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. ANN NEUROL 2019;86:743-753.
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. ANN NEUROL 2019;86:743-753.
Authors: Karim Mithani; Simeon M Wong; Mirriam Mikhail; Haatef Pourmotabbed; Elizabeth Pang; Roy Sharma; Ivanna Yau; Ayako Ochi; Hiroshi Otsubo; O Carter Snead; Elizabeth Donner; Cristina Go; Elysa Widjaja; Abbas Babajani-Feremi; George M Ibrahim Journal: Neuroimage Clin Date: 2020-02-04 Impact factor: 4.881
Authors: Karim Mithani; Alexandre Boutet; Jurgen Germann; Gavin J B Elias; Alexander G Weil; Ashish Shah; Magno Guillen; Byron Bernal; Justin K Achua; John Ragheb; Elizabeth Donner; Andres M Lozano; Elysa Widjaja; George M Ibrahim Journal: Sci Rep Date: 2019-12-09 Impact factor: 4.379
Authors: James E Baumgartner; Linda S Baumgartner; Michael E Baumgartner; Ernest J Moore; Steven A Messina; Michael D Seidman; David R Shook Journal: Stem Cells Transl Med Date: 2020-10-09 Impact factor: 6.940