Prediction of electroencephalographic spectra from neurophysiology

Abstract

A recent neurophysical model of propagation of electrical waves in the cortex is extended to include a physiologically motivated subcortical feedback loop via the thalamus. The electroencephalographic spectrum when the system is driven by white noise is then calculated analytically in terms of physiological parameters, including the effects of filtering of signals by the cerebrospinal fluid, skull, and scalp. The spectral power at low frequencies is found to vary as f(-1) when awake and f(-3) when asleep, with a breakpoint to a steeper power-law tail at frequencies above about 20 Hz in both cases; the f(-1) range concurs with recent magnetoencephalographic observations of such a regime. Parameter sensitivities are explored, enabling a model with fewer free parameters to be proposed, and showing that spectra predicted for physiologically reasonable parameter values strongly resemble those observed in the laboratory. Alpha and beta peaks seen near 10 Hz and twice that frequency, respectively, in the relaxed wakeful state are generated via subcortical feedback in this model, thereby leading to predictions of their frequencies in terms of physiological parameters, and of correlations in their occurrence. Subcortical feedback is also predicted to be responsible for production of anticorrelated peaks in deep sleep states that correspond to the occurrence of theta rhythm at around half the alpha frequency and sleep spindles at 3/2 times the alpha frequency. An additional positively correlated waking peak near three times the alpha frequency is also predicted and tentatively observed, as are two new types of sleep spindle near 5/2 and 7/2 times the alpha frequency, and anticorrelated with alpha. These results provide a theoretical basis for the conventional division of EEG spectra into frequency bands, but imply that the exact bounds of these bands depend on the individual. Three types of potential instability are found: one at zero frequency, another in the theta band at around half the alpha frequency, and a third at the alpha frequency itself.

Modelling event-related responses in the brain

“…We have shown that neural mass models (David and Friston, 2003;Jansen and Rit, 1995;Lopes da Silva et al, 1997;Nunez, 1974;Rennie et al, 2002;Robinson et al, 2001;Stam et al, 1999;Suffczynski et al, 2001;Valdes et al, 1999;Wendling et al, 2002) can reproduce a large variety of MEG/EEG signal characteristics. The potential advantage they afford, in comparison to standard data analysis, is their ability to pinpoint specific neuronal mechanisms underlying normal or pathological activity.…”

“…This means that MEG/EEG observation models rely upon simplifying assumptions and empirical priors (David and Friston, 2003;Freeman, 1978;Lopes da Silva et al, 1974;Robinson et al, 2001;Stam et al, 1999;Valdes et al, 1999;Van Rotterdam et al, 1982;Wendling et al, 2000). The primary aim of this paper is to describe a candidate forward model and establish its face validity.…”

“…The majority of neural mass models of MEG/EEG have been designed to generate alpha rhythms (Jansen and Rit, 1995;Lopes da Silva et al, 1974;Stam et al, 1999;Van Rotterdam et al, 1982). Recent studies have shown that it is possible to reproduce the whole spectrum of MEG/EEG oscillations, using appropriate values of model parameters (David and Friston, 2003;Robinson et al, 2001). In addition, these models have been used to test specific hypotheses about brain function, e.g., focal attention (Suffczynski et al, 2001).…”

Control of absence seizures induced by the pathways connected to SRN in corticothalamic system

“…Unless otherwise noted, we use the parameter values as listed in Appendix for numerical simulations. These values result from physiological experiments, and can be found in previous studies (Breakspear et al 2006;Chen et al 2014;Robinson et al 1998Robinson et al , 2001Robinson et al , 2003. At the same time, some parameter values are slightly adjusted to generate stable SWDs oscillation in corticothalamic circuit.…”

Anticipation of natural stimuli modulates EEG dynamics: physiology and simulation

“…Instead, it considers responses that are pooled across a group of cells. Dynamics of such models have been discussed by several authors (David and Friston 2003;Freeman 1975;Jansen and Rit 1995;Jirsa and Haken 1997;Nunez and Srinivasan 2006;Robinson et al 1997Robinson et al , 1998Robinson et al , 2001Rennie et al 2000). Although these models incorporate a nonlinear response function similar to the one in Eq.…”