Streamline Count Research Articles

Structural and Resting State Functional Connectivity of the Subthalamic Nucleus: Identification of Motor STN Parts and the Hyperdirect Pathway

Deep brain stimulation (DBS) for Parkinson’s disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN’s connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called “hyperdirect” pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.

Equi-flux streamline seeding for three-dimensional vector fields

Appropriate streamline placement is important in the visualisation of 3D vector fields and many streamline seeding algorithms exist that produce evenly spaced or feature-based streamlines. However, in many areas of applied science, such as 3D coil design for magnetic resonance imaging hardware or in meteorology, interest lies with obtaining streamlines that represent equal flux across the entire 3D vector field. In this article, an equi-flux streamline seeding method is presented in the context of a 3D volumetric current density. Streamline density is controlled by a density map that evolves with the placement of each streamline. A novel Gaussian filter is described as part of the process, which has amplitude equal to the updated density map maximum and variance related to the inverse cube root of this amplitude such that each streamline represents approximately fixed flux or integrated current. The method is verified by applying it to simple 1D, 2D and 3D examples for which exact results can be obtained for comparison. In addition, the method allows streamline count to be varied in a straightforward manner, and can be applied to complicated 3D vector fields for which no exact result can be obtained.