Journal: Behavior Research Methods

Article Title: The underwood project: A virtual environment for eliciting ambiguous threat

doi: 10.3758/s13428-022-02002-3

Figure Lengend Snippet: Relationship between affect and memory. One latent variable (LV) emerged ( p < .01) in the partial least squares (PLS) analysis demonstrating an association between memory measures and overall emotions. Ratings of unpredictability and surprise contributed to the LV, which correlated with memory bias for both the scenes and the prelude, and with sensitivity ( d ′) and “confabulation” about the prelude (see main text)

Article Snippet: For a partial least squares (PLS) analysis, we used the PLS command line package (Version 6.15, 2015) in MATLAB R2021a.

Techniques:

Journal: NeuroImage : Clinical

Article Title: Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome

doi: 10.1016/j.nicl.2017.09.022

Figure Lengend Snippet: A. Significantly reduced cortical thickness (CT) in the Metabolic Syndrome (MetS) group in left 1) inferior parietal, 2) rostral middle frontal, 3) lateral occipital regions, and right 4) precentral regions relative to the control group as assessed with a whole-brain general linear model (GLM). The significance map is presented on the pial surface of an average brain (“fsaverage”, provided by FreeSurfer) with dark gray representing sulci and light gray representing gyri. The color scale indicates p -values (thresholded at vertex- and cluster-wise p < 0.050). Results were multiple comparison corrected with Monte-Carlo simulations. See for more information. B. Average CT in the GLM-derived clusters. Horizontal lines indicate group means. Symbols without color filling indicate participants scanned at the Veterans Administration Boston Healthcare System (VA).

Article Snippet: Follow-up multivariate PLS analyses were used to investigate how individual MetS risk factors relate to average CT in GLM-derived clusters that are associated with MetS (“Behavioral PLS” from the PLS toolbox version 6.15.1 run in MATLAB version R2015b; https://www.rotman-baycrest.on.ca/pls ).

Techniques: Control, Comparison, Derivative Assay

Journal: NeuroImage : Clinical

Article Title: Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome

doi: 10.1016/j.nicl.2017.09.022

Figure Lengend Snippet: A. Significant latent variable from the partial least squares correlation (PLS) analysis that maximizes the covariance between cortical thickness (CT) in the clusters derived from the whole-brain general linear model (GLM; A) and individual risk factors for Metabolic Syndrome (MetS). Shown are the correlations between “brain scores” (representing how well clusters contribute to the overall latent variable pattern) and raw values of MetS risk factors, as indicated with gray bars, and when age was included in the model, with white bars. WC = waist circumference; TRIG = triglycerides; HDL = high-density lipoprotein cholesterol; GLUC = fasting blood glucose; s = systolic and d = diastolic BP = blood pressure. Error bars represent 95% confidence intervals (CI) and indicate the significance of the relationship between the latent variable and MetS risk factor and age variables. Risk factors with error bars crossing the horizontal axis are not significant contributors to the latent variable. Of note is the minimal change in correlation magnitude and CI size between the two PLS analyses. See for sample characteristics. B. Raw values of MetS risk factor components plotted against brain scores. Group membership of participants is indicated to provide additional information; symbols without color filling indicate participants scanned at the Veterans Administration Boston Healthcare System (VA). Note: high-density lipoprotein cholesterol is expected to show a positive relationship as lower values on this variable indicate worse health.

Article Snippet: Follow-up multivariate PLS analyses were used to investigate how individual MetS risk factors relate to average CT in GLM-derived clusters that are associated with MetS (“Behavioral PLS” from the PLS toolbox version 6.15.1 run in MATLAB version R2015b; https://www.rotman-baycrest.on.ca/pls ).

Techniques: Derivative Assay