Meeting Banner
Abstract #0255

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

Ricardo Jorge Maximiano1, Tiago Constantino1,2,3, André Santos-Ribeiro1,4, 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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here