Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network.

Publication Type	Academic Article
Authors	Ngo G, Khosla M, Jamison K, Kuceyeski A, Sabuncu M
Journal	Neuroimage
Volume	248
Pagination	118849
Date Published	12/26/2021
ISSN	1095-9572
Keywords	Brain Mapping, Connectome, Emotions, Magnetic Resonance Imaging, Neural Networks, Computer
Abstract	Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.
DOI	10.1016/j.neuroimage.2021.118849
PubMed ID	34965456
PubMed Central ID	PMC10155599