@conference { GWG*:2011, title = {Fiber Stippling: An Illustrative Rendering for Probabilistic Diffusion Tractography}, booktitle = {IEEE BioVis Proceedings}, year = {2011}, pages = {23-30}, abstract = {One of the most promising avenues for compiling anatomical brain connectivity data arises from diffusion magnetic resonance imaging (dMRI). dMRI provides a rather novel family of medical imaging techniques with broad application in clinical as well as basic neuroscience as it offers an estimate of the brain{\textquoteright}s fiber structure completely non-invasively and in vivo. A convenient way to reconstruct neuronal fiber pathways and to characterize anatomical connectivity from this data is the computation of diffusion tractograms. In this paper, we present a novel and effective method for visualizing probabilistic tractograms within their anatomical context. Our illustrative rendering technique, called fiber stippling, is inspired by visualization standards as found in anatomical textbooks. These illustrations typically show slice-based projections of fiber pathways and are typically hand-drawn. Applying the automatized technique to diffusion tractography, we demonstrate its expressiveness and intuitive usability as well as a more objective way to present white-matter structure in the human brain.}, author = {Mathias Goldau and Alexander Wiebel and Nico Stephan Gorbach and Corina Melzer and Mario Hlawitschka and Gerik Scheuermann and Tittgemeyer, Marc} } @article {425, title = {Hierarchical Information-based Clustering for Connectivity-based Cortex Parcellation}, journal = {Frontiers in Neuroinformatics}, volume = {5}, year = {2011}, abstract = {One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (\"the functional fingerprint\") is closely related to its anatomical connections (\"the connectional fingerprint\") and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation, and requires essentially clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several nontrivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as pre-motoric areas and those associated with pre-frontal areas.}, issn = {1662-5196}, doi = {10.3389/fninf.2011.00018}, url = {http://www.frontiersin.org/Journal/Abstract.aspx?s=752\&name=neuroinformatics\&ART_DOI=10.3389/fninf.2011.00018}, author = {Nico Stephan Gorbach and Christoph Sch{\"u}tte and Corina Melzer and Mathias Goldau and Olivia Sujazow and Jenia Jitsev and Tania Douglas and Tittgemeyer, Marc} }