Martine Dumont

Altered Sleep Brain Functional Connectivity in Acutely Depressed Patients

Recent evidence suggests that problems in information processing within neural networks may underlie depressive disease. In this study, we investigated whether sleep functional brain networks are abnormally organized during a major depressive episode (MDE). We characterized spatial patterns of functional connectivity by computing the “synchronization likelihood” (SL) of 19 sleep EEG channels in 11 acutely depressed patients [42 (20–51) years] and 14 healthy controls [32.9 (27–42) years]. To test whether disrupting an optimal pattern [“small‐world network” (SWN)] of functional brain connectivity underlies MDE, graph theoretical measures were then applied to the resulting synchronization matrices, and a clustering coefficient (C, measure of local connectedness) and a shortest path length (L, measure of overall network integration) were determined. In the depressed group, the mean SL was lower in the delta, theta and sigma frequency bands. Acutely depressed patients showed a significantly lower path length in the theta and delta frequency bands, whereas the cluster coefficient showed no significant changes. The present study provides further support that sleep functional brain networks exhibit “small‐world” properties. Sleep neuronal functional networks in depressed patients are characterized by a functional reorganization with a lower mean level of global synchronization and loss of SWN characteristics. These results argue for considering an MDE as a problem of neuronal network organization and a problem of information processing. Hum Brain Mapp, 2009.

A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men

OBJECTIVE We investigated the interactions between heart rate variability and sleep electroencephalogram power spectra. METHODS Heart rate and sleep electroencephalogram signals were recorded in 8 healthy young men. Spectral analysis was applied to electrocardiogram and electroencephalogram recordings. Spectral components of RR intervals were studied across sleep stages. The cross-spectrum maximum was determined as well as coherencies, gains and phase shifts between normalized high frequency of RR intervals and all electroencephalographic frequency bands, calculated over the first 3 NREM-REM cycles. RESULTS RR intervals increased from awake to NREM and decreased during REM. Normalized low frequency decreased from awake to NREM and increased during REM while normalized high frequency evolved conversely. Low to high frequency ratio developed in opposition to RR intervals. Coherencies between normalized high frequency and power spectra were high for all bands. The gain was highest for delta band. Phase shift between normalized high frequency and delta differed from zero and modifications in normalized high frequency preceded changes in delta by 41+/-14 degrees. CONCLUSIONS Our study demonstrates that: (1) all electroencephalographic power bands are linked to normalized high frequency; (2) modifications in cardiac vagal activity show predominantly parallel changes and precede changes in delta band by a phase shift corresponding to a lead of 12+/-5 min.

The impact of chronic primary insomnia on the heart rate ― EEG variability link

OBJECTIVE To determine if chronic insomnia alters the relationship between heart rate variability and delta sleep determined at the EEG. METHODS After one night of accommodation, polysomnography was performed in 14 male patients with chronic primary insomnia matched with 14 healthy men. ECG and EEG recordings allowed the determination of High Frequency (HF) power of RR-interval and delta sleep EEG power across the first three Non Rapid Eye Movement (NREM)-REM cycles. Interaction between normalized HF RR-interval variability and normalized delta sleep EEG power was studied by coherency analysis. RESULTS Patients showed increased total number of awakenings, longer sleep latency and wake durations and shorter sleep efficiency and REM duration than controls (p<.01). Heart rate variability across first three NREM-REM cycles and sleep stages (NREM, REM and awake) were similar between both groups. In each group, normalized HF variability of RR-interval decreased from NREM to both REM and awake. Patients showed decreased linear relationship between normalized HF RR-interval variability and delta EEG power, expressed by decreased coherence, in comparison to controls (p<.05). Gain and phase shift between these signals were similar between both groups. CONCLUSIONS Interaction between changes in cardiac autonomic activity and delta power is altered in chronic primary insomniac patients, even in the absence of modifications in heart rate variability and cardiovascular diseases. SIGNIFICANCE This altered interaction could reflect the first step to cardiovascular disorders.

On kinetic phase transitions in surface reactions

Abstract The steady-state properties for the bimolecular surface reaction A + 1 2 B 2 → AB are investigated. Kinetic phase transition points, bistability, and hysteresis characteristics are specified by using kinetic equations of the mean-field-approximation type. Comparisons with Monte Carlo simulation results are made.

Interdependency between heart rate variability and sleep EEG: linear/non-linear?

OBJECTIVE To investigate whether the interdependency between heart rate variability (HRV) and sleep electroencephalogram (EEG) power spectra is linear or non-linear. METHODS Heart rate and sleep EEG signals were recorded in 8 healthy young men. Spectral analysis was applied to electrocardiogram and EEG sleep recordings. Synchronization likelihood was computed over the first 3 non-rapid eye movement-rapid eye movement sleep cycles between normalized high frequency of RR intervals (RRI) and all electroencephalographic frequency bands. Comparison to surrogate data of different types was used to attest statistical significance of the coupling between RRI and EEG power bands and its linear or non-linear character. RESULTS Synchronization likelihood values were statistically greater than univariate surrogate synchronization for all sleep bands both at the individual and the group levels. With reference to multivariate surrogates, synchronization values were statistically greater at the group level and, in a majority of cases, for individual comparison except for sigma and beta bands. CONCLUSIONS While all electroencephalographic power bands are linked to normalized high frequency RRI band, this interdependency is non-linear for delta, theta and alpha bands. SIGNIFICANCE Non-linear description is required to capture the full interdependent dynamics of HRV and sleep EEG power bands.

On surface reaction kinetics in the presence of islands

Abstract Preliminary results of Monte Carlo simulations of surface reactions inducing the clustering of adsorbates are reported. A kinetic model which accounts for island formation in CO oxidation on Pt at ambient pressure is proposed.

Kinetic phase transitions in a surface reaction model with local interactions

The steady‐state properties of the CO oxidation on a catalyst surface are investigated by Monte Carlo simulations. We extend the irreversible model of Ziff, Gulari, and Barshad [Phys. Rev. Lett. 56, 2553 (1986)] by means of adsorption probabilities depending on the instantaneous local coverages. We almost follow Kaukonen and Nieminen [J. Chem. Phys. 91, 4380 (1989)] simulation procedure. It results in a qualitative change in the original phase diagram with, especially, the vanishing of the second order phase transition of the adsorbate. We also show how the addition of the diffusion of CO in our simulations shifts the transition points obtained so far.

Monte Carlo simulation of surface reactions

Monte Carlo simulation of bimolecular surface reactions such as A+12B2→AB is presented. This technique is a powerful tool for testing and modelling the kinetics of these reactions, especially in the presence of inhomogeneities (clusters). We proceed by induction starting from the simplest case (molecular adsorption) and ending with the parallel simulation of the following elementary steps: physisorption, molecular and dissociative chemisorption, molecular and associative desorption and surface reactions (Langmuir-Hinshelwood mechanism).

Progressive aging does not alter the interaction between autonomic cardiac activity and delta EEG power.

OBJECTIVE We tested the hypothesis that the reductions of the changes in the respective influence of the cardiac sympathetic and vagal activity control and delta EEG activity with aging alter the interactions between the heart rate variability (HRV) and the delta sleep EEG power band. METHODS A polysomnography was performed on 16 healthy young men and 19 healthy middle-aged men across the first 3 NREM-REM cycles. Spectral analysis was applied to electrocardiogram and electroencephalogram recordings. High Frequency (HF(nu)) of HRV as well as the maximum of cross-spectrum, coherency, gain and phase shifts between HF(nu) and delta sleep EEG power band were compared between both groups. RESULTS Young men experienced more deep sleep than middle-aged men (P<0.001). In middle-aged subjects, HF(nu) was lower than the HF(nu) of their younger counterparts (P<0.001), but they showed similar increases during NREM sleep and similar decreases during REM sleep as the young subjects. Cross-spectrum values, coherency, gain and phase shifts between HF(nu) and delta were identical between the two groups. Modifications in HF(nu) show parallel changes and precede changes in delta EEG band by a similar leads of 11+/-6min in young men and 9+/-7 min in middle-aged men (P=0.23). CONCLUSIONS Reduced changes in the respective influence of the cardiac sympathetic and vagal activity and delta EEG activity with progressive aging do not alter the relationship and phase difference between changes in the relative predominant cardiac vagal activity and delta power in middle-aged men. SIGNIFICANCE Interaction between the cardiac sympathetic and vagal activity with delta EEG activity is maintained in middle-aged men.

Scale-free dynamics of the synchronization between sleep EEG power bands and the high frequency component of heart rate variability in normal men and patients with sleep apnea-hypopnea syndrome.

OBJECTIVE To investigate the dynamics of the synchronization between heart rate variability and sleep electroencephalogram power spectra and the effect of sleep apnea-hypopnea syndrome. METHODS Heart rate and sleep electroencephalogram signals were recorded in controls and patients with sleep apnea-hypopnea syndrome that were matched for age, gender, sleep parameters, and blood pressure. Spectral analysis was applied to electrocardiogram and electroencephalogram sleep recordings to obtain power values every 20s. Synchronization likelihood was computed between time series of the normalized high frequency spectral component of RR-intervals and all electroencephalographic frequency bands. Detrended fluctuation analysis was applied to the synchronizations in order to qualify their dynamic behaviors. RESULTS For all sleep bands, the fluctuations of the synchronization between sleep EEG and heart activity appear scale free and the scaling exponent is close to one as for 1/f noise. We could not detect any effect due to sleep apnea-hypopnea syndrome. CONCLUSIONS The synchronizations between the high frequency component of heart rate variability and all sleep power bands exhibited robust fluctuations characterized by self-similar temporal behavior of 1/f noise type. No effects of sleep apnea-hypopnea syndrome were observed in these synchronizations. SIGNIFICANCE Sleep apnea-hypopnea syndrome does not affect the interdependence between the high frequency component of heart rate variability and all sleep power bands as measured by synchronization likelihood.