Prediction of Cognitive Status from the Resting-State fMRI Data by Machine Learning

Background: Machine learning-based approaches can provide quantitative identification of the cognitive status of the brain by fMRI, which is essential to evaluate human mental activities. However, the performance of traditional machine learning algorithms is not optimal.. Methods: The data was retrieved from an open fMRI dataset of movie-watching fMRI data. Specifically, dynamic functional connectivity analysis (DFC) was calculated using a sliding-window algorithm. A gradient boosting machine learning approach was used with the DFC matrices as the features to predict the cognitive status of the human brain. Conclusion: The area under the curve (AUC) of the gradient boosting classifier with DFC measures was higher than that using conventional machine learning methods. Our findings are expected to provide a better theoretical basis for the neural mechanisms underlying cognitive status of the human brain and shed light on future machine learning-aided mental health. Risk and Safety: There are no significant risk and safety concerns in this study.