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The NITRC 1000 Functional Connectomes Project And The Human Connectome Project

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The NITRC 1000 Functional Connectomes Project and the Human Connectome Project have recently made large amounts of human connectome data available for public research in the form of connectivity matrices. These data have been extensively analyzed with respect to their network topology, which has revealed a number of complex network properties, including small-world and scale-free characteristics and high modularity and clustering. However, these studies have been conducted almost exclusively on binarized networks generated by thresholding to remove negative edge weights associated with negative temporal correlations in fMRI data. The present study proposes a comparison of the characteristics of networks generated exclusively from either …show more content…

In particular, this data lends itself well to the tools and techniques for network analysis, as the human connectome is itself a complex network II. RELATED WORK In 2009, the US National Institute of Health launched the Human Connectome Project (HCP). The goal of the project is to build a network map of the neural connections of the human brain in order to investigate the normal functionality of the brain, as well as to examine the anatomical anomalies that occur in diseases such as schizophrenia and autism. Data from the early stages of the project were recently made publicly available. The dataset includes group-averaged data which have been extensively analyzed, as well as largely unprocessed data from individual subjects. The HCP experimental group consists of twins with non-twin siblings who are 22-35 years in age [1]. The inclusion of twins and non-twin siblings is intended to provide discrete levels of similarity between individuals within the study group. Monozygotic twins should exhibit the greatest degree of similarity with respect to genetic and environmental factors, whereas dizygotic twins and non-twin siblings are exposed to similar environmental factors but have diminished

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