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RT Journal Article
SR Electronic
T1 Disconnection-Based Prediction of Poststroke Dysphagia
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 57
OP 65
DO 10.3174/ajnr.A8074
VO 45
IS 1
A1 Yoon, Kyung Jae
A1 Park, Chul-Hyun
A1 Rho, Myung-Ho
A1 Kim, Minchul
YR 2024
UL http://www.ajnr.org/content/45/1/57.abstract
AB BACKGROUND AND PURPOSE: Dysphagia is a common deficit after a stroke and is associated with serious complications. It is not yet fully clear which brain regions are directly related to swallowing. Previous lesion symptom mapping studies may have overlooked structural disconnections that could be responsible for poststroke dysphagia. Here, we aimed to predict and explain the relationship between poststroke dysphagia and the topologic distribution of structural disconnection via a multivariate predictive framework.MATERIALS AND METHODS: We enrolled first-ever ischemic stroke patients classified as full per-oral nutrition (71 patients) and nonoral nutrition necessary (43 patients). After propensity score matching, 43 patients for each group were enrolled (full per-oral nutrition group with 17 women, 68 ± 15 years; nonoral nutrition necessary group with 13 women, 75 ± 11 years). The structural disconnectome was estimated by using the lesion segmented from acute phase diffusion-weighted images. The prediction of poststroke dysphagia by using the structural disconnectome and demographics was performed in a leave-one-out manner.RESULTS: Using both direct and indirect disconnection matrices of the motor network, the disconnectome-based prediction model could predict poststroke dysphagia above the level of chance (accuracy = 68.6%, permutation P = .001). When combined with demographic data, the classification accuracy reached 72.1%. The edges connecting the right insula and left motor strip were the most informative in prediction.CONCLUSIONS: Poststroke dysphagia could be predicted by using the structural disconnectome derived from acute phase diffusion-weighted images. Specifically, the direct and indirect disconnection within the motor network was the most informative in predicting poststroke dysphagia.CPMconnectome-based predictive modelingDOSSDysphagia Outcome and Severity ScaleFAfractional anisotropyHCPHuman Connectome ProjectLOOCVleave-one subject-out cross-validationMNIMontreal Neurological InstituteSSPLshortest structural path lengthSVMsupport vector machineVFSSvideofluoroscopic swallowing study