Among patients affected by the virus COVID-19, physicians have observed ventilation disorders. It is relevant to assess neurological involvement, including the role of diaphragmatic function. Its possible impairment could be related to the systemic inflammatory response and disease progression that both typify COVID-19 infection. We distinguished two groups (severe group (SG) and mild group (MG)) according to the severity of respiratory symptomatology. We performed neurophysiological and sonography studies to evaluate the diaphragmatic function. Regarding the sonography variables, we identified statistically significant differences in the right mean diaphragmatic thickness along with the expiration, showing 1.56 mm (SEM: 0.11) in the SG vs 1.92 mm (SEM: 0.19) in the MG (p = 0.042). The contractibility of both hemidiaphragms was 15% lower in the severe group, though this difference is not statistically significant. In our examination of the neurophysiological variables, in the amplitude responses, we observed a greater difference between responses from both phrenic nerves as follows: the raw differences in amplitude were 0.40 μV (SEM: 0.14) in the SG vs 0.35 μV (SEM: 0.19) in the MG and the percentage difference was 25.92% (SEM: 7.22) in the SG vs 16.28% (SEM: 4.38%) in the MG. Although diaphragmatic dysfunction is difficult to detect, our combined functional and morphological approach with phrenic electroneurograms and chest ultrasounds could improve diagnostic sensitivity. We suggest that diaphragmatic dysfunction could play a relevant role in respiratory disturbance in hospitalised patients with severe COVID-19.

Deep brain stimulation of the subthalamic nucleus is efficient for the treatment of motor symptoms (i.e., tremors) in patients with Parkinson’s disease. Gait disorders usually appear during advanced stages of idiopathic Parkinson’s disease in up to 80% of patients and have an important impact on their quality of life. The effects of deep brain stimulation of the subthalamic nucleus on gait and balance are still controversial. For this reason, alternative targets have been considered, such as stimulation of the pedunculopontine nucleus and the pars reticulata of substantia nigra, involved in the integration of the functional connections for gait. Due to the proximity of the subthalamic nucleus to the substantia nigra, their combined stimulation is feasible and may lead to better outcomes, improving axial symptoms. Our objective was to prospectively compare simultaneous stimulation of both structures versus conventional subthalamic stimulation in improving gait disorders. In ten patients with advanced Parkinson’s disease, deep brain stimulation leads (eight linear contacts) were implanted, and gait analysis was performed 6 months after surgery in off-stimulation and after 4 weeks of dual or single subthalamic stimulation. An improvement in gait parameters was confirmed with both stimulation conditions, with better results with combined substantia nigra and subthalamic stimulation compared with conventional subthalamic stimulation. Further studies are needed to determine if this effect remains after long-term dual-target stimulation.

Objective: Due to the absence of studies supporting the role of intraoperative neurophysiological monitoring (IONM) in intradural spinal tumors, this study evaluates the clinical outcome after these surgeries in relation to the use of the advanced intraoperative neurophysiological techniques. Methods: This is an observational, descriptive and retrospective study of two cohort groups in relation to the presence or absence of IONM during the intervention and the subsequent evaluation of the clinical and functional results in the short and medium terms. Ninety-six patients with extra- or intramedullary intradural spinal tumors operated on by the neurosurgery team of our center completed the current study. Results: We observed improvements in the Prolo, Brice and McKissock and McCormick scales scores in the monitored patients. These results examine the usefulness of IONM to preserve neurological functions and, therefore, its impact on quality of life. The rate of neurological deficits in the unmonitored patients was 14.5%, whereas it was 8.3% of the patients whose treatment included IONM. Conclusions: It is important to emphasize the importance of implementing IONM for early recognition of possible neurological damage, the improvement of postoperative functional outcomes, and for decreasing the rate of neurological complications. Significance: This study provides reliable results on the importance of IONM in intradural spinal tumor surgeries.

Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases. This study aimed to create a seizure detection methodology incorporating detailed information from each EEG channel and accounts for frequency band variations linked to the primary brain pathology leading to ICU admission, enhancing our ability to identify epilepsy onset. This study involved 460 video-electroencephalography recordings from 71 patients under monitoring. We applied signal preprocessing and conducted a numerical quantitative analysis in the frequency domain. Various machine learning algorithms were assessed for their efficacy. The k-nearest neighbours (KNN) model was the most effective in our overall sample, achieving an average F1 score of 0.76. For specific subgroups, different models showed superior performance: Decision Tree for ‘Epilepsy’ (average F1 score of 0.80) and ‘Craniencephalic Trauma’ (average F1 score of 0.84), Random Forest for ‘Cardiorespiratory Arrest’ (average F1 score of 0.89) and ‘Brain Haemorrhage’ (average F1 score of 0.84). In the categorisation of seizure types, Linear Discriminant Analysis was most effective for focal seizures (average F1 score of 0.87), KNN for generalised (average F1 score of 0.84) and convulsive seizures (average F1 score of 0.88), and logistic regression for non-convulsive seizures (average F1 score of 0.83). Our study demonstrates the potential of using classifier models based on quantified EEG data for diagnosing seizures in ICU patients. The performance of these models varies significantly depending on the underlying cause of the seizure, highlighting the importance of tailored approaches. The automation of these diagnostic tools could facilitate early seizure detection.