A novel data-driven model for detecting cognitive state transitions In functional magnetic resonance imaging

Purpose This work introduces a novel model for understanding the nuanced dynamics of the brain signal during transition periods between stimuli presented to patients in functional magnetic resonance imaging (fMRI). Design/methodology/approach This new mathematical method takes as input the voxel data from the relevant brain region, applies dimension reduction and fits a spline through each feature. The features from the constructed spline, such as the mean first derivative, mean absolute second derivative, etc., are used to classify the state per sliding window by labeling it as either transition or non-transition between stimuli. For the classification task, a random forest classifier is used in an experiment with data from six participants, and with 5-fold time series cross-validation. Findings The results from data of 6 participants from the Haxby dataset show an impressive 68% accuracy on average for the random forest classifier with splines, compared to the 60% baseline random forest classifier trained using a wavelet transform and wave reconstruction approach. Originality/value This work introduces a new mathematical framework for detecting cognitive state transitions in fMRI based on geometric features, demonstrating a substantial improvement over baseline feature representations.