Modeling the interrelationships between brain activity and trait attention measures to predict individual differences in reaction times in children during a Go/No-Go task

Abstract

Many researchers are utilizing event-related potentials (ERPs) to better understand brain-behavior relationships across development. The present study demonstrates how structural equation modeling (SEM) techniques can be used to refine descriptions of brain-behavior relationships in a sample of neurotypical children. We developed an exploratory latent variable model in which trait measures of maturation and attention are related to neural processing and task behaviors obtained during a cued Go/No-Go task. Model findings are compared to results of traditional analysis techniques such as bivariate correlations. The data suggest that more sophisticated statistical approaches are beneficial to accurately interpreting the nature of brain-behavior relationships.

Sensory Processing and Attention Profiles Among Children With Sensory Processing Disorders and Autism Spectrum Disorders

“…Manly et al (2007) demonstrated that for 6-to 16-year-old Australian children, the age-standardized scores of the nine subtests can be combined into a three-factor configuration representing the three different types of attention, naming them as sustained, selective, and control/shift. In a more recent examination of the factor structure of the TEA-Ch conducted on children aged 6-13 years in the United States, the best-fitting model using structural equation modeling resulted in just two factors (Taylor et al, 2018). The first factor included the sustained subtests, and the second factor was a combination of the subtests representing both the selective and control/shift subtests.…”

“…This structure of selective and control/shift collapsing into one factor is supported by Petersen and Posner (2012) who suggest that the neural networks for selective and control/shift may not be differentiated in young children. Taylor et al (2018) named this combined factor control attention. Thus, the resulting two factors revealed in young children were sustained and control attention (Taylor et al, 2018).…”

“…Taylor et al (2018) named this combined factor control attention. Thus, the resulting two factors revealed in young children were sustained and control attention (Taylor et al, 2018). Based on Taylor et al (2018), to obtain standardized coefficients, each participant's standard score was multiplied by the unstandardized coefficients and then averaged to obtain the sustained and control attention domains (see Table 1 for unstandardized coefficient values).…”

“…Thus, the resulting two factors revealed in young children were sustained and control attention (Taylor et al, 2018). Based on Taylor et al (2018), to obtain standardized coefficients, each participant's standard score was multiplied by the unstandardized coefficients and then averaged to obtain the sustained and control attention domains (see Table 1 for unstandardized coefficient values). To categorize individuals into three attention performance categories, typical performance, probable difference, and definite difference, the mean and standard deviation (SD) of the total score (sustained + control) of the TD group were used as the standard score.…”

“…Age-standardized scores of the TEA-Ch were used to analyze group differences in attention. Consistent with the factor structure of the TEA-Ch found for young children by Taylor et al (2018), the standard scores of the nine individual test items were consolidated to represent two subtypes of attention, To obtain an index of control and sustained attention for an individual using this model, the standard score for the four subtests within each attention domain (control and sustained) were multiplied by its associated unstandardized coefficient and then summed.…”