CL-ADDA: Contrastive Learning with Amplitude-Driven Data Augmentation for fMRI-Based Individualized Predictions

Effective representations of human brain function are essential for fMRI-based predictions of individual traits and classifications of neuropsychiatric disorders. Contrastive learning techniques can be favorable choices for representations of human brain function, if it were not for their requirement of large batch sizes. In this study, we proposed a novel method, namely, contrastive learning with amplitude-driven data augmentation (CL-ADDA), for effective representations of human brain function and ultimately fMRI-based individualized predictions. SimSiam, which sets no requirement on large batches, was used in this study to obtain discriminative representations among subjects to facilitate later predictions of individuals' traits. The fMRI data in this study was augmented based on recent neuroscience findings that fMRI frames with high- and low-amplitude are of quite different functional significance. Accordingly, we generated a positive pair by concatenating the fMRI frames with high-amplitude into one augmented sample and the frames with low-amplitude into another sample. The two augmented samples were used as inputs for CL-ADDA, and individualized predictions were made in an end-to-end way. The performance of the proposed CL-ADDA was evaluated with individualized age and IQ predictions based on a public dataset (Cam-CAN). The experimental results demonstrate that the proposed CL-ADDA can substantially improve the prediction performance as compared to the existing methods.