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Noise Induces Oscillation and Synchronization of the Circadian Neurons.

ABSTRACT: The principle clock of mammals, named suprachiasmatic nucleus (SCN), coordinates the circadian rhythms of behavioral and physiological activity to the external 24 h light-dark cycle. In the absence of the daily cycle, the SCN acts as an endogenous clock that regulates the ~24 h rhythm of activity. Experimental and theoretical studies usually take the light-dark cycle as a main external influence, and often ignore light pollution as an external influence. However, in modern society, the light pollution such as induced by electrical lighting influences the circadian clock. In the present study, we examined the effect of external noise (light pollution) on the collective behavior of coupled circadian oscillators under constant darkness using a Goodwin model. We found that the external noise plays distinct roles in the network behavior of neurons for weak or strong coupling between the neurons. In the case of strong coupling, the noise reduces the synchronization and the period of the SCN network. Interestingly, in the case of weak coupling, the noise induces a circadian rhythm in the SCN network which is absent in noise-free condition. In addition, the noise increases the synchronization and decreases the period of the SCN network. Our findings may shed new light on the impact of the external noise on the collective behavior of SCN neurons.

SUBMITTER: Gu C

PROVIDER: S-EPMC4687094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups' EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1-4 Hz), θ oscillation (4-8 Hz), α oscillation (8-13 Hz), and β oscillation (13-30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.

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Noise Induces Oscillation and Synchronization of the Circadian Neurons.

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