Biblio

The availability of commercial fully immersive virtual reality systems allows the proposal and development of new applications that offer novel ways to visualize and interact with multidimensional neuroimaging data. We propose a system for the visualization and interaction with Magnetic Resonance Imaging (MRI) scans in a fully immersive learning environment in virtual reality. The system extracts the different slices from a DICOM file and presents the slices in a 3D environment where the user can display and rotate the MRI scan, and select the clipping plane in all the possible orientations. The 3D environment includes two parts: 1) a cube that displays the MRI scan in 3D and 2) three panels that include the axial, sagittal, and coronal views, where it is possible to directly access a desired slice. In addition, the environment includes a representation of the brain where it is possible to access and browse directly through the slices with the controller. This application can be used both for educational purposes as an immersive learning tool, and by neuroscience researchers as a more convenient way to browse through an MRI scan to better analyze 3D data.

The purpose of this article is to present an algorithm which maximizes prediction accuracy under a linear regression model while preserving data privacy. This approach anonymizes the data such that the privacy of the original features is fully guaranteed, and the deterioration in predictive accuracy using the anonymized data is minimal. The proposed algorithm employs two stages: the first stage uses a probabilistic latent factor approach to anonymize the original features into a collection of lower dimensional latent factors, while the second stage uses an optimization algorithm to tune the anonymized data further, in a way which ensures a minimal loss in prediction accuracy under the predictive approach specified by the user. We demonstrate the advantages of our approach via numerical studies and apply our method to high-dimensional neuroimaging data where the goal is to predict the behavior of adolescents and teenagers based on functional magnetic resonance imaging (fMRI) measurements.