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Response time intra-subject variability: commonalities between children with autism spectrum disorders and children with ADHD

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Abstract

Despite the common co-occurrence of symptoms of attention deficit hyperactivity disorder (ADHD) in individuals with autism spectrum disorders (ASD), the underlying mechanisms are under-explored. A potential candidate for investigation is response time intra-subject variability (RT-ISV), a hypothesized marker of attentional lapses. Direct comparisons of RT-ISV in ASD versus ADHD are limited and contradictory. We aimed to examine whether distinct fluctuations in RT-ISV characterize children with ASD and with ADHD relative to typically developing children (TDC). We applied both a priori-based and data-driven strategies to RT performance of 46 children with ASD, 46 with ADHD, and 36 TDC (aged 7–11.9 years). Specifically, we contrasted groups relative to the amplitude of four preselected frequency bands as well as to 400 frequency bins from 0.006 to 0.345 Hz. In secondary analyses, we divided the ASD group into children with and without substantial ADHD symptoms (ASD+ and ASD−, respectively). Regardless of the strategy employed, RT-ISV fluctuations at frequencies between 0.20 and 0.345 Hz distinguished children with ADHD, but not children with ASD, from TDC. Children with ASD+ and those with ADHD shared elevated amplitudes of RT-ISV fluctuations in frequencies between 0.18 and 0.345 Hz relative to TDC. In contrast, the ASD− subgroup did not differ from TDC in RT-ISV frequency fluctuations. RT-ISV fluctuations in frequencies 0.18–0.345 Hz (i.e., periods between 3 and 5 s) are associated with ADHD symptoms regardless of categorical diagnosis and may represent a biomarker. These results suggest that children with ADHD and those with ASD+ share common underlying pathophysiological mechanisms of RT-ISV.

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Acknowledgments

The authors thank all children and parents for their participation to this research, as well as the research staff of the Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience for help in participant recruitment, assessment, data collection and data entry. The authors also wish to thank Drs. Katherine Johnson for sharing the fixed version of SART, Clare Kelly for help in some aspects of RT data preparation, and Philip Reiss for the development of the Functional Data Analysis applied here and for helpful discussion during manuscript preparation. This work was supported in part by grants from the National Institute of Mental Health (K23MH087770 to A.D.M., R01MH081218 to F.X.C.) from the National Institute of Child Health and Human Development (R01HD065282), Autism Speaks, the Stavros Niarchos Foundation, awarded to F.X.C., the Brain and Behavior Research Foundation (previously known as NARSAD) and the Leon Levy Foundation awarded to A.D.M.

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Authors and Affiliations

  1. Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, NYU Child Study Center, New York University Langone Medical Center, One Park Avenue, 8th floor, New York, NY, 10016, USA

    Nicoletta Adamo, Samantha Adelsberg, F. Xavier Castellanos & Adriana Di Martino

  2. Division of Biostatistics, NYU Child Study Center, New York, USA

    Lan Huo & Eva Petkova

  3. Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

    Eva Petkova & F. Xavier Castellanos

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  1. Nicoletta Adamo
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  2. Lan Huo
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  5. F. Xavier Castellanos
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  6. Adriana Di Martino
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Correspondence to Adriana Di Martino.

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787_2013_428_MOESM1_ESM.pdf

Supplementary Fig. 1 Average amplitude of each frequency band examined for the first (left) and the second (right) halves of the SART for TDC, ADHD and ASD groups, respectively. The X-axis represents the frequency spectrum examined for the task (0.006–0.345 Hz) with the a priori selected frequency bands delimited by dashed lines. The amplitude of each frequency is plotted on the Y-axis. To examine the effect of time on task, we computed the average amplitude of the examined frequencies separately for the first and second halves of the datasets within each group. Repeated measures ANOVA revealed no significant differences across all low-frequency bands (Slow-2 F (2,125) = 0.4, p = 0.70; Slow-3 F (2,125) = 0.4, p = 0.65; Slow-4 F (2,125) = 2.5, p = 0.09; Slow-5 F (2,125) = 0.6, p = 0.58). Similarly, analyses with SD-RT did not reveal any significant group differences (F (2,125) = 0.8, p = 0.43) (PDF 90 kb)

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Adamo, N., Huo, L., Adelsberg, S. et al. Response time intra-subject variability: commonalities between children with autism spectrum disorders and children with ADHD. Eur Child Adolesc Psychiatry 23, 69–79 (2014). https://doi.org/10.1007/s00787-013-0428-4

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