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)
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Biswal, B.B., et al.: Toward discovery science of human brain function. Proc. Natl. Acad. Sci. 107(10), 4734–4739 (2010)
Kelly, C., Biswal, B.B., Cameron Craddock, R., Xavier Castellanos, F., Milham, M.P.: Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn. Sci. 16(3), 181–188 (2012)
Khosla, M., Jamison, K., Ngo, G.H., Kuceyeski, A., Sabuncu, M.R.: Machine learning in resting-state fMRI analysis. Magn. Reson. Imaging 64, 101–121 (2019)
Dosenbach, N.U.F., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358–1361 (2010)
Finn, E.S., et al.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18(11), 1664 (2015)
Tavor, I., Jones, O.P., Mars, R.B., Smith, S.M., Behrens, T.E., Jbabdi, S.: Task-free MRI predicts individual differences in brain activity during task performance. Science 352(6282), 216–220 (2016)
Cole, M.W., Ito, T., Bassett, D.S., Schultz, D.H.: Activity flow over resting-state networks shapes cognitive task activations. Nat. Neurosci. 19(12), 1718 (2016)
Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013)
Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: Ensemble learning with 3d convolutional neural networks for functional connectome-based prediction. Neuroimage 199, 651–662 (2019)
Chiyu, M.J., Huang, J., Kashinath, K., Prabhat, P.M., Niessner, M.: Spherical CNNs on unstructured grids. In: International Conference on Learning Representations (2019)
Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J., Coalson, T.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22(10), 2241–2262 (2012)
Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272–284 (1999)
Wu, J., et al.: Accurate nonlinear mapping between MNI volumetric and freesurfer surface coordinate systems. Hum. Brain Mapp. 39(9), 3793–3808 (2018)
Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017)
Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014–2023 (2016)
Baumgardner, J.R., Frederickson, P.O.: Icosahedral discretization of the two-sphere. SIAM J. Numer. Anal. 22(6), 1107–1115 (1985)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015)
Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Smith, S.M., et al.: Resting-state fMRI in the human connectome project. Neuroimage 80, 144–168 (2013)
Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)
Amico, E., Goñi, J.: The quest for identifiability in human functional connectomes. Sci. Rep. 8(1), 1–14 (2018)
Yeo, B.T.T., et al.: Functional specialization and flexibility in human association cortex. Cereb. Cortex 25(10), 3654–3672 (2015)
Ngo, G.H., et al.: Beyond consensus: embracing heterogeneity in curated neuroimaging meta-analysis. NeuroImage 200, 142–158 (2019)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-59728-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59727-6
Online ISBN: 978-3-030-59728-3
eBook Packages: Computer ScienceComputer Science (R0)
