We present myPLS toolbox, a Matlab-based analysis pipeline that uses Partial Least Squares (PLS).
PLS is a data-driven multivariate statistical technique that aims to maximize the covariance between the two data matrices by deriving latent components that are weighted linear combinations of the original variables (McIntosh et al., 2004).
myPLS deploys Behavior PLS, which aims to optimally relate neuroimaging to behavioral data. myPLS can be used with different types of neuroimaging data formats : 1D (e.g., region-wise graph metrics), 2D (e.g., correlation matrix) or 3D (e.g., voxelwise cortical volume). Behavioral data is usually 1D (e.g., clinical scores).
A flowchart of the PLS analysis and statistical tests for interpreting the PLS results can be found in docu/PLS_analysis_interpretation.pdf
Requirements:
• SPM for saving results onto volume (https://www.fil.ion.ucl.ac.uk/spm/)
• Slice overlay (slover) to display slice maps (http://imaging.mrc-cbu.cam.ac.uk/imaging/DisplaySlices)
• Function ploterr (Copyright (c) 2008, Felix Zoergiebel) for bar plots (https://www.mathworks.com/matlabcentral/fileexchange/22216-ploterr)
You can test if all PLS functionalities work by running myPLS_test.m. If everything works, you can procede with these 2 steps:
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Load your neuroimaging and behavioral data, specify any grouping, specify paths and parameters (normalization, number of permutations and bootstrap samples, threshold for visualization) in myPLS_inputs.m
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The main script to run PLS is myPLS_main.m, which will run the PLS analysis and plot the results.
For general descriptions of PLS for medical image analysis, we refer to:
• Krishnan, A., Williams, L.J., McIntosh, A.R., Abdi, H., 2011. Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. Neuroimage 56, 455–475. doi:10.1016/j.neuroimage.2010.07.034
• McIntosh, A.R., Lobaugh, N.J., 2004. Partial least squares analysis of neuroimaging data: Applications and advances. Neuroimage 23, 250–263. doi:10.1016/j.neuroimage.2004.07.020
Code from this toolbox has been used in these papers:
Behavior PLS with brain network-based measures:
• Zöller, D., Sandini, C., Karahanoğlu, F.I., Padula, M.C., Schaer, M., Eliez, S., Van De Ville, D., 2019. Large-scale brain network dynamics provide a measure of psychosis and anxiety in 22q11.2 deletion syndrome. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4(10), 881-892. doi:10.1016/j.bpsc.2019.04.004
Behavior PLS with connectivity measures and different grouping in PLS and resampling:
• Kebets, V., Holmes, A.J., Orban, C., Tang, S., Li, J., Sun, N., Kong, R., Poldrack, R.A., Yeo, B.T.T., 2019. Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol. Psychiatry 86(10), 779-791. doi:10.1016/j.biopsych.2019.06.013
Contrast PLS for multivariate analysis of group differences and developmental effects:
• Zöller, D., Schaer, M., Scariati, E., Padula, M.C., Eliez, S., Van De Ville, D., 2017. Disentangling resting-state BOLD variability and PCC functional connectivity in 22q11.2 deletion syndrome. Neuroimage 149, 85–97. doi:10.1016/j.neuroimage.2017.01.064
• Zöller, D., Padula, M.C., Sandini, C., Schneider, M., Scariati, E., Van De Ville, D., Schaer, M., Eliez, S., 2018. Psychotic symptoms influence the development of anterior cingulate BOLD variability in 22q11.2 deletion syndrome. Schizophr. Res. 193, 319–328. doi:10.1016/j.schres.2017.08.003
Code written by Prof. Dimitri Van De Ville, Daniela Zoeller and Valeria Kebets, with subfunctions borrowed from PLS toolbox by Rotman Baycrest (https://www.rotman-baycrest.on.ca/index.php?section=84)
These scripts and functions are based on myPLS scripts previously published on https://miplab.epfl.ch/index.php/software/PLS
• Release v1.0 (25/09/2019): Initial release of myPLS toolbox
You can ask questions, report bugs or discuss ideas for modifications on this Slack channel: myplstoolbox.slack.com
or contact Daniela Zoller at XXXX or Valeria Kebets at [email protected]