Raw birdsong data for "Vocal changes in a zebra finch model of Parkinson’s Disease characterized by alpha-synuclein overexpression in the song-dedicated anterior forebrain pathway"

  • Cesar A. Medina (Creator)
  • Eddie Vargas (Creator)
  • Stephanie J. Munger (Creator)
  • Julie Elizabeth Miller (Creator)
  • Cesar A. Medina (Creator)

Dataset

Description

Parkinson's disease is the second most common neurodegenerative disease affecting motor behavior. In addition to the typical peripheral limb motor symptoms, vocal motor issues such as deteriorated vocal quality are present in Parkinson's patients. Unfortunately, very little is known about the biological mechanisms underlying these vocal changes. Here, we developed a Parkinsonian model using the zebra finch songbird to study the underlying neural mechanisms of these changes. Finch song and supportive brain areas are similar to human speech and brain pathways. To do this, we recorded song for 2 hours from zebra finches injected with an adeno-associated virus expressing either the causally-related human Parkinsonian gene SNCA or control GFP protein in finch brain. Song was then segmented using a specially designed matlab program called Vocal Inventory Clustering Engine (VoICE) into unique syllables. Acoustic features such as duration, pitch, mean frequency, amplitude, Wiener Entropy, frequency modulation, and amplitude modulation were measured from 75 individual copies of these syllables. The mean, mean variance, and coefficient of variation of these syllables were also measured. Motif-level data was also collected to monitor how much each bird sang (i.e., how many copies of song were uttered over a 2 hour period). We found that overexpression of SNCA led to shorter and poorer quality syllables that are similar to Parkinsonian vocal changes. To cite this work, please cite both the associated PLoS One paper "Vocal changes in a zebra finch model of Parkinson’s Disease characterized by alpha-synuclein overexpression in the song-dedicated anterior forebrain pathway" and this dataset. For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu
Date made available2022
PublisherUniversity of Arizona Research Data Repository

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