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Network-level analysis of various features, especially if it can be individualized for a singlesubject, is proving to be a valuable tool in many applications(Raamana and Strother 2017; Evans 2013; Palaniyappan et al. 2015; Tijms et al. 2012; Xu et al. 2017; Raamana et al. 2015; Lerch et al. 2006; He, Chen, and Evans 2007). 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. Input features could be from any modality (fMRI, MEG, EEG, eye-tracking), so long as they can be turned into numbers. 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). A rough scheme of computation is shown in Figure 1.

Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience