Dataset Information

Computational Analysis of Neonatal Mouse Ultrasonic Vocalization.

ABSTRACT: Neonatal vocalization is structurally altered in mouse models of autism spectrum disorder (ASD). Our published data showed that pup vocalization, under conditions of maternal separation, contains sequences whose alterations in a genetic mouse model of ASD impair social communication between pups and mothers. We describe details of a method which reveals the statistical structure of call sequences that are functionally critical for optimal maternal care. Entropy analysis determines the degree of non-random call sequencing. A Markov model determines the actual call sequences used by pups. Sparse partial least squares discriminant analysis (sPLS-DA) identifies call sequences that differentiate groups and reveals the degrees of individual variability in call sequences between groups. These three sets of analyses can be used to identify the otherwise hidden call structure that is altered in mouse models of developmental neuropsychiatric disorders, including not only autism but also schizophrenia. © 2018 by John Wiley & Sons, Inc.

SUBMITTER: O Broin P

PROVIDER: S-EPMC6055925 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Computational Analysis of Neonatal Mouse Ultrasonic Vocalization.

Ó Broin Pilib P Beckert Michael V MV Takahashi Tomohisa T Izumi Takeshi T Ye Kenny K Kang Gina G Pouso Patricia P Topolski Mackenzie M Pena Jose L JL Hiroi Noboru N

Current protocols in mouse biology 20180521 2

Neonatal vocalization is structurally altered in mouse models of autism spectrum disorder (ASD). Our published data showed that pup vocalization, under conditions of maternal separation, contains sequences whose alterations in a genetic mouse model of ASD impair social communication between pups and mothers. We describe details of a method which reveals the statistical structure of call sequences that are functionally critical for optimal maternal care. Entropy analysis determines the degree of ...[more]

PMID: 29927553

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Project description:Mice produce ultrasonic vocalizations (USV) in multiple communicative contexts, including adult social interaction (e.g., male to female courtship), as well as pup calls when separated from the dam. Assessment of pup USV has been widely applied in models of social and communicative disorders, dozens of which have shown alterations to this conserved behavior. However, features such as call production rate can vary substantially even within experimental groups and it is unclear to what extent aspects of USV represent stable trait-like influences or are vulnerable to an animal's state. To address this question, we have employed a mixed modeling approach to describe consistency in USV features across time, leveraging multiple large cohorts recorded from two strains, and across ages/times. We find that most features of pup USV show consistent patterns within a recording session, but inconsistent patterns across postnatal development. This supports the conclusion that pup USV is most strongly influenced by "state"-like variables. In contrast, adult USV call rate and call duration show higher consistency across sessions and may reflect a stable "trait." However, spectral features of adult song such as the presence of pitch jumps do not show this level of consistency, suggesting that pitch modulation is more susceptible to factors affecting the animal's state at the time of recording. Overall, the utility of this work is three-fold. First, as variability necessarily affects the sensitivity of the assay to detect experimental perturbation, we hope the information provided here will be used to help researchers plan sufficiently powered experiments, as well as prioritize specific ages to study USV behavior and to decide which features to consider most strongly in analysis. Second, via the mouseTube platform, we have provided these hundreds of recordings and associated data to serve as a shared resource for other researchers interested in either benchmark data for these strains or in developing algorithms for studying features of mouse song. Finally, we hope that this work informs both interpretation of USV studies in models of developmental disorder, and helps to further research into understanding the neural processes that contribute to the production and predictability of USV behavior.

Dataset Information

Computational Analysis of Neonatal Mouse Ultrasonic Vocalization.

Publications

Computational Analysis of Neonatal Mouse Ultrasonic Vocalization.

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