Abstract
A novel direction to existing neural mass modeling technique is proposed where the commonly used “alpha function” for representing synaptic transmission is replaced by a kinetic framework of neurotransmitter and receptor dynamics. The aim is to underpin neuro-transmission dynamics associated with abnormal brain rhythms commonly observed in neurological and psychiatric disorders. An existing thalamocortical neural mass model is modified by using the kinetic framework for modeling synaptic transmission mediated by glutamatergic and GABA (gamma-aminobutyric-acid)-ergic receptors. The model output is compared qualitatively with existing literature on in vitro experimental studies of ferret thalamic slices, as well as on single-neuron-level model based studies of neuro-receptor and transmitter dynamics in the thalamocortical tissue. The results are consistent with these studies: the activation of ligand-gated GABA receptors is essential for generation of spindle waves in the model, while blocking this pathway leads to low-frequency synchronized oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations increase with increased levels of post-synaptic membrane conductance for AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid) receptors, and blocking this pathway effects a quiescent model output. In terms of computational efficiency, the simulation time is improved by a factor of 10 compared to a similar neural mass model based on alpha functions. This implies a dramatic improvement in computational resources for large-scale network simulation using this model. Thus, the model provides a platform for correlating high-level brain oscillatory activity with low-level synaptic attributes, and makes a significant contribution toward advancements in current neural mass modeling paradigm as a potential computational tool to better the understanding of brain oscillations in sickness and in health.
Highlights
Neural mass computational models mimicking synchronous behavior in populations of thalamocortical neurons are often used to study brain oscillations (David and Friston, 2003; Suffczynski et al, 2004; Breakspear et al, 2006; Sotero et al, 2007; Deco et al, 2008; Izhikevich and Edelman, 2008; Pons et al, 2010; Robinson et al, 2011; de Haan et al, 2012)
A decrease and increase, respectively of the theta and alpha band components imply an overall increase in frequency with increasing values of gTamCRpa and gTamRNpa correspond to gampa ≡ {gTamCRpa, gTamRNpa}, where the incoming signal from the retina and thalamocortical relay (TCR), respectively in the model
These observations are consistent with similar reports of a transition in the state of the model output with increasing values of gampa in Golomb et al (1996; pp. 756–757), accompanied by an abrupt increase in the ratio of the frequency of oscillation of the TCR and the thalamic reticular nucleus (TRN) cell populations; we have not studied the latter aspect in this work
Summary
Neural mass computational models mimicking synchronous behavior in populations of thalamocortical neurons are often used to study brain oscillations (David and Friston, 2003; Suffczynski et al, 2004; Breakspear et al, 2006; Sotero et al, 2007; Deco et al, 2008; Izhikevich and Edelman, 2008; Pons et al, 2010; Robinson et al, 2011; de Haan et al, 2012). Da Silva et al (1974) used a neural mass model of a simple thalamocortical circuitry to simulate EEG (Electroencephalography) alpha rhythms (8–13 Hz) This model has been the basis of several research (Zetterberg et al, 1978; Stam et al, 1999; Suffczynski, 2000; Bhattacharya et al, 2011a), albeit with modifications and enhancements; of special mention is the modification introduced by Jansen and Rit (1995) where the model is expressed as a set of ordinary differential equations (ODE). The alpha function is a fair estimate of the synaptic process (Bernard et al, 1994), it does not allow an insight into the underlying cellular mechanisms of synaptic transmission associated with abnormal brain oscillations—an aspect emphasized to be crucial as an Frontiers in Computational Neuroscience www.frontiersin.org
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