Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Abstract Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.