Abstract
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many machine learning tasks, such as predicting subject-specific behavioral traits or task-evoked activity. In this work, we propose a surface-based convolutional neural network (BrainSurfCNN) model to predict individual task contrasts from their resting-state fingerprints. We introduce a reconstructive-contrastive loss that enforces subject-specificity of model outputs while minimizing predictive error. The proposed approach significantly improves the accuracy of predicted contrasts over a well-established baseline. Furthermore, BrainSurfCNN’s prediction also surpasses test-retest benchmark in a subject identification task. (Source code is available at https://github.com/ngohgia/brain-surf-cnn)
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Acknowledgements
This work was supported by NIH grants R01LM012719 (MS), R01AG053949 (MS), R21NS10463401 (AK), R01NS10264601A1 (AK), the NSF NeuroNex grant 1707312 (MS), the NSF CAREER 1748377 grant (MS), Jacobs Scholar Fellowship (GN), and Anna-Maria and Stephen Kellen Foundation Junior Faculty Fellowship (AK). The authors would like to thank the reviewers for their helpful comments, Ms. Hao-Ting Wang for her pointers on preprocessing HCP data and Mr. Minh Nguyen for his comments on the early drafts.
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Ngo, G.H., Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R. (2020). From Connectomic to Task-Evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-State Functional Connectivity. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_7
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