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Abstract #0025

Thalamic Connectome Based Machine Learning for Predicting Individual Symptoms after Mild Traumatic Brain Injury

Chia-Feng Lu1, Yu-Chieh Jill Kao2,3,4, Li-Chun Hsieh3,4,5, Sho-Jen Cheng3,5, Nai-Chi Chen3, and Cheng-Yu Chen3,4,5

1Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, 2Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan, 3Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan, 4Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, 5Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan

Mild traumatic brain injury (mTBI) can cause persistent post-concussion symptoms in 15-20% patients, however the presented type and severity of symptoms differ largely between patients. This study recruited 53 mTBI patients and 44 healthy controls to demonstrate the feasibility in using the imaging features of thalamic connectome combined with machine-learning regression models for the individualized prediction of clinical symptoms.

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