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Abstract #4302

False-positive potential of tractography-derived connections improves network reconstruction for disease spreading models

Anna Schroder1, Neil P. Oxtoby1, Marco Palombo1, Simona Schiavi2, Alessandro Daducci2, and Daniel C. Alexander1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Department of Computer Science, University of Verona, Verona, Italy

Network spreading models of disease propagation utilise functional or anatomical connectivity to predict regional pathology in-vivo. Models based on anatomical connectivity are likely to be disrupted by the inevitable presence of errors from tractography. To mitigate this problem, we propose a method to evaluate the potential of each tractography-derived connection to be false-positive. By incorporating this false-positive potential into a network spreading model, we are able to predict a pattern of tau accumulation more closely aligned with the pathology observed in a cohort of Alzheimer’s Disease patients.

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