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Abstract #3430

Brain network modular fingerprint of premature born children

Elda Fischi-Gomez 1,2 , Alessandra Griffa 1,3 , Emma Muoz-Moreno 4 , Lana Vasung 2 , Cristina Borradori-Tolsa 2 , Franois Lazeyras 5 , Jean-Philippe Thiran 1,3 , and Petra Susan Hppi 2

1 Signal Processing Laboratory 5, cole Polytechnique Fdrale de Lausanne (EPFL), Lausanne, (VD), Switzerland, 2 Division of Development and Growth. Department of Pediatrics, University of Geneva, Geneva, (GE), Switzerland, 3 Department of Radiology, University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, (VD), Switzerland, 4 Fetal and Perinatal Medicine Research Group, Institut d'Investigacions Biomediques August Pi i Sunyer, IDIBAPS, Barcelona, (B), Spain, 5 Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, (GE), Switzerland

In this work we characterize the modular topology of structural brain networks of children born extreme premature and/or with additional growth restrictions, and we quantify the similarity of their brain community structure using information theory derived metrics. In order to characterize the communities fingerprint in such cases, we used the consensus-clustering algorithm as a means to estimate a smooth representative group partition for each cohort.

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