Neurobiological processes during the Cambridge gambling task.

Although vast research has been conducted concerning gambling behavior this is the first study combining behavioral and functional magnetic resonance imaging (fMRI) data while using the Cambridge Gambling Task (CGT). We tested 20 healthy right-handed men and chose an event-related design to allow for precise temporal separation of gambling stages. In the color decision stage participants had to guess whether a yellow token was hidden behind red or blue boxes presented in varying color ratios, then stake wagers during the bet decision stage. In the final stage the outcome (won or lost) was presented. Analyzing the blood-oxygenation level-dependent (BOLD) contrasts in the decision stages we found increases of activation in brain areas involved in decision making, working memory and learning, when participants bet on the majority choice. During the outcome stage increased brain activation was found in parts of the reward system and areas involved in decision making and impulse control, when winning. When losing, activation increased in areas involved in risk aversion and management of uncertainties. When participants lost unexpectedly (i.e. lost although they bet on the majority), increased activation was found in the insula, compared to winning expectedly. The more unexpectedly participants won the higher the increase of brain activation in parts of the reward system and areas involved in executive functions. Our study gives an extensive overview of brain areas involved in different stages of gambling and during various outcomes, with corresponding behavioral data (e.g. speed and quality of decision making) illustrating underlying tendencies.

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.