Eelke Visser1,2, Emil Nijhuis1,3, Marcel P. Zwiers1,2
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; 2Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; 3Department of Technical Medicine, University of Twente, Enschede, Netherlands
Finding clusters among the many streamlines produced by tractography algorithms can improve interpretability and can provide a starting point for further analysis. A problem with many clustering methods is their handling of large datasets. We propose to overcome this problem by repeatedly clustering complementary subselections of streamlines. The execution time of the algorithm scales linearly with the number of streamlines, while working memory usage remains constants. The method produces anatomically plausible and coherent clusters in a single subject. When applied to a large group dataset, results are similar and consistent across subjects.