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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

Top: Frequency distributions of the assortativity coefficient derived from data recorded during pre-ictal (solid) and inter-ictal periods (dashed) for one patients (left) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (āpre − āinter)/āinter during pre-ictal and inter-ictal periods for each patient (patients 4 and 6 had no seizures during the recording period). āinter and āpre denote median values of the respective distributions.
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Figure 3: Top: Frequency distributions of the assortativity coefficient derived from data recorded during pre-ictal (solid) and inter-ictal periods (dashed) for one patients (left) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (āpre − āinter)/āinter during pre-ictal and inter-ictal periods for each patient (patients 4 and 6 had no seizures during the recording period). āinter and āpre denote median values of the respective distributions.

Mentions: In the upper part of Figure 3, we show the distribution of the assortativity coefficient for data from the pre-ictal and inter-ictal period from patient 1 as well as for the pooled data from all patients. Interestingly, the pre-ictal phase appears to be characterized by a slightly (about 10%) decreased assortative mixing, and in only one patient, we could observe a pre-ictal increase of degree-degree correlations (cf. lower part of Figure 3). Although these findings may help to further improve the understanding on how and which network reconfigurations promote seizure generation, more sophisticated analysis techniques (Andrzejak et al., 2009) applied to a larger dataset would be needed in order to statistically judge the observed pre-ictal changes.


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

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

Top: Frequency distributions of the assortativity coefficient derived from data recorded during pre-ictal (solid) and inter-ictal periods (dashed) for one patients (left) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (āpre − āinter)/āinter during pre-ictal and inter-ictal periods for each patient (patients 4 and 6 had no seizures during the recording period). āinter and āpre denote median values of the respective distributions.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Top: Frequency distributions of the assortativity coefficient derived from data recorded during pre-ictal (solid) and inter-ictal periods (dashed) for one patients (left) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (āpre − āinter)/āinter during pre-ictal and inter-ictal periods for each patient (patients 4 and 6 had no seizures during the recording period). āinter and āpre denote median values of the respective distributions.
Mentions: In the upper part of Figure 3, we show the distribution of the assortativity coefficient for data from the pre-ictal and inter-ictal period from patient 1 as well as for the pooled data from all patients. Interestingly, the pre-ictal phase appears to be characterized by a slightly (about 10%) decreased assortative mixing, and in only one patient, we could observe a pre-ictal increase of degree-degree correlations (cf. lower part of Figure 3). Although these findings may help to further improve the understanding on how and which network reconfigurations promote seizure generation, more sophisticated analysis techniques (Andrzejak et al., 2009) applied to a larger dataset would be needed in order to statistically judge the observed pre-ictal changes.

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