Re: Groupwise For Mac Os X Download
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In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.
The imreggroupwise function uses the total variation method to perform deformable registration of slices in a series of grayscale images. You can use this function to reduce sliding motion between slices in a series of medical images, such as a timeseries. Registering all slices of the series to one of the slices using deformable registration in a for loop can introduce bias towards the artifacts of one slice in all the slices. In contrast, the imreggroupwise function reduces the overall range of sliding motion across all slices.
[dispField,reg] = imreggroupwise(moving) transforms the slices in the image series moving so that they are groupwise registered, and returns the displacement field dispField and the registered image series reg.
Visualize the output of the groupwise registration. The mean image of the moving image series shows that the slices are misaligned. In contrast, the mean image of the registered image series indicates alignment across slices.
Image series to be registered, specified as a 3-D numeric array. The slices in the image series must capture the same anatomical slice of the body. For example, the image series can be a collection of the same slice imaged at different times.
Progress information output, specified as a numeric or logical 1 (true) or 0 (false). Specify DisplayProgress as true to display information such as the number of iterations, normalized root mean square error (RMSE), function local minima, step size, and closeness to optimal solution.
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