Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures.

functional connectivity generalized tonic-clonic seizures support vector machine

Journal

Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588

Informations de publication

Date de publication:
15 Mar 2022
Historique:
received: 24 01 2022
revised: 18 02 2022
accepted: 09 03 2022
entrez: 25 3 2022
pubmed: 26 3 2022
medline: 26 3 2022
Statut: epublish

Résumé

This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.

Identifiants

pubmed: 35329938
pii: jcm11061612
doi: 10.3390/jcm11061612
pmc: PMC8951449
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Natural Science Foundation of China
ID : 81871345
Organisme : National Natural Science Foundation of China
ID : 81790650
Organisme : National Natural Science Foundation of China
ID : 81790653
Organisme : National Natural Science Foundation of China
ID : 81701680
Organisme : National Key Research& Development Program of Ministry of Science& Technology of PR. China
ID : 2018YFA0701703
Organisme : National Key Research& Development Program of Ministry of Science& Technology of PR. China
ID : 2017YFC0108805
Organisme : grants of the key talent project in Jiangsu province
ID : ZDRCA2016093
Organisme : Natural scientific foundation-social development
ID : BE2016751
Organisme : Post-doctoral grants of China
ID : 2016M603064
Organisme : Jiangsu Province
ID : 1501169B

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Auteurs

Hsinyu Hsieh (H)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Qiang Xu (Q)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Fang Yang (F)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Qirui Zhang (Q)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Jingru Hao (J)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Gaoping Liu (G)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Ruoting Liu (R)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Qianqian Yu (Q)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Zixuan Zhang (Z)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Wei Xing (W)

Department of Radiology, Third Affiliated Hospital of Soochow University/Changzhou First People's Hospital, Changzhou 213004, China.

Boris C Bernhardt (BC)

McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada.

Guangming Lu (G)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Zhiqiang Zhang (Z)

Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.

Classifications MeSH