Analysis of Functional NeuroImages

One of AFNI's initial offerings improved the approach to transforming scans of individual brains onto a shared standardized space. Since each person's individual brain is unique in size and shape, comparing across a number of brains requires warping (rotating, scaling, etc.) individual brains into a standard shape. Unfortunately, functional MRI data at the time of AFNI's development was too low resolution for effective transformations. Instead, researchers use the higher resolution anatomical brain scans, often acquired at the beginning of an imaging session.

AFNI allows researchers to overlay a functional image to the anatomical, providing tools for aligning the two into the same space. Processes engaged to warp an individual anatomical scan to standard space are then applied also to the functional scan, improving the transformation process.[6]

Another feature available in AFNI is the SUMA tool, developed by Ziad Saad. This tool allows users to project the 2D data onto a 3D cortical surface map. In this way researchers can view activation patterns while more easily taking into account physical cortical features like gyri.[7]

"afni_proc.py" is a pre-made script that will run fMRI data from a single subject through a series of pre-processing steps, starting with the raw data. The default settings will perform the following pre-processing steps and finish with a basic regression analysis:[8]

1. Slice timing:[9] Each 3D brain image is composed of multiple 2D images, "slices". Although acquired at approximately the same time, up to several seconds could separate the first slice acquired from the last. Through interpolation, the slices are aligned to the same time point. Generally, any introduced noise from interpolation errors is thought to be outweighed by improvements in signal.[10]
2. Motion correction: Head movements can create sources of error in the analysis. Each 3D acquisition in a scan is collected on a 3D grid, with each small cube of grid space, "voxel", representing a single image intensity value. Ideally, voxels will always represent the same part of the brain in each acquisition, rather than vary from one 3D image to the next. To correct small motion artifacts, AFNI's motion correction tool employs a linear least squares algorithm that attempts to align each 3D image acquired to the first image acquired in the scan.[11]
3. Smoothing: To account for random noise in the image, a smoothing kernel is applied. While smoothing can increase the signal-to-noise ratio of the image, it reduces image resolution.[12][13]
4. Mask: Removes any non-brain areas, such as skull, from the fMRI image.
5. Scale: Scale each voxel so that changes in intensity represent percentage of signal change over the course of the scan. The default sets the mean of each voxel equal to 100.