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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 - PubMed

  • ️Mon Jan 01 2018

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

Brittany K Taylor et al. Neuropsychologia. 2018.

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.

Keywords: Brain-behavior relationships; Development; ERPs; Event-related potentials; SEM; Structural equation modeling.

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Figures

Figure 1
Figure 1

Trial-by-trial ERPs (thin black lines) and averaged ERPs (thick black lines) obtained during correct Go trials of the cued Go/No-Go task in two separate sessions from a single 8-year-old participant, and a single 12-year-old participant. The E-wave component window (1800–2000ms) is highlighted with a red box. Additionally, a section of the averaged ERP (1600–2000ms) has been extracted and enlarged to better show the E-wave component. All ERPs are shown at site Cz. Go trial stimuli from the cued Go/No-Go task are shown next to the vertical hash lines denoting the presentation of stimuli.

Figure 2
Figure 2

The structural model exploring interrelationships among age, means and standard deviations of E-wave amplitudes, and means and standard deviations of reaction times (RT) during Go trials for both sessions. Note: All reported coefficients are standardized. Unique variances are reported next to each manifest variable in small font. Non-statistically significant relationships are shown as gray, dotted lines. Statistical significance is indicated as follows: * p< .05, ** p < .01, *** p < .001. Note: RT = reaction time; M1 = mean session 1; M2 = mean session 2; SD1 = standard deviation session 1; SD2 = standard deviation session 2.

Figure 3
Figure 3

The latent variable model exploring interrelationships between means of E-wave amplitudes and reaction times (RT) during Go trials for both sessions. Note: All reported coefficients are standardized. Unique variances are reported next to each manifest variable, and disturbances are reported below latent variables in small font. Non-statistically significant relationships are shown as gray, dotted lines. Statistical significance is indicated as follows: * p < .05, ** p < .01, *** p < .001. Note: RT = reaction time; M1 = mean session 1; M2 = mean session 2; SD1 = standard deviation session 1; SD2 = standard deviation session 2; the triangle with the “1” indicates that the latent variables were identified by fixing their means to “0” and their variances to “1”, a statistical approach which allows all contributing manifest variables to be freely estimated.

Figure 4
Figure 4

The latent variable model exploring interrelationships between TEA-Ch attention factors, the E-wave, and reaction time (RT). Note: All reported coefficients are standardized. Unique variances are reported next to each manifest variable, and disturbances are reported above the latent variables in small font. Non-statistically significant relationships are shown as gray, dotted lines. Statistical significance is indicated as follows: * p < .05, ** p < .01, *** p < .001. Note: RT = reaction time; M1 = mean session 1; M2 = mean session 2; SD1 = standard deviation session 1; SD2 = standard deviation session 2; the triangle with the “1” indicates that the latent variables were identified by fixing their means to “0” and their variances to “1”, a statistical approach which allows all contributing manifest variables to be freely estimated.

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