University of Alberta

Year: 2011

Active expiration induced by excitation of ventral medulla in adult anesthetized rats

PMID: 21414911

Pagliardini S, Janczewski WA, Tan W, Dickson CT, Deisseroth K, Feldman JL

J. Neurosci. 2011 Feb;31(8):2895-905


Data from perinatal and juvenile rodents support our hypothesis that the preBötzinger complex generates inspiratory rhythm and the retrotrapezoid nucleus-parafacial respiratory group (RTN/pFRG) generates active expiration (AE). Although the role of the RTN/pFRG in adulthood is disputed, we hypothesized that its rhythmogenicity persists but is typically silenced by synaptic inhibition. We show in adult anesthetized rats that local pharmacological disinhibition or optogenetic excitation of the RTN/pFRG can generate AE and transforms previously silent RTN/pFRG neurons into rhythmically active cells whose firing is correlated with late-phase active expiration. Brief excitatory stimuli also reset the respiratory rhythm, indicating strong coupling of AE to inspiration. The AE network location in adult rats overlaps with the perinatal pFRG and appears lateral to the chemosensitive region of adult RTN. We suggest that (1) the RTN/pFRG contains a conditional oscillator that generates AE, and (2) at rest and in anesthesia, synaptic inhibition of RTN/pFRG suppresses AE.

A better oscillation detection method robustly extracts EEG rhythms across brain state changes: the human alpha rhythm as a test case

PMID: 20807577

Whitten TA, Hughes AM, Dickson CT, Caplan JB

Neuroimage 2011 Jan;54(2):860-74


Oscillatory activity is a principal mode of operation in the brain. Despite an intense resurgence of interest in the mechanisms and functions of brain rhythms, methods for the detection and analysis of oscillatory activity in neurophysiological recordings are still highly variable across studies. We recently proposed a method for detecting oscillatory activity from time series data, which we call the BOSC (Better OSCillation detection) method. This method produces systematic, objective, and consistent results across frequencies, brain regions and tasks. It does so by modeling the functional form of the background spectrum by fitting the empirically observed spectrum at the recording site. This minimizes bias in oscillation detection across frequency, region and task. Here we show that the method is also robust to dramatic changes in state that are known to influence the shape of the power spectrum, namely, the presence versus absence of the alpha rhythm, and can be applied to independent components, which are thought to reflect underlying sources, in addition to individual raw signals. This suggests that the BOSC method is an effective tool for measuring changes in rhythmic activity in the more common research scenario wherein state is unknown.

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