Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

All-night spectral and microstate EEG analysis in patients with recurrent isolated sleep paralysis

The pathophysiology of recurrent isolated sleep paralysis (RISP) has yet to be fully clarified. Very little research has been performed on electroencephalographic (EEG) signatures outside RISP episodes. This study aimed to investigate whether sleep is disturbed even without the occurrence of a RISP episode and in a stage different than conventional REM sleep. 17 RISP patients and 17 control subjects underwent two consecutive full-night video-polysomnography recordings. Spectral analysis was performed on all sleep stages in the delta, theta, and alpha band. EEG microstate (MS) analysis was performed on the NREM 3 phase due to the overall high correlation of subject template maps with canonical templates. Spectral analysis showed a significantly higher power of theta band activity in REM and NREM 2 sleep stages in RISP patients. The observed rise was also apparent in other sleep stages. Conversely, alpha power showed a downward trend in RISP patients' deep sleep. MS maps similar to canonical topographies were obtained indicating the preservation of prototypical EEG generators in RISP patients. RISP patients showed significant differences in the temporal dynamics of MS, expressed by different transitions between MS C and D and between MS A and B. Both spectral analysis and MS characteristics showed abnormalities in the sleep of non-episodic RISP subjects. Our findings suggest that in order to understand the neurobiological background of RISP, there is a need to extend the analyzes beyond REM-related processes and highlight the value of EEG microstate dynamics as promising functional biomarkers of RISP.

Alterations of sleep initiation in NREM parasomnia after sleep deprivation - A multimodal pilot study

Objectives

NREM parasomnias also known as disorders of arousal (DOA) are characterised by abnormal motor and autonomic activation during arousals primarily from slow wave sleep. Dissociative state between sleep and wake is likely responsible for clinical symptoms of DOA. We therefore investigated potential dissociation outside of parasomnic events by using simultaneous 256-channel EEG (hdEEG) and functional magnetic resonance imaging (fMRI).

Methods

Eight DOA patients (3 women, mean age = 27.8; SD = 4.2) and 8 gender and age matched healthy volunteers (3 women, mean age = 26,5; SD = 4.0) were included into the study. They underwent 30-32 h of sleep deprivation followed by hdEEG and fMRI recording. We determined 2 conditions: falling asleep (FA) and arousal (A), that occurred outside of deep sleep and/or parasomnic event. We used multimodal approach using data obtained from EEG, fMRI and EEG-fMRI integration approach.

Results

DOA patients showed increase in delta and beta activity over postcentral gyrus and cuneus during awakening period. This group expressed increased connectivity between motor cortex and cingulate during arousals unrelated to parasomnic events in the beta frequency band. They also showed lower connectivity between different portions of cingulum. In contrast, the greater connectivity was found between thalamus and some cortical areas, such as occipital cortex.

Conclusion

Our findings suggest a complex alteration in falling asleep and arousal mechanisms at both subcortical and cortical levels in response to sleep deprivation. As this alteration is present also outside of slow wave sleep and/or parasomnic episodes we believe this could be a trait factor of DOA.

Removal of physiological artifacts from simultaneous EEG and fMRI recordings

OBJECTIVE

Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.

METHODS

We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG.

RESULTS

We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts.

CONCLUSIONS

Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods.

SIGNIFICANCE

Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2–4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs (n=10). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG–fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.

Schrödinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact

In EEG data acquired in the presence of fMRI, gradient-related spike artifacts contaminate the signal following the common preprocessing step of average artifact subtraction. Spike artifacts compromise EEG data quality since they overlap with the EEG signal in frequency, thereby confounding frequency-based inferences on activity. As well, spike artifacts can inflate or deflate correlations among time series, thereby confounding inferences on functional connectivity. We present Schrödinger filtering, which uses the Schrödinger equation to decompose the spike-containing input. The basis functions of the decomposition are localized and pulse-shaped, and selectively capture the various input peaks, with the spike components clustered at the beginning of the spectrum. Schrödinger filtering automatically subtracts the spike components from the data. On real and simulated data, we show that Schrödinger filtering (1) simultaneously accomplishes high spike removal and high signal preservation without affecting evoked activity, and (2) reduces spurious pairwise correlations in spontaneous activity. In these regards, Schrödinger filtering was significantly better than three other despiking techniques: median filtering, amplitude thresholding, and wavelet denoising. These results encourage the use of Schrödinger filtering in future EEG-fMRI pipelines, as well as in other spike-related applications (e.g., fMRI motion artifact removal or action potential extraction).