Date of Award

5-2027

Document Type

Doctoral Research Project

Degree Name

Doctor of Psychology (PsyD)

Department

Psychology

First Advisor

Brian Fisak

Second Advisor

Eric Perlman

Third Advisor

Julie S. Costopoulos

Fourth Advisor

Lisa A. Steelman

Abstract

This study aimed to provide evidence for a white matter abnormality (WMA) screener by investigating whether commonly used neuropsychological measures of executive functioning and processing speed have predictive power for identifying high WMA burden.

Participants were administered a brief neuropsychological evaluation by a doctoral student under the direct supervision of a board-certified clinical neuropsychologist and underwent an MRI scan. A neurologist rated each participant’s MRI results using the Fazekas scale. A total of 178 participants were included, divided into two groups based on their Fazekas rating: low WMA burden (46%) and high WMA burden (54%).

A confirmatory factor analysis indicated that the hypothesized model did not accurately fit the current sample. Therefore, an exploratory factor analysis was conducted to develop a more robust model. Executive functioning demonstrated a significant relationship with WMA burden, whereas processing speed did not. Age was significantly correlated with WMA burden but did not moderate any of the individual neuropsychological measures; however, gender, years of education, diabetes, depression, and anxiety were not significantly correlated. A combination of the M-WCST, Trails A, and age was the best model for predicting WMAs in the current study, correctly classifying 69.5% of cases.

Individual measures of executive functioning are the most effective predictors of WMAs. However, in a sample of cognitively impaired individuals, measures of processing speed may capture some unique aspects of executive functioning. To establish a robust model for predicting WMAs, a combination of the M-WCST, Trails A, and age should be considered.

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