title: Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.
abstract: 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 7T, 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 T2weighted 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 T2weighted 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.
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA; Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, The Netherlands. Electronic address: michelle.moerel@maastrichtuniversity.nl.
authors.forename: Michelle
authors.initials: M
authors.lastname: Moerel
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, The Netherlands. Electronic address: f.demartino@maastrichtuniversity.nl.
authors.forename: Federico
authors.initials: F
authors.lastname: De Martino
authors.affiliation: Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, The Netherlands. Electronic address: valentin.kemper@maastrichtuniversity.nl.
authors.forename: Valentin G
authors.initials: VG
authors.lastname: Kemper
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA; Department of Biomedical Magnetic Resonance, Physikalisch-Technische Bundesanstalt, Berlin, Germany. Electronic address: sschmitt@umn.edu.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schmitter
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Advanced MRI Technologies, Sebastopol, CA, USA. Electronic address: anvu@berkeley.edu.
authors.forename: An T
authors.initials: AT
authors.lastname: Vu
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA. Electronic address: kamil@cmrr.umn.edu.
authors.forename: Kâmil
authors.initials: K
authors.lastname: Uğurbil
authors.affiliation: Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, The Netherlands. Electronic address: e.formisano@maastrichtuniversity.nl.
authors.forename: Elia
authors.initials: E
authors.lastname: Formisano
authors.affiliation: Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA. Electronic address: yaco0006@umn.edu.
authors.forename: Essa
authors.initials: E
authors.lastname: Yacoub
mesh_terms: D000465:Algorithms; D001303:Auditory Cortex / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Human auditory cortex; Sensitivity; Specificity; Ultra-high field fMRI