Distributed source imaging of alpha activity using a maximum entropy principle

Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson's disease

STUDY OBJECTIVES

The body-first Parkinson's disease (PD) hypothesis suggests initial gut Lewy body pathology initially propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels, a disease course presumed to likely occur in PD with rapid eye movement sleep behavior disorder (RBD). We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers.

METHODS

In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative analysis of NREM sleep and wake electroencephalography (EEG), confirmed it with automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined probable Arizona PD stage classifications based on sleep and wake EEG features.

RESULTS

The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to probable Arizona PD stage classifications.

CONCLUSIONS

Colonic PASH is strongly associated with widespread brain sleep and wake dysfunction, suggesting an extensive diffusion of the pathologic process in PD. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD.

CLINICAL TRIAL

Name: SYNAPark, URL: https://clinicaltrials.gov/study/NCT01748409, registration: NCT01748409.

Comparison of connectivity analyses for resting state EEG data

OBJECTIVE

In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated.

APPROACH

The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory.

MAIN RESULTS

The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization.

SIGNIFICANCE

Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

The dynamics of EEG entropy

The scaling properties of human EEG have so far been analyzed predominantly in the framework of detrended fluctuation analysis (DFA). In particular, these studies suggested the existence of power-law correlations in EEG. In DFA, EEG time series are tacitly assumed to be made up of fluctuations, whose scaling behavior reflects neurophysiologically important information and polynomial trends. Even though these trends are physiologically irrelevant, they must be eliminated (detrended) to reliably estimate such measures as Hurst exponent or fractal dimension. Here, we employ the diffusion entropy method to study the scaling behavior of EEG. Unlike DFA, this method does not rely on the assumption of trends superposed on EEG fluctuations. We find that the growth of diffusion entropy of EEG increments of awake subjects with closed eyes is arrested only after approximately 0.5 s. We demonstrate that the salient features of diffusion entropy dynamics of EEG, such as the existence of short-term scaling, asymptotic saturation, and alpha wave modulation, may be faithfully reproduced using a dissipative, first-order, stochastic differential equation-an extension of the Langevin equation. The structure of such a model is utterly different from the "noise+trend" paradigm of DFA. Consequently, we argue that the existence of scaling properties for EEG dynamics is an open question that necessitates further studies.

Cortical and Subcortical Correlates of Electroencephalographic Alpha Rhythm Modulation

Neural correlates of electroencephalographic (EEG) alpha rhythm are poorly understood. Here, we related EEG alpha rhythm in awake humans to blood-oxygen-level-dependent (BOLD) signal change determined by functional magnetic resonance imaging (fMRI). Topographical EEG was recorded simultaneously with fMRI during an open versus closed eyes and an auditory stimulation versus silence condition. EEG was separated into spatial components of maximal temporal independence using independent component analysis. Alpha component amplitudes and stimulus conditions served as general linear model regressors of the fMRI signal time course. In both paradigms, EEG alpha component amplitudes were associated with BOLD signal decreases in occipital areas, but not in thalamus, when a standard BOLD response curve (maximum effect at ∼6 s) was assumed. The part of the alpha regressor independent of the protocol condition, however, revealed significant positive thalamic and mesencephalic correlations with a mean time delay of ∼2.5 s between EEG and BOLD signals. The inverse relationship between EEG alpha amplitude and BOLD signals in primary and secondary visual areas suggests that widespread thalamocortical synchronization is associated with decreased brain metabolism. While the temporal relationship of this association is consistent with metabolic changes occurring simultaneously with changes in the alpha rhythm, sites in the medial thalamus and in the anterior midbrain were found to correlate with short time lag. Assuming a canonical hemodynamic response function, this finding is indicative of activity preceding the actual EEG change by some seconds.

Flash-related synchronization and desynchronization revealed by a multiple band frequency analysis

The fast Fourier transform (FFT) is a good method to estimate power spectral density (PSD), but the frequency resolution is limited to the sampling window, and thus the precise characteristics of PSD for short signals are not clear. To relax the limitation, a multiple band-pass filter was introduced to estimate the precise course of PSDs for flash visual evoked potentials (VEPs). Signals were recorded during -200 and 600 ms using balanced noncephalic electrodes, and sampled at 1,000 Hz in 12 bits. With 1 Hz and 10 ms resolutions, PSDs were estimated between 10 and 100 Hz. Background powers at the alpha- and beta-bands were high over the posterior scalp, and powers around 200 ms were evoked at the same bands over the same region, corresponding to P110 and N165 of VEPs. Normalized PSDs showed evoked powers around 200 ms and suppressed powers following the evoked powers over the posterior scalp. The evoked powers above the 20 Hz band were not statistically significant, however, the gamma band was significantly evoked intra-individually; details in the gamma bands were varied among the subjects. Details of PSDs were complicated even for a simple task such as watching flashes; both synchronization and desynchronization occurred with different distributions and different time courses.