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Characterization of Neurodevelopmental Disease Trajectories using Richly Annotated Sequences of Graphs (RICHGRAPH)

Subject Area Human Cognitive and Systems Neuroscience
Term from 2016 to 2021
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 290781790
 
Final Report Year 2020

Final Report Abstract

To reduce current problems of tractography and connectomics we developed a novel tractography algorithm which establishes a new state of the art for bundlespecific reconstruction of white matter pathways. We evaluated this new algorithm, called TractSeg, in several extensive studies and showed that it has three major advantages over all existing algorithms and toolkits: It runs faster, it is easier to use and it is more accurate. We made this algorithm openly available as an easy to use toolkit so it can be used by researchers all over the world. Our toolkit has already found huge adoption in the research community: We know of at least 40 research labs which are using our toolkit including labs from renowned universities like Cambridge, Oxford, Harvard, MIT, Cornell or Charite. We were able to register over 2700 unique downloads of our toolkit which indicated that a lot more people we do not know of are using the toolkit we developed in this work. Moreover, we were able to contribute to research of catatonia by doing an extensive analysis of its implication for white matter pathways which has not been done before. This can help in understanding how this disease works and eventually in finding potential treatments. Moreover, the extension of this work towards other graph-based connectomic methods including NBS, dynamic network analysis and generative network models and their application in the context of imaging genetics, characterization of pathophysiology and intermediate phenotypes in psychiatric disorders proved successful/promising as evidenced by our publications and will be further extended in future studies.

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