Quantification of membrane properties of trigeminal root ganglion neurons in guinea pigs

Abstract

Passive and active (voltage- and time-dependent) membrane properties of trigeminal root ganglion neurons of decerebrate guinea pigs have been determined using frequency-domain analyses of small-amplitude perturbations of membrane voltage. The complex impedance functions of trigeminal ganglion neurons were computed from the ratios of the fast Fourier transforms of the intracellularly recorded voltage response from the neuron and of the input current, which had a defined oscillatory waveform. The impedance magnitude functions and corresponding impedance locus diagrams were fitted with various membrane models such that the passive and active properties were quantified. The complex impedances of less than one-quarter of the 105 neurons which were investigated extensively could be described by the complex impedance function for a simple RC-electrical circuit. In such neurons, the voltage responses to constant-current pulses, using conventional bridge-balance techniques, could be fitted with single exponential curves, also suggesting passive membrane behavior. A nonlinear least-squares fit of the complex impedance function for the simple model to the experimentally observed complex impedance yielded estimates of the resistance of the electrode, and of input capacitance (range, 56 to 490 pF) and input resistance (range, 0.8 to 30 M omega) of the neurons. The majority of trigeminal ganglion neurons were characterized by a resonance in the 50- to 250-Hz bandwidth of their impedance magnitude functions. Such neurons when injected with "large" hyperpolarizing current pulses using bridge-balance techniques showed membrane voltage responses that "sagged" (time-dependent rectification). Also, repetitive firing commonly occurred with depolarizing current pulses; this characteristic of neurons with resonance in their impedance magnitude functions was not observed in neurons with "purely" passive membrane behavior. A nonlinear least-squares fit of a five-parameter impedance fitting function based on a membrane model to the impedance locus diagram of a neuron with resonance yielded estimates of its membrane properties: input capacitance, the time-invariant part of the conductance, the conductance activated by the small oscillatory input current, and the relaxation time constant for this conductance. The ranges of the estimates for input capacitance and input resistance were comparable to the ranges of corresponding properties derived for neurons exhibiting "purely" passive behavior.(ABSTRACT TRUNCATED AT 400 WORDS)

Nicotine Blocks the Hyperpolarization-Activated CurrentIhand Severely Impairs the Oscillatory Behavior of Oriens-Lacunosum Moleculare Interneurons

“…Therefore, in a first set of experiments, we have tested whether nicotine affects the subthreshold resonance behavior of O-LM interneurons. We have applied the ZAP method (Puil et al, 1986;Hutcheon and Yarom, 2000) during whole-cell current-clamp recordings. Because subthreshold resonance strictly depends on HCN channels, responsible for I h (Hu et al, 2002), the membrane potential of the cells was manually clamped at Ϫ70 and Ϫ90 mV, respectively, by injecting a steady current through the recording electrode.…”

“…In some experiments, the impedance ( Z) amplitude profile (ZAP) was used to measure the electric resonance behavior of O-LM cells (Puil et al, 1986;Hutcheon and Yarom, 2000). The voltage response during injection through the patch pipette of a sinusoidal current (the ZAP current) with constant amplitude and linearly increasing frequency (0.5-30 Hz for 30 s; using Matlab; MathWorks) was recorded in current-clamp conditions.…”

Resonance or Integration? Self-Sustained Dynamics and Excitability of Neural Microcircuits

“…In subthreshold conditions, many neurons display marked resonant properties (i.e., have preferred input frequencies to which they respond maximally). A common technique for characterizing subthreshold behavior is to stimulate the neuron with a subthreshold sinusoidal current having gradually increasing frequency (also called ZAP function) and computing the frequency-dependent impedance of the neuron (Puil et al 1986). We used here the following subthreshold input stimulus…”

“…The frequency-dependent impedance Z( f ) in the subthreshold regime can be computed by dividing the frequency spectra of the responses (membrane potential fluctuations around the resting potential) by the frequency spectrum of the input current, for each frequency component f (Puil et al 1986)…”

“…1). For each neuron type, we next determined the frequency-dependent impedance by dividing the spectra of the membrane potentials by the spectrum of the input current and taking the complex amplitude as a value for the impedance (Puil et al 1986). …”