Abstracts

Rationale: Very fast oscillations in LFP and EEG, ranging in frequency between 80 Hz and 250 Hz, have been observed in spatial and temporal patterns corresponding to the epileptogenic zone in patients with epilepsy and in experimental models of epilepsy (Fisher et al., J Clin Neurophysiol, 1992; Traub et al., Epilepsia, 2001). On the other hand, high-gamma activity (HGA) in overlapping frequencies (~60-200 Hz) have been observed during task-related cortical activation in humans (Crone et al., Prog Brain Res, 2006) and in animals (Ray et al., J Neurosci, 2008), and have been used to map normal brain function and to decode commands in brain-computer interfaces. To understand the role that high-gamma activity (HGA) plays in both normal and pathological brain states, deeper insights into its generating mechanisms are essential. Because the neural populations recorded by LFPs and EEG cannot be comprehensively recorded at scales that are likely to be relevant, we used a biologically based computational model of a cortical network to investigate the mechanisms generating HGA. Methods: The computational model included excitatory pyramidal regular-spiking (PY) and inhibitory fast-spiking (I) neurons described by Hodgkin - Huxley dynamics. We compared activity generated by this model with HGA that was observed in LFP recorded in monkey somatosensory cortex during vibrotactile stimulation. These animal data were used because simultaneously recorded LFP and single unit activity were available, in contrast to the human case. Sensory input was modeled as uncorrelated Poisson spike input arriving to subpopulation of excitatory and inhibitory neurons. Input rate was modeled as fast ON response followed by slowly decaying response simulating responses of fast adapting and slowly adapting receptors to step stimulation. Results: Increase of firing rate and broadband HGA responses in LFP signals generated by the model were in agreement with experimental results (Figure). Blocking the I?PY connections in the model abolishes the HGA while blocking PY?I, PY-PY or I-I does not. Thus, these HGA appear to be mediated mostly by an excited population of inhibitory fast-spiking interneurons firing at high-gamma frequencies and pacing excitatory regular-spiking pyramidal cells, which fire at lower rates but in phase with the population rhythm. HGA were generated for a broad range of model parameters and sensory input values and did not require setting the network close to pathological regime. Conclusions: HGA reflects local cortical activation under normal conditions and as such is a good candidate for mapping cortical areas engaged by a specific task. Pathological conditions are not necessary to observe HGA in the model. There might be different mechanisms leading to activity in similarly high frequencies and frequency alone may not be sufficient to distinguish between normal and pathologic oscillatory activity. The mechanisms of HGA, in this model of local cortical circuits, appear to be similar to those proposed for hippocampal ripples generated by subset of interneurons that regulate discharge of principal cells.