Symptom network analysis of prefrontal seizures

Auteurs

Gauld C, Bartolomei F, Micoulaud‐Franchi JA, McGonigal A

Les auteurs en lien sont membres de l'INS.

Équipes

DynaMap

Résumé

Abstract Objective Prefrontal seizures pose significant challenges in accurately identifying the complex interactions between clinical manifestations and brain electrophysiological activities. This proof‐of‐concept study aims to propose a new approach to rigorously support electroclinical reasoning in the field of epilepsy. Methods We analyzed stereoelectroencephalographic data from 42 patients with drug‐resistant focal epilepsy, whose seizures involved prefrontal cortex at seizure onset. Semiological and brain activities features were scored by expert observers. We performed a symptom network analysis of semiological feature and a hybrid network analysis, coupling semiological features with network analysis of ictal brain activities. Centrality measures were used to identify the most influential features in the networks. Results Our analysis identified impairment of consciousness as the most central feature in the semiological network. In the hybrid network, the anterior cingulate area (here incorporating Brodmann area [BA]‐32 and/or rostral part of BA‐24) emerged as the most central brain activity feature. Significance By integrating semiological features with brain electrophysio

Abstract Objective Prefrontal seizures pose significant challenges in accurately identifying the complex interactions between clinical manifestations and brain electrophysiological activities. This proof‐of‐concept study aims to propose a new approach to rigorously support electroclinical reasoning in the field of epilepsy. Methods We analyzed stereoelectroencephalographic data from 42 patients with drug‐resistant focal epilepsy, whose seizures involved prefrontal cortex at seizure onset. Semiological and brain activities features were scored by expert observers. We performed a symptom network analysis of semiological feature and a hybrid network analysis, coupling semiological features with network analysis of ictal brain activities. Centrality measures were used to identify the most influential features in the networks. Results Our analysis identified impairment of consciousness as the most central feature in the semiological network. In the hybrid network, the anterior cingulate area (here incorporating Brodmann area [BA]‐32 and/or rostral part of BA‐24) emerged as the most central brain activity feature. Significance By integrating semiological features with brain electrophysio

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