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

Christian Geier,Stephan Bialonski,Klaus Lehnertz

doi:10.3389/fnhum.2015.00462

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.

Highlights

Improving our understanding of the emergence of epileptogenesis and ictogenesis from large-scale epileptic brain networks calls for approaches that take into account the interplay between the dynamic properties of network constituents and the network topology
We observe a strong component at about 24 h with less pronounced contributions at the subharmonics at about 12 and 8 h. This may point toward an influence of daily rhythms, while components of even longer time scales might be related to alterations of antiepileptic medication during the presurgical evaluation
We investigated the long-term variability of degree-degree correlations of functional brain networks constructed from invasive EEG recordings from seven patients suffering from focal epilepsies with neocortical and/or hippocampal origin

Summary

Introduction

Network theory has proven successful in characterizing interactions among the constituents of diverse complex systems, ranging from technological and biological to social systems (Albert and Barabási, 2002; Barabási and Oltvai, 2004; Boccaletti et al, 2006; Arenas et al, 2008; Bullmore and Sporns, 2009; Barabási et al, 2011; Barthélemy, 2011; Bashan et al, 2012; Holme and Saramäki, 2012; Newman, 2012; Stam and van Straaten, 2012; Borgatti et al, 2013; Csermely et al, 2013; Pessoa, 2014; Stam, 2014). Findings achieved so far for these seizure networks are quite intriguing, given the similarity of their topological evolution across different types of epilepsies, seizures, medication, age, gender, and other clinical features, which might point to a common biophysical mechanism underlying ictogenesis. This similarity, is contrasted by strong intra- and interindividual fluctuations of local and global statistical network properties seen for the temporal evolution of epileptic brain networks over periods of days (Kuhnert et al, 2010; Kramer et al, 2011; Geier et al, 2013)

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Conclusion