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Abstract #0714

Analysis of feature importance in deep neural networks in psychiatric disorders using magnetic resonance imaging

Irina Sánchez1, Carles Soriano-Mas2,3, Antonio Verdejo-García4, Narcís Cardoner3,5, Fernando Fernández-Aranda2,6, José Manuel Menchón2,3, Paulo Rodrigues1, Vesna Prčkovska1, and Matt Rowe1

1QMENTA Inc, Barcelona, Spain, 2Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain, 3CIBERSAM, Madrid, Spain, 4School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Melbourne, Australia, 5Department of Mental Health, Corporació Sanitaria Parc Taulí, Sabadell, Spain, 6CIBEROBN, Madrid, Spain

Current methods to diagnose psychiatric disorders are based on possible manifestations of the disease and behavioral criteria. Symptoms and manifestations overlap between disorders rendering the diagnostic process extremely difficult. Neuroimaging provides information about the structure and function of the brain. This information, combined with DL techniques, has a huge potential shortening and improving the diagnostic process. In this work, we train a neural network to differentiate between healthy subjects and patients of six different mental illnesses with accuracy of 64%. Finally, we analysed the network weights of the model to identify the most important regions of the brain for classification.

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