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Abstract #5329

Investigating the feasibility of classifying independent components in resting state BOLD fMRI with sparse paradigm free mapping

Cesar Caballero-Gaudes1, Oihane Ezama1, Manuel Delgado-Alvarado2,3, and Maria Cruz Rodriguez-Oroz1,4,5,6

1Basque Center of Cognition, Brain and Language, Donostia - San Sebastian, Spain, 2Biodonostia Health Research Institute, Donostia-San Sebastian, Spain, 3Neurology Department, University Hospital Marqués de Valdecilla, Santander, Spain, 4Neuroscience Area, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain, 5Centro de Investigacion Biomedicas en Red Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Spain, 6Ikerbasque. Basque Foundation for Science, Bilbao, Spain

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.

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