Abstract #0714

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

Irina Sánchez¹, Carles Soriano-Mas^2,3, Antonio Verdejo-García⁴, Narcís Cardoner^3,5, Fernando Fernández-Aranda^2,6, José Manuel Menchón^2,3, 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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