2dTCA For Detection of Irregular, Transient fMRI Activation
Morgan V, Abou-Khalil B, Li Y
The Temporal Clustering Algorithm (TCA) has been developed in order to detect irregular, transient fMRI activation signals when the timing of the stimulus is unknown. Unfortunately, these methods can be especially sensitive to signal changes caused by motion and physiological noise. We have recently developed a modified TCA technique, 2dTCA, that can detect more than one different activation timing pattern within one dataset so that motion or noise can be detected separately from BOLD activation. This work demonstrates the increased sensitivity of 2dTCA over TCA in a set of phantoms with varying motion and one and two activation time courses.