Abstract
Functional connectomes can successfully predict behavioral measures. While the majority of the literature uses a single connectome to predict a single behavioral measure, there is ample evidence that combining different connectomes and behavioral measures reveals more robust neural correlates. Here, we proposed a prediction framework that combines connectomes from multiple sources (e.g. task and resting-state fMRI) and predicts a latent phenotype, derived from a battery of behavioral measures. The framework relies on a novel generalization of canonical correlation analysis with both a closed-form and an iterative solution. We applied the framework to data from the Human Connectome Project (HCP) to predict a latent, general intelligence factor. Prediction accuracy was higher for this latent factor than any single measure of intelligence, showing the advantage of combining multiple connectomes and behavioral measures in a single predictive model.
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References
Dubois, J., Adolphs, R.: Building a science of individual differences from fMRI. Trends Cogn. Sci. 20(6), 425–443 (2016)
Greene, A.S., Gao, S., Scheinost, D., Todd Constable, R.: Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9(1), 2807 (2018)
Gao, S., Greene, A.S., Todd Constable, R., Scheinost, D.: Combining multiple connectomes via canonical correlation analysis improves predictive models. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 349–356. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_40
Dubois, J., Galdi, P., Paul, L.K., Adolphs, R.: A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philos. Trans. R. Soc. B: Biol. Sci. 373(1756), 20170284 (2018)
Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Shen, X., et al.: Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protocols 12(3), 506–518 (2017)
Asendorf, N.A., Informative data fusion: Beyond canonical correlation analysis (2015)
Stewart, G.W.: A krylov-schur algorithm for large eigenproblems. SIAM J. Matrix Anal. Appl. 23(3), 601–614 (2002)
Alexander, D.L.J., Tropsha, A., Winkler, D.A.: Beware of R\(^{2}\): simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model. 55(7), 1316–1322 (2015)
Acknowledgements
Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Gao, S., Shen, X., Todd Constable, R., Scheinost, D. (2019). Combining Multiple Behavioral Measures and Multiple Connectomes via Multipath Canonical Correlation Analysis. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_86
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DOI: https://doi.org/10.1007/978-3-030-32248-9_86
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