Epilepsy & behavior : E&B · 2026
Identification of cognitive phenotypes in temporal lobe epilepsy and genetic generalized epilepsy using robotic assessment.
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Aliyianis Theodore S, Finn Spencer, Garcia-Ramo Karla Batista, Beattie Brooke C, Boissé Lomax Lysa, Shukla Garima, Scott Stephen H, Winston Gavin P
Аннотация
BACKGROUND: Cognitive dysfunction is common in people with epilepsy (PWE). Although expectations exist for deficits based on diagnosis, phenotypic variation of cognitive deficits is observed within epilepsy subtypes. Classification by severity and type of cognitive deficits has been shown across multiple studies of people with temporal lobe epilepsy (TLE). In this study, we replicate these findings using robotic assessment in TLE and apply the same method in genetic generalized epilepsy (GGE) to uncover potential cognitive phenotypes.
METHOD: Participants with TLE and GGE were recruited to participate in robotic assessment battery of neurocognitive function. We used 7 task scores to form clusters that includes functions from various cognitive and motor domains. Calinski-Harabasz method seeded at zero was used to find the optimal value of c, and fuzzy C-means clustering was used to assess the cluster membership from the data in c-groups.
RESULTS: We found 3 clusters among TLE (n = 33): minimal deficits (45 % of participants with TLE), partial deficits (27 %), and global deficits (27 %). Likewise, we found 3 clusters among GGE (n = 25): memory and executive deficits (40 % of participants with GGE), processing speed deficits (36 %), and complex motor deficits (24 %).
CONCLUSIONS: Robotic assessment can be used with clustering methods to classify cognitive dysfunction in epilepsy. These clusters show similar patterns to previous research, which suggests that robotic assessment can distinguish the cognitive phenotypes of PWE.
METHOD: Participants with TLE and GGE were recruited to participate in robotic assessment battery of neurocognitive function. We used 7 task scores to form clusters that includes functions from various cognitive and motor domains. Calinski-Harabasz method seeded at zero was used to find the optimal value of c, and fuzzy C-means clustering was used to assess the cluster membership from the data in c-groups.
RESULTS: We found 3 clusters among TLE (n = 33): minimal deficits (45 % of participants with TLE), partial deficits (27 %), and global deficits (27 %). Likewise, we found 3 clusters among GGE (n = 25): memory and executive deficits (40 % of participants with GGE), processing speed deficits (36 %), and complex motor deficits (24 %).
CONCLUSIONS: Robotic assessment can be used with clustering methods to classify cognitive dysfunction in epilepsy. These clusters show similar patterns to previous research, which suggests that robotic assessment can distinguish the cognitive phenotypes of PWE.