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

@article{Shehzad2014AMD,
  title={A multivariate distance-based analytic framework for connectome-wide association studies},
  author={Zarrar Shehzad and Clare Kelly and Philip T. Reiss and R. Cameron Craddock and John W. Emerson and Katie L. McMahon and David A. Copland and F. Xavier Castellanos and Michael P. Milham},
  journal={NeuroImage},
  year={2014},
  volume={93 Pt 1},
  pages={74-94}
}
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… CONTINUE READING