Mo Y, et al. J Clin Neurol. 2020 Jul;16(3):438-447. https://doi.org/10.3988/jcn.2020.16.3.438

This study explored the association of early dynamic changes in inflammatory markers with 30-day mortality in in-hospital cardiac arrest (IHCA) patients.This study retrospectively enrolled 85 IHCA ...

Background: Feeding intolerance (FI) exhibits a high incidence rate among patients with severe neurological conditions during the implementation of enteral nutrition and often interrupts the administration of enteral nutrition, and the effects are seriously compromised. This study aims to investigate the current status of FI among patients with severe neurological conditions and to further determine the correlation between FI and their poor prognosis. Methods: A retrospective analysis of the medical data of 58 patients who stayed for more than 3 days in the neurointensive care unit (NICU) of the Department of Neurology of the First Affiliated Hospital of Chongqing Medical University between January 2017 and December 2017, was carried out. These patients’ diagnoses included five categories (Stroke, Paroxysmal disease, Peripheral nerve disease, Central nervous system infection, Other diseases of the nervous system). Patients were divided into two groups according to modified Rankin Scale (mRS) scores: the case group including patients with poor prognoses (mRS of 3-6) and the observation group including patients with good prognoses (mRS of 0-2). Logistic regression was used to determine the relationship between FI and a poor prognosis of patients with severe neurological conditions and to analyze the relevant factors for the poor prognosis of these patients. Results: General data analysis showed that age and diagnosis(stroke) were significantly different between the two groups (P<0.05). Univariate analysis showed that APACHE II score, vomiting within 3 days of NICU admission, gastrointestinal bleeding within 3 days of NICU admission and occurrence of FI within 3 days of NICU admission were all risk factors for a poor prognosis of these patients(P<0.05). Multivariate logistic regression analysis showed that FI within 3 days of NICU admission(OR 8.026, 95%CI(1.550-26.039)) and diagnosis(stroke)(OR 10.654, 95%CI (1.746-21.291)) were independent factors for a poor prognosis of patients with severe neurological conditions. Conclusion: The incidence of early FI in stroke patients is correlated with a poor prognosis.

The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis.

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of the central nervous system. It remains unclear whether pathological changes in ALS can lead to abnormalities in neural dynamics and how these abnormalities relate to key clinical characteristics of ALS. Nonlinear neural dynamics analyses of electroencephalography (EEG) sensorimotor channels were conducted using recurrence quantification analysis (RQA), permutation Lempel-Ziv complexity (PLZC), shannon entropy (ShannonE) and permutation entropy (PermEn). Whole-brain spatiotemporal topological dynamics were assessed using microstate analysis. The study shows that the nonlinear neural dynamics across all frequency bands in the sensorimotor channels of ALS patients are impaired (reduction in Shannon Entropy of Diagonal Line Length Distribution [ENTR]). Frequency-specific nonlinear neural dynamics indicate increased nonlinear neural dynamics in high-frequency bands (with increases in PLZC in beta2 (20-25 Hz)). Nonlinear neural dynamics in low-frequency bands decreases (with decreases in ShannonE in theta (4-7 Hz)) and is negatively correlated with disease duration. ENTR across all frequency bands and ShannonE in the theta band of the sensorimotor channels are potential protective factors for ALS. Furthermore, sensorimotor channel analysis shows a close relationship with whole-brain spatiotemporal topological neural dynamics. Duration A is positively correlated with RR, DET and ENTR, while Occurrence B is negatively correlated with it. The study demonstrates widespread abnormalities in the neural dynamics of ALS, which are closely related to clinical characteristics of ALS. There is also a close relationship between the neural dynamics of sensorimotor channels and whole-brain spatiotemporal topological dynamics in ALS patients.