Characterizing brain connectivity using-radial nodes : application to autism classification

Whole brain tractography studies of can generate up to and over half-a-million tracts per brain which form the basis for constructing edges in an extremely large 3D graph. Currently there is no agreed-upon method for constructing the brain anatomical connectivity graphs out of large number of white matter tracts. In this paper, we present an efficient framework for building and analyzing graphs using tractography in a normalized space. We then apply the constructed graphs in a classification setting of autistic vs. typically developing individuals and obtain prediction accuracy of 87%. This suggests that efficiently characterizing anatomical connectivities of the brain may be used to characterize discriminant patterns in different populations.

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