Methods and tools for a collaborative environment supporting experiments in neuroscience
Andrea Schenone (DIST, University of Genova, Italy)

Current trends in modern e-Science require the adoption of advanced tools in order to collect, organize, annotate and access data. This is evident in life science and especially in neuroinformatics. Nowadays, more and more, common issues like storage and sharing of large amounts of produced data, as well as availability of dedicated computational resources have to be faced within these contexts.

Another key aspect is multimodal and multiscale data integration. There is need for software platforms able to manage data from different modalities on a variety of scales (organ/body image and signals, tissue/sub-organ images, molecular and genomic data). Moreover, data and resources should be accessible through a unified Web interface, and studies (collection of data about a patient) from different domains (structural and functional neuroradiology, oncology, psychology, and psychiatry) should be available for a given patient.

The XTENS (eXTensible Environment for NeuroScience) platform consists in an highly extensible environment for collaborative work that improve repeatability of experiment and provides data storage and analysis capabilities. The platform is divided in repository and application domains, branched in services with different purpose. The first domain is the central component of the platform and consists in a multimodal repository with a client-server architecture. The second one provides remote tools for image and signal visualization and analysis . The main issue for such a platform is not only to provide an extensible collaborative environment, but also to build a development platform for testing models and algorithms in neuroscience. For these reasons a Grid approach has been considered. because of the added value provided by the possibility of exploiting both computational (also taking advantage of dedicated HPC nodes) and data Grids infrastructures to analyze large datasets of distributed data.

The most important improvements of XTENS in comparison with other solutions are:

as regards functions, it is designed to fully support clinical practice during neuroinformatics multidisciplinary studies, not just managing a Grid based radiology information system, but also providing integrated services and tools;
as regards data, it is designed to handle not only radiological data, and thus its metadata support has to be fully multimodal, enabling the injection in the repository of several different data types according to structured schemas;
as regards extensibility, it has been designed in a very general way in order to be able to fit different requirements coming from a large variety of applications simply through a custom configuration.

According to these requirements, our collaborative environment has been designed to fully support diagnostic and therapeutic paths by using hierarchical structures defining all the different events (visits, acquisitions, data manipulations, data analysis steps) in the experimental sequence. Two different scenarios have been considered as references in the development of the Xtens platform. In the first scenario, the architecture has been deployed to support surgical planning for patients affected by drug resistant epilepsy. In that scenario, a complex analysis for a fully multimodal dataset including different image modalities, EEG and video is required to localize the origin of the ictal discharge and critical brain areas.

The second scenario concerns the identification of biomarkers in the development of Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) through the correlation between quantitative results of morphological and functional neuroimaging studies, and genetic data related to biological markers.