Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/

How to Build an Embodiment Lab: Achieving Body Representation Illusions in Virtual Reality

Advances in computer graphics algorithms and virtual reality (VR) systems, together with the reduction in cost of associated equipment, have led scientists to consider VR as a useful tool for conducting experimental studies in fields such as neuroscience and experimental psychology. In particular virtual body ownership, where the feeling of ownership over a virtual body is elicited in the participant, has become a useful tool in the study of body representation, in cognitive neuroscience and psychology, concerned with how the brain represents the body. Although VR has been shown to be a useful tool for exploring body ownership illusions, integrating the various technologies necessary for such a system can be daunting. In this paper we discuss the technical infrastructure necessary to achieve virtual embodiment. We describe a basic VR system and how it may be used for this purpose, and then extend this system with the introduction of real-time motion capture, a simple haptics system and the integration of physiological and brain electrical activity recordings