Abstract
Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2–200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.
Highlights
The brain is a complex dynamic system in which the transient interaction between spatially segregated neural networks results in functional responses to both internal and external environments
SUBJECTS A characterization of scale-free brain activity in awake and slow wave sleep (SWS) states was performed with ECoG data from six pediatric subjects with intractable partial epilepsy
By choosing to allow the data to define the regions over which linear slopes could be fit to log–log plots of signal power by frequency, we show that ECoG spectra are not well characterized by a single linear fit across a defined set of frequencies, but are best described by a set of discrete linear fits across the full range of available frequencies
Summary
The brain is a complex dynamic system in which the transient interaction between spatially segregated neural networks results in functional responses to both internal and external environments. While there is a longstanding interest in defining state fluctuations in human neurophysiology on short time scales (microstates, see Fingelkurts and Fingelkurts, 2010; Van De Ville et al, 2010; Latchoumane and Jaeseung, 2011), the best defined stable neural state change is that seen between awake and asleep, where both clear behavioral and electrophysiological changes have been well characterized (Hobson and Pace-Schott, 2002; Saper et al, 2005; McCarley, 2007) This shift in neurobehavioral state is generally seen as representing a global change in brain state reflected in shifts in electrophysiological signaling, and in metabolic activity (Braun et al, 1997; Maquet and Phillips, 1998).
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