Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

• Published in: Microscopy research and technique Vol. 85; no. 6; pp. 2083 - 2094
• Main Authors: Saba, Tanzila, Rehman, Amjad, Shahzad, Mirza Naveed, Latif, Rabia, Bahaj, Saeed Ali, Alyami, Jaber
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Brain - pathology; Brain cancer; Brain mapping; Brain Mapping - methods; brain tumor; functional connectivity; Functional magnetic resonance imaging; healthcare; human & diseases; Humans; Kernel functions; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neuroimaging; Patients; Polynomials; Post traumatic stress disorder; Psychological stress; Radial basis function; resting‐state functional magnetic resonance imaging; Stress Disorders, Post-Traumatic - diagnostic imaging; Stress Disorders, Post-Traumatic - pathology; Support Vector Machine; Support vector machines; Variance analysis; resting-state functional magnetic resonance imaging; human & diseases; brain tumor; post-traumatic stress disorder; functional connectivity; healthcare

Early detection of post‐traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess int...