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CCA

  • Canonical Correlates Analysis (CCA) is a multivariate analysis technique for maximizing the correlations between orthagonalized sets of independent and dependent variates
  • The codes provided here are for the CCA as performed in Perry et al., (2017, in review), where
  • And are modified from Smith et al's., (2015) HCP investigation

edgebund

*Adapted from Perry et al., (2017)

Codes included within repository:

  • Normalization and decomposition of functional networks
  • CCA
  • Basic visualization output
  • Parcellation templates used in functional network construction

Required dependencies

Getting started

1. Data required:

  • Functional network matrices of all subjects (i.e. dependent variates in CCA) (connectivity matrices)
  • Design Matrix of non-imaging measures (i.e. independent variates in CCA) (DM)
  • Motion parameters (i.e. framewise displacement) of functional images (motionFD)
  • Centroids of parcellation template employed in functional network construction (COG)

2. Performing the CCA

  • Within a MatLab terminal run:
    • [CCAout] = cca_functional(connectivitymatrices, DM, motionFD, COG)

3. Extracting CCA results

  • The resultant data will be stored within the Matlab structure CCAout
  • Which stores important information in the fields of CCAout, such as:
    • grotU: Individual subjects weights for non-imaging measures captured by each CCA mode
    • grotV: Individual subjects weights for functional connectivity patterns captured by each CCA mode
    • grotR: Correlation between the orthagonalized non-imaging and connectivity patterns
    • conload: Loadings of non-imaging measures onto each modes connectivity patterns (i.e. grotV)
    • grotstats: Parametric statistical output

4. Visualising the CCA output

  • Extracted are the connectivity edges and nodes that are most strongly expressed (i.e. top 250 connections) by the first CCA mode
    • For both positive and negative associations with CCA mode
    • Users may want to modify their code, depending on their number of significant CCA modes
  • Data is extracted in the .nodes and .edge format required for BrainNet Viewer:
  • For example, for the top positive associations:
    • Nodes : CCA_nodes250topposcons_mode1.nodes
    • Edges : CCA_250topposcons_mode1.edge

bnvhelpgithub

hbm-mode1-poscons-axview
For any questions and more advanced codes/data please contact Alistair Perry (QIMR Berghofer) (alistairgperry at gmail.com)