Classification of partial seizures based on functional connectivity: A MEG study with support vector machine

doi:10.3389/fninf.2022.934480

CASIA OpenIR > 类脑智能研究中心

	Classification of partial seizures based on functional connectivity: A MEG study with support vector machine
	Wang, Yingwei 1; Li, Zhongjie 2; Zhang, Yujin3,4 ; Long, Yingming 1; Xie, Xinyan 1; Wu, Ting 1,5
发表期刊	FRONTIERS IN NEUROINFORMATICS
	2022-08-18
卷号	16 页码:14
通讯作者	Wu, Ting(fsyy00598@njucm.edu.cn)
摘要	Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification.
关键词	temporal lobe epilepsy resting-state functional connectivity MEG machine learning classification
DOI	10.3389/fninf.2022.934480
关键词[WOS]	TEMPORAL-LOBE EPILEPSY ; COMPLEX PARTIAL SEIZURES ; OROALIMENTARY AUTOMATISMS ; OPERCULAR CORTEX ; BRAIN NETWORKS ; HIPPOCAMPAL ; ABSENCE ; EEG ; PROPAGATION ; PERFUSION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China ; [82172022]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000861312600001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50453
专题	类脑智能研究中心
通讯作者	Wu, Ting
作者单位	1.Nanjing Univ Chinese Med, Affiliated Hosp, Dept Radiol, Nanjing, Peoples R China 2.Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China 5.Nanjing Med Univ, Nanjing Brain Hosp, Dept Magnetoencephalog, Nanjing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yingwei,Li, Zhongjie,Zhang, Yujin,et al. Classification of partial seizures based on functional connectivity: A MEG study with support vector machine[J]. FRONTIERS IN NEUROINFORMATICS,2022,16:14.
APA	Wang, Yingwei,Li, Zhongjie,Zhang, Yujin,Long, Yingming,Xie, Xinyan,&Wu, Ting.(2022).Classification of partial seizures based on functional connectivity: A MEG study with support vector machine.FRONTIERS IN NEUROINFORMATICS,16,14.
MLA	Wang, Yingwei,et al."Classification of partial seizures based on functional connectivity: A MEG study with support vector machine".FRONTIERS IN NEUROINFORMATICS 16(2022):14.