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Longitudinal Changes in Processing Speed and Corresponding White Matter Microstructure in Autism Spectrum Disorder (ASD)

Saturday, 4 May 2013: 09:00-13:00
Banquet Hall (Kursaal Centre)
B. G. Travers1, E. D. Bigler2, N. Adluru1, D. P. Tromp1, C. Ennis1, M. Prigge3, A. L. Froehlich4, N. Lange5, A. L. Alexander6 and J. E. Lainhart7, (1)Waisman Center, University of Wisconsin, Madison, WI, (2)Brigham Young University, Provo, UT, (3)University of Utah, Salt Lake City, UT, (4)Psychiatry, University of Utah, Salt Lake City, UT, (5)McLean Hospital, Belmont, MA, (6)University of Wisconsin, Madison, WI, (7)Psychiatry, Waisman Center, University of Wisconsin-Madison, Madison, WI
Background: Slower processing speeds have been commonly reported in individuals with ASD (Mayes & Calhoun, 2003, 2008; Oliveras-Renta et al., 2011; Wechsler, 2003). However, little is known regarding how processing speed matures and develops from childhood into adulthood in this population. Given that processing speed is a fundamental cognitive process that relates to higher order skills such as communication ability, it is important to examine processing speed changes over time in ASD. Further, it is important to examine underlying neural substrates of processing speed that may be affected in ASD.    

Objectives: To compare longitudinal measures of processing speed from early childhood to adulthood in persons with ASD and typically developing controls, and to examine whether processing speed depends on the average fractional anisotropy (FA) of whole-brain white matter, as measured with Diffusion Tensor Imaging (DTI).     

Methods: In our accelerated longitudinal design, participants included 86 males with ASD (age range 6.3-42.6 years) and 60 males with typical development (age range 6.9-39.8 years). Participants completed standardized processing speed measures of the Wechsler Intelligence Scale for Children, 3rd edition (WISC-III) and the Wechsler Adult Intelligence Scale, 3rdedition (WAIS-III) 1-3 times over the last 10 years. Linear mixed effect models examined processing speed measures as a function of diagnostic group and age, while controlling for full-scale IQ. Pearson correlations (r) were used to quantify the relation between processing speed index scores and average whole-brain FA.

Results: After accounting for age, full-scale IQ, and individual growth curves, persons with ASD scored on average 12.0 points lower than typically developing controls on the processing speed index (PSI) of the WISC-III and WAIS-III (p < .001). Group differences also emerged in the raw scores of the WAIS-III coding (p < .001) and symbol search subtests (p < .001), but not in the raw scores of the WISC-III coding (p = .10) or symbol search subtests (p = .19). There were no significant age-by-group interactions. Collapsing across all time points, whole-brain average FA was correlated with PSI scores when both groups were included in the model, r(149) = +.23, p =.005 , but not within each group separately, ASD: r(93) = +.12, p =.25 ; TD: r(55) = +.19, p= .15. This small-sized correlation further diminished across all participants when age was included as a covariate.    

Conclusions: Individuals with ASD exhibited slower processing speed index scores across a wide age range (6-42 years) compared to individuals with typical development. However, group differences in subtest raw scores were only significant in the adult versions of the test. These results suggest that processing speed impairments are present in ASD and may be more pronounced in adult IQ tests, even though the rate of age-related processing speed changes were similar across groups. The average FA of the whole-brain white matter was not related to processing speed index scores when age was included in the model. Future analyses will examine correlations between processing speed and region-specific white matter tracts, as well as correlations between processing speed and ASD symptom severity measures.

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