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Combining Multiple Behavioral Measures and Multiple Connectomes via Multipath Canonical Correlation Analysis

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11766))

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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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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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Correspondence to Siyuan Gao .

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32247-2

  • Online ISBN: 978-3-030-32248-9

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