Abstract #2028
Diagnosis of Schizophrenia, Bipolar, and Attention Deficit Hyperactivity Disorders using Graph-Theory Features of Resting-State fMRI
Faezeh Ghasemi1,2, Hosna Tavakoli3,4, Saeed Masoudnia 4, Narges Hoseini Tabatabaei5, Reza Rostami6, and Mohammad-Reza Nazem-Zadeh4,7
1Medical Physics and Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Islamic Republic of), 2Research Center for Biomedical Technologies and Robotics, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 3cognitive neuroscience, Institute for Cognitive Science Studies, Tehran, Iran (Islamic Republic of), 4Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 5Medical School, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 6Department of Psychology, University of Tehran, Tehran, Iran (Islamic Republic of), 7Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)
Synopsis
Accurate and specific diagnosis of mental disorders is very important for effective, customized, and personalized treatments, which would be made more possible based on individual neuroimaging data. In this study, we classified schizophrenia (SZ), bipolar disorder (BD), and attention deficit hyperactivity disorder (ADHD) cohorts, vs. healthy control (HC) cohort, using extracted graph features from Resting-State fMRI (rs-fMRI) images. The graph-based connectivity features of limbic, auditory, visual, and default mode networks were identified as the most separating features for the SZ, BD and, ADHD groups from the HC group among brain networks.
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