Multivoxel Pattern of Blood Oxygen Level Dependent Activity can be sensitive to stimulus specific fine scale responses

Luca Vizioli, Federico De Martino, Lucy S. Petro, Daniel Kersten, Kamil Ugurbil, Essa Yacoub, Lars Muckli

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


At ultra-high field, fMRI voxels can span the sub-millimeter range, allowing the recording of blood oxygenation level dependent (BOLD) responses at the level of fundamental units of neural computation, such as cortical columns and layers. This sub-millimeter resolution, however, is only nominal in nature as a number of factors limit the spatial acuity of functional voxels. Multivoxel Pattern Analysis (MVPA) may provide a means to detect information at finer spatial scales that may otherwise not be visible at the single voxel level due to limitations in sensitivity and specificity. Here, we evaluate the spatial scale of stimuli specific BOLD responses in multivoxel patterns exploited by linear Support Vector Machine, Linear Discriminant Analysis and Naïve Bayesian classifiers across cortical depths in V1. To this end, we artificially misaligned the testing relative to the training portion of the data in increasing spatial steps, then investigated the breakdown of the classifiers’ performances. A one voxel shift led to a significant decrease in decoding accuracy (p < 0.05) across all cortical depths, indicating that stimulus specific responses in a multivoxel pattern of BOLD activity exploited by multivariate decoders can be as precise as the nominal resolution of single voxels (here 0.8 mm isotropic). Our results further indicate that large draining vessels, prominently residing in proximity of the pial surface, do not, in this case, hinder the ability of MVPA to exploit fine scale patterns of BOLD signals. We argue that tailored analytical approaches can help overcoming limitations in high-resolution fMRI and permit studying the mesoscale organization of the human brain with higher sensitivities.

Original languageEnglish (US)
Article number7565
JournalScientific reports
Issue number1
StatePublished - May 5 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).


  • Algorithms
  • Brain Mapping/methods
  • Brain/physiology
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Magnetic Resonance Imaging/methods
  • Models, Theoretical
  • Oxygen/metabolism
  • Support Vector Machine

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't


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