A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity

• Published in: PloS one Vol. 19; no. 5; p. e0293053
• Main Authors: Hassanzadeh, Reihaneh, Abrol, Anees, Pearlson, Godfrey, Turner, Jessica A., Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.05.2024; Public Library of Science (PLoS)
• Subjects: 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0293053

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer’s disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient’s age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders’ development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.