Recognition memory deficits detected through eye-tracking in well-controlled children with self-limited epilepsy with centrotemporal spikes.

OBJECTIVE: Children with self-limited epilepsy characterized by centrotemporal spikes (SeLECTS) exhibit cognitive deficits in memory during the active phase, but there is currently a lack of studies and techniques to assess their memory development after well-controlled seizures. In this study, we employed eye-tracking techniques to investigate visual memory and its association with clinical factors and global intellectual ability, aiming to identify potential risk factors by examining encoding and recognition processes. METHODS: A total of 26 recruited patients diagnosed with SeLECTS who had been seizure-free for at least 2 years, along with 24 control subjects, underwent Wechsler cognitive assessment and an eye-movement-based memory task while video-electroencephalographic (EEG) data were recorded. Fixation and pupil data related to eye movements were utilized to detect distinct memory processes and subsequently to compare the cognitive performance of patients exhibiting different regression patterns on EEG. RESULTS: The findings revealed persistent impairments in visual memory among children with SeLECTS after being well controlled, primarily observed in the recognition stage rather than the encoding phase. Furthermore, the age at onset, frequency of seizures, and interictal epileptiform discharges exhibited significant correlations with eye movement data. SIGNIFICANCE: Children with SeLECTS exhibit persistent recognition memory impairment after being well controlled for the disease. Controlling the frequency of seizures and reducing prolonged epileptiform activity may improve memory cognitive development. The application of the eye-tracking technique may provide novel insights into exploring memory cognition as well as underlying mechanisms associated with pediatric epilepsy.

Seizure Prediction Analysis of Infantile Spasms.

Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms ( θ , α , ß , γ ) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of 79.78 % , 94.46% , 75.46% accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction.

Scalp EEG functional connection and brain network in infants with West syndrome.

The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz), ß (13-30 Hz), and γ (30-80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes inßandγfrequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome.

A new crane fly species of the genus Libnotes Westwood, 1876 (Diptera, Limoniidae) from Jilin, China.

Background: Twenty-eight Libnotes Westwood, 1876 species belonging to three subgenera have been known to occur in China, of which 13 belong to the nominotypical subgenus. Amongst the 13 Chinese Libnotes (s. str.) species, eight are from the Chinese mainland and five are from Taiwan. New information: A Libnotes (s. str.) species from Jilin, China, L. (L.) changbaishana sp. nov. is described and illustrated as new to science. The genus Libnotes is recorded from Jilin Province for the first time. The new species can be distinguished from congeners mainly by its body colour, wing and male genitalia.