A multivariate distance-based analytic framework for connectome-wide association studies

Shehzad, Zarrar, Kelly, Clare, Reiss, Philip T., Craddock, R. Cameron, Emerson, John W., McMahon, Katie, Copland, David A., Castellanos, F. Xavier and Milham, Michael P. (2014) A multivariate distance-based analytic framework for connectome-wide association studies. NeuroImage, 93 P1: 74-94. doi:10.1016/j.neuroimage.2014.02.024

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Author Shehzad, Zarrar
Kelly, Clare
Reiss, Philip T.
Craddock, R. Cameron
Emerson, John W.
McMahon, Katie
Copland, David A.
Castellanos, F. Xavier
Milham, Michael P.
Title A multivariate distance-based analytic framework for connectome-wide association studies
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
1095-9572
Publication date 2014-06-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2014.02.024
Open Access Status File (Author Post-print)
Volume 93
Issue P1
Start page 74
End page 94
Total pages 21
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.
Keyword Brain-behavior relationships
Connectome
Discovery
Functional connectivity
Phenotype
Resting-state
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID BRAINS R01MH094639
1FDN2012-1
Institutional Status UQ

 
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