A six-year longitudinal study of neurocognitive problems in children with epilepsy

Analysis of related factors for neuropsychiatric comorbidities in children with epilepsy

OBJECTIVE

To analyze the risk factors affecting psychiatric behavior and study the psychobehavioral conditions of children with epilepsy.

METHOD

We randomly selected and enrolled 294 children with epilepsy who visited and were hospitalized in the pediatric clinic of Hebei General Hospital between January 2017 and January 2022, as the study participants. We comprehensively assessed their cognitive functions using the Gesell development schedule or Wechsler Intelligence Scales. The participants were divided into the study group (n = 123) with cognitive impairment and the control group (n = 171) with normal cognitive functions, for analysis.

RESULTS

There were statistically significant differences between the two groups in disease course, frequency of epilepsy, status epilepticus, and the number of antiseizure medications (ASMs) used (P < 0.05), while there were no statistically significant differences in age, gender, age of onset, form of onset, interictal epileptiform discharge, history of febrile convulsion, and the time from onset to initial visit (P > 0.05). Based on multivariate logistic regression analysis, the course of disease, frequency of onset, status epilepticus and number of ASMs used were identified as high-risk factors for cognitive impairment in children with epilepsy. Similarly, early onset, long course of disease, known etiology, and combination of multiple drugs have a negative impact on behavioral problems, school education, and social adaptability.

CONCLUSION

The course of disease, the frequency of onset, status epilepticus, and the number of ASMs used are high-risk factors for cognitive impairment in children with epilepsy, which can be prevented and controlled early. When selecting ASMs, their advantages and disadvantages should be weighed. Moreover, the availability of alternative treatment options must be considered. With the help of genomic technology, the causes of epilepsy should be identified as early as possible, and precision medicine and gene therapy for children with epilepsy should be actively developed.

Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

OBJECTIVE

Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.

METHODS

EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.

RESULTS

EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.

SIGNIFICANCE

Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.