ArticleFrontiers in neurology2012
Characterization of scale-free properties of human electrocorticography in awake and slow wave sleep States.
Article in Frontiers in neurology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Biological current source imaging method based on acoustoelectric effect: A systematic review.Frontiers in neuroscience · 2022Pooled it
- Temporally resolved analyses of aperiodic features track neural dynamics during sleep.Communications psychology · 2025Article
- Article
- Scale-free and oscillatory spectral measures of sleep stages in humans.Frontiers in neuroinformatics · 2022Article
- Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
- Time rescaling reproduces EEG behavior during transition from propofol anesthesia-induced unconsciousness to consciousness.Scientific reports · 2018Article
- Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Behavioral state classification in epileptic brain using intracranial electrophysiology.Journal of neural engineering · 2017Article
- Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task.Behavioural neurology · 2017Article
- Self-organized dynamical complexity in human wakefulness and sleep: different critical brain-activity feedback for conscious and unconscious states.Physical review. E, Statistical, nonlinear, and soft matter physics · 2015Article
- Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications.PloS one · 2015Article
- Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEG.PLoS computational biology · 2014Article
- Scale-free fluctuations in behavioral performance: delineating changes in spontaneous behavior of humans with induced sleep deficiency.PloS one · 2014Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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