Visual maps from anatomy

Benson and his colleagues (including Aguirre and Brainard) developed methods to identify the positions of the early visual field maps from anatomical (T1-weighted) MRI data. They can achieve this because the detailed properties of the cortical folds are well-correlated with the positions of these functional maps. The general principle - correlation between structural folds and functional regions - is an emerging theme from work in occipital and temporal cortex (e.g., see the papers by Weiner on face and Witthoft on hV4 in the Grill-Spector lab).

The docker container (also a Flywheel Gear) that we describe here implements Noah Benson's algorithm for identifying V1/V2/V3 from an anatomical T1.

It also returns the assignments of all the visual field maps represented in the Wang et al. () atlas onto the T1-weighted image.

Input

T1 anatomy nifti file. This file is selected as the input from the Flywheel GUI.

Outputs

scanner.template_areas.nii.gz - A nifti file at the same resolution as the T1 anatomical file. The value at each voxel indicates the visual area. For example, the visual area template will consist of values ranging from 0-3 (1: V1, 2: V2, 3: V3, 0: none of the above).

scanner.wang2015_atlas.nii.gz - A nifti file at the same resolution as the T1 anatomical file. The value at each voxel indicates the visual area from the Wang et al. atlas.

A few comments on things we should change.

• The word 'scanner' is not a good idea. The output files are in the same coordinate frame as the T1-weighted input. Those data may or may not be in scanner (image) coordinates. Indeed, we often run the container on T1-weighted data that have been registered into AC-PC space.
• We should probably regularize 'wang2015_atlas' and 'template_areas' to both be, say, 'wang2015_atlas' and benson2014_atlas'. We think that would be helpful.
• The algorithm also returns gifti files. These can be downloaded and viewed with a gifti viewer, such as XXX.

Visualizing

In Flywheel simply select the visualization icon and choose Papaya or Slice Drop. You may want to select the color map (Papaya) to spectrum to separate out the colors better.

References

Benson NC, Butt OH, Datta R, Radoeva PD, Brainard DH, Aguirre GK. The retinotopic organization of striate cortex is well predicted by surface topology. Current Biology. 2012;22(21):2081–2085. doi: 10.1016/j.cub.2012.09.014. pmid:23041195

Correction of Distortion in Flattened Representations of the Cortical Surface Allows Prediction of V1-V3 Functional Organization from Anatomy Noah C. Benson, Omar H. Butt, David H. Brainard, Geoffrey K. Aguirre Published: March 27, 2014http://dx.doi.org/10.1371/journal.pcbi.1003538 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003538