Literature DB >> 27984100

Air-puff induced vocalizations: A novel approach to detecting negative affective state following concussion in rats.

Jenny R Browning1, Ashley C Whiteman2, Lai Yee Leung2, Xi-Chun May Lu2, Deborah A Shear2.   

Abstract

BACKGROUND: Negative emotional states resulting from concussion are of increasing concern. In the current study, we developed a model to investigate negative affect following concussion in the projectile concussive impact (PCI) model. High frequency ultrasonic vocalizations (22kHz USVs) are associated with negative affective stimuli in rats. Changes in negative affective state were examined following PCI using a mild air-puff stimulus to elicit 22kHz USVs. NEW
METHOD: Forty-eight hours post-injury, animals were placed into a clean acrylic box lined with bedding. A 5min baseline recording was followed by 15 air puffs (55psi) spaced 15s apart aimed at the upper back and neck.
RESULTS: Injured animals produced on average 153.5±55.13 more vocalizations than shams, vocalizing on average 4min longer than shams. Additionally, concussed animals vocalized to fewer air-puffs, exhibiting a 1.5 fold lower threshold for the expression of negative affect. COMPARISON WITH EXISTING
METHODS: Studies currently used to test negative affective states following concussion in animals, such as the elevated plus maze and forced swim task have, as of yet, been unsuccessful in demonstrating injury effects in the PCI model. While the air-puff test has been applied in other fields, to our knowledge it has not been utilized to study traumatic brain injury.
CONCLUSION: The current study demonstrates that the air-puff vocalization test may be a valuable tool in assessing negative mood states following concussion in rat models and may be used to evaluate novel therapies following brain injury for the treatment of mood dysfunction. Published by Elsevier B.V.

Entities:  

Keywords:  22kHz vocalizations; Concussion; Negative affect; Projectile concussive impact model; Ultrasonic vocalizations

Mesh:

Year:  2016        PMID: 27984100     DOI: 10.1016/j.jneumeth.2016.10.017

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  5 in total

1.  DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations.

Authors:  Kevin R Coffey; Russell G Marx; John F Neumaier
Journal:  Neuropsychopharmacology       Date:  2019-01-04       Impact factor: 7.853

Review 2.  Inflammation in Traumatic Brain Injury.

Authors:  Teodor T Postolache; Abhishek Wadhawan; Adem Can; Christopher A Lowry; Margaret Woodbury; Hina Makkar; Andrew J Hoisington; Alison J Scott; Eileen Potocki; Michael E Benros; John W Stiller
Journal:  J Alzheimers Dis       Date:  2020       Impact factor: 4.472

Review 3.  Effect of Estrous Cycle on Behavior of Females in Rodent Tests of Anxiety.

Authors:  Thelma A Lovick; Hélio Zangrossi
Journal:  Front Psychiatry       Date:  2021-08-31       Impact factor: 4.157

4.  Examining affective structure in chickens: valence, intensity, persistence and generalization measured using a Conditioned Place Preference Test.

Authors:  Elizabeth S Paul; Joanne L Edgar; Gina Caplen; Christine J Nicol
Journal:  Appl Anim Behav Sci       Date:  2018-10       Impact factor: 2.448

5.  A Systematic Review of Closed Head Injury Models of Mild Traumatic Brain Injury in Mice and Rats.

Authors:  Colleen N Bodnar; Kelly N Roberts; Emma K Higgins; Adam D Bachstetter
Journal:  J Neurotrauma       Date:  2019-03-06       Impact factor: 5.269

  5 in total

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