Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field

Michelle Moerel, Federico De Martino, Valentin G. Kemper, Sebastian Schmitter, An T. Vu, Kâmil Uğurbil, Elia Formisano, Essa Yacoub

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7 T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T2* weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T2* weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T2* weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked.

Original languageEnglish (US)
Pages (from-to)18-31
Number of pages14
StatePublished - Jan 1 2018

Bibliographical note

Funding Information:
This work was supported by the Netherlands Organization for Scientific Research (NWO; Rubicon Grant 446-12-010 to M.M., Veni Grant 451-15-012 to M.M., VIDI Grant 864-13-012 to F.D.M., and VICI grant 453-12-002 to E.F.), the National Institutes of Health (NIH grants P41 EB015894 , P30 NS076408 , and S10 RR026783 ), European Research Council (ERC grant number 269853 ), and the WM KECK Foundation . This research has been made possible with the support of the Dutch Province of Limburg.


  • Human auditory cortex
  • Sensitivity
  • Specificity
  • Ultra-high field fMRI

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