Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and Harmonization.

Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and Harmonization.
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DOI：
10.1109/embc48229.2022.9871869
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发表时间：
2022-07-01
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
通讯作者：
Vergara, Victor M
Vergara, Victor M
中科院分区：
其他
文献类型：
--
作者：
Bostami, Biozid;Espinoza, Flor A;Vergara, Victor M

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Traumatic brain injury (TBI) can drastically affect an individual's cognition, physical, emotional wellbeing, and behavior. Even patients with mild TBI (mTBI) may suffer from a variety of long-lasting symptoms, which motivates researchers to find better biomarkers. Machine learning algorithms have shown promising results in detecting mTBI from resting-state functional network connectivity (rsFNC) data. However, data collected at multiple sites introduces additional noise called site-effects, resulting in erroneous conclusions. Site errors are controlled through a process called harmonization, but its use in classifying neuroimaging data has been addressed lightly. With the ongoing need to improve mTBI detection, this study shows that harmonization should be integrated into the machine learning process when working with multi-site neuroimaging datasets.