| Autor: |
Wiltschko, Alexander B, Tsukahara, Tatsuya, Zeine, Ayman, Anyoha, Rockwell, Gillis, Winthrop, Markowitz, Jeffrey, Peterson, Ralph, Katon, Jesse, Johnson, Matthew J, Datta, Sandeep Robert |
| Rok vydání: |
2020 |
| DOI: |
10.5281/zenodo.3951697 |
| Popis: |
Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically-inspired behavioral analysis method that identifies modular components of 3D mouse body language called “syllables.” Here we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuro- and psychoactive drugs administered to a cohort of nearly 700 mice. These data reveal that MoSeq can identify syllables that are characteristic of individual drugs; we leverage this finding to characterize the on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior, and suggest that behavioral syllables represent a new class of druggable target.Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically-inspired behavioral analysis method that identifies modular components of 3D mouse body language called “syllables.” Here we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuro- and psychoactive drugs administered to a cohort of nearly 700 mice. These data reveal that MoSeq can identify syllables that are characteristic of individual drugs; we leverage this finding to characterize the on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior, and suggest that behavioral syllables represent a new class of druggable target. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
