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Abstract #2134

Group-Representative Partitions of Human Brain Structural Networks

Alessandra Griffa1, 2, Richard Betzel3, Kim Q. Do4, Philippe Conus5, Patric Hagmann, 16, Jean-Philippe Thiran1, 6

1Signal Processing Laboratory (LTS5), Ecole Polytechnique Fdrale de Lausanne (EPFL), Lausanne, Vaud, Switzerland; 2Department of Radiology, University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Vaud, Switzerland; 3Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States; 4Center for Psychiatric Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Vaud, Switzerland; 5Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Vaud, Switzerland; 6Department of Radiology, Univerity Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Vaud, Switzerland

Diffusion MRI, tractography and graph analysis allowed to characterize the brain structural architecture as a small-world, hierarchically modular network. The study of the brain modular topology is raising new interest, and could be a key approach for the understanding of neurodevelopmental disorder. In this framework, it is important to individuate representative partitions for whole groups of subjects. In this work we use information theory-derived measures to quantify the inter-subjects variability of structural network modular decomposition, and we propose different approaches (and particularly the consensus clustering algorithm) to individuate a group-representative partition.

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Keywords

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