Abstract
The human brain is a complex network of interacting nonstationary subsystems, whose complicated spatial–temporal dynamics is still poorly understood. Deeper insights can be gained from recent improvements of time-series-analysis techniques to assess strength and direction of interactions together with methodologies for deriving and characterizing evolving networks from empirical time series. We here review these developments, and by taking the example of evolving epileptic brain networks, we discuss the progress that has been made in capturing and understanding brain dynamics that varies on time scales ranging from seconds to years.
Highlights
Due to its complex structure and its immense functionality, the human brain is one of the most complex and fascinating systems in nature
We summarized developments of bivariate time-seriesanalyses techniques that aim at assessing strength and direction of interactions as well as methodologies for deriving and characterizing evolving networks from empirical time series
At the example of evolving epileptic brain networks, we discussed the progress that has been made in capturing and improving our understanding of brain dynamics that varies on time scales ranging from seconds to years
Summary
Due to its complex structure and its immense functionality, the human brain is one of the most complex and fascinating systems in nature. The highly interconnected networks in the brain, which are neither random nor entirely regular, span multiple spatial scales, from individual cells and synapses via cortical columns to (sub)cortical areas These networks support a rich repertoire of behavioral and cognitive functions [5,6,7,8,9,10,11,12,13,14,15,16,17] that are – for the most part – shared among all individuals, despite enormous differences in morphology and connection structure. Recent investigations that simultaneously assess dynamics from the macro- (gross neural activities) and the micro-scale (single/multi-neuron activities) point to complex relationships between these scales, and combining insights from both these dynamics is a key challenge for the future [29] Before we draw our conclusions, we illustrate recent efforts on characterizing aspects of human epileptic brain dynamics varying on time scales spanning up to seven orders of magnitude
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