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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