Fig. 1.

Experimental design of the data set. Diffusion-weighted imaging sequences were acquired on two separate testing sessions (Session 1 and Session 2) and with a 12-channel (12ch) and 32-channel (32ch) head-coil. On each of these four resulting diffusion tensor imaging data sets, two independent runs of global tractography were performed (Run 1 and Run 2). During each tracking run, streamlines were reconstructed once with 1 reconstruction repetition (1 Rep) and once with 10 reconstruction repetitions (10 Rep). For each of these tractography data sets, the selection of streamlines was performed with four different variants of selection parameters: endpoint_nofuzzy (end_nofuz; blue shading); endpoint_fuzzy (end_fuz; red shading); visiting_nofuzzy (vis_nofuz; green shading); and visiting_fuzzy (vis_fuz; purple shading), resulting in 64 subject-specific data sets of streamline counts. Statistical analyses aimed at probing the effect of number of reconstruction repetitions, type of head-coil, and streamline selection variant on within-session and between-session test-retest reproducibility, resulting in four analyses: Model 1, within-session reproducibility of reconstruction repetitions×streamline selection variant; Model 2, between-session reproducibility of reconstruction repetitions×streamline selection variant; Model 3, within-session reproducibility of type of head-coil×streamline selection variant; Model 4, between-session reproducibility of type of head-coil×streamline selection variant. Black bars indicate which sub-set of data entered these four statistical models.