Functional tolerance to mechanical deformation developed from organotypic hippocampal slice cultures
In this study, we measured changes in electrophysiological activity after mechanical deformation of living organotypic hippocampal brain slice cultures at tissue strains and strain rates relevant to traumatic brain injury (TBI). Electrophysiological activity was measured throughout the hippocampus w...
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Veröffentlicht in: | Biomechanics and modeling in mechanobiology 2015-06, Vol.14 (3), p.561-575 |
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Sprache: | eng |
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Zusammenfassung: | In this study, we measured changes in electrophysiological activity after mechanical deformation of living organotypic hippocampal brain slice cultures at tissue strains and strain rates relevant to traumatic brain injury (TBI). Electrophysiological activity was measured throughout the hippocampus with a 60-electrode microelectrode array. Electrophysiological parameters associated with unstimulated spontaneous activity (neural event firing rate, duration, and magnitude), stimulated evoked responses (the maximum response
R
max
, the stimulus current necessary for a half-maximal response
I
50
, and the electrophysiological parameter
m
that is representative of firing uniformity), and paired-pulse responses (paired-pulse ratio at varying interstimulus intervals) were quantified for each hippocampal region (CA1, CA3, and DG). We present functional tolerance criteria for the hippocampus in the form of mathematical relationships between the input tissue-level injury parameters (strain and strain rate) and altered neuronal network function. Most changes in electrophysiology were dependent on strain and strain rate in a complex fashion, independent of hippocampal anatomy, with the notable exception of
R
max
. Until it becomes possible to directly measure brain tissue deformation in vivo, finite element (FE) models will be necessary to simulate and predict the in vivo consequences of TBI. One application of our study is to provide functional relationships that can be incorporated into these FE models to enhance their biofidelity of accident and collision reconstructions by predicting biological outcomes in addition to mechanical responses. |
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ISSN: | 1617-7959 1617-7940 |
DOI: | 10.1007/s10237-014-0622-4 |