Detection and evaluation of activation instances as change points in functional MR images

Change point analysis is an efficient method for understanding the unexpected behavior of the data used in many different disciplines including medical imaging. It is important to find the instances the activations occur as much as finding the activation areas in the analysis of functional magnetic resonance imaging (fMRI). Change point detection algorithms can be used to find the activation instances. In this study, a regression based point detection method is proposed to find the activation instances in fMRI experiments. The proposed method is applied to a fMRI experiment which includes a motor task. A linear based evaluation method is also proposed. The analyses show that the activations are in accordance with the established methods in the literature.