Literature DB >> 1481693

Non-linearity of skin resistance response to intraneural electrical stimulation of sudomotor nerves.

M Kunimoto1, K Kirnö, M Elam, T Karlsson, B G Wallin.   

Abstract

Intraneural electrical stimuli (0.3 mA, 0.2 ms) were delivered via a tungsten microelectrode inserted into a cutaneous fascicle in the median nerve at the wrist in 16 normal subjects, and the effects on the sweat glands within the innervation zone were recorded as changes of skin resistance. In order to examine the relationship between the skin resistance level and the amplitude of transient resistance responses, trains of high frequency stimulation were used to reduce the skin resistance level and then transient resistance responses were evoked by single stimuli at 0.1 Hz. Regional anaesthesia of the brachial plexus in the axilla eliminated spontaneous sympathetic activity and reflex effects. At high skin resistance levels response amplitudes to single stimuli were low but they increased successively to a maximum at intermediate levels and then decreased again at low resistance levels. Repeated stimulation sequences evoked qualitatively similar response curves but quantitatively both response amplitudes and skin resistance levels were slightly reduced upon repetition. We suggest that the changes of response amplitudes are due to variable resistivity of the corneal layer. The shifts of the response curves with repetition of stimulation may result from increased hydration of the corneum. It is concluded that the variability of response amplitudes to constant stimuli makes the amplitude of a skin resistance response unsuitable as an indicator of the strength of sympathetic sudomotor nerve traffic.

Mesh:

Year:  1992        PMID: 1481693     DOI: 10.1111/j.1748-1716.1992.tb09433.x

Source DB:  PubMed          Journal:  Acta Physiol Scand        ISSN: 0001-6772


  10 in total

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8.  Testing a linear time invariant model for skin conductance responses by intraneural recording and stimulation.

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9.  Human Pavlovian fear conditioning conforms to probabilistic learning.

Authors:  Athina Tzovara; Christoph W Korn; Dominik R Bach
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  10 in total

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