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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords