Abstract #0255

Automatic Classification of Brain Connectivity Matrices - a toolbox for supporting neuropsychiatric diagnosis

Ricardo Jorge Maximiano¹, Tiago Constantino^1,2,3, André Santos-Ribeiro^1,4, and Hugo Alexandre Ferreira¹

¹Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal, ²Spitalzentrum Biel, Bienne, Switzerland, ³Lisbon School of Health Technology - ESTeSL, Lisbon, Portugal, ⁴Centre for Neuropsychopharmacology, Imperial College London, London, United Kingdom

In this work, a user-friendly toolbox that aims to classify automatically brain connectivity matrices is described. To test this tool, we used the Parkinson’s Progression Markers Initiative (PPMI) data which includes structural and functional Magnetic Resonance Imaging data of healthy subjects, patients with “scans without evidence for dopaminergic deficit” (SWEDD) and patients diagnosed with Parkinson’s Disease (PD). Using default parameters, this tool was able to achieve a maximum accuracy of 85.4% in classifying the 3 groups of subjects by selecting features that were related to the rostral middle frontal gyrus and splenium, which are in agreement with PD literature.

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