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Rhythm generation through period concatenation in rat somatosensory cortex

Lookup NU author(s): Dr Anita Roopun, Lucy Carracedo, Professor Miles Whittington

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Abstract

Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems. Contemporary observations suggest that coexistent oscillations interact and, in time, may switch in dominance. We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex. We found that following an initial interval of coexistent gamma (≈25 ms period) and beta2 (≈40 ms period) rhythms in the superficial and deep cortical layers, respectively, a transition to a synchronous beta1 (≈65 ms period) rhythm in all cortical layers occurred. We proposed that the switch to beta1 activity resulted from the novel mechanism of period concatenation of the faster rhythms: gamma period (25 ms)+beta2 period (40 ms) = beta1 period (65 ms). In this article, we investigate in greater detail the fundamental mechanisms of the beta1 rhythm. To do so we describe additional in vitro experiments that constrain a biologically realistic, yet simplified, computational model of the activity. We use the model to suggest that the dynamic building blocks (or motifs) of the gamma and beta2 rhythms combine to produce a beta1 oscillation that exhibits cross-frequency interactions. Through the combined approach of in vitro experiments and mathematical modeling we isolate the specific components that promote or destroy each rhythm. We propose that mechanisms vital to establishing the beta1 oscillation include strengthened connections between a population of deep layer intrinsically bursting cells and a transition from antidromic to orthodromic spike generation in these cells. We conclude that neural activity in the superficial and deep cortical layers may temporally combine to generate a slower oscillation. © 2008 Kramer et al.

Publication metadata

Author(s): Kramer MA, Roopun AK, Carracedo LM, Traub RD, Whittington MA, Kopell NJ

Publication type: Article

Publication status: Published

Journal: PLoS Computational Biology

Year: 2008

Volume: 4

Issue: 9

Print publication date: 01/09/2008

Date deposited: 21/01/2010

ISSN (print): 1553-734X

ISSN (electronic): 1553-7358

Publisher: Public Library of Science

URL: http://dx.doi.org/10.1371/journal.pcbi.1000169

DOI: 10.1371/journal.pcbi.1000169

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