Visual vector inversion during memory antisaccades--a TMS study

In the memory antisaccade task, subjects are instructed to look at an imaginary point precisely at the opposite side of a peripheral visual stimulus presented short time previously. To perform this task accurately, the visual vector, i.e., the distance between a central fixation point and the peripheral stimulus, must be inverted from one visual hemifield to the other. Recent data in humans and monkeys suggest that the posterior parietal cortex (PPC) might be critically involved in the process of visual vector inversion. In the present study, we investigated the temporal dynamics of visual vector inversion in the human PPC by using transcranial magnetic stimulation (TMS). In six healthy subjects, single pulse TMS was applied over the right PPC during a memory antisaccade task at four different time intervals: 100 ms, 217 ms, 333 ms, or 450 ms after target onset. The results indicate that for rightward antisaccades, i.e., when the visual target was presented in the left screen-half, TMS had a significant effect on saccade gain when applied 100 ms after target onset, but not later. For leftward antisaccades, i.e., when the visual target was presented in the right screen-half, a significant TMS effect on gain was found for the 333 ms and 450 ms conditions, but not for the earlier ones. This double dissociation of saccade gain suggests that the initial process of vector inversion can be disrupted 100 ms after onset of the visual stimulus and that TMS interfered with motor saccade planning based on an inversed vector signal at 333 ms and 450 ms after stimulus onset.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).