scholarly journals Temporal dynamics of functional networks in long-term infant scalp EEG

2020 ◽  
Author(s):  
Rachel J. Smith ◽  
Ehsan Alipourjeddi ◽  
Cristal Garner ◽  
Amy L. Maser ◽  
Daniel W. Shrey ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Time Scales ◽  
Temporal Dynamics ◽  
Time Of Day ◽  
Term Infant ◽  
Clustering Coefficient ◽  
Functional Networks ◽  
Principle Components Analysis ◽  
Long Time

AbstractHuman functional connectivity networks are modulated on time scales ranging from milliseconds to days. Rapid changes in connectivity over short time scales are a feature of healthy cognitive function, and variability over long time scales can impact the likelihood of seizure occurrence. However, relatively little is known about modulation of healthy functional networks over long time scales. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings from 19 healthy infants. Networks were subject-specific, as inter-subject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. This enabled automatic separation of wakefulness and sleep states via principle components analysis of the functional network time series, with median classification accuracy of 91%. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in both wakefulness and sleep. Together, these results suggest that modulation of healthy functional networks occurs over long timescales and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

scholarly journals Infant functional networks are modulated by state of consciousness and circadian rhythm

2021 ◽  
pp. 1-36
Author(s):  
Rachel J. Smith ◽  
Ehsan Alipourjeddi ◽  
Cristal Garner ◽  
Amy L. Maser ◽  
Daniel W. Shrey ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Functional Connectivity ◽  
Principal Component ◽  
Time Of Day ◽  
Clustering Coefficient ◽  
Functional Networks ◽  
Temporal Properties ◽  
Healthy Infants ◽  
Infant Brain ◽  
Eeg Recordings

Abstract Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hours) from 19 healthy infants. Networks were subjectspecific, as inter-subject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ~24 hours and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

The Spread of Pollutants from Harbour Sludge Depots

1984 ◽  
Vol 16 (3-4) ◽  
pp. 623-633
Author(s):  
M Loxham ◽  
F Weststrate
Keyword(s):  
Time Scales ◽  
Molecular Diffusion ◽  
Near Field ◽  
Parameter Analysis ◽  
Migration Patterns ◽  
Long Time ◽  
Dilution Effects ◽  
Short Time ◽  
Harbour Sludge

It is generally agreed that both the landfill option, or the civil techniques option for the final disposal of contaminated harbour sludge involves the isolation of the sludge from the environment. For short time scales, engineered barriers such as a bentonite screen, plastic sheets, pumping strategies etc. can be used. However for long time scales the effectiveness of such measures cannot be counted upon. It is thus necessary to be able to predict the long term environmenttal spread of contaminants from a mature landfill. A model is presented that considers diffusion and adsorption in the landfill site and convection and adsorption in the underlaying aquifer. From a parameter analysis starting form practical values it is shown that the adsorption behaviour and the molecular diffusion coefficient of the sludge, are the key parameters involved in the near field. The dilution effects of the far field migration patterns are also illustrated.

Simulated Local and Remote Biophysical Effects of Afforestation over the Southeast United States in Boreal Summer*

2012 ◽  
Vol 25 (13) ◽  
pp. 4511-4522 ◽  
Author(s):  
Guang-Shan Chen ◽  
Michael Notaro ◽  
Zhengyu Liu ◽  
Yongqiang Liu
Keyword(s):  
United States ◽  
Carbon Sequestration ◽  
Time Scales ◽  
Boreal Summer ◽  
Surface Cooling ◽  
Local Cooling ◽  
Southeast United States ◽  
Long Time ◽  
Biophysical Effects

Abstract Afforestation has been proposed as a climate change mitigation strategy by sequestrating atmospheric carbon dioxide. With the goal of increasing carbon sequestration, a Congressional project has been planned to afforest about 18 million acres by 2020 in the Southeast United States (SEUS), the Great Lake states, and the Corn Belt states. However, biophysical feedbacks of afforestation have the potential to counter the beneficial climatic consequences of carbon sequestration. To assess the potential biophysical effects of afforestation over the SEUS, the authors designed a set of initial value ensemble experiments and long-term quasi-equilibrium experiments in a fully coupled Community Climate System Model, version 3.5 (CCSM3.5). Model results show that afforestation over the SEUS not only has a local cooling effect in boreal summer [June–August (JJA)] at short and long time scales but also induces remote warming over adjacent regions of the SEUS at long time scales. Precipitation, in response to afforestation, increases over the SEUS (local effect) and decreases over adjacent regions (remote effect) in JJA. The local surface cooling and increase in precipitation over SEUS in JJA are hydrologically driven by the changes in evapotranspiration and latent heat flux. The remote surface warming and decrease in precipitation over adjacent regions are adiabatically induced by anomalous subsidence. Our results suggest that the planned afforestation efforts should be developed carefully by taking account of short-term (local) and long-term (remote) biophysical effects of afforestation.

scholarly journals Evaluating the impacts of environmental factors on soil moisture temporal dynamics at different time scales

2020 ◽  
Author(s):  
Qi Chai ◽  
Tiejun Wang ◽  
Chongli Di
Keyword(s):  
Soil Moisture ◽  
Environmental Factors ◽  
Time Scales ◽  
Temporal Dynamics ◽  
Soil Moisture Dynamics ◽  
Temporal Variance ◽  
The Impact ◽  
Moisture Dynamics ◽  
Different Time Scales

Abstract Soil moisture displays complex spatiotemporal patterns across scales, making it important to disentangle the impacts of environmental factors on soil moisture temporal dynamics at different time scales. This study evaluated the factors affecting soil moisture dynamics at different time scales using long-term soil moisture data obtained from Nebraska and Utah. The empirical mode decomposition method was employed to decompose soil moisture time series into different temporal components with several intrinsic mode functions (IMFs) and one residual component. Results showed that the percent variance contribution (PVC) of IMFs to the total soil moisture temporal variance tended to increase for the IMFs with longer time periods. It indicated that the long-term soil moisture variations in study regions were mainly determined by low-temporal frequency signals related to seasonal climate and vegetation variations. Besides, the PVCs at short- and medium-temporal ranges were positively correlated with climate dryness, while negatively at longer temporal ranges. Moreover, the results suggested that the impact of climate on soil moisture dynamics at different time scales might vary across different climate zones, while soil effect was comparatively less in both regions. It provides additional insights into understanding soil moisture temporal dynamics in regions with contrasting climatic conditions.

scholarly journals Long-Term Brightness Changes in Cool Pulsating Variables

1993 ◽  
Vol 139 ◽  
pp. 425-427
Author(s):  
John R. Percy
Keyword(s):  
Time Scales ◽  
Large Amplitude ◽  
Variable Star ◽  
American Association ◽  
Long Term Changes ◽  
Long Time ◽  
Red Giant

AbstractSeveral types of cool pulsating variables show unexplained long-term changes in brightness, typically on time scales of 10 to 20 times the basic (pulsational) period. The visual and photoelectric programs of the American Association of Variable Star Observers (AAVSO) are well-suited for detecting and studying these changes. Some examples are given here, including yellow hypergiants, RV Tauri stars, small- and large-amplitude red giant and super giant variables. The study of pulsating variables on long time scales provides “new perspectives” on their behavior.

scholarly journals X-ray variability in LINERs

2013 ◽  
Vol 9 (S304) ◽  
pp. 399-402
Author(s):  
Josefa Masegosa ◽  
Lorena Hernández-García ◽  
Isabel Márquez ◽  
Omaira González-Martín
Keyword(s):  
Active Galactic Nuclei ◽  
Time Scales ◽  
Galactic Nuclei ◽  
Spectral Variability ◽  
X Ray ◽  
Line Region ◽  
Variability Pattern ◽  
Long Time ◽  
Public Archives

AbstractOne of the most important features in active galactic nuclei (AGN) is the variability of their emission. Variability has been discovered at X-ray, UV, and radio frequencies on time scales from hours to years. Among the AGN family and according to theoretical studies, Low-Ionization Nuclear Emission Line Region (LINER) nuclei would be variable objects on long time scales. Our purpose is to investigate spectral X-ray variability in LINERs and to understand the nature of these kinds of objects, as well as their accretion mechanism. Chandra and XMM–Newton public archives were used to compile X-ray spectra of LINER nuclei at different epochs with time scales of years. To search for variability we fit all the spectra from the same object with a set of models, in order to identify the parameters responsible for the variability pattern. We found that long term spectral variability is very common, with variations mostly related to hard energies (2-10 keV). These variations are due to changes in the soft excess, and/or changes in the absorber, and/or intrinsic variations of the source.

scholarly journals Optical variability of quasars: a damped random walk

2013 ◽  
Vol 9 (S304) ◽  
pp. 395-398 ◽  
Author(s):  
Željko Ivezić ◽  
Chelsea MacLeod
Keyword(s):  
Random Walk ◽  
Time Scales ◽  
Model Parameters ◽  
Optical Wavelength ◽  
Long Time ◽  
Asymptotic Variability ◽  
Short Time ◽  
Quasar Variability ◽  
Best Fit

AbstractA damped random walk is a stochastic process, defined by an exponential covariance matrix that behaves as a random walk for short time scales and asymptotically achieves a finite variability amplitude at long time scales. Over the last few years, it has been demonstrated, mostly but not exclusively using SDSS data, that a damped random walk model provides a satisfactory statistical description of observed quasar variability in the optical wavelength range, for rest-frame timescales from 5 days to 2000 days. The best-fit characteristic timescale and asymptotic variability amplitude scale with the luminosity, black hole mass, and rest wavelength, and appear independent of redshift. In addition to providing insights into the physics of quasar variability, the best-fit model parameters can be used to efficiently separate quasars from stars in imaging surveys with adequate long-term multi-epoch data, such as expected from LSST.

scholarly journals On the differences between two semi-empirical sea-level models for the last two millennia

2012 ◽  
Vol 8 (4) ◽  
pp. 3551-3581 ◽  
Author(s):  
M. Vermeer ◽  
S. Rahmstorf ◽  
A. Kemp ◽  
B. Horton
Keyword(s):  
North Carolina ◽  
Sea Level Rise ◽  
Sea Level ◽  
Time Scales ◽  
Long Time ◽  
Global Mean Sea Level ◽  
Semi Empirical ◽  
The Last Two Millennia ◽  
Global Mean

Abstract. We compare hindcasts of global mean sea level over the past millennium obtained using two semi-empirical models linking temperature and sea-level rise. The models differ in that one of them includes a term for a very long-term sea-level rise component unfolding over many millennia. On short (century) time scales, both models give very similar results. Proxy sea-level reconstructions from the northern (North Carolina) and southern (New Zealand and Tasmania) hemispheres are used to test the ability of both models to reproduce the longer-term sea-level evolution. In both comparisons the model including the second term produces a markedly better fit from 1000 AD to the present. When both models are used for generating sea-level projections, they behave similarly out to 2100 AD. Further out, to 2300–2500 AD, the projections differ significantly, in no small part due to different values for the sea-level response time scale τ obtained. We conclude that careful model validation on long time scales is important before attempting multi-century projections.

scholarly journals Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Science ◽  
2021 ◽  
Vol 372 (6539) ◽  
pp. eabf4588
Author(s):  
Nicholas A. Steinmetz ◽  
Cagatay Aydin ◽  
Anna Lebedeva ◽  
Michael Okun ◽  
Marius Pachitariu ◽  
...  
Keyword(s):  
Time Scales ◽  
Small Animals ◽  
High Quality ◽  
Site Density ◽  
Long Time ◽  
Free Behavior ◽  
Post Hoc ◽  
Density Probe ◽  
Data Processing Methods

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

scholarly journals Cyclic variations in the dynamics of flu incidence in Azerbaijan, 1976–2000

2014 ◽  
Vol 143 (1) ◽  
pp. 13-22
Author(s):  
B. D. DIMITROV ◽  
E. S. BABAYEV
Keyword(s):  
Temporal Dynamics ◽  
Statistical Significance ◽  
Monthly Data ◽  
Cyclic Patterns ◽  
Long Time ◽  
Cyclic Variations ◽  
Cyclic Dynamics ◽  
Time Structures ◽  
Parametric Regression Models

SUMMARYMulticomponent cyclicity in influenza (flu) incidence had been observed in various countries (e.g. periods T = 1, 2–3, 5–6, 8·0, 10·6–11·3, 13, 18–19 years) and its close similarity with cycles in natural environmental phenomena as meteorological factors and heliogeophysical activity (HGA) suggested. This report aimed at verifying previous results on cyclic patterns of flu incidence by exploring whether flu annual cyclicity (seasonality) and trans-year (13 to <24 months) and/or multiannual (long-term, ⩾24 months) cycles might be present. For this purpose, a relatively long monthly flu incidence dataset consisting of absolute numbers of new cases from the Grand Baku area, Azerbaijan, for the years 1976–2000 (300 months) was analysed. The exploration of underlying chronomes or, time structures, was done by linear and nonlinear parametric regression models, autocorrelation, spectral analysis and periodogram regression analysis. We analysed temporal dynamics and described multicomponent cyclicity, determining its statistical significance. The analysis, considering the flu data specifically stratified in three distinct intervals (1976–1990, 1991–1995, 1996–2000), and also combinations thereof, indicated that the main cyclic pattern was a seasonal one, with a period of T = 12 months. Further, a number of multiannual cycles with periods T in the ranges of 26–36, 62–85 or 113–162 months were observed, i.e. average periods of 2·5, 6·1 and 11·5 years, respectively. Indeed, most of these cycles correspond to similar cyclic parameters of HGA and further analyses are warranted to investigate such relationships. In conclusion, our study revealed the presence of multicomponent cyclic dynamics in influenza incidence by using relatively long time-series of monthly data. The specific cyclic patterns of flu incidence in Azerbaijan allows further, more specific modelling and correlations with environmental factors of similar cyclicity, e.g. HGA, to be explored. These results might contribute more widely to a better understanding of influenza dynamics and its aetiology as well as to the derivation of more precise forecasted estimates for planning and prevention purposes.

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