation, emotional functioning, and daily functioning. The human figure graphic representation has emerged as a holistic tool to assess post-stroke outcomes. Objectives: This systematic review examines the methodologies of human figure representation tests and their application in assessing post-stroke body representation, emphasizing its role in bridging subjective patient experiences with objective metrics. Methods: This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A literature search was conducted through the databases PubMed, Scopus, Embase, Web of Science, and Google Scholar, collecting publications eligible for qualitative analysis. We selected studies where patients drew human figures in the study design to assess body representation, involving exclusively the adult stroke population. The Newcastle–Ottawa Scale was used to assess the risk of bias. Results: Ten studies were analyzed. The tool demonstrated versatility in capturing unilateral spatial neglect, emotional disturbances, and functional independence. Qualitative metrics and quantitative indices correlated with cognitive deficits, mood disorders, and activities of daily living. Human figure representation also predicted rehabilitation outcomes, with improvements aligning with motor recovery. Innovations included digital quantification of evaluation metrics. Conclusions: Human figure graphic representation is a low-cost, adaptable tool bridging motor, cognitive, and emotional assessments in stroke survivors. While methodological variability persists, AI-driven analytics and standardized frameworks could enhance objectivity. Future research should prioritize validating parameters and developing hybrid models combining traditional qualitative insights with machine learning, thus advancing precision neurorehabilitation and personalized care.

Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants' movements from single-camera video recordings. Methods: A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed using deep learning to capture infants' body landmarks and extract a set of kinematic parameters. Results: Our findings revealed significant differences between infants later diagnosed with NDD and typically developing (TD) infants in three lower limb features at 10 days old: 'Median Velocity', 'Area differing from moving average', and 'Periodicity'. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed through a comparative analysis with a validated semi-automatic algorithm (Movidea), obtaining a Pearson correlation (R) of 93.96% (88.61-96.60%) and a root mean square error (RMSE) of 9.52 pixels (7.29-12.37). Conclusions: This research highlights the potential of AI movement analysis for the early detection of NDDs, providing valuable insights into the motor development of infants at risk.

This paper examines different computational models for Calcium wave propagation in astrocytes. Through a comparative analysis of models by Goldbeter, De Young-Keizer, Atri, Li-Rinzel, and De Pittà and of experimental data, the study highlights the model contributions for the understanding of Calcium dynamics. Tracing the evolution from simple to complex models, this work emphasizes the importance of integrating experimental data in order to further refine these models. The results allow to improve our understanding of the physiological functions of astrocytes, suggesting the importance of more accurate astrocyte models.

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer’s Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data, leading to potential overfitting. This study explores Transfer Learning (TL) approaches to enhance AD diagnosis using a Baseline model consisting of a 3D-Convolutional Neural Network trained on 80 3T MRI scans.Two scenarios are explored: (A) utilizing historical data to address changes in MRI acquisitions (from 1.5T to 3T MRI), and (B) adapting 2D models pre-trained on ImageNet (ResNet18, ResNet50, ResNet101) for 3D image processing when historical data is unavailable. In both scenarios, two modeling approaches are tested. The General Approach involves distinct feature extraction and classification steps, using Radiomic features and TL-based features evaluated with six classifiers. The Deep Approach integrates these steps by fine-tuning the pre-trained models for AD diagnosis.In scenario (A), TL significantly boosts the Baseline’s accuracy from 63% to 99%. In scenario (B), Radiomic features better represents 3D MRI than TL-features in the General Approach. Nonetheless, fine-tuning models pre-trained on natural images can increase the Baseline’s accuracy by up to 12 percentage points, achieving an overall accuracy of 83%.

Introduction:Several trials documented safety and efficacy of omalizumab, but there are a few data about its effects after discontinuation. This study aims to evaluate the maintenance of efficacy of omalizumab in pediatric asthmatic patients one year after its suspension. Methods:A retrospective analysis was conducted on 17 subjects aged 6-18 years, divided into two groups: Group A (9 patients) who discontinued omalizumab after 18 months, and Group B (8 patients) who continued the therapy. Data on respiratory function (FEV1%), the number of exacerbations, need for hospitalizations, use of oral corticosteroids, and Asthma Control Test (ACT) scores were collected and analyzed at three time points: baseline (T0), after 18 months of treatment (T1), and 36 months (T2). Results:In Group A, significant differences were observed between T0 and T1, and T1 and T2, in FEV1% values, the number of exacerbations, the need for oral corticosteroids, and ACT scores. Group B showed significant differences in these parameters over time, with a notable reduction in exacerbations and improvement in ACT scores. The comparative analysis revealed that Group B had a higher number of exacerbations compared to Group A at T0 and greater use of oral cortico-steroids at T1. By T2, Group A had a higher ACT score than Group B at T0, whereas Group B showed higher ACT scores at T2 compared to Group A. Discussion:The study confirmed the efficacy and safety of omalizumab, with its benefits persisting one year after treatment discontinuation in terms of lung function, reduction in exacerbations, decreased need for oral corticosteroids, and improved quality of life. Further research is necessary.