Abstract
Interpreting a Multivariate Analysis of Functional Neuroimaging Data
Highlights
Over a decade ago, Nestor et al (2002) employed a data-driven multivariate statistical algorithm to better understand brainbehavior correlates in schizophrenia
A recent study employed partial least squares (PLS) in order to indentify functional activation patterns from functional MRI data that covaried with their behavior measure, working memory performance, and to further identify how these patterns differ in patients with schizophrenia versus healthy comparison subjects (Kim et al, 2010)
Kim et al (2010) interpret their results as “an exaggerated change in the BOLD signal” or “increased activation” in schizophrenia patients relative to healthy controls. These claims are based on a greater magnitude of brain scores, which essentially represents the degree to which each behavior contributes to the covariance pattern of functional activation
Summary
Over a decade ago, Nestor et al (2002) employed a data-driven multivariate statistical algorithm to better understand brainbehavior correlates in schizophrenia. A recent study employed PLS in order to indentify functional activation patterns from functional MRI (fMRI) data that covaried with their behavior measure, working memory performance, and to further identify how these patterns differ in patients with schizophrenia versus healthy comparison subjects (Kim et al, 2010). The group noted that the brain scores for the healthy control sample were stable irrespective of task complexity, the schizophrenia patients showed larger brain scores for the intermediate memory load condition.
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