Spatial band-pass filtering aids decoding musical genres from auditory cortex 7T fMRI

Sengupta, Ayan; Pollmann, Stefan; Hanke, Michael

doi:10.12688/f1000research.13689.2

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…Due to the broadband nature of the orientation-related signal, superior decoding performance can be achieved via BP filtering strategies that remove high and low frequency noise components. The present results offer no basis for conclusive recommendations on the specific configuration of a BP filter, however, we recently showed that this finding is not limited to orientation decoding from visual cortex, but a similar effect can be observed for decoding musical genre from auditory cortex (Sengupta et al, 2018). Given the potential generality of this finding future studies focused on the analysis of distributed activity patterns using, for example, decoding analysis or encoding models should investigate this possibility.…”

Section: Discussioncontrasting

confidence: 60%

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

Sengupta

Speck

Yakupov

et al. 2018

Preprint

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Previously published results indicate that the accuracy of decoding visual orientation from 7 Tesla fMRI data of V1 peaks at spatial acquisition resolutions that are routinely accessible with more conventional 3 Tesla scanners. This study directly compares the decoding performance between a 3 Tesla and a 7 Tesla dataset that were acquired using the same stimulation paradigm by applying an identical analysis procedure. The results indicate that decoding models built on 3 Tesla data are comparatively impaired. Moreover, we found no evidence for a strong coupling of BOLD signal change magnitude or temporal signal to noise ratio (tSNR) with decoding performance. Direct enhancement of tSNR via multiband fMRI acquisition at the same resolution did not translate into improved decoding performance. Additional voxel selection can boost 3 Tesla decoding performance to the 7 Tesla level only at a 3 mm acquisition resolution. In both datasets the BOLD signal available for orientation decoding is spatially broadband, but, consistent with the size of the BOLD point-spread-function, decoding models at 3 Tesla utilize spatially coarser image components.

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Section: Discussioncontrasting

confidence: 60%

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

Sengupta

Speck

Yakupov

et al. 2018

Preprint

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“…Further, Case y (2017) and Sengupta et al. (2018) used fMRI data with five distinct music genres, followed by activity‐based multi‐class classification using SVM. However, these studies did not provide answers to how cortical representations of music genres contribute to genre classification.…”

Section: Discussionmentioning

confidence: 99%

“…However, there remains considerable uncertainty as to how such genre categories are perceived from complex auditory stimuli and how the human brain subserves this categorization. Neuroimaging studies have decoded music genres from brain activity using support vector machines (SVM) (Case y, 2017; Ghaemmaghami & Sebe, 2016; Sengupta et al., 2018); however, these studies did not clarify how cortical representations of music genres contribute to genre classification.…”

Section: Introductionmentioning

confidence: 99%

Correspondence of categorical and feature‐based representations of music in the human brain

2020

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Introduction Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music‐related features. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations. Methods We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel‐wise modeling approach to music‐evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature‐brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features. Results Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro‐temporal modulation‐transfer function model. Conclusion Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.

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“…Ghaemmaghami and Sebe (2017) used magnetoencephalogram and electroencephalogram datasets to classify musical stimuli as either pop or rock, using support vector machines (SVM) (24). Casey (2017) and Sengupta et al (2018) used fMRI data with 5 distinct music genres, then performed activity-based multi-class classification using SVM (25,26). These studies, however, did not provide how cortical representations of music genres contributed to genre classification.…”

Section: Discussionmentioning

confidence: 99%

Representation of music genres based on the spectro-temporal modulation responses of the human brain

Nakai

Koide-Majima

Nishimoto

2018

Preprint

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Music genre is an essential category for understanding human musical preferences and is provided based on the abstract categorization upon complex auditory stimuli. Previous neuroimaging studies have reported the involvement of the superior temporal gyrus (STG) in response to general music-related features. However, it remains largely unclear how abstract categories of music genre are represented in the brain and what acoustic features are more suited for explaining such representations. Here we examined comprehensive cortical representations and functional organization of music genres using 540 music clips. We applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging (fMRI). We observed distinct cortical organizations for different music genres in the bilateral STG, which revealed the representational relationship between various music genres, e.g., classical and hip-hop music showed opposite representations. Representations of music genres were largely explained by spectro-temporal modulation, which was modeled by a biologically plausible spectro-temporal modulation-transfer function (MTF) model. Our results elucidate the quantitative representation of music genres in the human cortex and indicate the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity. Significance statementMusic genre is an essential category for understanding human preferences of music. However, it is largely unknown how abstract categories of music genre are represented in the brain. Here, we examined comprehensive cortical representations of music genres by building voxel-wise models of fMRI data collected while human subjects listened to 540 music clips. We found distinct cortical organizations for various music genres in the bilateral STG. Such genre-specific cortical organization was explained by the biologically plausible MTF model. The current study elucidates the quantitative representation of music genres in the human cortex for the first time and indicates the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity.

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Spatial band-pass filtering aids decoding musical genres from auditory cortex 7T fMRI

Cited by 7 publications

References 18 publications

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

Correspondence of categorical and feature‐based representations of music in the human brain

Representation of music genres based on the spectro-temporal modulation responses of the human brain

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