Cardiovascular mechanisms underlying vocal behavior in freely moving macaque monkeys
Cristina Risueno-Segovia,
Okan Koç,
Pascal Champéroux,
Steffen R. Hage
Affiliations
Cristina Risueno-Segovia
Neurobiology of Social Communication, Department of Otolaryngology-Head and Neck Surgery, Hearing Research Centre, University of Tübingen, Medical Center, Elfriede-Aulhorn-Strasse 5, 72076 Tübingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Street 25, 72076 Tübingen, Germany; Graduate School of Neural and Behavioural Sciences-International Max Planck Research School, University of Tübingen, Österberg-Street 3, 72074 Tübingen, Germany; Corresponding author
Okan Koç
Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Street 25, 72076 Tübingen, Germany
Pascal Champéroux
European Research Biology Center, ERBC, Chemin de Montifault, 18800 Baugy, France
Steffen R. Hage
Neurobiology of Social Communication, Department of Otolaryngology-Head and Neck Surgery, Hearing Research Centre, University of Tübingen, Medical Center, Elfriede-Aulhorn-Strasse 5, 72076 Tübingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Street 25, 72076 Tübingen, Germany; Corresponding author
Summary: Communication is a keystone of animal behavior. However, the physiological states underlying natural vocal signaling are still largely unknown. In this study, we investigated the correlation of affective vocal utterances with concomitant cardiorespiratory mechanisms. We telemetrically recorded electrocardiography, blood pressure, and physical activity in six freely moving and interacting cynomolgus monkeys (Macaca fascicularis). Our results demonstrate that vocal onsets are strengthened during states of sympathetic activation, and are phase locked to a slower Mayer wave and a faster heart rate signal at ∼2.5 Hz. Vocalizations are coupled with a distinct peri-vocal physiological signature based on which we were able to predict the onset of vocal output using three machine learning classification models. These findings emphasize the role of cardiorespiratory mechanisms correlated with vocal onsets to optimize arousal levels and minimize energy expenditure during natural vocal production.
