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

Ayan Sengupta,Michael Hanke,Stefan Pollmann

doi:10.12688/f1000research.13689.2

Abstract

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation - primarily in the visual cortex. Previous research indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we applied an analysis strategy from a previous study on decoding visual orientation from V1 to publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex. The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

Highlights

We recently reported[1] that spatial band-pass filtering of 7 Tesla BOLD fMRI data boosts accuracy of decoding visual orientations from human V1
Maximum decoding performance was observed for a band of 5–8 mm full width at half maximum (FWHM), indicating that low spatial frequency fMRI components contribute to noise with respect to orientation discrimination
Stimulus and fMRI data Data were taken from a published dataset[2] which were repeatedly analyzed previously[4,5], and publicly available from the studyforrest.org project of 20 participants passively listening to five natural, stereo, high-quality music stimuli (6 s duration; 44.1 kHz sampling rate) for each of five different musical genres: 1) Ambient, 2) Roots Country 3) Heavy Metal, 4) 50s Rock’n’Roll, and 5) Symphonic, while fMRI data were recorded in a 7 Tesla Siemens scanner (1.4 mm isotropic voxel size, TR=2 s, matrix size 160×160, 36 axial slices, 10% interslice gap). fMRI data were scanner-side corrected for spatial distortions[6]

Summary

Introduction

We recently reported[1] that spatial band-pass filtering of 7 Tesla BOLD fMRI data boosts accuracy of decoding visual orientations from human V1. We observed this result in comparison to data without any dedicated spatial filtering applied, and spatially low-pass filtered data – a typical preprocessing strategy for BOLD fMRI. This effect was present across a range of tested spatial acquisition resolutions, ranging from 0.8 mm to 2 mm isotropic voxel size (Figure 4 in 1). I believe it was axial, but Figure 1 could mislead people into thinking it was coronal.

Objectives

Methods

Results