A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity - Hassanzadeh, Reihaneh; Abrol, Anees; Pearlson, Godfrey; Turner, Jessica A.; Calhoun, Vince D. (PloS one, 2024)
Keywords: Abnormalities; Advertising executives; Age; Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - physiopathology; Alzheimer's disease; Biology and Life Sciences; Biomarkers; Brain - diagnostic imaging; Brain - physiopathology; Case-Control Studies; Cerebellum; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - physiopathology; Comparative analysis; Computer and Information Sciences; Confounding (Statistics); Connectome - methods; Data mining; Datasets; Deep learning; Dementia; Development and progression; Diagnosis; Diagnostic imaging; Diseases; Disorders; Female; Functional magnetic resonance imaging; Gender; Genetic factors; Humans; Identification and classification; Learning algorithms; Machine Learning; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical research; Medicine and Health Sciences; Medicine, Experimental; Mental disorders; Middle Aged; Motor task performance; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neural networks; Neurodegenerative diseases; Physiological aspects; Regression analysis; Research and Analysis Methods; Rest - physiology; Review boards; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; Sensorimotor integration; Sensory integration; Social Sciences; Statistical analysis; Support vector machines; Variables; Visual observation; United States