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Neuroimage. 2014 Jun;93 Pt 1:74-94. doi: 10.1016/j.neuroimage.2014.02.024. Epub 2014 Feb 28.

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

Author information

  • 1Department of Psychology, Yale University, New Haven, CT, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA. Electronic address: zarrar.shehzad@gmail.com.
  • 2Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, Department of Child and Adolescent Psychiatry, New York University, New York, NY, USA.
  • 3Division of Biostatistics, Department of Child and Adolescent Psychiatry, New York University, New York, NY, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
  • 4Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
  • 5Department of Statistics, Yale University, New Haven, CT, USA.
  • 6Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
  • 7Centre for Clinical Research and School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia.
  • 8Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, Department of Child and Adolescent Psychiatry, New York University, New York, NY, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
  • 9Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA. Electronic address: michael.milham@childmind.org.

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.

Copyright © 2014 Elsevier Inc. All rights reserved.

KEYWORDS:

Brain–behavior relationships; Connectome; Discovery; Functional connectivity; Phenotype; Resting-state

PMID:
24583255
[PubMed - indexed for MEDLINE]
PMCID:
PMC4138049
[Available on 2015-06-01]
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