Symptom network analysis of prefrontal seizures
By integrating semiological features with brain electrophysiological activities into hybrid networks, symptom network analysis offers an effective quantitative tool for examining the relationships between seizure semiology and brain activity correlates in prefrontal seizures. This study provides an opportunity to advance a novel approach to rigorously investigate the intricacies of electroclinical correlations, sustaining the development of dynamic models, on different series of focal epilepsies, larger cohorts, and semiological features automatically extracted by artificial intelligence, that better reflect the temporal and spatial complexities of seizure propagation in patients with complex seizures.
Authors
- Aileen McGonigal (ORCID: https://orcid.org/0000-0001-6775-5318)
- Christophe Gauld (ORCID: https://orcid.org/0000-0003-4510-6186)
- Jean‐Arthur Micoulaud‐Franchi (ORCID: https://orcid.org/0000-0002-5203-8444)
- Fabrice Bartoloméi (ORCID: https://orcid.org/0000-0002-1678-0297)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Université de Bordeaux (FR)
- Inserm (FR)
- The University of Queensland (AU)
- Aix-Marseille Université (FR)
- Mater Research (AU)
- Bordeaux Population Health (FR)
- Institut de Neurosciences des Systèmes (FR)
- Mater Adult Hospital (AU)
- Hôpital de la Timone (FR)
- Sommeil, Addiction et Neuropsychiatrie (FR)
Publication Details
- Journal
- Epilepsia
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1111/epi.18372
- Citations
- 5
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 10.59
Funders
- Mater Foundation
- University of Queensland