Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion

• Published in: MedComm (2020) Vol. 5; no. 6; pp. e585 - n/a
• Main Authors: Lei, Yu, Zhang, Xin, Ni, Wei, Gao, Chao, Li, Yanjiang, Yang, Heng, Gao, Xinjie, Xia, Ding, Zhang, Xia, Osipowicz, Karol, Doyen, Stephane, Sughrue, Michael E., Gu, Yuxiang, Mao, Ying
• Format: Journal Article
• Language: English
• Published: China John Wiley & Sons, Inc 01.06.2024; John Wiley and Sons Inc; Wiley
• Subjects: brain ischemia; cerebral revascularization; Ischemia; machine learning; neural networks; Original; cerebral revascularization; brain ischemia; machine learning; neural networks
• ISSN: 2688-2663; 2688-2663
• DOI: 10.1002/mco2.585

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno‐occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8 ± 20.5 months. Using multimodal parcellation with connectivity‐based and pathological distortion‐independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single‐subject level. Thirty‐three machine‐learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC > 0.7). Notably, the FC feature‐based model for long‐term neurological outcomes performed very well (AUC > 0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate “fingerprints” of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion. We applied individual brain connectome in chronic steno‐occlusive vasculopathy to map both ischemic symptoms and their postbypass changes. Using multimodal parcellation with connectivity‐based and pathological distortion‐independent approach, areal MR features of brain connectome were generated with three measurements at the single‐subject level. Machine‐learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes. Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate “fingerprints” of brain connectome.