From the start of the COVID-19 pandemic, the use of surgical masks became widespread. However, they occlude an important part of the face and make it difficult to decode and interpret other people’s emotions. To clarify the effect of surgical masks on configural and featural processing, participants completed a facial emotion recognition task to discriminate between happy, sad, angry, and neutral faces. Stimuli included fully visible faces, masked faces, and a cropped photo of the eyes or mouth region. Occlusion due to the surgical mask affects emotion recognition for sadness, anger, and neutral faces, although no significative differences were found in happiness recognition. Our findings suggest that happiness is recognized predominantly via featural processing.

Clinical findings suggest that transcutaneous spinal direct current stimulation (tsDCS) can modulate ascending sensitive, descending corticospinal, and segmental pathways in the spinal cord (SC). However, several aspects of the stimulation have not been completely understood, and realistic computational models based on MRI are the gold standard to predict the interaction between tsDCS-induced electric fields and anatomy. Here, we review the electric fields distribution in the SC during tsDCS as predicted by MRI-based realistic models, compare such knowledge with clinical findings, and define the role of computational knowledge in optimizing tsDCS protocols. tsDCS-induced electric fields are predicted to be safe and induce both transient and neuroplastic changes. This could support the possibility to explore new clinical applications, such as spinal cord injury. For the most applied protocol (2–3 mA for 20–30 min, active electrode over T10–T12 and the reference on the right shoulder), similar electric field intensities are generated in both ventral and dorsal horns of the SC at the same height. This was confirmed by human studies, in which both motor and sensitive effects were found. Lastly, electric fields are strongly dependent on anatomy and electrodes’ placement. Regardless of the montage, inter-individual hotspots of higher values of electric fields were predicted, which could change when the subjects move from a position to another (e.g., from the supine to the lateral position). These characteristics underlines the need for individualized and patient-tailored MRI-based computational models to optimize the stimulation protocol. A detailed modeling approach of the electric field distribution might contribute to optimizing stimulation protocols, tailoring electrodes’ configuration, intensities, and duration to the clinical outcome.

Long-COVID is a clinical condition in which patients affected by SARS-CoV-2 usually report a wide range of physical and cognitive symptoms from 3 to 6 months after the infection recovery. The aim of the current study was to assess the link between self-reported long-COVID symptoms and reaction times (RTs) in a self-administered Visual Detection Task (VDT) in order to identify the predictor symptoms of the slowing in reaction times to determine attention impairment. In total, 362 participants (age (mean ± S.D.: 38.56 ± 13.14); sex (female–male: 73.76–26.24%)) responded to a web-based self-report questionnaire consisting of four sections: demographics, disease-related characteristics, and medical history questions. The final section consisted of a 23 item 5-point Likert-scale questionnaire related to long-term COVID-19 symptoms. After completing the questionnaire, subjects performed a VDT on a tablet screen to assess reaction times (RTs). An exploratory factorial analysis (EFA) was performed on the 23 long-COVID symptom questions, identifying 4 factors (cognition, behavior, physical condition, presence of anosmia and/or ageusia). The most important predictors of RTs were cognition and physical factors. By dissecting the cognitive and physical factors, learning, visual impairment, and headache were the top predictors of subjects’ performance in the VDT. Long-COVID subjects showed higher RTs in the VDT after a considerable time post-disease, suggesting the presence of an attention deficit disorder. Attention impairment due to COVID-19 can be due to the presence of headaches, visual impairments, and the presence of cognitive problems related to the difficulty in learning new activities. The link between the slowing of reaction times and physical and cognitive symptoms post-COVID-19 suggests that attention deficit disorder is caused by a complex interaction between physical and cognitive symptoms. In addition, the study provides evidence that RTs in a VDT represent a reliable measure to detect the presence of long-COVID neurological sequelae.

Background: Large language models (LLMs) such as ChatGPT (OpenAI) and Gemini (Google) are increasingly explored for their potential in medical diagnostics, including neurology. Their real-world applicability remains inadequately assessed, particularly in clinical workflows where nuanced decision-making is required. Objective: This study aims to evaluate the diagnostic accuracy and appropriateness of clinical recommendations provided by not-specifically-trained, freely available ChatGPT and Gemini, compared to neurologists, using real-world clinical cases. Methods: This study consisted of an experimental evaluation of LLMs’ diagnostic performance presenting real-world neurology cases to ChatGPT and Gemini, comparing their performance with that of clinical neurologists. The study was conducted simulating a first visit using information from anonymized patient records from the Neurology Department of the ASST Santi Paolo e Carlo Hospital, ensuring a real-world clinical context. The study involved a cohort of 28 anonymized patient cases covering a range of neurological conditions and diagnostic complexities representative of daily clinical practice. The primary outcome was diagnostic accuracy of both neurologists and LLMs, defined as concordance with discharge diagnoses. Secondary outcomes included the appropriateness of recommended diagnostic tests, interrater agreement, and the extent of additional prompting required for accurate responses. Results: Neurologists achieved a diagnostic accuracy of 75%, outperforming ChatGPT (54%) and Gemini (46%). Both LLMs demonstrated limitations in nuanced clinical reasoning and overprescribed diagnostic tests in 17%‐25% of cases. In addition, complex or ambiguous cases required further prompting to refine artificial intelligence–generated responses. Interrater reliability analysis using Fleiss Kappa showed a moderate-to-substantial level of agreement among raters (κ=0.47, SE 0.077; z=6.14, P<.001), indicating agreement between raters. Conclusions: While LLMs show potential as supportive tools in neurology, they currently lack the depth required for independent clinical decision-making when using freely available LLMs without previous specific training. The moderate agreement observed among human raters underscores the variability even in expert judgment and highlights the importance of rigorous validation when integrating artificial intelligence tools into clinical workflows. Future research should focus on refining LLM capabilities and developing evaluation methodologies that reflect the complexities of real-world neurological practice, ensuring effective, responsible, and safe use of such promising technologies.

Background: Mindfulness trainings have shown promising results as treatment for behavioural symptoms in several pathologies. In addition, mindfulness protocols induced an improvement in memory and attention. Therefore, mindfulness could be an effective intervention for patients affected by Parkinson’s disease (PD) and mild cognitive impairment (MCI), who are characterized by both behavioural and cognitive dysfunctions. Methods: We assessed differences in Montreal Cognitive Assessment (MoCA) scores and in Beck Depression Inventory II (BDI-II) scores in patients affected by PD and MCI enrolled in two different rehabilitation programs (an experimental vs. an usual structured program for cognitive rehabilitation). Participants in the experimental group (MILC-tr) underwent innovative rehabilitation program involving mindfulness and reminiscence activities. Assessments were performed before (T0) and at the end of the rehabilitation program (T1). Results: Friedman test showed a significant improvement between timepoints in MoCA global score (x² = 4.000, p = 0.046), MoCA memory sub-scale score (x² = 4.571, p = 0.033), and BDI-II cognitive and affective factors (x² = 4.000, p = 0.046) only for patients in MILC-tr group. Mann–Whitney test showed a significant difference between group comparing differences in Δ scores between T0 and T1 in the MoCA memory sub-scale score (U = 190.50, p = 0.035). Conclusions: Mindfulness-based rehabilitation programs could be effective in patients affected by PD and MCI.

Depressive disorders are one of the leading causes of disability worldwide. Transcranial direct current stimulation (tDCS) is a safe, simple, non-invasive brain stimulation technique showing considerable effectiveness in improving depressive symptoms. Most studies to date have applied anodal tDCS to the left dorsolateral prefrontal cortex (DLPFC), in line with the hypothesis that depressed patients exhibit relative hypoactivity in the left DLPFC compared to the right. Considering the emerging role of the cerebellum in emotional processes, we aimed to study the effect of combining bilateral cerebellar tDCS with the commonly used bifrontal stimulation in patients with severe depression. This open-label pilot study entailed the simultaneous administration of bilateral cerebellar (anode over the left cerebellum, cathode over the right cerebellum) and bilateral frontal (anode over the left DLPFC, cathode over the right DLPFC) tDCS to patients (N = 12) with treatment-resistant depression. The 21-item Hamilton Depression Rating Scale (HDRS) and Beck’s Depression Inventory-II (BDI-II) were selected as outcome measures. Electric fields distribution originating from this novel electrode montage was obtained by a computational method applied to a realistic human head model. We observed a 30% reduction of both clinician-rated and self-reported severity of depressive symptoms after only five days (10 sessions) of treatment. Younger age was associated with greater clinical improvement. Adverse events were similar to those of the conventional electrodes montage. The modelling studies demonstrated that the electric fields generated by each pair of electrodes are primarily distributed in the cortical areas under the electrodes. In conclusion, the cerebellum could represent a promising adjunctive target for tDCS interventions in patients with TRD, particularly for younger patients.