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Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network

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Medical imaging technologies have become an essential component in different areas related to health care. Volume visualization (VV) of medical data is an invaluable support in tasks such as clinical diagnosis, treatment planning, surgery rehearsal, education, and research. Several algorithms and systems have been developed to enable the visualization and interaction with volumetric data. Slice-based visualization methods dominate the field of medical volumes scanning since they allow more detailed analysis of the data. However, an intensive training is usually required for the user to be able to effectively explore the data. In this paper, we present novel a slice-based methodology which objective is to facilitate the exploration of medical volumetric data. The proposed method consist of the use of augmented reality principles to determine the spatial position and orientation of rigid planar objects within a defined space in the real-world which represents the medical volumetric information. The results obtained by a usability study indicate the feasibility of employing this technique for a natural human-computer interaction with the medical data, having the potential of making the process of medical volume exploration more easy and intuitive.

Modern medical image technologies are capable of providing meaningful structural and functional information in the form of volumetric digital data. However current standard systems for the visualization and interaction with such data fail to provide a natural-intuitive way to interact with these data. In this paper, we present our advances towards the development of computational methods for the natural and intuitive visualization of volumetric medical data.