This paper is under review which means review has begun. You can track the progress of this review on GitHub over here »

Network-level analysis of various features, esp. if it can be individualized for a single-subject, is proving to be quite a valuable tool in many applications. This package extracts single-subject (individualized, or intrinsic) networks from node-wise data by computing the edge weights based on histogram distance between the distributions of values within each node. Individual nodes could be an ROI or a patch or a cube, or any other unit of relevance in your application. This is a great way to take advantage of the full distribution of values available within each node, relative to the simpler use of averages (or another summary statistic).

Archive DOI: pending