Abstract
The current study aimed to establish a fine-grained, efficient characterization of the concurrent neuropsychological contributions to social functioning in neuropsychologically-referred youth. A secondary aim was to demonstrate a useful statistic approach for such investigations (Partial Least Squares Regression; PLSR), which is underutilized in this field. Forty-five participants (70 – 164 months; Mage = 110.89; 34 male) were recruited from a large neuropsychological assessment clinic. Participants completed subtests from the NEPSY-II focusing on neuropsychological constructs that have been linked to social functioning (affect decoding, social memory, motor skills, visuomotor skills, response inhibition, attention and set-shifting, and verbal comprehension). Mothers completed the BASC-2, from which Atypicality and Social Skills scales were analyzed. PLSR revealed that difficulty with social memory, sensorimotor integration, and the ability to attend to and accurately discriminate auditory stimuli combine to best predict atypical or “odd” behavior. In terms of social skills, two factors emerged. The first factor indicated that, counterintuitively, greater emotional perception, visuospatial perception, ability to attend to and accurately discriminate auditory stimuli, and understand instructions was related to poorer social skills. The second factor indicated that a pattern of better facial memory, and sensorimotor ability (execution & integration) characterized a distinct profile of greater social ability. PLSR results were compared to traditional OLS and Backwards Stepwise regression approaches to demonstrate utility. Results also suggested that these findings were consistent across age, gender, and diagnostic group, indicating common neuropsychological substrates of social functioning in this sample of referred youth. Overall, this study provides the first characterization of optimized combinations of neuropsychological variables in predicting social functioning in assessment clinic-referred youth, and introduces to this literature a valuable statistical approach for obtaining such characterizations.
Highlights
Challenges associated with social behavior are central concerns for many youth populations referred for neuropsychological testing [1,2,3]
We consider an array of well-specified individual neuropsychological variables in predicting concurrent social functioning in neuropsychologically-referred populations, and employ an under-used statistical approach to specify a configuration of these variables that optimally predicts social functioning
We evaluated the appropriate number of independent variables by examining the Root Mean Squared Error of Prediction (RMSEP) of each Partial Least-Squares Regression (PLSR) component, using Random Segment Cross-Validation on the entire sample, seeking the smallest value after the intercept
Summary
Challenges associated with social behavior are central concerns for many youth populations referred for neuropsychological testing [1,2,3]. Indices of best fit in PLSR may be seen as a proxy for empirical generalizability of obtained independent variable patterns; as such, this approach addresses some of the usual questions applied to neuropsychology clinic-referred samples in terms of generalizability of effects. Overall, these features capitalize on structural features of neuropsychological data that have traditionally presented challenges to traditional regression models, and it is important to introduce such approaches to this field. Given the theoretical and empirical differences in social functioning among children of varying diagnostic groups [30], ages [66], and gender [67], the differences in these combinations by each of these factors were examined
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