Sign in
Animal Behavior & CognitionbioRxiv
Preprint

Vocal biomarkers of aging and Parkinson's disease in a songbird

Lucas Manca Dal'AvaiD, Plínio Almeida BarbosaiD, Julie E. MilleriD

Abstract

PurposeSimilar vocal impairments occur in normative aging and Parkinsons disease (PD), making differential diagnosis challenging in early stages. The brains role in age and PD-related vocal dysfunction is unclear. This motivated our use of the zebra finch songbird model. We applied human acoustic analysis to evaluate aging and parkinsonian-like birdsong for shared and distinct features. MethodTwo open-access datasets of zebra finch song were analyzed using an expanded set of parameters that, in the case of humans, can detect subtle changes in voice. The aging dataset included younger, middle-aged, and older birds. The PD dataset included birds receiving viral injections into Area X to induce -synuclein overexpression or control treatment, recorded pre- and post-injection. Songs were automatically segmented into motifs using cross-correlation. Acoustic measures were extracted from harmonic, noisy, and mixed syllables. ResultsMiddle-aged birds differed from younger and older birds, showing non-linear trajectories in fundamental frequency measures and age-related increases in spectral slope, spectral emphasis, and intensity variability, whereas temporal parameters remained stable. In PD, pre-post changes occurred in both ASYN and controls, with ASYN birds exhibiting larger effects for fundamental frequency variability, decreasing frequency modulation, and vocal intensity measures. ConclusionsVocal aging in zebra finches was characterized by non-linear trajectories and prominent spectral and intensity-related changes. In a gene-driven PD state there were greater alterations in fundamental frequency variability and intensity regulation, features that may distinguish aging-from disease-related vocal variability. These findings further support the zebra finch as a translational model for vocal biomarker research.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISGordon '26Dominguez '26Rodgers '25Yeganegi '23Maxwell '21Dickinson '21
84% semantic matchNeuroscience
Michelle L Gordon, Arpita Gulati, Robin A. Samlan+1 more·Behavioral Neuroscience·Aug 10, 2026