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Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy
被引:2
|作者:
Chen, Hao
[1
]
Wang, Yi
[1
]
Ji, Taoyun
[2
,3
]
Jiang, Yuwu
[2
,3
]
Zhou, Xiao-Hua
[1
,4
,5
]
机构:
[1] Peking Univ, Beijing Int Ctr Math Res, Beijing, Peoples R China
[2] Peking Univ First Hosp, Dept Pediat, Beijing, Peoples R China
[3] Peking Univ First Hosp, Pediat Epilepsy Ctr, Beijing, Peoples R China
[4] Peking Univ, Sch Publ Hlth, Dept Biostat, Beijing, Peoples R China
[5] Pazhou Lab, Guangzhou, Peoples R China
基金:
中国博士后科学基金;
中国国家自然科学基金;
关键词:
brain functional connectivity;
ictal EEG recording;
scalp EEG;
support vector machine;
vagus nerve stimulation;
RESISTANT EPILEPSY;
CHILDREN;
SEIZURES;
VNS;
D O I:
10.1111/cns.14257
中图分类号:
Q189 [神经科学];
学科分类号:
071006 ;
摘要:
ObjectiveAlthough vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. MethodsTwenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes. ResultsThe postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. SignificanceThis study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.
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页码:3259 / 3268
页数:10
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