ИСТИНА

This work compares approaches to solving the classification problem based on fMRI data of the original dimension using the big data platform Spark. The original data is 4D fMRI time series with time resolution (TR) = 0.5 s for one sample recording. Participants have to solve 6 tasks, requiring activating various types of thinking, during 30 min session. A large number of subjects and a short time resolution generated the dataset with more than 85 000 samples, which allowed applying machine learning methods to solve this problem, instead of classical statistical maps. The random forest model was used to solve the binary classification problem. The paper analyzes model performance dependence upon time during the problem-solving sessions.