This work proposes a novel method for the classification of ICs in resting-state fMRI data based on sparse paradigm free mapping (PFM), a deconvolution approach that enables detecting BOLD events without prior information of their timing. This approach uses a single temporal feature, the significance of the deconvolution model estimated with PFM. Our results demonstrate that despite its simplicity this approach achieves similar sensitivity in classifying the neuronal-related BOLD components to the more complex classification method of ICA-AROMA, but with less specificity in classifying noise components. In addition, it can improve the identification of physiological noise components.
This abstract and the presentation materials are available to members only; a login is required.