It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement Learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents.

High-frequency noise is present in several modalities of medical images. It originates from the acquisition process, scanner configurations, the scanned body, or to other external factors. This way, prospective filters are an important tool to improve image quality. In this paper, we propose a non-local weighted operational anisotropic diffusion filter and evaluate its effect on magnetic resonance images and on kV/CBCT radiotherapy images. We also provide a detailed analysis of non-local parameter settings. Results show that the new filter enhances previous local implementations and has potential application in radiotherapy treatments.

Brain connectivity analysis is a critical component of ongoing human connectome projects to decipher the healthy and diseased brain. Recent work has highlighted the power-law (multi-time scale) properties of brain signals; however, there remains a lack of methods to specifically quantify short- vs. long- time range brain connections. In this paper, using detrended partial cross-correlation analysis (DPCCA), we propose a novel functional connectivity measure to delineate brain interactions at multiple time scales, while controlling for covariates. We use a rich simulated fMRI dataset to validate the proposed method, and apply it to a real fMRI dataset in a cocaine dependence prediction task. We show that, compared to extant methods, the DPCCA-based approach not only distinguishes short and long memory functional connectivity but also improves feature extraction and enhances classification accuracy. Together, this paper contributes broadly to new computational methodologies in understanding neural information processing.

Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based on fMRI classification. We improve the traditional methodology of the literature called multi-voxel pattern analysis (MVPA), which is used for feature extraction and classification. In addition, we propose new features that use specific functional connectivity measures. An extensive evaluation was conducted comparing our methodology with MVPA, as well as, several learning methods with distinct feature sets. We could identify the neural patterns that lead to improve classification accuracies and evaluate the advantages of employing an information fusion approach through an ensemble of classifiers. Experimental results show an improvement of final accuracy over the state-of-the-art methods.

In this work, we propose a novel approach for improving center of gravity (COG) estimation of the brain in magnetic resonance (MR) images, that uses 3D Haar-like features. We hypothesize that better pose estimation will advance the posterior skull-stripping results of the popular Brain Extraction Tool (BET). The proposed methodology is quantitatively validated in 20 T1- and T2-weighted images of the brain. As compared to the native BET COG algorithm, our method produced COGs 87.3% closer to the expected coordinates for the T1-weighted dataset, and importantly this resulted in an average enhancement of 15.4% to the accuracy of skull-stripping masks. As far the authors know, we are first in analyzing the impact of COG estimation over skull-stripping of MR images.

Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.