scholarly journals More Severe Insomnia Complaints in People with Stronger Long-Range Temporal Correlations in Wake Resting-State EEG

2016 ◽  
Vol 7 ◽  
Author(s):  
Michele A. Colombo ◽  
Yishul Wei ◽  
Jennifer R. Ramautar ◽  
Klaus Linkenkaer-Hansen ◽  
Enzo Tagliazucchi ◽  
...  
Keyword(s):  
Long Range ◽  
Resting State ◽  
Temporal Correlations ◽  
Insomnia Complaints

scholarly journals Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

2015 ◽  
Vol 6 ◽  
Author(s):  
Maria Botcharova ◽  
Luc Berthouze ◽  
Matthew J. Brookes ◽  
Gareth R. Barnes ◽  
Simon F. Farmer
Keyword(s):  
Long Range ◽  
Resting State ◽  
Finger Movement ◽  
Phase Synchrony ◽  
Temporal Correlations

scholarly journals Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger-tapping

2015 ◽  
Author(s):  
Maria Botcharova ◽  
Luc Berthouze ◽  
Matthew J Brookes ◽  
Gareth R Barnes ◽  
Simon F Farmer
Keyword(s):  
White Noise ◽  
Long Range ◽  
Phase Difference ◽  
Resting State ◽  
Temporal Structure ◽  
Gaussian White Noise ◽  
Kuramoto Model ◽  
Finger Tapping ◽  
Temporal Correlations ◽  
Left And Right

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilise a recently introduced method for characterising the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterise phase relationships within a Kuramoto model of oscillators in its sub-critical, critical and super-critical synchronisation states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state towards a more random state.

scholarly journals Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia

2019 ◽  
Vol 10 ◽  
Author(s):  
James K. Moran ◽  
Georgios Michail ◽  
Andreas Heinz ◽  
Julian Keil ◽  
Daniel Senkowski
Keyword(s):  
Long Range ◽  
Resting State ◽  
Beta Oscillations ◽  
Temporal Correlations

scholarly journals Long-range Temporal Correlations in the Broadband Resting state Activity of the Human Brain revealed by Neuronal Avalanches

2020 ◽  
Author(s):  
Fabrizio Lombardi ◽  
Oren Shriki ◽  
Hans J. Herrmann ◽  
Lucilla de Arcangelis
Keyword(s):  
Long Range ◽  
Power Law ◽  
Resting State ◽  
Brain Activity ◽  
Neural Systems ◽  
Scaling Exponents ◽  
Long Range Correlations ◽  
Temporal Correlations ◽  
Neuronal Avalanches ◽  
The Brain

AbstractResting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. At criticality, a crucial role is played by long-range power-law correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, this approach is not suitable for assessing long-range correlations in broadband resting-state cortical signals. To overcome such limitation, here we propose to characterize the correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms.

scholarly journals Unsupervised characterization of dynamic functional connectivity reveals age-associated differences in temporal stability and connectivity states during rest and task

2021 ◽  
Author(s):  
Nisha Chetana Sastry ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Resting State ◽  
Network Stability ◽  
Dynamic Functional Connectivity ◽  
Functional Brain ◽  
Temporal Correlations ◽  
The Stability ◽  
And Task

Understanding brain functions as an outcome of underlying neuro-cognitive network mechanisms in rest and task requires accurate spatiotemporal characterization of the relevant functional brain networks. Recent endeavours of the Neuroimaging community to develop the notion of dynamic functional connectivity is a step in this direction. A key goal is to detect what are the important events in time that delimits how one functional brain network defined by known patterns of correlated brain activity transitions into a 'new' network. Such characterization can also lead to more accurate conceptual realization of brain states, thereby, defined in terms of time-resolved correlations. Nonetheless, identifying the canonical temporal window over which dynamic functional connectivity is operational is currently based on an ad-hoc selection of sliding windows that can certainly lead to spurious results. Here, we introduce a data-driven unsupervised approach to characterize the high dimensional dynamic functional connectivity into dynamics of lower dimensional patterns. The whole-brain dynamic functional connectivity states bearing functional significance for task or rest can be explored through the temporal correlations, both short and long range. The present study investigates the stability of such short- and long-range temporal correlations to explore the dynamic network mechanisms across resting state, movie viewing and sensorimotor action tasks requiring varied degrees of attention. As an outcome of applying our methods to the fMRI data of a healthy ageing cohort we could quantify whole-brain temporal dynamics which indicates naturalistic movie watching task is closer to resting state than the sensorimotor task. Our analysis also revealed an overall trend of highest short range temporal network stability in the sensorimotor task, followed by naturalistic movie watching task and resting state that remains similar in both young and old adults. However, the stability of neurocognitive networks in the resting state in young adults is higher than their older counterparts. Thus, healthy ageing related differences in quantification of network stability along task and rest provides a blueprint of how our approach can be used for cohort studies of mental health and neurological disorders.

scholarly journals Negative mood and mind wandering increase long-range temporal correlations in attention fluctuations

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196907 ◽  
Author(s):  
Mona Irrmischer ◽  
C. Natalie van der Wal ◽  
Huibert D. Mansvelder ◽  
Klaus Linkenkaer-Hansen
Keyword(s):  
Long Range ◽  
Negative Mood ◽  
Mind Wandering ◽  
Temporal Correlations
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