On application of kernel PCA for generating stimulus features for fMRI during continuous music listening
Tsatsishvili, V., Burunat, I., Cong, F., Toiviainen, P., Alluri, V., & Ristaniemi, T. (2018). On application of kernel PCA for generating stimulus features for fMRI during continuous music listening. Journal of Neuroscience Methods, 303, 1-6. https://doi.org/10.1016/j.jneumeth.2018.03.014
Published inJournal of Neuroscience Methods
© Elsevier Ltd, 2018. This is a final draft version of an article whose final and definitive form has been published by Elsevier Ltd. Published in this repository with the kind permission of the publisher.
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. ...
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Related funder(s)Research Council of Finland
Funding program(s)Research post as Academy Professor, AoF
Additional information about fundingThe first author wishes to thank Fabian Prezja and Virpi-Liisa Kykyri for their support. Part of this work was financially supported by the Academy of Finland [project numbers 272250 and 274037]
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