Identification of dynamic functional connectivity pattern in resting state after acute mild traumatic brain injury

Yuxiang Li; Hongwei Xie; Hui Shen

doi:10.1117/12.2581322

10 November 2020 Identification of dynamic functional connectivity pattern in resting state after acute mild traumatic brain injury

Yuxiang Li, Hongwei Xie, Hui Shen

Author Affiliations +

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841S (2020) https://doi.org/10.1117/12.2581322
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

As a major health problem, traumatic brain injury has received increasing attention in recent years. Mild traumatic brain injury (mTBI) is the representative of the vast majority of traumatic brain injury. However, most studies have focused on moderate to severe traumatic brain injury. According to reports, mTBI patients during the acute period have the most prominent cognitive disorders. Hence, we collected the functional magnetic resonance imaging data in resting-state (rsfMRI) of the acute-mTBI patients and the healthy controls to explore the differences of brain functional connectivity between them. We chose dynamic functional connectivity in resting-state as features and performed feature selection/extraction. Finally, the classifier based on machine learning methods achieved a good classification accuracy of 85.48%. Most of the extracted brain regions of interest (ROIs) with high identification power belong to the sensorimotor and functional connectivities extracted within the sensorimotor has the highest proportion which suggested the sensorimotor may be the most severely damaged part of mTBI patients in the acute stage.

Citation Download Citation

Yuxiang Li, Hongwei Xie, and Hui Shen "Identification of dynamic functional connectivity pattern in resting state after acute mild traumatic brain injury", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841S (10 November 2020); https://doi.org/10.1117/12.2581322

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