title: Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.
abstract: Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.
authors.affiliation: Department of Statistics, Northwestern University, Evanston, Illinois, United States of America; Department of Psychology, Drexel University, Philadelphia, Pennsylvania, United States of America.
authors.forename: Fengqing
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Department of Statistics, Northwestern University, Evanston, Illinois, United States of America.
authors.forename: Ji-Ping
authors.initials: JP
authors.lastname: Wang
authors.affiliation: The Roxelyn and Richard Pepper Department of Communication Science and Disorders, Northwestern University, Evanston, Illinois, United States of America.
authors.forename: Jieun
authors.initials: J
authors.lastname: Kim
authors.affiliation: Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America.
authors.forename: Todd
authors.initials: T
authors.lastname: Parrish
authors.affiliation: The Roxelyn and Richard Pepper Department of Communication Science and Disorders, Northwestern University, Evanston, Illinois, United States of America; Department of Linguistics & Modern Languages, The Chinese University of Hong Kong, Shatin, Hong Kong, PRC; Department of Otolaryngology-Head & Neck Surgery, Northwestern University, Chicago, Illinois, United States of America.
authors.forename: Patrick C M
authors.initials: PC
authors.lastname: Wong
mesh_terms: D001303:Auditory Cortex / Q000502:physiology; D001307:Auditory Perception / Q000502:physiology*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D013016:Sound; D060388:Support Vector Machine; D013702:Temporal Lobe / Q000502:physiology*; D055815:Young Adult