Abstract #0963

A multi-compartment model for pathological connectomes

Sara Bosticardo^1,2, Matteo Battocchio^1,3, Simona Schiavi⁴, Cristina Granziera^2,5,6, and Alessandro Daducci¹

¹Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy, ²Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, University of Basel, Basel, Switzerland, ³Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, QC, Canada, ⁴Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, ⁵Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University of Basel, Basel, Switzerland, ⁶Department of Neurology, MS Center, University Hospital Basel, Basel, Switzerland

Synopsis

Keywords: Diffusion/other diffusion imaging techniques, Brain ConnectivityThe white matter is the complex system of neuronal fibers in the brain. Any disruption to this circuitry may lead to a wide range of neurological diseases and, thus, it is fundamental to be able of detecting pathological conditions at early stages. State-of-the-art methods for studying brain connectivity assume constant properties along the fibers, but this assumption is not valid in pathological conditions that locally affect the tissue, e.g. multiple sclerosis. Here, we present a model for “multi-compartment connectomes” to explicitly consider the presence of focal lesions during the estimation of connectivity and show its effectiveness using realistic numerical simulations.

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