Abstracts

Rationale: Infantile Epileptic Spasms Syndrome (IESS) is a severe infantile epilepsy that can progress into Lennox-Gastaut Syndrome (LGS), which can be associated with cognitive impairments, intellectual problems, and psychiatric disorders. Early diagnosis and effective treatment of LGS improves prognosis and lowers healthcare costs [1]. Ensuring appropriate treatment of LGS is paramount, with many children not receiving ideal seizure medications. Therefore, there is a need for biomarkers of the progression from IESS to LGS and to assess treatment response. Based on prior work associating IESS and LGS with strong functional connectivity networks [2], [3], we hypothesize that functional connectivity strength is a robust biomarker for the presence of IESS and LGS and will be modulated by treatment response.

Methods: Five children from the Children’s Hospital of Orange County who were diagnosed with IESS and later progressed to LGS were included in this study. Each patient had EEG recordings at the time of IS and LGS diagnosis, with varying numbers of EEGs in between and at least one EEG following LGS diagnosis. We clipped ten minutes of awake-state EEG from each recording and calculated functional connectivity networks by performing the statistical analysis of cross-correlation between electrode pairs using previously established methods [2]. The number of strong connections and the mean strength of the top 10% of connections were computed and correlated to the disease state progression and response (or non-response) to treatment, as well as the child’s age at the time of the EEG.

Results: Consistent with our hypothesis, the connectivity strength was high at the time of IS diagnosis, and a positive treatment outcome was associated with a decrease in strength. Despite varying etiologies, LGS diagnosis was also associated with an increase in functional connectivity strength. After LGS diagnosis, a decrease in connectivity strength was associated with effective LGS treatment, and an unchanged or increased strength reflected a lack of response to treatment. We found that connectivity strength was not correlated to age, suggesting that these network changes are not due to age-related physiological changes. The number of strong connections and the mean connection strength gave approximately equivalent results.

Conclusions: Overall, functional connectivity strength reflected the presence of IS and LGS, as well as positive or negative response to treatment. Computational EEG analysis of functional connectivity could therefore be applied in clinical practice to enable earlier treatment of LGS and guide treatment selection based on response, thus improving the prognosis of LGS patients. However, it is critical to extend this analysis to a larger cohort of subjects to increase the power of the study and validate these results.

References:_x000D_ 1. J. E. Piña-Garza et al., Epilepsy Behav, vol. 73, pp. 46-50, 2017._x000D_ 2. R. J. Smith et al, Epilepsy Res, vol. 176, p. 106704, 2021._x000D_ 3. W. Stacey et al., Epilepsy Res, vol. 159, p. 106255, 2020.

Funding: This work was supported by the Lennox Gastaut Syndrome Foundation and the UC Irvine California-Catalonia Engineering Program through a Balsells Mobility Fellowship to BRM.