BioScience Trends. 2019;13(1):98-104. (DOI: 10.5582/bst.2019.01002)
Combined machine learning and functional magnetic resonance imaging allows individualized prediction of high-altitude induced psychomotor impairment: The role of neural functionality in putamen and pallidum.
Chen XM, Zhang Q, Wang JY, Xin ZL, Chen JY, Luo WJ
Hypoxia exposure during high-altitude expedition cause psychomotor impairment. Neuroimaging studies indicated that the impairment may be significantly associated with neuron loss and decreased regional homogeneity (ReHo) in several brain regions, suggesting the neural functionality in these regions may be utilized to predict psychomotor impairment under exposure. In this study, 69 subjects come from Shaanxi-Tibet immigrant cohort. Reaction time (RT) tasks were performed to measure the subject's psychomotor function before and after 2-year high-altitude exposure. For each individual, the RT differences between pre-exposure and post-exposure were calculated, which were referred to as "targets" in model establishment. Rs-fMRI data were acquired at the same time with RT tasks. For each individual, the map of ReHo alteration was generated, from which the patterns would be recognized. A pattern recognition procedure was utilized to train and test the predictive models. Two different cross-validation strategies were utilized to evaluate the model performance: leave-one-out cross-validation and four-fold cross-validation. For the models displaying significant R2 and MSE, weight maps were built. As a result, the predictive models were able to decode the changes of simple and recognition reaction time from the alterations of brain activation under the exposure. The regions with highest contributions to the predictions were bilateral putamen and bilateral pallidum, suggesting that predictions were mainly based on the patterns concentrated in these regions. This study was a proof of concept study designed to examine whether individual-level psychomotor impairment under high-altitude exposure could be predicted by a combination of pattern recognition approach and neuroimaging data.
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