Literature DB >> 11085614

Modulation of prepulse inhibition by an augmented acoustic environment in DBA/2J mice.

J E Jeskey1, J F Willott.   

Abstract

DBA/2J (DBA) mice exhibit progressive hearing loss, evident for high frequencies (>20 kHz) at age 3-4 weeks and severe by 12-16 weeks. From age 25 days to 12 weeks, DBA mice were exposed for 12 hr nightly to an augmented acoustic environment (AAE): moderately intense broadband noise bursts. After AAE treatment, prepulse inhibition (PPI) to tone prepulses (4-24 kHz, 70 dB SPL) was stronger, and baseline acoustic startle responses were larger, compared with results for age-matched DBA mice (testing performed with AAE off). Nightly AAE treatment was then terminated, and both AAE effects were largely gone 1 week later. Reinstatement of AAE treatment after the 4-week period had no significant effect on startle magnitude, but PPI improved significantly, with the AAE effect reacquired after 3 weeks. It is proposed that AAE modulates neural plasticity induced by high-frequency hearing loss in auditory system components of the PPI pathway.

Entities:  

Mesh:

Year:  2000        PMID: 11085614     DOI: 10.1037//0735-7044.114.5.991

Source DB:  PubMed          Journal:  Behav Neurosci        ISSN: 0735-7044            Impact factor:   1.912


  4 in total

Review 1.  Cochlin and glaucoma: a mini-review.

Authors:  Sanjoy K Bhattacharya; Neal S Peachey; John W Crabb
Journal:  Vis Neurosci       Date:  2005 Sep-Oct       Impact factor: 3.241

2.  Effects of a high-frequency augmented acoustic environment on parvalbumin immunolabeling in the anteroventral cochlear nucleus of DBA/2J and C57BL/6J mice.

Authors:  James F Willott; Justine Vandenbosche; Toru Shimizu
Journal:  Hear Res       Date:  2010-01-07       Impact factor: 3.208

3.  Acoustic experience alters the aged auditory system.

Authors:  Jeremy G Turner; Jennifer L Parrish; Loren Zuiderveld; Stacy Darr; Larry F Hughes; Donald M Caspary; Esma Idrezbegovic; Barbara Canlon
Journal:  Ear Hear       Date:  2013 Mar-Apr       Impact factor: 3.570

4.  Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms.

Authors:  Timothy J Fawcett; Chad S Cooper; Ryan J Longenecker; Joseph P Walton
Journal:  MethodsX       Date:  2020-12-01
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.