Coordinate-based coactivation-based parcellation

[tsalo]

Add coactivation-based parcellation algorithm, as described in Bzdok et al. (2013).

References

Bzdok, D., Laird, A. R., Zilles, K., Fox, P. T., & Eickhoff, S. B. (2013). An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Human brain mapping, 34(12), 3247-3266. https://doi.org/10.1002/hbm.22138

[tsalo]

Here's a draft of my understanding of the necessary steps:

1. For each voxel in the mask, identify studies in dataset corresponding to that voxel. Selection criteria can be either based on a distance threshold (e.g., all studies with foci within 5mm of voxel) or based on a minimum number of studies (e.g., the 50 studies reporting foci closest to the voxel).
2. For each voxel, perform MACM (meta-analysis) using the identified studies.
3. Correlate statistical maps between voxel MACMs to generate n_voxels X n_voxels correlation matrix.
4. Convert correlation coefficients to correlation distance (1 - r) values.
5. Perform clustering on correlation distance matrix.

[tsalo]

Will compare Bzdok 2013 approach to Chase 2020.