Magnetic resonance imaging (MRI) images were acquired using a GE MR750 3T scanner. For each participant, a resting-state functional connectivity (RSFC) matrix was created from 8 minutes of resting-state functional MRI images. These matrices were entered as features into multivariate pattern analysis (MVPA), a machine learning technique, to predict an individual＇s agehis/her cognitive performance.

This technique can accurately predict an individual＇s agecognitive performance based on fast resting-state acquisitionmultivariate analysis. Our current predictor has the second highest accuracy among published MRI-based age predictors around the world.

This technique can be applied in clinical practice for detectionscreening purposes. In the future, this technique could help prevent dementiapsychiatric diseases through client-based intervention programs utilizing individual predictions.