Abstract
In recent years, the incidence of mild traumatic brain injury (mTBI) has been increasing, especially in the military because of soldiers’ special working environment. Current diagnostic systems relying too much on patients’ self-description leads to improper diagnosis and treatment. Finding biomarkers of mTBI becomes significant for diagnostic accuracy improvement. We used machine learning methods to extract highly discriminative functional connectivity features and to discriminate mTBI patients from healthy controls. 31 mTBI patients and 31 healthy controls with matching age, gender and education level underwent resting-state functional magnetic resonance imaging. A promising classification accuracy of 75.81% was achieved using resting-state functional connectivity as features. Moreover, some functional connectivities between certain brain regions of the cerebellum and the sensorimotor were found to exhibit the highest discriminative power, which might provide a new idea for the discovery of stable biomarkers of mTBI.
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Li, Y., Shen, H., Xie, H., Hu, D. (2022). Research on Machine Learning Classification of Mild Traumatic Brain Injury Patients Using Resting-State Functional Connectivity. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_37
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DOI: https://doi.org/10.1007/978-981-16-9247-5_37
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