Pretraining for Large-Scale Functional Connectome Fingerprinting Supports Generalization and Transfer Learning in Functional Neuroimaging.

Functional MRI (fMRI) currently supports a limited application space stemming from modest dataset sizes, large interindividual variability, and heterogeneity among scanning protocols. These constraints have made it difficult for fMRI researchers to take full advantage of modern deep-learning tools that have revolutionized other fields such as NLP, speech transcription, and image recognition. To help address these issues, we scaled up functional connectome fingerprinting as a neural network pretraining task, drawing inspiration from speaker recognition research, to learn a generalizable representation of brain function. This approach achieves strong performance for neural fingerprinting on a previously unseen scale, across multiple public fMRI datasets (individual recognition from held-out scan sessions, 93% on MPI-Leipzig, 94% on NKI-Rockland, 73% on OASIS-3, and 99% on HCP). Performance is maintained even when evaluation scan duration is truncated to <2 min. We show that this representation can also generalize to support accurate neural fingerprinting for completely new datasets and participants of either sex not used in training. Finally, we demonstrate that the representation learned by the network encodes features related to individual variability that partially transfers to new tasks. These results support the development of scalable transfer-learning approaches for future clinical and cognitive neuroimaging applications.