Optimisation of a post-processing method to remove the pulse artifact from EEG data recorded during fMRI: an application to P300 recordings during e-fMRI.

In functional cerebral studies, it has been established that co-registered electroencephalography (EEG) measurements and functional magnetic resonance imaging (fMRI) were complementary. However, EEG data recorded inside an MRI scanner are heavily distorted, mainly by the most prominent artifact, the cardiac pulse artifact (PA). We describe an original algorithm which yields a high-quality PA filter and demonstrates how this tool can be used to improve the quality of P300 ERP measurements during event-related fMRI (e-fMRI) experiments. EEG data were acquired in interleaved mode during e-fMRI while six healthy volunteers performed a visual odd-ball task, involving Distractors, Target and Novel stimuli, to elicit P300 components. The PA was corrected with the original algorithm. The temporal variations in the PA were evidenced using a principal component analysis (PCA), on each EEG channel. The procedure yielded several PA templates, which were regressed from the EEG data. The PA removal procedure was optimised, and then implemented to improve the measured P300 components. Regressing the most adequate PA template resulted in a high-quality reduction in spectral power at frequencies associated with the cardiac PA. More reliable P300 component measurements were obtained, evidencing higher amplitudes for Novels (9.76-11.20 microV) than for to Targets (6.3-9.09 microV) in centro-parietal and prefrontal areas. The improvement of the processing of EEG data acquired simultaneously with fMRI data provides a new tool and casts perspectives to study the functional organisation of the brain.

P300 recordings during event-related fMRI: a feasibility study.

Analysis of combined event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) can provide a high temporal and high spatial resolution to study functional cerebral processes. However, EEG data recorded inside an MR scanner is heavily distorted by artifacts. It is important in cognitive studies to ensure that recorded data reflect the same brain activity, and this is achieved through interleaved electroencephalographic (EEG) and fMRI measurements. Here, we demonstrate the feasibility of recording P300 ERPs during fMRI using a three-stimulus visual oddball task and involving a small number of trials for each stimulus. Ten EEG channels were acquired interleaved with fMRI images in five healthy subjects. The stimuli, including rare targets "X," frequent repetitive distractors "O," and rare distractors referred to as novels, were randomly presented every 2 +/- 1 s. The post hoc filter presented here was designed and applied to EEG data to remove the cardiac pulse artifact. Interleaved EEG/fMRI acquisition evidenced two P300 ERPs evoked at Fz, Cz, and Pz by targets and novels. Novel-related ERPs were of higher amplitude than their target-related counterparts. The fMRI maps acquired concurrently showed stronger BOLD response for target condition. We have shown that interleaved acquisition allows to obtain reliable P300 data and fMRI results, likely to shed light on the anatomical location of brain regions involved in cognitive ERPs relevant to many disorders affecting CNS functions. These noninvasive multimodal neuroimaging techniques can be used to explore and better understand processes underlying the functional brain organization.

Oscillations in sympatho-vagal balance oppose variations in delta-wave activity and the associated renin release.

To determine the potential role of the sympathetic nervous system in the generation of the oscillations in PRA over the 24-h period, we used the autocorrelation coefficient of RR interval (rRR), a new tool to evaluate the sympatho-vagal balance continuously. We determined the influence of the sympathetic nervous system both on the nocturnal PRA oscillations associated to increases in delta-wave activity and on the daytime oscillations that occur randomly in awake subjects. PRA and rRR were determined every 10 min during 24 h in nine healthy subjects under continuous bed rest. Electroencephalographic spectral analysis was used to establish the variations in delta-wave activity during sleep, from 2300-0700 h. The overnight profiles in PRA, rRR and delta-wave activity were analyzed using a modified version of the pulse detection program ULTRA. The temporal link among the profiles of rRR, PRA, and delta-wave activity was quantified using cross-correlation analysis. During sleep, large oscillations in PRA were strongly linked to variations in delta-wave activity. They were preceded by opposite oscillations in rRR, decreases in rRR reflecting predominant vagal activity, and increases in rRR reflecting sympathetic dominance. During the waking periods, the levels of rRR were higher, with smaller variations. The daytime PRA oscillations were not associated with any significant changes in rRR, and conversely, significant oscillations in rRR were not followed by any significant changes in PRA. In conclusion, the sympathetic nervous system is not directly involved in the generation of renin oscillations observed under basal conditions. During sleep, the oscillations in sympatho-vagal balance are inversely related to the variations in delta-wave activity and the associated renin release. The processes that give the intermittent signal for concomitant increases in slow wave activity and renin release from the kidney remain to be identified.

Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man.

In previous sleep studies, it has been demonstrated that Poincare plots of RR intervals, which provide a beat to beat dynamic measure of heart rate variability, have distinctive and characteristic patterns according to sleep stages. This study was designed to evaluate the temporal relationship between heart rate variability and sleep electroencephalographic activity (EEG) by using the Pearson's interbeat autocorrelation coefficients of RR intervals derived from the Poincare plots. The coefficients were calculated in 12 subjects over each minute and were related to the profiles of EEG mean frequency (0.5-35 Hz) computed using a Fast Fourier Transformation algorithm. Overnight profiles of interbeat autocorrelation coefficients and of EEG mean frequency were found to be related with highly significant cross-correlation coefficients ranging between 0.216 and 0.638 (P < 0.001). The variations in heart rate variability preceded changes in brain activity by 1-2 min. These results demonstrate that beat to beat heart rate variability and EEG activity are closely linked during sleep in normal man.

Ultradian rhythms in hydromineral hormones.

The maintenance of hydromineral homeostasis depends on the coordinated action of arginine vasopressin (AVP), atrial natriuretic peptide (ANP), the renin-angiotensin-aldosterone system and other recently identified endocrine or paracrine hormones. Several reports have pointed out the changes in urinary excretion and osmolality during the sleep-wake cycle and the rapid eye movement (REM)-non(N)REM sleep cycles. No such changes occur for ANP levels which have a flat profile over 24 h. The pulsatile fluctuations of AVP are described as random. The ultradian rhythm of plasma renin activity (PRA) depends on the regularity of the REM-NREM sleep cycles and the nocturnal curves reflect all disturbances in the internal sleep structure. A study with a shift in the normal sleep time clearly demonstrated that both PRA and aldosterone oscillations are sleep-stage dependent. These hormones could account for the ultradian variations in renal function. The nocturnal oscillations in sympathovagal balance may play an additional role. It is suggested that a central generator synchronizes endocrine, renal, autonomic and sleep processes.

Where arousal meets attention: a simultaneous fMRI and EEG recording study.

In this fMRI study, we looked for the regions supporting interaction between cortical arousal and attention during three conditions: detection, observation, and rest. Arousal measurements were obtained from the EEG low-frequency (LF) power (5-9.5 Hz) recorded continuously together with fMRI. Whatever the condition, arousal was positively correlated with the fMRI signal of the right dorsal-lateral prefrontal and superior parietal cortices, closely overlapping regions involved in the maintenance of attention. Although the inferior temporal areas also presented a correlation with arousal during detection, path analysis suggests that this influence may be indirect, through the top-down influence of the previously mentioned network. However, those visual-processing areas could account for the correlation between arousal and performances. Lastly, the medial frontal cortex, frontal opercula, and thalamus were inversely correlated with arousal but only during detection and observation so that they could account for the control of arousal.

Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men.

We have recently demonstrated that the overnight profiles of cardiac interbeat autocorrelation coefficient of R-R intervals (rRR) calculated at 1-min intervals are related to the changes in sleep electroencephalographic (EEG) mean frequency, which reflect depth of sleep. Other quantitative measures of the Poincaré plots, i.e., the standard deviation of normal R-R intervals (SDNN) and the root mean square difference among successive R-R normal intervals (RMSSD), are commonly used to evaluate heart rate variability. The present study was designed to compare the nocturnal profiles of rRR, SDNN, and RMSSD with the R-R spectral power components: high-frequency (HF) power, reflecting parasympathetic activity; low-frequency (LF) power, reflecting a predominance of sympathetic activity with a parasympathetic component; and the LF-to-HF ratio (LF/HF), regarded as an index of sympathovagal balance. rRR, SDNN, RMSSD, and the spectral power components were calculated every 5 min during sleep in 15 healthy subjects. The overnight profiles of rRR and LF/HF showed coordinate variations with highly significant correlation coefficients (P < 0.001 in all subjects). SDNN correlated with LF power (P < 0.001), and RMSSD correlated with HF power (P < 0.001). The overnight profiles of rRR and EEG mean frequency were found to be closely related with highly cross-correlated coefficients (P < 0. 001). SDNN and EEG mean frequency were also highly cross correlated (P < 0.001 in all subjects but 1). No systematic relationship was found between RMSSD and EEG mean frequency. In conclusion, rRR appears to be a new tool for evaluating the dynamic beat-to-beat interval behavior and the sympathovagal balance continuously during sleep. This nonlinear method may provide new insight into autonomic disorders.