Unified neurophysical model of EEG spectra and evoked potentials

Abstract

Evoked potentials -- the brain's transient electrical responses to discrete stimuli -- are modeled as impulse responses using a continuum model of brain electrical activity. Previous models of ongoing brain activity are refined by adding an improved model of thalamic connectivity and modulation, and by allowing for two populations of excitatory cortical neurons distinguished by their axonal ranges. Evoked potentials are shown to be modelable as an impulse response that is a sum of component responses. The component occurring about 100 ms poststimulus is attributed to sensory activation, and this, together with positive and negative feedback pathways between the cortex and thalamus, results in subsequent peaks and troughs that semiquantitatively reproduce those of observed evoked potentials. Modulation of the strengths of positive and negative feedback, in ways consistent with psychological theories of attentional focus, results in distinct responses resembling those seen in experiments involving attentional changes. The modeled impulse responses reproduce key features of typical experimental evoked response potentials: timing, relative amplitude, and number of peaks. The same model, with further modulation of feedback, also reproduces experimental spectra. Together, these results mean that a broad range of ongoing and transient electrocortical activity can be understood within a common framework, which is parameterized by values that are directly related to physiological and anatomical quantities.

Effects of the anesthetic agent propofol on neural populations

“…$ X K E,I (x)dx = 1. This nonlocal approach generalizes previous diffusive models ( Steyn-Ross et al 2001a;Rennie et al 2002;Bojak and Liley 2005) by considering higher spatial derivatives Coombes et al 2007).…”

“…This finding may be important for future modeling studies and even may be compared to experimental measurements Further the derivation of the power spectrum in section ''The general power spectrum'' takes into account both the excitatory and inhibitory membrane potentials, whose difference represents the effective membrane potential (Freeman 1992) or, equivalently, the effective dendritic current generating experimental signals such as LFPs (Nicholson and Freeman 1975) and EEG (Nunez 2000). This approach is different to most previous studies of the power spectrum of neural populations (Steyn-Ross et al 2001a;Wilson et al 2006;Molaee-Ardekani et al 2007;Bojak and Liley 2005;Liley and Bojak 2005;Rennie et al 2002;Robinson 2003;Robinson et al 2004), which consider the power spectrum of either the excitatory or inhibitory membrane potential only.…”

“…This difference between EEG and LFP is expected to yield different power spectra of LFP and EEG. The effect of spatial modes on the power spectrum in neural populations has been investigated in great detail by Robinson's group (Rennie et al 2002;Robinson et al 2001Robinson et al , 2004Robinson 2003). Further we mention the study of Fell et al (2005), who measured synchronously the EEG on the scalp and Local Field Potentials intracranially in humans as a function of the propofol concentration.…”

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.…”

Stochastic models of neuronal dynamics

“…Usually, two operators are required: linking membrane responses to input from afferent neurons (pulse-to-wave) and the dependence of action potential density on membrane potential (wave-to-pulse; see Jirsa 2004; for an excellent review). They are divided into lumped models (Lopes da Silva et al 1976;David & Friston 2003) where populations of neurons are modelled as discrete nodes or regions that interact through cortico-cortical connections or as continuous neural fields (Jirsa & Haken 1996;Wright et al 2001Wright et al , 2003Rennie et al 2002;Robinson et al 2003), where the cortical sheet is modelled as a continuum, on which the cortical dynamics unfold. Frank (2000) and Frank et al (2001) have extended the continuum model to include stochastic effects with an application to MEG data.…”