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Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing.

Geier C, Lehnertz K, Bialonski S - Front Hum Neurosci (2015)

Bottom Line: Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities.Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way.We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

View Article: PubMed Central - PubMed

Affiliation: Department of Epileptology, University of Bonn Bonn, Germany ; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn Bonn, Germany.

ABSTRACT
We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

No MeSH data available.


Related in: MedlinePlus

Temporal evolutions of the assortativity coefficient of functional brain networks derived from patient 4 (upper part) and from patient 1 (lower part). Time profiles were smoothed using a moving average over 30 windows corresponding to 10.24 min for better legibility. Discontinuities are due to recording gaps. Tics on x-axes denote midnight. Vertical red lines mark the times of electrical onset of seizures, and the horizontal black lines (standard deviation is shown in green) denote the mean assortativity coefficient of Erdős-Rényi networks having the same number of nodes and the same link density as the functional networks. Below the time courses, we show the respective frequency distributions of the assortativity coefficient and power spectral density estimates of the temporal evolutions (Lomb-Scargle periodograms, computed by applying the algorithm proposed in Press and Rybicki, 1989 to the full, unfiltered, demeaned time profiles).
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Figure 1: Temporal evolutions of the assortativity coefficient of functional brain networks derived from patient 4 (upper part) and from patient 1 (lower part). Time profiles were smoothed using a moving average over 30 windows corresponding to 10.24 min for better legibility. Discontinuities are due to recording gaps. Tics on x-axes denote midnight. Vertical red lines mark the times of electrical onset of seizures, and the horizontal black lines (standard deviation is shown in green) denote the mean assortativity coefficient of Erdős-Rényi networks having the same number of nodes and the same link density as the functional networks. Below the time courses, we show the respective frequency distributions of the assortativity coefficient and power spectral density estimates of the temporal evolutions (Lomb-Scargle periodograms, computed by applying the algorithm proposed in Press and Rybicki, 1989 to the full, unfiltered, demeaned time profiles).

Mentions: In Figure 1, we show time courses of the assortativity coefficient a derived from the temporal sequences of functional brain networks from two patients. We observe exclusively assortative mixing for patient 4, whereas for patient 1 repeated switches between phases of assortative and dissortative mixing can be observed (see Table 2 for the temporal means and their standard deviations of the assortativity coefficient for each patient). Of all patients, patient 1 was the only case for which we could observe long periods (up to several hours) of dissortative mixing. Shorter periods (up to several minutes) of dissortative mixing were observed in two other patients (patient 5 and 6). Since the number of recording sites Nrs (i.e., the size of a network) varied across patients, we checked whether this might have led to the large fluctuations seen in time-resolved degree-degree correlations but could not observe a clearcut relationship (the Pearson correlation coefficient between Nrs and mean assortativity coefficients amounted to 0.12).


Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing.

Geier C, Lehnertz K, Bialonski S - Front Hum Neurosci (2015)

Temporal evolutions of the assortativity coefficient of functional brain networks derived from patient 4 (upper part) and from patient 1 (lower part). Time profiles were smoothed using a moving average over 30 windows corresponding to 10.24 min for better legibility. Discontinuities are due to recording gaps. Tics on x-axes denote midnight. Vertical red lines mark the times of electrical onset of seizures, and the horizontal black lines (standard deviation is shown in green) denote the mean assortativity coefficient of Erdős-Rényi networks having the same number of nodes and the same link density as the functional networks. Below the time courses, we show the respective frequency distributions of the assortativity coefficient and power spectral density estimates of the temporal evolutions (Lomb-Scargle periodograms, computed by applying the algorithm proposed in Press and Rybicki, 1989 to the full, unfiltered, demeaned time profiles).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542502&req=5

Figure 1: Temporal evolutions of the assortativity coefficient of functional brain networks derived from patient 4 (upper part) and from patient 1 (lower part). Time profiles were smoothed using a moving average over 30 windows corresponding to 10.24 min for better legibility. Discontinuities are due to recording gaps. Tics on x-axes denote midnight. Vertical red lines mark the times of electrical onset of seizures, and the horizontal black lines (standard deviation is shown in green) denote the mean assortativity coefficient of Erdős-Rényi networks having the same number of nodes and the same link density as the functional networks. Below the time courses, we show the respective frequency distributions of the assortativity coefficient and power spectral density estimates of the temporal evolutions (Lomb-Scargle periodograms, computed by applying the algorithm proposed in Press and Rybicki, 1989 to the full, unfiltered, demeaned time profiles).
Mentions: In Figure 1, we show time courses of the assortativity coefficient a derived from the temporal sequences of functional brain networks from two patients. We observe exclusively assortative mixing for patient 4, whereas for patient 1 repeated switches between phases of assortative and dissortative mixing can be observed (see Table 2 for the temporal means and their standard deviations of the assortativity coefficient for each patient). Of all patients, patient 1 was the only case for which we could observe long periods (up to several hours) of dissortative mixing. Shorter periods (up to several minutes) of dissortative mixing were observed in two other patients (patient 5 and 6). Since the number of recording sites Nrs (i.e., the size of a network) varied across patients, we checked whether this might have led to the large fluctuations seen in time-resolved degree-degree correlations but could not observe a clearcut relationship (the Pearson correlation coefficient between Nrs and mean assortativity coefficients amounted to 0.12).

Bottom Line: Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities.Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way.We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

View Article: PubMed Central - PubMed

Affiliation: Department of Epileptology, University of Bonn Bonn, Germany ; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn Bonn, Germany.

ABSTRACT
We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

No MeSH data available.


Related in: MedlinePlus