Abstract #0025

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

Chia-Feng Lu¹, Yu-Chieh Jill Kao^2,3,4, Li-Chun Hsieh^3,4,5, Sho-Jen Cheng^3,5, Nai-Chi Chen³, and Cheng-Yu Chen^3,4,5

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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