Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

Abstract

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

“…Thirdly, we omitted the investigation of the effect of sex due to the unbalanced representation of females and males across the age groups and the whole sample. Finally, information about the time of the day in which acquisitions took place was not available and circadian and ultradian rhythms can impact spectral characteristics of EEG signals (Lehnertz et al, 2021). These limitations might be addressed by the addition of further information and extensions to the LEMON dataset or combining it with other openly available datasets.…”

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

“…With many of the aforementioned techniques to derive characteristics of pairwise interactions from observations, interactions are assumed to be constant (at least during the investigated time interval). This assumption might not be fully justified for the inherently nonstationary system brain, notwithstanding the wide range of endogenous and exogenous biological rhythms impacting differently on its structure and function (Lehnertz et al, 2021). Another and long-standing issue (e.g., Zentgraf, 1975) centers around identifying and characterizing higher-order interactions, i.e., interactions that cannot be reduced to pairwise interactions.…”

Seizure forecasting: Where do we stand?

“…Early network‐based seizure‐prediction studies 48–51 have reported limited predictive performance from temporal changes of global network characteristics that assess the network's functional segregation, integration, or robustness. This can be ascribed to various biological rhythms that distinctly affect brain networks 52 . In contrast, a well‐above‐chance‐level 37 predictive performance was reported recently for temporal changes of characteristics of single network constituents, 53–55 based on which network mechanism for the epileptic brain's transition into the pre‐ictal state was proposed.…”

“…This can be ascribed to various biological rhythms that distinctly affect brain networks. 52 In contrast, a wellabove-chance-level 37 predictive performance was reported recently for temporal changes of characteristics of single network constituents, [53][54][55] based on which network mechanism for the epileptic brain's transition into the pre-ictal state was proposed. The transition is characterized by a rearrangement of the larger epileptic network's path structure, which results in a formation of bottlenecks that impairs physiologic brain communication-an important factor in brain disorders.…”