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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: Exemplary frequency distributions of the assortativity coefficient derived from data recorded during day (solid) and night times (dashed) for two patients (left and middle) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (ānight − āday)/āday during day and night times for each patient. āday and ānight denote median values of the respective distributions.
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Figure 2: Top: Exemplary frequency distributions of the assortativity coefficient derived from data recorded during day (solid) and night times (dashed) for two patients (left and middle) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (ānight − āday)/āday during day and night times for each patient. āday and ānight denote median values of the respective distributions.

Mentions: Interestingly, the time courses shown in Figure 1 indicate that less assortative (or even dissortative) mixing can be observed preferentially during night times. In order to investigate whether this observation extends beyond exemplary data, we split the data recorded during night times (ranging from 22 p.m. to 6 a.m.) and during day times (ranging from 6 a.m. to 22 p.m.). In the upper part of Figure 2, we show distributions of the assortativity coefficient for patients 1 and 4 as well as for the pooled data from all patients. Given the large interindividual variability, differences between distributions are diverse (see lower part of Figure 2), but a preferentially less assortative mixing during night times can be observed for the group of patients investigated here.


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: Exemplary frequency distributions of the assortativity coefficient derived from data recorded during day (solid) and night times (dashed) for two patients (left and middle) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (ānight − āday)/āday during day and night times for each patient. āday and ānight denote median values of the respective distributions.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Top: Exemplary frequency distributions of the assortativity coefficient derived from data recorded during day (solid) and night times (dashed) for two patients (left and middle) and for the pooled data from all patients (right). Bottom: Relative changes of assortativity (ānight − āday)/āday during day and night times for each patient. āday and ānight denote median values of the respective distributions.
Mentions: Interestingly, the time courses shown in Figure 1 indicate that less assortative (or even dissortative) mixing can be observed preferentially during night times. In order to investigate whether this observation extends beyond exemplary data, we split the data recorded during night times (ranging from 22 p.m. to 6 a.m.) and during day times (ranging from 6 a.m. to 22 p.m.). In the upper part of Figure 2, we show distributions of the assortativity coefficient for patients 1 and 4 as well as for the pooled data from all patients. Given the large interindividual variability, differences between distributions are diverse (see lower part of Figure 2), but a preferentially less assortative mixing during night times can be observed for the group of patients investigated here.

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