High-resolution T1-mapping using inversion-recovery EPI and application to cortical depth-dependent fMRI at 7 Tesla
Sriranga Kashyap1, Dimo Ivanov1, Martin Havlíček1, Benedikt A Poser1, and Kâmil Uludağ1

1Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands

Synopsis

Cortical-depth dependent fMRI usually relies on the definition of depths on an anatomical image (eg. MPRAGE). The geometric dissimilarities of the functional compared to the anatomical data require further spatial processing of the functional data to ensure good co-registration. We propose an alternative approach that uses an optimised inversion-recovery EPI derived T1 image, whose resolution and readout, hence distortions, are identical to that of the functional data, in order to delineate cortical depths. As a result, the cortical-depth specific fMRI data can be analysed in the native space without any spatial confounds stemming from distortion correction and inaccurate registration.

Introduction

Recently, there has been growing interest in cortical depth-dependent fMRI, particularly due to increased accessibility of ultra-high field scanners. Since the cortical depths are usually determined on an anatomical image, good co-registration with the distorted functional data is crucial in high-resolution studies. The standard approach has been to correct the EPI for distortions using algorithms such as FSL-TOPUP [1] and co-registering the undistorted EPI to the anatomical data with the depth definitions. However, distortion correction might reduce laminar information in the fMRI data due to smoothing and interpolation, and sub-optimal registration might lead to erroneous classification of depths. The present study builds upon previous work [2][3] to acquire, for the first time, sub-millimetre distortion-matched quantitative T1 image using inversion-recovery EPI (IR-EPI) at 7T and demonstrates the feasibility of using the IR-EPI T1 map to define cortical-depths for fMRI analyses. In this study, cortical depth-dependent haemodynamic responses at 7T were investigated using a full-contrast static and reduced-contrast flickering hemi-annulus, a stimulus combination that is well-suited to study the laminar distribution of the positive BOLD amplitude and the post-stimulus undershoot [4].

Methods

Images were acquired with a Siemens 7T scanner (Erlangen, Germany) using a 32-channel head coil (Nova Medical, USA). Two subjects participated in this study after written informed consent. Sequence parameters (Table 1) were optimised in pilot studies. IR-EPI: Thirty inversion times (TI) were acquired by placing EPI readouts following an optimized tr-FOCI [5] inversion pulse and permuting the slice order [6] each TR [2][3]. Ten repetitions of each inversion time were averaged and T1 and M0 maps were obtained from the mean IR-EPI by modelling each voxel with $$$M(TI) = M_{0}*(1-2*E*e^{(-TI/T_{1})}+e^{(-TR/T_{1})})$$$, where E is the inversion efficiency, using a non-linear least squares algorithm in MATLAB (MathWorks, USA). Experiment: Static and flickering hemi-annuli were created using PsychoPy [7] and presented to the left hemi-field in a block design for 20s followed by 40s of isoluminant grey background. Three runs of ten blocks each were acquired for both conditions. fMRI analyses: 3D gradient-echo (GE) EPI runs were motion-corrected and registered using SPM12. The statistical analyses were done using FSL FEAT with standard haemodynamic predictors. The IR-EPI T1 image was co-registered to the 3D GE-EPI. The region-of-interest (ROI) was manually delineated on the IR-EPI T1 map using ITK-SNAP [8] and ten cortical-depths were defined within the ROI using an equidistant-layering algorithm in MATLAB.

Results

Figure 1 shows the enhanced grey-white matter contrast in the distortion-matched IR-EPI T1 map compared to the mean EPI image. The co-registration is qualitatively presented by the statistical map overlaid on both the images (Fig. 1 a, b). Ten cortical depths were defined in the ROI between the CSF-GM and GM-WM boundaries as illustrated in Figure 2. It can be seen that the depths are accurately positioned within the grey matter. The cortical depth-dependent haemodynamic responses for both conditions are shown in Figure 3. Both the flickering and static stimuli evoked similar positive BOLD responses as a function of depth followed by post-stimulus timecourses depending on the depth and stimulus. Cortical-depth profiles were then plotted for positive BOLD (Fig. 4a) and the post-stimulus undershoot (PSU) (Fig. 4b), both of which, approximately decrease linearly in amplitude as a function of depth. Since the PSU has similar depth profile as the positive BOLD signal, the ratio of the PSU to the peak BOLD amplitude is shown in Figure 4c. The ratio has its maximum magnitude in the middle GM depth.

Discussion

The present study demonstrates, for the first time, the feasibility of acquiring sub-millimetre resolution distortion-matched anatomical images using IR-EPI at 7T. The advantage of the IR-EPI lies in the fact that minimal spatial processing of the functional data is required and its enhanced grey-matter contrast can be used to define cortical depths. Therefore, the anatomical reference obtained by IR-EPI approach is a viable alternative to the standard approaches in high-resolution fMRI studies. Using this approach, we demonstrated that even with gradient-echo (GE) fMRI laminar specificity could be achieved. GE signal is largely macrovasculature weighted [9] and this influences the spatial specificity of the haemodynamic response across cortical depths. Thus, the positive BOLD response alone may not reveal cortical-depth dependent differences in neuronal activity. However, deconvolving the standard cortical-depth profile of the response may be necessary to obtain information about laminar neuronal activity from GE functional data. In this study, we normalized the post-stimulus undershoot with the positive BOLD response. This analysis revealed the cortical-depth dependent modulation of the PSU by removing the standard cortical response profile of the GE BOLD response.

Acknowledgements

This work was financially supported by the VIDI grant (452-11-002).

References

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Figures

Table 1: Optimised sequence parameters for the IR-EPI and the functional 3D GE-EPI.

Figure 1: Enhanced grey-white matter contrast in the IR-EPI T1 (a) compared to the mean 3D GE-EPI (b) with functional activation overlaid in color-code.

Figure 2: Two representative slices of the functional ROI from the IR-EPI T1 (a, c) and cortical depth profiles defined in the grey-matter of the ROI (b, d).

Figure 3: Average haemodynamic responses sampled from the same voxels for both conditions at different depths (3 out of 10 depths plotted for illustrative clarity). Error bars indicate ±SEM.

Figure 4: Cortical depth profiles (% signal change as a function of depth) of the positive BOLD amplitude (a), post-stimulus undershoot (b) and ratio of the post-stimulus undershoot to the peak BOLD amplitude (c) for the two conditions. Error bars indicate ±SEM.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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