Abstract #2028

Diagnosis of Schizophrenia, Bipolar, and Attention Deficit Hyperactivity Disorders using Graph-Theory Features of Resting-State fMRI

Faezeh Ghasemi^1,2, Hosna Tavakoli^3,4, Saeed Masoudnia ⁴, Narges Hoseini Tabatabaei⁵, Reza Rostami⁶, and Mohammad-Reza Nazem-Zadeh^4,7

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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