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Neuroimage. 2019 Aug 15;197:212-223. doi: 10.1016/j.neuroimage.2019.04.060. Epub 2019 Apr 27.

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors.

Author information

1
Department of Psychology, Yale University, USA. Electronic address: kwangsun.yoo@yale.edu.
2
Department of Psychology, Yale University, USA.
3
Interdepartmental Neuroscience Program, Yale University, USA.
4
Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA.
5
Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA.
6
Department of Psychology, Yale University, USA; Interdepartmental Neuroscience Program, Yale University, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA.

Abstract

Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.

KEYWORDS:

Connectome-based predictive modeling; Distance correlation; Fluid intelligence; Functional connectivity; Functional connectome fingerprinting; Multivariate dependency; Test-retest reliability

PMID:
31039408
PMCID:
PMC6591084
[Available on 2020-08-15]
DOI:
10.1016/j.neuroimage.2019.04.060

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