Valeri Tsatsishvili1, Iballa Burunat2, Fengyu Cong3, Petri Toiviainen2, Vinoo Alluri4, Tapani Ristaniemi5. 1. Faculty of Information Technology, University of Jyvaskyla, Finland. Electronic address: valeri.tsatsishvili@jyu.fi. 2. Department of Music, Art and Culture Studies, University of Jyvaskyla, Finland. 3. Faculty of Information Technology, University of Jyvaskyla, Finland; Department of Biomedical Engineering, Dalian University of Technology, China. 4. International institute of information technology, Hyderabad, India. 5. Faculty of Information Technology, University of Jyvaskyla, Finland.
Abstract
BACKGROUND: There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. RESULTS: The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. COMPARISON WITH EXISTING METHOD: Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. CONCLUSIONS: Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing.
BACKGROUND: There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. RESULTS: The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. COMPARISON WITH EXISTING METHOD: Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. CONCLUSIONS: Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing.
Authors: Anna V Kasdan; Andrea N Burgess; Fabrizio Pizzagalli; Alyssa Scartozzi; Alexander Chern; Sonja A Kotz; Stephen M Wilson; Reyna L Gordon Journal: Neurosci Biobehav Rev Date: 2022-03-05 Impact factor: 9.052