Owing to difficulties in measuring ventilation symmetry, good evidence of different right/left respiratory movements has not yet been provided. We investigated VT differences between paretic and healthy sides during quiet breathing, voluntary hyperventilation, and hypercapnic stimulation in patients with hemiparesis. We studied eight patients with hemiparesis and nine normal sex- and age-matched subjects. Right- and left-sided VT was reconstructed using optoelectronic plethysmography. In control subjects, no asymmetry was found in the study conditions. VTs of paretic and healthy sides were similar during quiet breathing, but paretic VT was lower during voluntary hyperventilation in six patients and higher during hypercapnic stimulation in eight patients (p = 0.02). The ventilatory response to hypercapnic stimulation was higher on the paretic than on the healthy side (p = 0.012). In conclusion, hemiparetic stroke produces asymmetric ventilation with an increase in carbon dioxide sensitivity and a decrease in voluntary ventilation on the paretic side.

Objective This international multicenter, prospective, observational study aimed at identifying predictors of short-term clinical outcome in patients with prolonged disorders of consciousness (DoC) due to acquired severe brain injury. Methods Patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS) or in minimally conscious state (MCS) were enrolled within 3 months from their brain injury in 12 specialized medical institutions. Demographic, anamnestic, clinical, and neurophysiologic data were collected at study entry. Patients were then followed up for assessing the primary outcome, that is, clinical diagnosis according to standardized criteria at 6 months postinjury. Results We enrolled 147 patients (44 women; mean age 49.4 [95% confidence interval 46.1–52.6] years; VS/UWS 71, MCS 76; traumatic 55, vascular 56, anoxic 36; mean time postinjury 59.6 [55.4–63.6] days). The 6-month follow-up was complete for 143 patients (VS/UWS 70; MCS 73). With respect to study entry, the clinical diagnosis improved in 72 patients (VS/UWS 27; MCS 45). Younger age, shorter time postinjury, higher Coma Recovery Scale–Revised total score, and presence of EEG reactivity to eye opening at study entry predicted better outcome, whereas etiology, clinical diagnosis, Disability Rating Scale score, EEG background activity, acoustic reactivity, and P300 on event-related potentials were not associated with outcome. Conclusions Multimodal assessment could identify patients with higher likelihood of clinical improvement in order to help clinicians, families, and funding sources with various aspects of decision-making. This multicenter, international study aims to stimulate further research that drives international consensus regarding standardization of prognostic procedures for patients with DoC.

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer’s disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCD vs MCI and HC vs SCD vs MCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models. Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HC vs SCD vs MCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74. Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.