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Related Experiment Video

Updated: Sep 30, 2025

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models.

Ugne Klibaite¹, Mikhail Kislin², Jessica L Verpeut²

¹Department of Organismic and Evolutionary Biology, Harvard University, 52 Oxford St, 02138, Cambridge, MA, USA. klibaite@fas.harvard.edu.

Molecular Autism

|March 13, 2022

Summary

Deep phenotyping using AI reveals distinct movement and adaptation deficits in mouse models of autism spectrum disorder (ASD). This advanced approach offers potential for quantitative diagnostic criteria in clinical settings.

Keywords:

Autism Behavior Cerebellum Clustering Mouse Pose estimation

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Last Updated: Sep 30, 2025

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders

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Published on: May 12, 2015

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Published on: July 18, 2019

Area of Science:

Neuroscience
Computational Biology
Genetics

Background:

Autism spectrum disorder (ASD) and other neurodevelopmental disorders are characterized by repetitive behaviors, environmental rigidity, and fine motor impairments.
Individual variability in these traits is significant, and conventional animal model phenotyping often incompletely captures these fine-scale variations.
Two genetic mutations, cerebellum-specific Tsc1 deletion and whole-brain Cntnap2 knockout, are linked to ASD and were investigated in mouse models.

Purpose of the Study:

To develop a novel computational framework for detailed behavioral characterization in rodent models.
To establish a dynamic baseline of adaptive movement in wild-type mice.
To investigate the specific behavioral consequences of Tsc1 and Cntnap2 gene mutations in mouse models of neurodevelopmental disorders.

Main Methods:

Utilized computer vision and deep learning for high-dimensional statistical analysis of mouse movement in the open-field test.
Developed a pipeline to process pose estimation data, identify behavioral clusters, and generate wavelet signatures.
Quantified spatial and temporal habituation, self-grooming, locomotion, and gait patterns over multiple days.

Main Results:

Both Cntnap2 knockout and L7-Tsc1 mutant mice exhibited forelimb lag during gait.
Mutant mice displayed significant deficits in multi-day adaptation, failing to show the typical increase in corner occupancy observed in wild-type mice.
L7-Tsc1 mutants showed persistent ambling, turning, locomotion, and reduced grooming, while Cntnap2 knockouts showed different behavioral state occupancy patterns.

Conclusions:

The developed deep phenotyping pipeline accurately identifies model-specific deviations in movement and adaptation.
The observed behavioral deficits in mutant mice represent robust ASD symptoms.
This automated deep phenotyping approach holds promise for quantitative diagnostic criteria in clinical settings.