Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion.

The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.

MRI default mode network connectivity is associated with functional outcome after cardiopulmonary arrest.

BACKGROUND: We hypothesized that the degree of preserved functional connectivity within the DMN during the first week after cardiopulmonary arrest (CPA) would be associated with functional outcome at hospital discharge. METHODS: Initially comatose CPA survivors with indeterminate prognosis at 72 h were enrolled. Seventeen CPA subjects between 4 and 7 days after CPA and 17 matched controls were studied with task-free fMRI. Independent component analysis was performed to delineate the DMN. Connectivity strength in the DMN was compared between CPA subjects and controls, as well as between CPA subjects with good outcome (discharge Cerebral Performance Category or CPC 1-2) and those with bad outcome (CPC 3-5). The relationship between connectivity strength in the posterior cingulate cortex (PCC) and precuneus (PC) within the DMN with discharge CPC was evaluated using linear regression. RESULTS: Compared to controls, CPA subjects had significantly lower connectivity strength in subregions of the DMN, the PCC and PC (p < 0.0001). Furthermore, connectivity strength in the PCC and PC was greater in CPA subjects with good outcome (n = 8) than those with bad outcome (n = 9) (p < 0.003). Among CPA subjects, the connectivity strength in the PCC and PC showed strong linear correlations with the discharge CPC (p < 0.005). CONCLUSIONS: Among initially comatose CPA survivors with indeterminate prognosis, task-free fMRI demonstrated graded disruption of DMN connectivity, especially in those with bad outcomes. If confirmed, connectivity strength in the PC/PCC may provide a clinically useful prognostic marker for functional recovery after CPA.