K-complexes, spindles, and ERPs as impulse responses: unification via neural field theory

• Published in: Biological cybernetics Vol. 111; no. 2; pp. 149 - 164
• Main Authors: Zobaer, M. S., Anderson, R. M., Kerr, C. C., Robinson, P. A., Wong, K. K. H., D’Rozario, A. L.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017; Springer Nature B.V
• Subjects: Bioinformatics; Biomedical and Life Sciences; Biomedicine; Cerebral Cortex; Complex Systems; Computer Appl. in Life Sciences; Electroencephalography; Evoked Potentials; Field theory; Humans; Mathematical models; Models, Neurological; Neurobiology; Neurosciences; Original Article; Physiology; Reproduction; Sleep; Spindles; Spontaneous; Thalamus; Waveform analysis; Waveforms; Corticothalamic system; K-complexes; Evoked response potentials; Neural field theory; Impulse responses; Electroencephalography; Sleep spindles; Modeling; Neurophysiology
• ISSN: 0340-1200; 1432-0770
• DOI: 10.1007/s00422-017-0713-2

To interrelate K-complexes, spindles, evoked response potentials (ERPs), and spontaneous electroencephalography (EEG) using neural field theory (NFT), physiology-based NFT of the corticothalamic system is used to model cortical excitatory and inhibitory populations and thalamic relay and reticular nuclei. The impulse response function of the model is used to predict the responses to impulses, which are compared with transient waveforms in sleep studies. Fits to empirical data then allow underlying brain physiology to be inferred and compared with other waves. Spontaneous K-complexes, spindles, and other transient waveforms can be reproduced using NFT by treating them as evoked responses to impulsive stimuli with brain parameters appropriate to spontaneous EEG in sleep stage 2. Using this approach, spontaneous K-complexes and sleep spindles can be analyzed using the same single theory as previously been used to account for waking ERPs and other EEG phenomena. As a result, NFT can explain a wide variety of transient waveforms that have only been phenomenologically classified to date. This enables noninvasive fitting to be used to infer underlying physiological parameters. This physiology-based model reproduces the time series of different transient EEG waveforms; it has previously reproduced experimental EEG spectra, and waking ERPs, and many other observations, thereby unifying transient sleep waveforms with these phenomena.