title: De-noising with a SOCK can improve the performance of event-related ICA.
abstract: Event-related ICA (eICA) is a partially data-driven analysis method for event-related fMRI that is particularly suited to analysis of simultaneous EEG-fMRI of patients with epilepsy. EEG-fMRI studies in epileptic patients are typically analyzed using the general linear model (GLM), often with assumption that the onset and offset of neuronal activity match EEG event onset and offset, the neuronal activation is sustained at a constant level throughout the epileptiform event and that associated fMRI signal changes follow the canonical HRF. The eICA method allows for less constrained analyses capable of detecting early, non-canonical responses. A key step of eICA is the initial deconvolution which can be confounded by various sources of structured noise present in the fMRI signal. To help overcome this, we have extend the eICA procedure by utilizing a fully standalone and automated fMRI de-noising procedure to process the fMRI data from an EEG-fMRI acquisition prior to running eICA. Specifically we first apply ICA to the entire fMRI time-series and use a classifier to remove noise-related components. The automated objective de-noiser, "Spatially Organized Component Klassificator" (SOCK) is used; it has previously been shown to distinguish a substantial fraction of noise from true activation, without rejecting the latter, in resting-state fMRI. A second ICA is then performed, this time on the event-related response estimates derived from the denoised data (according to the usual eICA procedure). We hypothesize that SOCK + eICA has the potential to be more sensitive than eICA alone. We test the effectiveness of SOCK by comparing activation obtained in an eICA analysis of EEG-fMRI data with and without the use of SOCK for 14 patients with rolandic epilepsy who exhibited stereotypical IEDs arising from a focus in the rolandic fissure.
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Hospital, The University of Melbourne Melbourne, VIC, Australia ; Department of Medicine, The University of Melbourne Melbourne, VIC, Australia.
authors.forename: Kaushik
authors.initials: K
authors.lastname: Bhaganagarapu
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Hospital, The University of Melbourne Melbourne, VIC, Australia ; Department of Medicine, The University of Melbourne Melbourne, VIC, Australia ; Department of Radiology, The University of Melbourne Melbourne, VIC, Australia.
authors.forename: Graeme D
authors.initials: GD
authors.lastname: Jackson
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Hospital, The University of Melbourne Melbourne, VIC, Australia ; Department of Medicine, The University of Melbourne Melbourne, VIC, Australia.
authors.forename: David F
authors.initials: DF
authors.lastname: Abbott
keywords: Benign epilepsy with centro-temporal spikes (BECTS); artifacts; automated classification; denoising; event related ICA; filter; functional magnetic resonance imaging (fMRI); independent component analysis (ICA)