Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation
推荐理由
首次提供了一个多中心、多模态的连接组学VNS疗效预测模型。利用DTI、MEG等信号，构建了准确性高的机器学习模型，对我们自己疗效预测研究的开展有借鉴意义。
| 文章简介 | |
|---|---|
| 期刊 | Annals of Neorology |
| 发表年份 | 2019 |
| DOI | 10.1002/ana.25574 |
| 类型 | 研究性工作 |
| 领域 | VNS |
| 引用量 | 73 |
| 推荐信息 | |
|---|---|
| 推荐人 | 郑冬阳 |
| 审核 | 郝老师 |
| 推荐小组 | 癫痫小组 |
摘要
Objective Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using structural and functional connectomic profiling. Methods Fifty-six children, comprising discovery (n = 38) and validation (n = 18) cohorts, were recruited from 3 separate institutions. Diffusion tensor imaging was used to identify group differences in white matter microstructure, which in turn informed beamforming of resting-state magnetoencephalography recordings. The results were used to generate a support vector machine learning classifier, which was independently validated. This algorithm was compared to a second classifier generated using 31 clinical covariates. Results Treatment responders demonstrated greater fractional anisotropy in left thalamocortical, limbic, and association fibers, as well as greater connectivity in a functional network encompassing left thalamic, insular, and temporal nodes (p < 0.05). The resulting classifier demonstrated 89.5% accuracy and area under the receiver operating characteristic (ROC) curve of 0.93 on 10-fold cross-validation. In the external validation cohort, this model demonstrated an accuracy of 83.3%, with a sensitivity of 85.7% and specificity of 75.0%. This was significantly superior to predictions using clinical covariates alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008). Interpretation This study provides the first multi-institutional, multimodal connectomic prediction algorithm for VNS, and provides new insights into its mechanism of action. Reliable identification of VNS responders is critical to mitigate surgical risks for children who may not benefit, and to ensure cost-effective allocation of health care resources.
细分领域
< | VNS癫痫疗效预测