Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award TR01-GM104948)National Institutes of Health (U.S.) (Grant R44NS071988)National Institute of Neurological Diseases and Stroke (U.S.) (Grant Grant R44NS071988

Dual array EEG-fMRI : An approach for motion artifact suppression in EEG recorded simultaneously with fMRI

Objective: Although simultaneous recording of EEG and MRI has gained increasing popularity in recent years, the extent of its clinical use remains limited by various technical challenges. Motion interference is one of the major challenges in EEG-fMRI. Here we present an approach which reduces its impact with the aid of an MR compatible dual-array EEG (daEEG) in which the EEG itself is used both as a brain signal recorder and a motion sensor. Methods: We implemented two arrays of EEG electrodes organized into two sets of nearly orthogonally intersecting wire bundles. The EEG was recorded using referential amplifiers inside a 3 T MR-scanner. Virtual bipolar measurements were taken both along bundles (creating a small wire loop and therefore minimizing artifact) and across bundles (creating a large wire loop and therefore maximizing artifact). Independent component analysis (ICA) was applied. The resulting ICA components were classified into brain signal and noise using three criteria: 1) degree of two-dimensional spatial correlation between ICA coefficients along bundles and across bundles; 2) amplitude along bundles vs. across bundles; 3) correlation with ECG. The components which passed the criteria set were transformed back to the channel space. Motion artifact suppression and the ability to detect interictal epileptic spikes following daEEG and Optimal Basis Set (OBS) procedures were compared in 10 patients with epilepsy. Results: The SNR achieved by daEEG was 11.05 +/- 3.10 and by OBS was 8.25 +/- 1.01 (p <0.00001). In 9 of 10 patients, more spikes were detected after daEEG than after OBS (p <0.05). Significance: daEEG improves signal quality in EEG-fMRI recordings, expanding its clinical and research potential. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision

Advancing Multimodal Approaches to Study Human Brain: Improvements in Simultaneous EEG-fMRI Acquisition

The primary aim of the study detailed in this dissertation was improving the quality of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments. Two common challenges to use concurrent EEG-fMRI tests are addressed herein. The first is the presence of EEG artifacts during simultaneous EEG-fMRI, which require more consideration than EEG data recorded outside the scanner. To mitigate this issue, a fully automated artifact correction pipeline was developed. In the proposed pipeline, magnetic resonance (MR) environmental (i.e., gradient and ballistocardiogram [BCG]) artifacts were reduced using optimal basis sets (OBS) and average artifact subtraction (AAS). Subsequently, independent component analysis (ICA) was leveraged for reducing physiological artifacts (e.g., eye blinks, saccade and muscle artifacts), in addition to residual BCG artifacts. To validate pipeline performance, both resting-state (time/frequency and frequency analysis) and task-based (event related potential [ERP]) EEG data from eight healthy participants were tested. This data was compared with the time/frequency and frequency results achieved by matching meticulously, manually corrected EEG data to the automatically corrected EEG data. No significant difference was found between results. A comparison between ERP results (e.g., amplitude measures and SNR) also showed no differences between manually corrected and fully automated EEG corrected data. The second challenge addressed in this work is the low experimental control over the subject's actual behavior during the eyes-open resting-state fMRI (rsfMRI). This technique has been widely used for studying the (presumably) awake and alert human brain using multimodal EEG-fMRI; however, objective and verified experimental measures to quantify the degree of alertness (e.g., vigilance) are not readily available. To this end, the study reported in this dissertation investigated whether simultaneous multimodal EEG, rsfMRI and eye-tracker experiments could be used to extract objective and robust biomarkers of vigilance in healthy human subjects (n = 10) during cross fixation. Frontal and occipital beta power (FOBP) were found to correlate (r = 0.306, p<0.001) with pupil size fluctuation, which is an indirect index for locus coeruleus activity implicated in vigilance regulation. Moreover, FOBP was also correlated with heart rate (r = 0.255, p<0.001) and several brain regions in an anti-correlated network, including the bilateral insula and inferior parietal lobule. Results support the conclusion that FOBP is an objective and robust biomarker of vigilance in healthy human subjects