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Observing Changes in Cognition, Mood, and White Matter in Chronic TBI Using Multiple Factor Analysis After Cognitive Intervention
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Background
Individuals who sustain traumatic brain injuries (TBIs) often continue to experience significant impairment of cognitive functions mediated by the prefrontal cortex well into chronic stages of recovery. Traditional brain training programs that focus on improving specific skills fall short of addressing integrative functions that draw upon multiple higher-order processes critical for social and vocational integration. In the current study, we compare the effects of two short-term, intensive, group-based cognitive rehabilitation programs for individuals with chronic TBI. One program emphasizes learning about brain functions and influences on cognition, while the other program adopts a top-down approach to improve abstract reasoning abilities that are largely reliant on the prefrontal cortex. These treatment programs are evaluated in civilian and military veteran TBI populations.
Methods/design
One hundred individuals are being enrolled in this double-blinded clinical trial (all measures and data analyses will be conducted by blinded raters and analysts). Each individual is randomly assigned to one of two treatment conditions, with each condition run in groups of five to seven individuals. The primary anticipated outcomes are improvement in abstract reasoning and everyday life functioning, measured through behavioral tasks and questionnaires, and attention modulation, as measured by functional neuroimaging. Secondary expected outcomes include improvements in the cognitive processes of working memory, attention, and inhibitory control.
Discussion
Results of this trial will determine whether cognitive rehabilitation aimed at teaching TBI-relevant information about the brain and cognition versus training in TBI-affected thinking abilities (e.g., memory, attention, and executive functioning) can improve outcomes in chronic military and civilian TBI patient populations. It should shed light on the nature of improvements and the characteristics of patients most likely to benefit. This trial will also provide information about the sustainability of treatment-related improvements 3 months post-training.
Trial registration
ClinicalTrials.gov Identifier: NCT01552473
Multiple factor analysis (mfa, also called multiple factorial analysis) is an exten-
sion of principal component analysis (pca) tailored to handle multiple data tables that
measure sets of variables collected on the same observations, or, alternatively, (in dual-
mfa) multiple data tables where the same variables are measured on di�erent sets of
observations. Mfa proceeds in two steps: First it computes a principal component
analysis (pca) of each data table and \normalizes" each data table by dividing all its
elements by the �rst singular value obtained from its pca. Second, all the normalized
data tables are aggregated into a grand data table that is analyzed via a (non nor-
malized) pca that gives a set of factor scores for the observations and loadings for the
variables. In addition, mfa provides for each data table a set of partial factor scores for
the observations that re
ects the speci�c \view-point" of this data table. Interestingly,
the common factor scores could be obtained by replacing the original normalized data
tables by the normalized factor scores obtained from the pca of each of these tables.
In this article, we present mfa, review recent extensions, and illustrate it with a
detailed example.
Multiple factor analysis (MFA, also called multiple factorial analysis) is an extension of principal component analysis (PCA) tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, (in dual‐MFA) multiple data tables where the same variables are measured on different sets of observations. MFA proceeds in two steps: First it computes a PCA of each data table and ‘normalizes’ each data table by dividing all its elements by the first singular value obtained from its PCA. Second , all the normalized data tables are aggregated into a grand data table that is analyzed via a (non‐normalized) PCA that gives a set of factor scores for the observations and loadings for the variables. In addition, MFA provides for each data table a set of partial factor scores for the observations that reflects the specific ‘view‐point’ of this data table. Interestingly, the common factor scores could be obtained by replacing the original normalized data tables by the normalized factor scores obtained from the PCA of each of these tables. In this article, we present MFA, review recent extensions, and illustrate it with a detailed example. WIREs Comput Stat 2013, 5:149–179. doi: 10.1002/wics.1246
This article is categorized under: Data: Types and Structure > Categorical Data
Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis
Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
Analyzing multi-block data: A tutorial of Multiple Factor Analysis in R. Workshop presented at the Society for Applied Multivariate Research
- A Dutcher
- M J Kmiecik
- D Beaton
Dutcher, A., Kmiecik, M.J., & Beaton, D. (2016, April). Analyzing multi-block data: A tutorial of Multiple Factor Analysis in R. Workshop presented at the
Society for Applied Multivariate Research, Dallas, Texas.