The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis

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2 Citations (Scopus)

Abstract

Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.

Original languageEnglish
Pages (from-to)32-42
Number of pages11
JournalNeuroimage
Volume132
DOIs
Publication statusPublished - 15 May 2016

Keywords

  • Multivariate pattern analysis
  • fMRI
  • 7 T
  • Spatial resolution
  • Spatial smoothing
  • Auditory cortex
  • PRIMARY VISUAL-CORTEX
  • INFORMATION FMRI
  • 7 TESLA
  • BRAIN
  • ORIENTATION
  • ACTIVATION
  • SIGNAL
  • V1
  • STIMULI
  • HUMANS

Cite this

@article{454e35d5669941b7a215abf659effb7f,
title = "The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis",
abstract = "Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.",
keywords = "Multivariate pattern analysis, fMRI, 7 T, Spatial resolution, Spatial smoothing, Auditory cortex, PRIMARY VISUAL-CORTEX, INFORMATION FMRI, 7 TESLA, BRAIN, ORIENTATION, ACTIVATION, SIGNAL, V1, STIMULI, HUMANS",
author = "Anna Gardumi and Dimo Ivanov and Lars Hausfeld and Giancarlo Valente and Elia Formisano and Kamil Uludag",
note = "Copyright {\circledC} 2016 Elsevier Inc. All rights reserved.",
year = "2016",
month = "5",
day = "15",
doi = "10.1016/j.neuroimage.2016.02.033",
language = "English",
volume = "132",
pages = "32--42",
journal = "Neuroimage",
issn = "1053-8119",
publisher = "Elsevier Science",

}

TY - JOUR

T1 - The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis

AU - Gardumi, Anna

AU - Ivanov, Dimo

AU - Hausfeld, Lars

AU - Valente, Giancarlo

AU - Formisano, Elia

AU - Uludag, Kamil

N1 - Copyright © 2016 Elsevier Inc. All rights reserved.

PY - 2016/5/15

Y1 - 2016/5/15

N2 - Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.

AB - Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.

KW - Multivariate pattern analysis

KW - fMRI

KW - 7 T

KW - Spatial resolution

KW - Spatial smoothing

KW - Auditory cortex

KW - PRIMARY VISUAL-CORTEX

KW - INFORMATION FMRI

KW - 7 TESLA

KW - BRAIN

KW - ORIENTATION

KW - ACTIVATION

KW - SIGNAL

KW - V1

KW - STIMULI

KW - HUMANS

U2 - 10.1016/j.neuroimage.2016.02.033

DO - 10.1016/j.neuroimage.2016.02.033

M3 - Article

C2 - 26899782

VL - 132

SP - 32

EP - 42

JO - Neuroimage

JF - Neuroimage

SN - 1053-8119

ER -