The NIH Toolbox Cognitive Battery for intellectual disabilities: three preliminary studies and future directions - PubMed (original) (raw)
The NIH Toolbox Cognitive Battery for intellectual disabilities: three preliminary studies and future directions
David Hessl et al. J Neurodev Disord. 2016.
Abstract
Background: Recent advances in understanding molecular and synaptic mechanisms of intellectual disabilities (ID) in fragile X syndrome (FXS) and Down syndrome (DS) through animal models have led to targeted controlled trials with pharmacological agents designed to normalize these underlying mechanisms and improve clinical outcomes. However, several human clinical trials have failed to demonstrate efficacy of these targeted treatments to improve surrogate behavioral endpoints. Because the ultimate index of disease modification in these disorders is amelioration of ID, the validation of cognitive measures for tracking treatment response is essential. Here, we present preliminary research to validate the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB) for ID.
Methods: We completed three pilot studies of patients with FXS (total n = 63; mean age 19.3 ± 8.3 years, mean mental age 5.3 ± 1.6 years), DS (n = 47; mean age 16.1 ± 6.2, mean mental age 5.4 ± 2.0), and idiopathic ID (IID; n = 16; mean age 16.1 ± 5.0, mean mental age 6.6 ± 2.3) measuring processing speed, executive function, episodic memory, word/letter reading, receptive vocabulary, and working memory using the web-based NIH-TB-CB, addressing feasibility, test-retest reliability, construct validity, ecological validity, and syndrome differences and profiles.
Results: Feasibility was good to excellent (≥80 % of participants with valid scores) for above mental age 4 years for all tests except list sorting (working memory). Test-retest stability was good to excellent, and convergent validity was similar to or better than results obtained from typically developing children in the normal sample for executive function and language measures. Examination of ecological validity revealed moderate to very strong correlations between the NIH-TCB composite and adaptive behavior and full-scale IQ measures. Syndrome/group comparisons demonstrated significant deficits for the FXS and DS groups relative to IID on attention and inhibitory control, a significant reading weakness for FXS, and a receptive vocabulary weakness for DS.
Conclusions: The NIH-TCB has potential for assessing important dimensions of cognition in persons with ID, and several tests may be useful for tracking response to intervention. However, more extensive psychometric studies, evaluation of the NIH-TCB's sensitivity to change, both developmentally and in the context of treatment, and perhaps establishing links to brain function in these populations, are required to determine the true utility of the battery as a set of outcome measures.
Keywords: Assessment; Cognition; Down syndrome; FMR1 gene; Fragile X syndrome; Outcome measures.
Figures
Fig. 1
Story board/picture schedule used in study 3 to increase motivation and understanding of assessment visit schedule. The participant prize or a representation of the prize for compliance and effort is placed in the center, and with the examiner’s assistance, the participant checks off each completed task with a dry erase pen. NIH-TCB tasks from top (clockwise) depicted are flanker, dimensional change card sort, picture sequence memory, picture vocabulary, oral reading, pattern comparison, and list sorting
Fig. 2
Scatterplot showing the association between the NIH-TCB cognitive composite and Stanford-Binet full-scale IQ (z deviation method). Dotted lines represent the 95 % confidence interval around the regression line. Note that the regression line and correlation (−12.76 + 1.17×; R 2 = .79) show that the composite is a strong predictor of IQ in these samples of individuals with ID
Fig. 3
Z scores (±1 SEM) of each NIH-TCB subtest by group. Z scores (age-adjusted) reflect the number of standard deviations from the average (0 for all subtests) in the normative sample from the general population. For example, the FXS + ID sample had a mean performance on flanker that is greater than 7 standard deviations below average, adjusted for age. Note that for picture sequence memory, only data from form A is shown
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