Verga, L. | bioRxiv Science

bioRxiv2023

Unravelling individual rhythmic abilities using machine learning

Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, for example in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp with single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on the behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that the variability of rhythmic abilities, and their link with formal and informal music experience, can be successfully captured by profiles including a minimal set of behavioral measures. These profiles can shed light on individual variability in healthy and clinical populations, and provide guidelines for personalizing rhythm-based interventions.

bioinformatics↗

bioRxiv2020

Neuroanatomy of the grey seal brain: bringing pinnipeds into the neurobiological study of vocal learning

Comparative studies of vocal learning and vocal non-learning animals can increase our understanding of the neurobiology and evolution of vocal learning and human speech. Mammalian vocal learning is understudied: most research has either focused on vocal learning in songbirds or its absence in non-human primates. Here we focus on a highly promising model species for the neurobiology of vocal learning: grey seals. We provide a neuroanatomical atlas (based on dissected brain slices and magnetic resonance images), a labelled MRI template, a 3D model with volumetric measurements of brain regions, and histological cortical stainings. Four main features of the grey seal brain stand out. (1) It is relatively big and highly convoluted. (2) It hosts a relatively large temporal lobe and cerebellum, structures which could support developed timing abilities and acoustic processing. (3) The cortex is similar to humans in thickness and shows the expected six-layered mammalian structure. (4) Expression of FoxP2 - a gene involved in vocal learning and spoken language - is present in deeper layers of the cortex. Our results could facilitate future studies targeting the neural and genetic underpinnings of mammalian vocal learning, thus bridging the research gap from songbirds to humans and non-human primates.

animal behavior and cognition↗