Search Results (681)

Background/Objectives: Impairment in self-awareness (ISA) is one of the common consequences of an acquired brain injury (ABI) and is associated with anosodiaphoria. Collectively, these co-occurring neuropsychological disorders pose significant obstacles in the neurorehabilitation of moderate-to-severe ABI patients. Individuals who recover from ISA may present with anxiety and/or depression as adaptive reactions to the ABI, along with related functional disabilities. The present study investigated whether the level of metacognitive self-awareness (SA) is associated with the presence of anxiety and depression, apathy, or anosodiaphoria in patients with moderate-to-severe ABI. It aimed also at investigating the possible relationship between the severity of disability and both psycho-emotional diseases and the presence of PTSD symptoms in patients with high metacognitive SA. Methods: Sixty patients with moderate-to-severe ABI and different levels of metacognitive SA completed a series of questionnaires, which assessed their self-reported metacognitive SA, anosodiaphoria, anxiety and depression, apathy, and PTSD symptoms. Results: Low-metacognitive-SA patients showed lower levels of anxiety and depression and higher anosodiaphoria than high-metacognitive-SA patients. Patients with high metacognitive SA and high levels of disability showed significant higher states of anxiety and PTSD symptoms than patients with high metacognitive SA and low levels of disability. Conclusions: The neurorehabilitation of individuals with moderate to severe ABI should address, in particular, the complex interaction between ISA and anxiety and depression in patients during the rehabilitation process. Full article

Background/Objectives: Cardiovascular diseases (CVDs) are frequently associated with psychiatric and cognitive comorbidities. These conditions have been shown to significantly impact quality of life and clinical outcomes. This study aims to evaluate the prevalence of anxiety, depression, and cognitive deficits in patients with CVD and to compare the results with existing evidence in the literature. Methods: A total of 74 patients were assessed using the following standardized screening tools: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory (BAI). A systematic review was then conducted to compare the findings with those reported in the literature. Results: Most previous studies using the MoCA reported an over 70% absence of cognitive impairment, whereas this study shows a balanced distribution between the absence of (32.4%) and mild (35%) or moderate (32%) impairment. Studies with the MMSE indicated high rates of absence of cognitive deficits (74–79%), but here, the rate of absence was lower (58%), with an increase in mild impairment (42%). Regarding depression, compared with studies showing only absence or moderate/severe forms, this study reveals a more balanced profile, with 57% without depression and with varying severity levels (22% mild, 19% moderate, and 3% severe). Finally, for anxiety, unlike previous asymmetric distributions, greater variability was observed, with 58% without anxiety and significant percentages of mild (26%), moderate (12%), and severe (4%) anxiety. Conclusions: The results highlight a significant and varied prevalence of anxiety, depression, and cognitive deficits, emphasizing the importance of a multidimensional assessment to improve clinical management and therapeutic outcomes. Full article

Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients’ performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation. Full article

Background: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. Methods: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions. Results: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system. Conclusions: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Background/Objectives: Cerebral hemorrhage (CH) has physical, cognitive, and emotional consequences. Recovery requires a complex rehabilitation process in which coping strategies play a fundamental role in supporting psychological adaptation. The aim of this study is to investigate and understand the extent and manner in which coping strategies have been assessed in the CH population within the scientific literature. Methods: Studies were identified through searches in the PubMed, Scopus, and Embase databases. Eight studies published between 2014 and 2024 were selected. Results: The most frequently adopted coping strategies include task-oriented coping, avoidance, emotion-focused coping, acceptance, planning, and emotional support. Task-oriented strategies and acceptance are associated with better psychological outcomes. Conversely, avoidant and emotion-focused strategies correlate with higher levels of anxiety, depression, and poorer adaptation. Resilience and social participation emerge as protective factors. Finally, Action/Distraction is associated with a better quality of life, while Trivialization/Resignation is linked to lower levels. Conclusions: Coping seems to represent a modifiable, patient-centered lever that can mitigate the psychosocial sequelae of intracranial hemorrhage when assessed systematically and addressed through tailored rehabilitation programs. Our findings lay the groundwork for evidence-based, coping-focused interventions and highlight critical avenues for future longitudinal and mechanistic research. Full article

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

Background: Art therapy was recently administered to stroke patients using immersive virtual reality technology, chosen to provide the illusion of being able to replicate an artistic masterpiece. This approach was effective in improving rehabilitative outcomes due to the so-called Michelangelo effect: patients’ interaction with artistic stimuli reduced perceived fatigue and improved performance. The aim of the present study was to investigate which factors may influence those outcomes (e.g., type of artwork, esthetic valence, perceived fatigue, clinical conditions). Methods: An observational study was conducted on 25 patients with stroke who performed the protocol of virtual art therapy (VAT). In each trial, patients were asked to rate the esthetic valence of the artworks and their perceived fatigue, whereas therapists assessed patients’ participation in the therapy (Pittsburgh Rehabilitation Participation Scale, PRPS). Moreover, before and after treatment, patients’ independence in daily living activities (Barthel Index, BI), and their upper limb functioning (Manual Muscle Test, MMT) and spasticity (Ashworth Scale, AS) were measured. Results: The after-treatment BI scores depended on the before-treatment BI score (p < 0.001) and on the PRPS score (p = 0.006), which, in turn, was increased by the subjective esthetic valence (p = 0.044). Perceived fatigue is a complex factor that may have influenced the outcomes (p = 0.049). Conclusions: There was a general effect of art in reducing fatigue and improving participation of patients during therapy. The variability observed among patients mainly depended on their clinical conditions, but also on the esthetic valence given to each artwork, that could also be intertwined with the difficulty of the task. Art therapy has a high potential to improve rehabilitation outcomes, especially if combined with new technologies, but psychometric investigation of the effects of each factor is needed to design the most effective protocols. Full article

Traumatic brain injury (TBI) leads to severe neurological dysfunction, disability, and even death. Surgical intervention and neurorehabilitation represent the current clinical management methods, yet there remains no effective treatment for recovery after TBI. Post-traumatic hyperinflammation and vascular injury are the key therapeutic challenges. Therefore, a novel-designed multifunctional HT/SAA/HSYA hydrogel based on hyaluronic acid (HA) co-loaded with salvianolic acid A (SAA) and hydroxysafflor yellow A (HSYA) was developed in order to simultaneously target inflammation and vascular injury, addressing key pathological processes in TBI. The HT hydrogel was formed through covalent cross-linking of tyramine-modified HA catalyzed by horseradish peroxidase (HRP). Results demonstrated that the HT hydrogel possesses a porous structure, sustained release capabilities of loaded drugs, suitable biodegradability, and excellent biocompatibility both in vitro and in vivo. WB, immunofluorescence staining, and PCR results revealed that SAA and HSYA significantly reduced the expression level of pro-inflammatory cytokines (IL-1β and TNF-α) and inhibited M1 macrophage polarization through the suppression of the TLR4/NF-κB inflammatory pathway. In vivo experiments confirmed that the HT/SAA/HSYA hydrogel exhibited remarkable pro-angiogenic effects, as evidenced by increased expression of CD31 and α-SMA. Finally, H&E staining showed that the HT/SAA/HSYA hydrogel effectively reduced the lesion volume in a mouse TBI model, and demonstrated more pronounced effects in promoting brain repair at the injury site, compared to the control and single-drug-loaded hydrogel groups. In conclusion, the HT hydrogel co-loaded with SAA and HSYA demonstrates excellent anti-inflammatory and pro-angiogenic effects, offering a promising therapeutic approach for brain repair following TBI. Full article

NGLY1 deficiency is a congenital disorder of deglycosylation, caused by pathogenic variants of the NGLY1 gene. It manifests as global developmental delay, hypo- or alacrima, hypotonia, and a primarily hyperkinetic movement disorder. The NGLY1 enzyme is involved in deglycosylation of misfolded N-glycosylated proteins before their proteasomal degradation and in the activation of transcription factors that control the expression of proteasomal subunits. Here, we have characterized the pathogenic NGLY1 variants found in three Swiss NGLY deficiency patients, as well as the most common pathogenic NGLY1 variant, Arg401*, found in about 20% of patients. Our functional and structural assessments of these variants show that they cause a profound reduction in NGLY1 activity, severely reduced expression of NGLY1 protein, and misprocessing of the transcription factor NFE2L1. Furthermore, transcription of proteasomal subunits and NGLY1 mRNA splicing are impaired by some of these variants. Our in silico structural analysis shows that the Arg390Gln substitution results in destabilization of NGLY1 structure due to a loss of an ionic interaction network of Arg390 and potentially impairment of protein–protein interactions. Our results provide important information on the functional and structural effects of pathogenic NGLY1 variants and pave the way for structure-based development of personalized treatment options. Full article

Background: Serious videogames have already demonstrated their positive impact on rehabilitation and of particular interest is the virtual reality (VR) technology. This immersive technology has been used in this study to create a neuroaesthetic experience based on the Michelangelo effect for the rehabilitation of patients with spinal cord injury. The aim of this study was to test the usability of a system for virtual art therapy and its capacity to assess patients’ deficits performances. Methods: A VR headset was worn by the participants who experienced a painting simulation of famous artworks (artistic stimuli) against a coloring canvas (non-artistic stimuli). The trajectories of the hand were studied to obtain different kinematic and spectral parameters to evaluate the user performances. A total of 13 healthy subjects and 13 patients with spinal cord injury participated in this study. Results: Significative differences were obtained for most of the parameters between the two groups, except for the normalized jerk and energy of the spectrum. Analysis in the frequency domain showed that both groups preferred horizontal movements for painting the canvas. The NASA and USEQ scores reported a comfortable and user-friendly system according to the patients’ point of view. Conclusions: The system can be a usable tool, the rehabilitative efficacy of which should be tested in patients with spinal cord injury. The kinematic and spectral parameters would allow for the evaluation of the performances alongside the clinical scales, distinguish pathological and physiological performances. Full article

Hemolytic uremic syndrome (HUS), a thrombotic microangiopathy primarily affecting the kidneys, can also involve the central nervous system (CNS), often leading to significant morbidity and mortality. Neurologic manifestations are among the most severe extra-renal complications, particularly in children and during outbreaks of Shiga toxin-producing Escherichia coli (STEC)-associated HUS (typical (tHUS)). This review explores the clinical spectrum, pathophysiology, diagnostic workup, and age-specific outcomes of neurologic involvement in both typical (tHUS) and atypical (aHUS). Neurologic complications occur in up to 11% of pediatric and over 40% of adult STEC-HUS cases in outbreak settings. Presentations include seizures, encephalopathy, focal deficits, movement disorders, and posterior reversible encephalopathy syndrome (PRES). Magnetic resonance imaging (MRI) commonly reveals basal ganglia or parieto-occipital lesions, though subtle or delayed findings may occur. Laboratory workup typically confirms microangiopathic hemolytic anemia (MAHA), thrombocytopenia, and kidney damage, with additional markers of inflammation or metabolic dysregulation. Eculizumab is the first-line treatment for aHUS with CNS involvement, while its utility in STEC-HUS remains uncertain. Although many children recover fully, those with early CNS involvement are at greater risk of developing epilepsy, cognitive delays, or fine motor deficits. Adults may experience lingering neurocognitive symptoms despite apparent clinical recovery. Differences in presentation and imaging findings between age groups emphasize the need for tailored diagnostic and therapeutic strategies. Comprehensive neurorehabilitation and long-term follow-up are crucial for identifying residual deficits. Continued research into predictive biomarkers, neuroprotective interventions, and standardized treatment protocols is needed for improving outcomes in HUS patients with neurological complications. Full article

Electroencephalography (EEG) and surface electromyography (sEMG) signals are widely used in human–machine interaction (HMI) systems due to their non-invasive acquisition and real-time responsiveness, particularly in neurorehabilitation and prosthetic control. However, existing deep learning approaches often struggle to capture both fine-grained local patterns and long-range spatio-temporal dependencies within these signals, which limits classification performance. To address these challenges, we propose a lightweight deep learning framework that integrates adaptive spatial attention with multi-scale temporal feature extraction for end-to-end EEG and sEMG signal decoding. The architecture includes two core components: (1) an adaptive attention mechanism that dynamically reweights multi-channel time-series features based on spatial relevance, and (2) a multi-scale convolutional module that captures diverse temporal patterns through parallel convolutional filters. The proposed method achieves classification accuracies of 79.47% on the BCI-IV 2a EEG dataset (9 subjects, 22 channels) for motor intent decoding and 85.87% on the NinaPro DB2 sEMG dataset (40 subjects, 12 channels) for gesture recognition. Ablation studies confirm the effectiveness of each module, while comparative evaluations demonstrate that the proposed framework outperforms existing state-of-the-art methods across all tested scenarios. Together, these results demonstrate that our model not only achieves strong performance but also maintains a lightweight and resource-efficient design for EEG and sEMG decoding. Full article

Background/Objectives: Spasticity in children with cerebral palsy (CP) can impair motor-related functions. The objective of this exploratory, prospective study was to examine if transcutaneous electrical nerve stimulation (TENS) in a whole-body suit leads to changes in spasticity and other related effects. Methods: Thirty-one children with CP GMFCS III–V, with a median age of 11.0 years (age range of 7–17 years), were consecutively included, and they used the suit with TENS for 24 weeks. The primary outcome was spasticity measured using the Modified Ashworth Scale (MAS). Functional motor-related tasks were evaluated by the Goal Attainment Scale (SMART GAS). The Modified Tardieu Scale (MTS), passive Range of Motion (pROM), GMFM-66, and Posture and Postural Ability Scale (PPAS) assessments were performed. Results: Seventeen subjects (17/31) completed the 24 weeks. Dropout was due to difficulty in donning the suit. The level of overall spasticity, most pronounced in the proximal arms and legs, was reduced according to the MAS, but not the MTS or pROM. Subject-relevant motor-related goals improved significantly in standing/walking and hand/arm function. Changes in the GMFM-66 and PPAS were not significant. Conclusions: Although there were statistically significant but underpowered changes in the MAS after 24 weeks, there were no clinically relevant effects. Exploratorily, we found observer-reliant motor-related functional improvements, which, however, we were unable to detect when trying to quantify them. Donning the suit led to dropout throughout the study. Caregivers need to allocate time, mental capacity and have the physical skill set for donning the suit for long-term use. Full article