Analysis Pipeline

Functional analysis for this project was completed using SPM8 and Automatic Analysis (aa) [1]. aa is a framework for MRI analysis that allows for efficient workflow. It allows for thorough tracking and repetition of complicated pipelines without the need for tedious manual analyses.

4.1.1

Raw Data Conversion

Functional data from the scanner is provided in 4D NIfTI files; a single file for each timecourse of functional scans. These 4D files are first split into a single NIfTI file for each 3D volume in the timecourse. This was the format aa modules were written to expect (although since this project began, support for 4D files has been incorporated into aa).

4.1.2

Slice Timing Correction

As our functional data were collected using a 2D EPI sequence, slices are acquired sequentially, distributed throughout our 2 second TR. The BOLD signal for different slices was therefore sampled at slightly different times. SPM’s slice timing correction takes into account the slice acquisition order (Interleaved) and a chosen reference slice (slice 1), interpolating the time-course for each voxel in time to match all voxels across each volume in time.

4.1.3

Image Realignment

To compensate for subject movement during the scan, SPM’s realign function estimates, for each time series volume, the rigid body movement parameters in three translational axes and three rotational axes required to align each volume with the first in the series. These alignment parameters are updates in each volume’s header, but we do not reslice (resample and interpolate the volumes so each voxel in each volume

represents the same point in real space) the EPI volumes as one normally might in this type of analysis. This is because later, in our laminar resampling step, we will sample and interpolate these volumes using structurally derived coordinates. By leaving them in their original space, some volumes will have data coordinates closer to some of our sampling coordinates (by nature of the slight subject movements over time), and we will gain some degree of super-resolution.

The realignment module also creates a mean EPI volume for the first EPI session. This is used in later steps, and is helpful for visualization of the functional volumes.

4.1.4

EPI Undistortion

We evaluated an EPI undistortion module written for automatic analysis (aa) [2]. This module requires both a phase and magnitude image from the field mapping sequence. The phase image was processed by a robust 3D phase unwrapping algorithm [3]. The magnitude image was fed to a brain extraction tool (BET) to create a brain mask. The masked phase data were dilated and smoothed, and the sequence parameters are used to calculate a forward distortion map, which can be used to remove distortions from each EPI volume. This module was not applied in the final pipeline as it was deemed

unnecessary (as explained in 3.1.4).

4.1.5

Image Coregistration

To make associations between the structural and functional images, we must first ensure they are properly aligned with one another. The SPM coregistration function co- registers the structural image to the mean EPI image by optimizing a rigid-body

information. The structural alignment parameters were updated, and the EPI’s weren’t changed.

4.1.6

Laminar Resampling

A custom aa module was written to perform the resampling necessary for the laminar analysis, based around SPM’s resampling function. The module operates on a one-instance-per-session level, running once for each session and for each subject. It is passed, as inputs, the functional images, the structural images, the BrainVoyager generated cortical maps, and a smoothing parameter.

The module begins by parsing the BrainVoyager text output and reformatting the vertices into usable matrices.

The module then loads the structural image and, using SPM’s resampling function and the list of vertices from BrainVoyager, creates a resampled structural volume in “BV space”. This volume is the width and length of the grid size, and as deep as the number of grids generated, each plane representing the flattened surface of one cortical depth grid. This volume is written to file.

The module then loops over the number of EPI volumes. For each, it loads the volume and, using the alignment parameters from this EPI and the alignment parameters from the structural volume, calculates the transformation matrix to convert coordinates from structural space to EPI space. It then uses this transformation matrix to convert the BrainVoyager vertices from structural space to EPI space. Then, looping over the number of BrainVoyager grids, it resamples the EPI volume with the transformed vertices and performs 2D Gaussian smoothing (FWHM=2 voxels) over the sampled surface. It then combines these resampled and smoothed surfaces into one 3D volume in “BV space”, and writes it to file.

The module then creates a resampled mean EPI volume in “BV space” in the same way as previously described for the structural, except using the transformed BrainVoyager vertices in EPI space.

Lastly, using the structural volume, and mean EPI volume, it creates two “vertex maps”, with the vertices from all of the BrainVoyager grids indicated in white atop the standard volume. These are written to file. This allows for easy confirmation that the transformation is occurring correctly and that the coregistration is adequate for properly resampling the intended grey matter of the EPI volumes.

4.1.7

Modeling

A modelling module was used to perform conventional univariate analysis. Each combination of the two conditions (low and high frequency), and two tasks (change detection and imagery) were modeled separately, along with the maintenance period and intertrial interval. A contrast module was used to produce a variety of contrast maps.