| Autor: |
Burkett ZD; 1] Department of Integrative Biology &Physiology, University of California, Los Angeles, California 90095 [2] Interdepartmental Program in Molecular, Cellular, &Integrative Physiology, University of California, Los Angeles, California 90095., Day NF; Department of Integrative Biology &Physiology, University of California, Los Angeles, California 90095., Peñagarikano O; 1] Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095 [2] Center for Autism Research &Treatment, Semel Institute for Neuroscience &Human Behavior, University of California, Los Angeles, California 90095., Geschwind DH; 1] Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095 [2] Center for Autism Research &Treatment, Semel Institute for Neuroscience &Human Behavior, University of California, Los Angeles, California 90095 [3] Center for Neurobehavioral Genetics, Semel Institute for Neuroscience &Human Behavior, University of California, Los Angeles, California 90095., White SA; 1] Department of Integrative Biology &Physiology, University of California, Los Angeles, California 90095 [2] Interdepartmental Program in Molecular, Cellular, &Integrative Physiology, University of California, Los Angeles, California 90095. |
| Abstrakt: |
The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization "types" by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories. |
