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

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The authors' statistical analysis revealed that FNC between the following network pairs is stronger in Alzheimer's disease 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 Alzheimer's disease for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Moreover, the authors achieved an accuracy of 85% in classifying subjects into Alzheimer's disease and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into Alzheimer's disease, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. SZ, males are more predictable than females.

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