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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

Two-dimensional histograms of the frequencies of occurrence of pairs (a, C) for patient 1 (left), patient 4 (middle), and aggregated for all patients (right). Histograms are normalized to the maximum bin count. ϱ denotes the Pearson correlation coefficient which we determined for the respective datasets.
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Figure 5: Two-dimensional histograms of the frequencies of occurrence of pairs (a, C) for patient 1 (left), patient 4 (middle), and aggregated for all patients (right). Histograms are normalized to the maximum bin count. ϱ denotes the Pearson correlation coefficient which we determined for the respective datasets.

Mentions: Eventually, we studied whether temporal changes in assortative mixing are correlated with temporal changes in network transitivity (as quantified by the clustering coefficient). The temporal evolutions of the clustering coefficient for exemplary patients are shown in Figure 4. Similar to the case of the assortativity coefficient, the time courses of the clustering coefficient show large fluctuations and periodic structures. These periodicities act on timescales of about 24 h, with less pronounced contributions from subharmonics at about 12 and 8 h, which confirms previous results (Kuhnert et al., 2010). The intra- and interindividual fluctuations of the clustering coefficient (cf. Table 2) are much less pronounced than the fluctuation of the assortativity coefficient. We only observed weak correlations between assortativity and clustering coefficient (as quantified by the Pearson correlation coefficient ϱ; cf. Figure 5) in five patients (Pearson correlation coefficient ranged from −0.20 to 0.28), while in two patients, the correlation vanished (/ϱ/ ≪ 0.1). These results indicate that for the functional brain networks investigated here, the assortativity coefficient provides information about the long-term evolution of the functional brain networks, which is complementary to the information provided by the clustering coefficient.


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

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

Two-dimensional histograms of the frequencies of occurrence of pairs (a, C) for patient 1 (left), patient 4 (middle), and aggregated for all patients (right). Histograms are normalized to the maximum bin count. ϱ denotes the Pearson correlation coefficient which we determined for the respective datasets.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 5: Two-dimensional histograms of the frequencies of occurrence of pairs (a, C) for patient 1 (left), patient 4 (middle), and aggregated for all patients (right). Histograms are normalized to the maximum bin count. ϱ denotes the Pearson correlation coefficient which we determined for the respective datasets.
Mentions: Eventually, we studied whether temporal changes in assortative mixing are correlated with temporal changes in network transitivity (as quantified by the clustering coefficient). The temporal evolutions of the clustering coefficient for exemplary patients are shown in Figure 4. Similar to the case of the assortativity coefficient, the time courses of the clustering coefficient show large fluctuations and periodic structures. These periodicities act on timescales of about 24 h, with less pronounced contributions from subharmonics at about 12 and 8 h, which confirms previous results (Kuhnert et al., 2010). The intra- and interindividual fluctuations of the clustering coefficient (cf. Table 2) are much less pronounced than the fluctuation of the assortativity coefficient. We only observed weak correlations between assortativity and clustering coefficient (as quantified by the Pearson correlation coefficient ϱ; cf. Figure 5) in five patients (Pearson correlation coefficient ranged from −0.20 to 0.28), while in two patients, the correlation vanished (/ϱ/ ≪ 0.1). These results indicate that for the functional brain networks investigated here, the assortativity coefficient provides information about the long-term evolution of the functional brain networks, which is complementary to the information provided by the clustering coefficient.

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