Functional image preprocessing

This section describes processing steps that were common to both the axial and sagittal protocols. For a schematic overview of the analysis pipeline, see Fig. S1.

First, DICOM images were converted to NIFTI using the ISISCONV converter (Fig. S1a). Motion correction was performed using SPM software (Statistical Parametric Mapping; SPM12) ⁵² and was done separately for nulled and not-nulled frames (Fig. S1b). A 4^th order spline function was used for spatial interpolation. Motion correction and registration across runs was done simultaneously. This minimized the effect of spatial resolution loss to one single resampling step ⁵³. Motion traces of nulled and not-nulled were visually inspected to ensure good overlap for the two contrasts (Fig. S1b).

Following these steps, frames were sorted into their respective contrast: not-nulled (BOLD) or nulled (VASO; Fig. S1c). Note that BOLD and VASO contrasts are kept separate from this point forward, and all analyses below were performed for each contrast individually.

Next, runs of the same contrast type were averaged (Fig. S1d), and within these average runs, trials of the same type were averaged (Fig. S1e). Because all runs have the same trial order, and all trials have the same epoch structure and timing, runs and trials can be averaged without deconvolving the hemodynamic response. This is an important feature of our experimental design, since hemodynamic responses differ across cortical depths ⁴⁶. Following trial averaging, VASO data were BOLD corrected using the dynamic division method (Fig. S1e). Thus, for each contrast (BOLD and VASO), for the axial protocol, each subject had four average trials: alphabetize, remember, action, and non-action. For the sagittal protocol, each subject had two average trials: alphabetize and remember.

In a parallel analysis, a region of interest (ROI) in the left dlPFC was defined for each subject (Fig. S1f, right). The approximate location of the ROI was taken from the 6-minute functional localizer (Fig. S1f, left) following GLM analysis with FSL FEAT (Version 5.98) ⁵⁴ . For the complete FEAT design protocol, please see (https://github.com/layerfMRI/repository/tree/master/DLPFC_Emily/Featdesign). The ROI was manually selected and drawn for every individual subject (see Fig. S3 for drawn ROIs in every subject). Rather than only acquire an additional T1-weighted image for anatomical reference, we used the functional EPI data itself to estimate the T1 contrast, and used this for manual delineation of two layers within this ROI, one superficial and one deep (Fig. S1f, right). The advantage of this approach is that it avoids the distortion correction and resampling steps necessary for registering EPI images to a separately acquired T1 image, preserving spatial specificity. See sections below for additional information about this layer-drawing procedure for both the axial and sagittal protocols.