Meeting Banner
Abstract #2131

3D Visualisation of Connectomes Using Streamtubes

Kerstin Pannek1, Roslyn Boyd1, Stephen Rose2

1The University of Queensland, Brisbane, Queensland, Australia; 2The Australian E-Health Research Centre, CSIRO, Brisbane, Australia

Connectomes are typically represented in matrix form. To improve visual assessment of the connectome, we suggest the use a streamtube representation of connections. Representation of connections contained in the connectome using streamtubes allows visualization of confidence in the existence of a connection (streamline number) and local attributes of the connections (e.g. FA). Tube thickness, colour and opacity can be fine-tuned for optimized visualization of cortical or deep structures. The opacity and colour of individual streamtubes can be adjusted to highlight subsets of connections, such as connections of altered connectivity.

Abstract PDF

Keywords

adjusted allow allows altered anatomical anterior arrowhead assessment attributes audience blue brain calculation cerebral children confidence connection connections connectivity contained context cortex cortical count deep deeper described determined developing deviation deviations diffusion directionally easily easy encoded encoding enhanced every example existence expected explore extracted facilitate fashion fine form generated gray green health highlight homogeneous identified identifying improve indicate indicates indicating individual individuals interpolated interpolation interpretation isotropic local located location locations matrix metrics network networks note often opacity opaque optimized outline overall pair palsy pathways plausibility plotted population populations posterior preprocessing previously putting recorded representation represented researchers resolution restricted rose sampled scaled shape significantly spline statistic statistical statistics streamline streamlines structural structures subnetworks subsets suggest tangent target thick thinner tract tracts transparent tube tubes tuned typically unilateral variability variables visual visualization visualize visually white whole