Resting State fMRI Analysis Using PSO Algorithm to Diagnose ADHD Disease

Studying the resting state functional magnetic resonance imaging (rs-fMRI) has been an important issue for diagnosing of the diseases related to brain function, nowadays. This paper proposes a novel method for diagnosis of the Attention-deficit/hyperactivity disorder (ADHD) diseases that includes three main modules. The first module is the feature extraction that extracts the correlation between the time series of the signals of the different brain regions. In the second module the SVM is used as the classifier. The third module is an optimization algorithm, i.e. particle swarm optimization algorithm (PSO). PSO chooses the appropriate features by changing the length of its particles. The results show that the proposed method has high classification accuracy in comparison with others.