Network-level analysis of various features, esp. if it can be individualized for a single-subject is proving to be a valuable tool in many applications. Ability to extract the networks for a given subject individually on its own, would allow for feature extraction conducive to predictive modeling, unlike group-wise networks which can only be used for descriptive and explanatory purposes. 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) to compare two nodes/ROIs within a given subject.

{"references": ["Raamana, P.R. and Strother, S.C., 2016, June. Novel histogram-weighted cortical thickness networks and a multi-scale analysis of predictive power in Alzheimer's disease. In Pattern Recognition in Neuroimaging (PRNI), 2016 International Workshop on (pp. 1-4). IEEE."]}

Software is beta, and needs to be tested in the wild by the community.