Abstract #5214

Adaptive linear discriminant analysis for complex networks to study extreme prematurity and intrauterine growth restriction effects at school age

Serafeim Loukas^1,2, Djalel Eddine Meskaldji^1,3, Elda Fischi Gomez^1,4, Lana Vasung⁵, Dimitri Van De Ville², and Petra Susan Huppi¹

¹Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland, ²Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³Applied statistics, Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴Signal Processing Laboratory LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁵Department of Pediatrics, Boston Children's Hospital, Boston, MA, United States

In this study, we combined complex network theory with machine learning in order to grasp potential biomarkers of brain development. The data consists of brain connectomes (brain connectivity matrices) of 53 children aged six years old. For each subject, we estimated brain network-based measures at four different levels: connection, node, module and global levels. Then we applied linear discriminant analysis and support vector machine in order to extract features and we compared their performances. We showed that node and module levels are the best choices to extract relevant and interpretable biomarkers in order to distinguish between different brain development conditions.

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