Dear PLS experts,

I'm running a behavioral PLS analysis, including average cortical thickness in 4 clusters and 6 physiological variables of 35 participants. Clusters were derived from a GLM comparing a patient and a control group; data were collapsed across both groups for PLS. Please see my script in the bottom.

The PLS analysis yielded 1 significant LV (“sprob”), accounting for 99% of covariance (“s.^2/sum(s.^2”) between thickness and physio variables. All four clusters are reliably associated with the LV (“compare_u”) and 4/6 physio variables are reliably (?!) correlated with the brainscores (“orig_corr”) as indicted by confidence intervals not including 0 (“ulcorr” and “llcorr”). Are these interpretations correct?

However, I am interested in the effects of physio variables on thickness, not thickness effects on physio variables, which I believe my current analysis represents. Should/could I switch the physio with the brain data (i.e., enter physio as 'datamat' and thickness as 'physiodata' in script below)? Or should I stick to my analysis but interpret other results?

Thank you very much in advance!

Nic

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MATLAB 2011b and PLS version 6.15.1

Data files

CT_data.txt: average CT for each of 35 participants (i.e., 35 rows) in the clusters (i.e., four columns).

Physio_data.txt: values for six physiological variables (i.e., six columns) for the participant (i.e., 35 rows).

Analysis

nsubjects=35;

ncolumns=4;

in1=fopen('CT_data.txt');

datamat=fscanf(in1,'%f',[ncolumns,inf])';

fclose(in1);

save 'datamat.mat' 'datamat'

ncolumns=6;

in2=fopen('Physio_data.txt');

physiodata=fscanf(in2,'%f',[ncolumns,inf])';

fclose(in2);

save 'physiodata.mat' 'physiodata'

opt.method=3;

opt.num_perm=1000;

opt.num_boot=1000;

opt.stacked_behavdata=physiodata;

results=pls_analysis({datamat},[nsubjects], 1, opt);

save 'results.mat' 'results'

Results

# Bootstrap ratios, representing reliability of cortical thickness clusters contributing to LV

results.boot_result.compare_u

# Statistical significance of latent variables

results.perm_result.sprob

# Percentage that LVs account for covariance between thickness and physio data

pct=(results.s.^2/sum(results.s.^2))

Figures

# Output used for barplots representing correlation between brain scores and physio data along with confidence intervals:

...

m=results.boot_result.orig_corr(:,LV)

El=results.boot_result.orig_corr(:,LV)-results.boot_result.llcorr(:,LV)

Eu=results.boot_result.ulcorr(:,LV)-results.boot_result.orig_corr(:,LV)

…

# Data in "results.usc" are plotted against physio data as another way to represent the correlation between physio data and brainscores (i.e., representations of cortical thickness)