Abstract
We propose a technical solution that enables 3D video-based in-bore movement quantification to be acquired synchronously with the BOLD function magnetic resonance imaging (fMRI) sequences. Our solution relies on in-bore video setup with 2 cameras mounted in a 90 degrees angle that allows tracking movments while acquiring fMRI sequences. In this study we show that using 3D motion quantification of a simple finger opposition paradigm we were able to map two different finger positions to two different BOLD response patterns in a typical block design protocol. The motion information was also used to adjust the block design to the actual motion start and stop improving the time accuracy of the analysis. These results reinforce the role of video based motion quantification in fMRI analysis as an independent regressor that allows new findings not discernable when using traditional block designs.

Abstract
The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a powerful tool to study brain function. In this study, we present a video-EEG-fMRI system where in-bore video, EEG and fMRI are acquired synchronously. To determine the added value of video in a typical EEG-fMRI scenario, we analyzed a simple motor activation paradigm (right index tapping). By using in-bore video, our results show that it is possible to determine different EEG potentials related to motion as well as to clearly distinguish the corresponding blood oxygen level dependent activations.

Abstract
EpiGauss is a method that combines single dipole model with dipole clustering to characterize active brain generators in space and time related to EEG events. EpiGauss was applied to study epileptogenic activity in 4 patients suffering of hypothalamic hamartoma related epilepsy, a rare syndrome with a unique epileptogenic source - the hamartoma lesion - and natural propagation hypothesis - from hamartoma to the surface EEG focus. The results are compared to Rap-MUSIC and Single Moving Dipole methods over the same patients. © Springer-Verlag Berlin Heidelberg 2006.

Abstract
Functional magnetic resonance imaging (fMRI) is an imaging technique that can be used to characterize brain physiological activity, usually presented as 3D volumes in function of time. In the context of our previous work in nonlinear association studies in electroencephalogram (EEG) time series, we were able to identify clinical relevant features useful in clinical diagnosis. The use of a similar approach in fMRI, now adapted for 3D time series, is both appealing and flew. Such time series analysis imposes challenging requirements regarding computational power and medical image management. In this paper we propose a grid architecture framework to support the typical analysis protocol of association analysis applied to fMRI. The system, implemented using the gLite middleware, provides the necessary support to manage brain images and run non-linear fMRI analysis methods.

Abstract
Magnetic Resonance Imaging has become one of the most important tools for anatomic and functional assessment of the complex brain entities. In neurology are present different indicators related with the brain volume measurements, which have a direct impact on several fields such as diagnosis, surgical planning, study of pathologies, disease preventing and tracking the evolution of diseases under (or not) medical treatments, etc. In this work we propose a new automatic brain volumetry estimation method based on the suitable combination of histogram analysis, optimal thresholding, prior geometric information and mathematical morphology techniques. To validate our method we compare our results with three different well established methods in the neuroscience community: Brain Extraction Tool, Brain Suite and Statistical Parametric Mapping. A dataset of 25 patient studies were evaluated concerning to precision, resolution as well as inter-examination features and statistically we demonstrated that our method present competitive results in relation to the others.

Abstract
Increasingly, medical institutions have access to clinical information through computers. The need to process and manage the large amount of data is motivating the recent interest in semantic approaches. Data regarding vaccination records is a common in such systems. Also, being vaccination is a major area of concern in health policies, numerous information is available in the form of clinical guidelines. However, the information in these guidelines may be difficult to access and apply to a specific patient during consultation. The creation of computer interpretable representations allows the development of clinical decision support systems, improving patient care with the reduction of medical errors, increased safety and satisfaction. This paper describes the method used to model and populate a vaccination ontology and the system which recognizes vaccination information on medical texts. The system identifies relevant entities on medical texts and populates an ontology with new instances of classes. An approach to automatically extract information regarding inter-class relationships using association rule mining is suggested.