Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth using a Mechanically-coupled Reaction-Diffusion Model

Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal evolution was simulated using the Finite-Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter fiber tracks is not a major determinant of tumor shape in the setting of reaction-diffusion based tumor growth models and for most locations across the brain

Dynamic pointing triggers shifts of visual attention in young infants

Pointing, like eye gaze, is a deictic gesture that can be used to orient the attention of another person towards an object or an event. Previous research suggests that infants first begin to follow a pointing gesture between 10 and 13 months of age. We investigated whether sensitivity to pointing could be seen at younger ages employing a technique recently used to show early sensitivity to perceived eye-gaze. Three experiments were conducted with 4.5- and 6.5-month-old infants. Our first goal was to examine whether these infants could show a systematic response to pointing by shifting their visual attention in the direction of a pointing gesture when we eliminated the difficulty of disengaging fixation from a pointing hand. The results from Experiments 1 and 2 suggest that a dynamic, but not a static, pointing gesture triggers shifts of visual attention in infants as young as 4.5 months of age. Our second goal was to clarify whether this response was based on sensitivity to the directional posture of the pointing hand, the motion of the pointing hand, or both. The results from Experiment 3 suggest that the direction of motion is necessary but not sufficient to orient infants’ attention toward a distal target. Infants shifted their attention in the direction of the pointing finger, but only when the hand was moving in the same direction. These results suggest that infants are prepared to orient to the distal referent of a pointing gesture which likely contributes to their learning the communicative function of pointing

From Sensorimotor Knowledge to Abstract Symbolic Representations

AbstractWe present two cognitive robotic experiments looking at different aspects of relations between symbolic representations and sensorimotor knowledge

Contingency allows the robot to spot the tutor and to learn from interaction

Aiming at artificial system learning from a human tutor elicit tutoring behavior, which we implemented on the robotic platform iCub. For the evaluation of the system with users, we considered a contingency module that is developed to elicit tutoring behavior, which we then evaluate by implementing this module on the robotic platform iCub and within an interaction with the users. For the evaluation of our system, we consider not only the participant's behavior but also the system's log-files as dependent variables (as it was suggested in [15] for the improvement of HRI design). We further applied Sequential Analysis as a qualitative method that provides micro-analytical insights into the sequential structure of the interaction. This way, we are able to investigate a closer interrelationship between robot's and tutor's actions and how they respond to each other. We focus on two cases: In the first case, the system module was reacting to the interaction partner appropriately; in the second case, the contingency module failed to spot the tutor. We found that the contingency module enables the robot to engage in an interaction with the human tutor who orients to the robot's conduct as appropriate and responsive. In contrast, when the robot did not engage in an appropriate responsive interaction, the tutor oriented more towards the object while gazing less at the robot

Towards learning by interacting

Abstrac