On application of kernel PCA for generating stimulus features for fMRI during continuous music listening
| Autor: | Valeri Tsatsishvili, Iballa Burunat, Petri Toiviainen, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Computer science Cognitive Neuroscience media_common.quotation_subject Speech recognition musiikki Sensory system Stimulus (physiology) ta3112 050105 experimental psychology Kernel principal component analysis naturalistic fMRI music stimulus Young Adult 03 medical and health sciences toiminnallinen magneettikuvaus 0302 clinical medicine Rhythm Neuroimaging Perception Humans 0501 psychology and cognitive sciences Active listening media_common Brain Mapping Principal Component Analysis Neural correlates of consciousness General Neuroscience 05 social sciences functional magnetic resonance imaging (fMRI) feature generation kernel PCA Brain Magnetic Resonance Imaging ta6131 Auditory Perception Female ärsykkeet Music 030217 neurology & neurosurgery musical features |
| Zdroj: | Journal of Neuroscience Methods. 303:1-6 |
| ISSN: | 0165-0270 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect