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Variability in cognitive task performance in early adolescence is associated with stronger between-network anticorrelation and future attention problems

  • Sarah E. Chang
    Correspondence
    Corresponding author: Sarah E. Chang, 760 Westwood Plaza, Rm A7-436 Los Angeles, CA 90095,
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
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  • Agatha Lenartowicz
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
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  • Gerhard S. Hellemann
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California

    Department of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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  • Lucina Q. Uddin
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
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  • Carrie E. Bearden
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California

    Department of Psychology, University of California, Los Angeles, Los Angeles, California
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Open AccessPublished:November 25, 2022DOI:https://doi.org/10.1016/j.bpsgos.2022.11.003

      ABSTRACT

      Background

      Intra-individual variability (IIV) during cognitive task performance is a key behavioral index of attention and a consistent marker of ADHD. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN) - thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are (i) associated with each other and (ii) can predict future attention-related deficits has not been examined in a developmental, population-based cohort.

      Methods

      We examined relationships at the baseline visit between IIV on three cognitive tasks, DMN-DAN anticorrelation, and parent-reported attention problems using data from the Adolescent Brain and Cognitive Development Study (n=11,878 participants, aged 9-10, female=47.8%). We also investigated whether behavioral and neural markers of attention at baseline predicted attention problems, 1, 2, and 3 years later.

      Results

      At baseline, greater DMN-DAN anticorrelation was associated with lower IIV across all three cognitive tasks (unstandardized β: 0.22-0.25). Older age at baseline was associated with stronger DMN-DAN anticorrelation and lower IIV (β: -0.005--0.0004). Weaker DMN-DAN anticorrelation and IIV were cross-sectionally associated with attention problems (β: 1.41-7.63). Longitudinally, lower IIV at baseline was associated with less severe attention problems 1-3 years later, after accounting for baseline attention problems (β: 0.288-0.77).

      Conclusions

      The results suggest that IIV in early adolescence is associated with worsening attention problems in a representative cohort of US youth. Attention deficits in early adolescence may be important for understanding and predicting future cognitive and clinical outcomes.

      Keywords

      Introduction

      Momentary lapses in attention can interfere with goal-directed behaviors. In individuals with attention deficits, these lapses are persistent and can hinder task completion at work or concentrating in school. A key metric of attentional lapses is intra-individual variability (IIV), which is defined as trial-by-trial fluctuation in reaction time during a timed cognitive task (
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      ). Higher IIV is a robust behavioral marker of attention-deficit/hyperactivity disorder (ADHD) (Hedge’s g = 0.76; ADHD vs. controls) (
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      ). Across the lifespan, IIV exhibits a U-shaped trajectory, with dramatic reductions during childhood and adolescence corresponding with a major developmental shift in sustained attention (
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      ). The anticorrelation of these two networks may be a fundamental property of brain organization that supports effective attention allocation, by focusing on the task at hand and suppressing internally-directed thoughts to support neurocognitive performance (
      • Kelly A.M.C.
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      Competition between functional brain networks mediates behavioral variability.
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      • Fox M.D.
      • Snyder A.Z.
      • Vincent J.L.
      • Corbetta M.
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      ).
      Consistent evidence from rsfMRI research across the lifespan and studies of ADHD links DMN-DAN anticorrelation with attention allocation. These networks become increasingly anticorrelated in infancy through adolescence and less anticorrelated through older adulthood (
      • Gao W.
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      Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.
      ,
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      Default network modulation and large-scale network interactivity in healthy young and old adults.
      ). Kelly et al. 2008 were the first to link DMN-DAN anticorrelation during rest and task with reduced IIV in healthy adults, which was later replicated and extended through task-based fMRI and EEG studies (
      • Kelly A.M.C.
      • Uddin L.Q.
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      Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.
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      ). Recently, Owens et al. 2020 linked DMN-DAN anticorrelation with less severe parent-reported attention problems using baseline data from the Adolescent Brain Cognitive Development (ABCD) study (
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      ). From the ADHD literature, a meta-analysis found that anticorrelation between DMN and task-positive networks was diminished in children and adolescents with ADHD compared with typically developing (TD) youth (
      • Sutcubasi B.
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      ). Furthermore, evidence from dynamic functional connectivity (FC) analyses, a method that measures moment-to-moment shifts in connectivity (
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      ), suggests that children with ADHD spent less total time in, and switched out of, anticorrelated states involving the DMN and task-relevant networks more frequently compared with TD children (
      • Shappell H.M.
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      ).
      While this cogent body of literature supports the notion that DMN-DAN anticorrelation is linked with IIV, several clinically relevant questions and literature gaps remain. While IIV is a robust marker of ADHD, it is unclear whether IIV can be used in the general population to predict future attention problems (
      • Kofler M.J.
      • Rapport M.D.
      • Sarver D.E.
      • Raiker J.S.
      • Orban S.A.
      • Friedman L.M.
      • Kolomeyer E.G.
      Reaction time variability in ADHD: a meta-analytic review of 319 studies.
      ). Case-control study design does not capture the full spectrum of attention problems because individuals with milder symptoms that do not reach clinical impairment may be excluded (
      • Barber A.D.
      • Jacobson L.A.
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      • Nebel M.B.
      • Caffo B.S.
      • Pekar J.J.
      • Mostofsky S.H.
      Connectivity supporting attention in children with attention deficit hyperactivity disorder.
      ,
      • Shappell H.M.
      • Duffy K.A.
      • Rosch K.S.
      • Pekar J.J.
      • Mostofsky S.H.
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      ,
      • Querne L.
      • Fall S.
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      • Bourel-Ponchel E.
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      ). Importantly, studies linking DMN-DAN anticorrelation at rest to IIV have only been established in adulthood and are relatively small (11, 37). Yet, studying IIV in early adolescence is particularly important. Evidence from executive function task performance from n>10,000 participants, aged 8-35, has shown that executive function (including inhibitory attention) undergoes the most rapid development in early adolescence (

      Tervo-Clemmens, B., Calabro, F. J., Parr, A. C., Fedor, J., Foran, W., & Luna, B. (2022). A Canonical Trajectory of Executive Function Maturation During the Transition from Adolescence to Adulthood. psyRvix.

      ). Whether DMN-DAN anticorrelation develops alongside, prior to, or in response to this dynamic development is unknown. Lastly, whether DMN-DAN anticorrelation can be used to predict future attention problems has not been examined in a large, population-based sample. Deviations in these neural or behavioral markers of attention during this important stage of cognitive development may lead to poorer cognitive outcomes in adulthood.
      To address these questions, we examine the relationship between intra-individual variability (IIV) in timed cognitive tasks and resting-state FC from the ABCD study, a population-based, longitudinal cohort of 11,878 9-10 year olds. In contrast to previous work, this study captures the full spectrum of attention problems during a period of dynamic cognitive and neural development (47, 48). The main hypotheses tested using cross-sectional data at baseline were the following: (1a) Stronger DMN-DAN anti-correlation will be associated with lower IIV, across cognitive tasks; (1b) within these models, older baseline age will be associated with stronger DMN-DAN anticorrelation and (1c) lower IIV. Secondly, we examined the cross-sectional relationship of lab-based cognitive and neuroimaging measures (IIV and DMN-DAN anticorrelation) with parent-report of their child’s attention problems. We predicted that both (2a) weaker DMN-DAN anticorrelation and (2b) higher IIV would be associated with more severe attention problems. Lastly, we tested two hypotheses using longitudinal data: (3a) weaker DMN-DAN anticorrelation and/or (3b) greater IIV would be associated with future attention symptoms, at 1-year, 2-year and 3-year follow-up timepoints, after accounting for baseline attention symptoms.
      In addition to these a priori hypotheses based on work conducted in adults (
      • Kelly A.M.C.
      • Uddin L.Q.
      • Biswal B.B.
      • Castellanos F.X.
      • Milham M.P.
      Competition between functional brain networks mediates behavioral variability.
      ), we also conduct three exploratory analyses. First, we examine brain-behavior associations via ex-Gaussian distribution modeling that parses the reaction-time distribution into the variability of extremely slow (tau) vs. fast (sigma) responses. Individuals with ADHD exhibit greater tau and greater sigma on reaction-time tasks, and neural mechanisms supporting tau and sigma may differ since they follow different developmental patterns (
      • Leth-Steensen C.
      • Elbaz Z.K.
      • Douglas V.I.
      Mean response times, variability, and skew in the responding of ADHD children: A response time distributional approach.
      ,
      • Williams B.R.
      • Hultsch D.F.
      • Strauss E.H.
      • Hunter M.A.
      • Tannock R.
      Inconsistency in reaction time across the life span.
      ,
      • van Belle J.
      • van Raalten T.
      • Bos D.J.
      • Zandbelt B.B.
      • Oranje B.
      • Durston S.
      Capturing the dynamics of response variability in the brain in ADHD.
      ,
      • Geurts H.M.
      • Grasman R.P.P.P.
      • Verté S.
      • Oosterlaan J.
      • Roeyers H.
      • van Kammen S.M.
      • Sergeant J.A.
      Intra-individual variability in ADHD, autism spectrum disorders and Tourette’s syndrome.
      ,
      • Hervey A.S.
      • Epstein J.N.
      • Curry J.F.
      • Tonev S.
      • Eugene Arnold L.
      • Keith Conners C.
      • et al.
      Reaction time distribution analysis of neuropsychological performance in an ADHD sample.
      ,
      • McAuley T.
      • Yap M.
      • Christ S.E.
      • White D.A.
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      ). Therefore, we investigated whether DMN-DAN anticorrelation was differentially associated with tau or sigma, to better characterize the nature of attentional variability. Second, we evaluated additional putative anticorrelated networks (DMN-Cingulo-Opercular Network, DMN-Frontoparietal Network, DMN-Ventral Attention Network) and their behavioral associations. Lastly, we evaluated whether IIV and/or DMN-DAN anticorrelation are linked with externalizing symptoms, as deficits in attention may contribute to problems with self-regulation more broadly (53-55).

      Methods

      Participants

      Data from 11,878 participants were obtained through the ABCD study, a prospective, longitudinal study tracking 9-11 year olds for the following 10 years, across 20 research collection sites in the United States (
      • Luciana M.
      • Bjork J.M.
      • Nagel B.J.
      • Barch D.M.
      • Gonzalez R.
      • Nixon S.J.
      • Banich M.T.
      Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery.
      ). Each site obtained informed consent from parents and assent from children, approved by each site’s Institutional Review Board (IRB), with centralized IRB approval at the University of California, San Diego. All de-identified raw and processed data for these analyses were from ABCD Data Release 4.0, accessed through the National Institute of Mental Health Data Archive (Collection 2573). Due to use of de-identified data, this study was exempt from IRB approval. Exclusion criteria included: (
      • Leth-Steensen C.
      • Elbaz Z.K.
      • Douglas V.I.
      Mean response times, variability, and skew in the responding of ADHD children: A response time distributional approach.
      ) missing demographic information, such as parental income (n=1022) or race/ethnicity information (n=2); (
      • Castellanos F.X.
      • Tannock R.
      Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes.
      ) missing trial-wise data for the Flanker (n=1310), Dimensional Change Card Sort (n=1687), or Pattern Comparison Processing Speed (n=1316) or (
      • Kofler M.J.
      • Rapport M.D.
      • Sarver D.E.
      • Raiker J.S.
      • Orban S.A.
      • Friedman L.M.
      • Kolomeyer E.G.
      Reaction time variability in ADHD: a meta-analytic review of 319 studies.
      ) missing psychiatric symptom data at baseline (n =10). In concordance with previous work, further exclusion criteria for neuroimaging analyses can be found in the Supplement (
      • Marek S.
      • Tervo-Clemmens B.
      • Nielsen A.N.
      • Wheelock M.D.
      • Miller R.L.
      • Laumann T.O.
      • et al.
      Identifying reproducible individual differences in childhood functional brain networks: An ABCD study.
      ,
      • Power J.D.
      • Barnes K.A.
      • Snyder A.Z.
      • Schlaggar B.L.
      • Petersen S.E.
      Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.
      ,
      • Cai Y.
      • Elsayed N.M.
      • Barch D.M.
      Contributions from resting state functional connectivity and familial risk to early adolescent-onset MDD: Results from the Adolescent Brain Cognitive Development study.
      ). Baseline demographic characteristics are reported in Table 1; see the Supplement for demographic tables for year 1, 2, and 3 follow-up timepoints.
      Table 1Baseline Demographics Table, based on imaging inclusion criteria
      Total Participants, N8446
      Sex, N (%)
      Female4201(49.75)
      Male4245(50.25)
      Age, mean (SD)9.94(0.63)
      Parent report of child’s race/ethnicity, N (%)
      White4474(52.97)
      Black1202(14.23)
      Hispanic1698(20.11)
      Asian164(1.94)
      Other906(10.73)
      Income Category, N (%)
      < 50k2219(26.28)
      50-99k2232(26.43)
      100k+3321(39.31)
      Refuse to report income332(3.93)
      Don't know income340(4.03)
      Parental education in years, mean (SD)16.67(2.67)
      CBCL Attention t-score, mean (SD)53.73(6.04)
      CBCL Externalizing t-score, mean (SD)45.57(10.24)
      Mean Framewise Displacement (FD), mean (SD)0.22(2.34e-05)

      Measures

      Neuropsychological Test Battery

      Participants completed a developmentally-appropriate neurocognitive battery, the National Institute of Health (NIH) Toolbox (56), which includes 7 tasks covering episodic memory, executive function, attention, working memory, processing speed and language abilities. This neurocognitive battery was designed to comprehensively assess these domains, has been used in longitudinal studies across childhood and adolescence, and is psychometrically sound (
      • Luciana M.
      • Bjork J.M.
      • Nagel B.J.
      • Barch D.M.
      • Gonzalez R.
      • Nixon S.J.
      • Banich M.T.
      Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery.
      ).
      The current study used the timed-reaction time tasks from NIH Toolbox, which includes the Flanker, Pattern Comparison Processing Speed, and Dimensional Change Card Sort tasks. The Flanker and Dimensional Change Card Sort task show excellent test-retest reliability (ICC=0.92 for both measures) and the Processing Speed task shows good test-retest reliability (ICC=0.84) in children and adolescents over a two-week interval (
      • Zelazo P.D.
      • Anderson J.E.
      • Richler J.
      • Wallner-Allen K.
      • Beaumont J.L.
      • Weintraub S.
      II. NIH Toolbox Cognition Battery (CB): Measuring executive function and attention.
      ,
      • Carlozzi N.E.
      • Tulsky D.S.
      • Kail R.V.
      • Beaumont J.L.
      VI. NIH Toolbox Cognition Battery (CB): Measuring processing speed.
      ). Details of these tasks have been published elsewhere (
      • Luciana M.
      • Bjork J.M.
      • Nagel B.J.
      • Barch D.M.
      • Gonzalez R.
      • Nixon S.J.
      • Banich M.T.
      Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery.
      ,
      • Anokhin A.P.
      • Luciana M.
      • Banich M.
      • Barch D.
      • Bjork J.M.
      • Gonzalez M.R.
      • et al.
      Age-related changes and longitudinal stability of individual differences in ABCD Neurocognition measures.
      ).
      Intra-individual variability for each task and participant was operationalized as the standard deviation in reaction time across all correct trials. To remove extreme outliers that suggest invalid task performance, IIV was winsorized at three standard deviations for each task. Because the Flanker task assesses attention, we focused first on testing our analyses using Flanker IIV. Then, we examined associations using IIV from the Dimensional Change Card Sort and the Pattern Comparison Processing Speed tasks, to assess whether the associations would generalize to these related cognitive domains. To verify these results, we also analyzed these associations using the raw reaction time data to derive IIV (Supplement). Lastly, we generated ex-Gaussian parameters (tau, sigma, and mu) from the raw reaction time data for each task and participant by applying the retimes package in R.

      Imaging Procedure: Acquisition

      ABCD imaging collection, acquisition, and analysis has been previously described (
      • Casey B.J.
      • Cannonier T.
      • Conley M.I.
      • Cohen A.O.
      • Barch D.M.
      • Heitzeg M.M.
      • et al.
      The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.
      ,
      • Garavan H.
      • Bartsch H.
      • Conway K.
      • Decastro A.
      • Goldstein R.Z.
      • Heeringa S.
      • et al.
      Recruiting the ABCD sample: Design considerations and procedures.
      ,
      • Hagler Jr., D.J.
      • Hatton S.
      • Cornejo M.D.
      • Makowski C.
      • Fair D.A.
      • Dick A.S.
      • et al.
      Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.
      ). All participants were scanned on 3T scanners, including Prisma (Siemens), Discovery MR750 (General Electric), Achieva dStream or Ingenia CX (Philips) with a 32-channel head coil. Participants completed T1-weighted and T2-weighted structural scans, as well as four 5-minute resting-state blood oxygen level-dependent scans, with their eyes open, fixed at a crosshair. Further details about the resting state imaging acquisition that varied by 3T scanner have been detailed previously (
      • Casey B.J.
      • Cannonier T.
      • Conley M.I.
      • Cohen A.O.
      • Barch D.M.
      • Heitzeg M.M.
      • et al.
      The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.
      ).

      Imaging Procedure: Processing

      The ABCD DAIC performs centralized processing of MRI data from ABCD, using the Multi-Modal Processing Stream (65; see Supplement). After processing, between-network connectivity was calculated by computing pairwise correlations between each ROI within a given network and each ROI within another network, defined by the Gordon parcellation (
      • Gordon E.M.
      • Laumann T.O.
      • Adeyemo B.
      • Huckins J.F.
      • Kelley W.M.
      • Petersen S.E.
      Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.
      ) These correlations were averaged and Fisher-Z transformed to generate a summary metric of between-network connectivity strength, which we considered anticorrelation if significantly negatively correlated.

      Attention Problems and Externalizing Symptoms

      Caregivers of the participants completed the Child Behavior Checklist (CBCL), a 112-item questionnaire used to detect emotional and behavioral problems in youth (

      Achenbach, T. M. The Child Behavior Checklist and related instruments. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment., 2nd Ed., 2(1999), 429–466.

      ). The longitudinal analyses use the CBCL attention and externalizing t-scores from follow-up years 1, 2, and 3. Demographic characteristics of participants by follow-up year, based on availability of the CBCL data, are included in the Supplement.

      Statistical Analyses

      Linear mixed models were conducted in R version 4.1.1 lmer package (R Foundation for Statistical Computing, Vienna, Austria), with research site and family unit (nested within site, to account for 1336 twins and 23 triplets in this study sample) as random effects, and age, sex, race/ethnicity, parental income and parental education modeled as covariates (

      Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting Linear Mixed-Effects Models using lme4.

      ). Imaging analyses additionally included mean framewise displacement (FD) as a covariate. All results reported were FDR corrected.
      Linear mixed models were used to test the following cross-sectional predictions: (1a) across cognitive tasks (each in a separate model), lower IIV is associated the stronger DMN-DAN anti-correlation, and (1b) there would be an age effect, such that greater baseline age would be associated with stronger DMN-DAN anticorrelation and (1c) lower IIV. Furthermore, we tested the following hypotheses regarding associations between the lab-based measures and neurobehavioral symptoms: (2a) Greater IIV and/or (2b) weaker DMN-DAN anticorrelation are linked with worse attention symptoms at baseline. Lastly, we tested the following predictions using longitudinal data: (3a) Across cognitive tasks (each in a separate model), greater IIV and (3b) weaker DMN-DAN anticorrelation, all assessed at baseline, will be associated with subsequent attention problems, 1-, 2-, and 3- years later, after accounting for baseline CBCL attention problems. Results are expressed as standardized coefficients with 95% confidence intervals. As a control analysis, we hypothesized that IIV and parent-reported attention problems would not be associated with between network connectivity between auditory and retrosplenial networks, since the Flanker, Dimensional Change Card Sort, and Pattern Comparison Processing speed tasks are not thought to engage either network (69). When investigating behavioral and neural measures of attentional variability, we examined other key factors associated with these variables, including sex, race/ethnicity and parental income, to better contexualize these associations.

      Results

      DMN-DAN Anticorrelation and Behavioral variability

      As hypothesized, lower IIV across all three neurocognitive tasks, modeled in separate linear mixed models, was associated with stronger anticorrelation between the DMN and DAN (Figure 1, Table 2, see Supplement for estimated marginal means visualization). Within this model, older baseline age was associated with stronger DMN-DAN anticorrelation. Of the other covariates, head motion, sex, race/ethnicity, and family income were associated with DMN-DAN anticorrelation across models.
      Figure thumbnail gr1
      Figure 1Default Mode Network (blue) and Dorsal Attention Network (red) from the Gordon Parcellation
      Table 2Associations between IIV and DMN-DAN Functional Connectivity
      Flanker IIVDCCS IIVProc Speed IIV
      Predictorsstd. Betastd. CIstd. Betastd. CIstd. Betastd. CI
      DMN-DAN

      Anticorrelation
      0.04 ∗∗0.02 – 0.060.05 ∗∗∗0.02 – 0.070.03 0.01 – 0.05
      Age-0.09 ∗∗∗-0.11 – -0.07-0.13 ∗∗∗-0.15 – -0.11-0.08 ∗∗∗-0.11 – -0.06
      Sex0.02-0.02 – 0.060.07 ∗∗0.02 – 0.110.12 ∗∗∗0.07 – 0.16
      Mean FD0.10 ∗∗∗0.07 – 0.120.11 ∗∗∗0.09 – 0.130.07 ∗∗∗0.05 – 0.09
      50-99k-0.23 ∗∗∗-0.29 – -0.17-0.30 ∗∗∗-0.36 – -0.23-0.11 ∗∗∗-0.17 – -0.04
      100k+-0.34 ∗∗∗-0.40 – -0.28-0.35 ∗∗∗-0.41 – -0.29-0.15 ∗∗∗-0.22 – -0.09
      Non-White0.19 ∗∗∗0.13 – 0.240.17 ∗∗∗0.12 – 0.220.12 ∗∗∗0.07 – 0.18
      Parental Education-0.02-0.04 – 0.00-0.02-0.04 – 0.01-0.00-0.03 – 0.02
      * p<0.05 ** p<0.01 *** p<0.001, FDR corrected
      Reference categories: sex (F), income (<50k), race/ethnicity (White)
      More negative values indicate stronger DMN-DAN anticorrelation.

      Age and Behavioral Variability

      We replicated neuropsychological literature showing that younger baseline age was also associated with greater IIV, across all three tasks after adjusting for covariates (
      • MacDonald S.W.S.
      • Nyberg L.
      • Bäckman L.
      Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity.
      ,
      • Williams B.R.
      • Hultsch D.F.
      • Strauss E.H.
      • Hunter M.A.
      • Tannock R.
      Inconsistency in reaction time across the life span.
      ). Within these three models, there were also effects of race/ethnicity, parental income, and sex (Supplement). In a supplemental analysis, greater baseline age was also related to greater accuracy (Supplement).

      Behavioral and Neural Associations with Attentional and Externalizing Symptoms at Baseline

      Higher IIV across the Flanker, Processing Speed, and Dimensional Change Card Sort was associated with more severe baseline attention symptoms (Table 3). Furthermore, weaker DMN-DAN anticorrelation was associated with more severe attention symptoms (Table 3).
      Table 3Behavioral and Neural Associations with Attention Problems at Baseline
      CBCL Attention BVCBCL Attention BVCBCL Attention BVCBCL Attention BV
      Predictorsstd. Betastd. CIstd. Betastd. CIstd. Betastd. CIstd. Betastd. CI
      Flanker IIV0.12 ∗∗∗0.10 – 0.14
      DCCS IIV0.13 ∗∗∗0.11 – 0.15
      Proc Speed IIV0.11 ∗∗∗0.09 – 0.13
      DMN-DAN Anticorrelation0.07 ∗∗∗0.05 – 0.09
      Mean FD0.05 ∗∗∗0.02 – 0.07
      Age0.01-0.01 – 0.030.01-0.01 – 0.030.00-0.01 – 0.020.01-0.02 – 0.03
      Sex0.11 ∗∗∗0.07 – 0.150.10 ∗∗∗0.06 – 0.140.10 ∗∗∗0.06 – 0.140.06 ∗∗0.02 – 0.11
      50-99k-0.15 ∗∗∗-0.21 – -0.10-0.14 ∗∗∗-0.20 – -0.08-0.17 ∗∗∗-0.23 – -0.12-0.17 ∗∗∗-0.23 – -0.11
      100k+-0.25 ∗∗∗-0.30 – -0.19-0.24 ∗∗∗-0.30 – -0.19-0.28 ∗∗∗-0.33 – -0.22-0.28 ∗∗∗-0.34 – -0.22
      Non-White-0.01-0.05 – 0.04-0.01-0.06 – 0.03-0.00-0.05 – 0.04-0.01-0.06 – 0.04
      Parental Education-0.01-0.03 – 0.01-0.01-0.03 – 0.01-0.01-0.03 – 0.01
      * p<0.05 ** p<0.01 *** p<0.001, FDR corrected
      Reference categories: sex (F), income (<50k), race/ethnicity (White)
      More negative values indicate stronger DMN-DAN anticorrelation.
      BV = Baseline Visit, DCCS = Dimensional Change Card Sort
      In an exploratory analysis, higher IIV across all cognitive tasks and weaker (less negative) DMN-DAN anticorrelation was associated with more severe externalizing symptoms, all measured contemporaneously at baseline (Supplement). Of the covariates, several effects are consistent across all 8 models (Table 3 for Attentional Symptoms and Supplement for Externalizing Symptoms); sex, parental income, and race/ethnicity were associated with attention and externalizing symptoms.

      Prospective Behavioral and Neural Associations with Attentional Symptoms, 1-3 years later

      Next, we evaluated if either IIV or DMN-DAN connectivity at baseline was predictive of attention symptoms, one, two, and three years after the baseline visit. For all three tasks, higher IIV at baseline predicted more severe attention symptoms at the participants’ 1-, 2-, and 3-year follow-up visits, after controlling for baseline attention problems (Flanker in Table 4, other tasks in Supplement). After accounting for baseline attention problems and covariates included in baseline models, DMN-DAN connectivity did not predict future attention symptoms (Supplement).
      Table 4Prospective Behavioral Associations with Attentional symptoms, 1-3 years later
      CBCL Attention Y1CBCL Attention Y2CBCL Attention Y3
      Predictorsstd. Betastd. CIstd. Betastd. CIstd. Betastd. CI
      (Intercept)0.05 0.01 – 0.090.08 ∗∗0.02 – 0.130.08 0.01 – 0.15
      Flanker IIV0.02 ∗∗0.01 – 0.030.03 ∗∗0.01 – 0.040.03 0.01 – 0.05
      Baseline CBCL Attention0.73 ∗∗∗0.71 – 0.740.68 ∗∗∗0.66 – 0.700.61 ∗∗∗0.59 – 0.63
      Age-0.00-0.02 – 0.010.01-0.01 – 0.030.01-0.01 – 0.03
      Sex0.01-0.02 – 0.04-0.01-0.05 – 0.02-0.06 -0.10 – -0.01
      50-99k-0.02-0.06 – 0.02-0.06 -0.10 – -0.01-0.04-0.10 – 0.03
      100k+-0.07 ∗∗∗-0.11 – -0.04-0.06 -0.11 – -0.01-0.06 -0.13 – -0.00
      Non-White-0.04 -0.07 – -0.01-0.07 ∗∗∗-0.11 – -0.03-0.05-0.10 – 0.00
      Parental Education0.00-0.01 – 0.020.00-0.02 – 0.020.01-0.01 – 0.04
      * p<0.05 ** p<0.01 *** p<0.001, FDR corrected
      Reference categories: sex (F), income (<50k), race/ethnicity (White)
      More negative values indicate stronger DMN-DAN anticorrelation.
      Y1 = Year 1 Follow-up, Y2 = Year 2 Follow-up, Y3 = Year 3 Follow-up

      Prospective Behavioral and Neural Associations with Externalizing Symptoms, 1-3 years later

      Higher IIV at baseline was also associated with increased externalizing symptoms at the 1-year follow-up visit after controlling for baseline externalizing symptom scores, but not at later time points (Supplement). Furthermore, DMN-DAN anticorrelation did not predict future externalizing symptoms at any time point after controlling for baseline externalizing symptoms.

      Ex-Gaussian Distribution Modeling

      Using ex-Gaussian approaches to parse reaction time distribution into variability in slow (tau) vs. fast (sigma) responses, we found that stronger DMN-DAN anticorrelation was associated with both lower sigma and lower tau, across all three tasks, after controlling for covariates (Supplement). We also found that DMN-CON anticorrelation was specifically associated with lower tau and lower sigma in the Dimensional Change Card Sort task, but not the other two tasks.

      Specificity and Robustness Analyses at Baseline

      To demonstrate specificity, we tested an association between IIV and between-network connectivity between the auditory and retrosplenial networks. The cognitive tasks require visuospatial attention but not auditory processing. Furthermore, these tasks require in-the-moment attention, and not the range of cognitive functions linked with the retrosplenial network, including episodic memory, navigation, imagination and planning for the future (
      • Vann S.D.
      • Aggleton J.P.
      • Maguire E.A.
      What does the retrosplenial cortex do?.
      ). The auditory-retrosplenial network connectivity was not associated with Flanker IIV (p=0.753) or CBCL attention t-score (p=0.857) at baseline, after accounting for the covariates included in previous models (Supplement).
      To evaluate the robustness of these results, we also formally tested the (i) effect of site and examined our hypotheses using (ii) raw reaction time data to calculate IIV and (iii) using a subset of low-motion subjects. First, we modeled site as a predictor using general linear models to compare site effects with the main effects in Aims 1-3. Our main findings remained significant, though a minority of sites were significant in these models (Supplement). Using raw non-winsorized data, all the results remain significant, with the only exception being that Processing Speed IIV was no longer associated with DMN-DAN anticorrelation (Supplement). Lastly, we applied a more stringent motion threshold which only included participants with 60% of resting-state frames or higher with framewise displacement less than 0.2 mm (n=7003). Greater DMN-DAN anticorrelation remained significantly associated with lower IIV in the Flanker and Dimensional Change Card Sort tasks, greater baseline age, and less severe baseline attention symptoms (Supplement).

      Discussion

      The current study provides the first evidence linking anticorrelation between brain networks at rest and neurocognitive measures of attentional variability in early adolescence. Importantly, both lab-based resting state FC and neurocognitive markers of attentional variability were associated with parent-reported attention symptoms at baseline. We also provide novel evidence that worse scores on lab-based measures of attentional variability (IIV) at 9-10 years old predict subsequent parent-reported attentional problems at 1, 2 and 3-year follow-up visits. Leveraging the ABCD cohort to evaluate longitudinal associations between functional connectivity, neurocognitive measures, and symptoms allows us to evaluate potential markers for attentional dysfunction in the general population and to understand the relationships between neural and behavioral development in adolescence.
      We provide the first evidence linking stronger DMN-DAN anticorrelation and lower IIV in early adolescence, connecting resting-state and neurocognitive literature, which have been largely investigated independently as measures of attention variability. Previous studies have shown that the DMN and DAN networks have an intrinsically antiphase relationship, first established one year after birth, increasing during development, and decreasing in older adulthood (
      • Fox M.D.
      • Snyder A.Z.
      • Vincent J.L.
      • Corbetta M.
      • Van Essen D.C.
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      The human brain is intrinsically organized into dynamic, anticorrelated functional networks.
      ,
      • Fransson P.
      Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis.
      ,
      • Gao W.
      • Gilmore J.H.
      • Shen D.
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      • Zhu H.
      • Lin W.
      The synchronization within and interaction between the default and dorsal attention networks in early infancy.
      ,
      • Chai X.J.
      • Ofen N.
      • Gabrieli J.D.E.
      • others
      Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.
      ,
      • Spreng R.N.
      • Schacter D.L.
      Default network modulation and large-scale network interactivity in healthy young and old adults.
      ,
      • Buckner R.L.
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      • Yeo B.T.T.
      Opportunities and limitations of intrinsic functional connectivity MRI.
      ). Kelly et al. 2008 was the first to link stronger DMN-DAN anticorrelation with lower Flanker IIV in 20 adults. The current results extend this work to early adolescence and to other goal-directed, cognitive tasks, showing that stronger DMN-DAN anticorrelation is associated with greater baseline age and lower IIV across cognitive domains. Furthermore, using ex-Gaussian distribution modeling, stronger DMN-DAN anticorrelation is also related to both lower tau and lower sigma across all tasks, indicating lower variability of slow and fast responses, respectively. Taken together, both Gaussian and ex-Gaussian approaches suggest that segregation of the DMN and DAN may be relevant to cognitive development during early adolescence.
      These results also indicate a remarkably consistent effect of age on neural and behavioral markers of attention, in a narrow age range from 9-11 years, that aligns with previous work in smaller samples (
      • MacDonald S.W.S.
      • Nyberg L.
      • Bäckman L.
      Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity.
      ,
      • Williams B.R.
      • Hultsch D.F.
      • Strauss E.H.
      • Hunter M.A.
      • Tannock R.
      Inconsistency in reaction time across the life span.
      ,
      • Tamnes C.K.
      • Fjell A.M.
      • Westlye L.T.
      • Østby Y.
      • Walhovd K.B.
      Becoming consistent: Developmental reductions in intraindividual variability in reaction time are related to white matter integrity.
      ,
      • Dykiert D.
      • Der G.
      • Starr J.M.
      • Deary I.J.
      Sex differences in reaction time mean and intraindividual variability across the life span.
      ). Neuropsychological task literature indicates that IIV decreases dramatically during childhood and adolescence, in conjunction with improved sustained attention (
      • MacDonald S.W.S.
      • Nyberg L.
      • Bäckman L.
      Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity.
      ,
      • Williams B.R.
      • Hultsch D.F.
      • Strauss E.H.
      • Hunter M.A.
      • Tannock R.
      Inconsistency in reaction time across the life span.
      ,
      • Tamnes C.K.
      • Fjell A.M.
      • Westlye L.T.
      • Østby Y.
      • Walhovd K.B.
      Becoming consistent: Developmental reductions in intraindividual variability in reaction time are related to white matter integrity.
      ,
      • Dykiert D.
      • Der G.
      • Starr J.M.
      • Deary I.J.
      Sex differences in reaction time mean and intraindividual variability across the life span.
      ). However, most of this literature focuses on IIV across the lifespan. Here, we focus on ages 9-11 and found that older baseline age was associated with lower IIV across the Flanker, Pattern Comparison Processing Speed, and Dimensional Change Card Sort Tasks. These consistent behavioral findings suggest that attentional variability is improving across different cognitive demands in early adolescence, even within the narrow age band of 9-11.
      The current study also found that higher IIV across three cognitive tasks is cross-sectionally associated with more severe attention symptoms. Previous work indicates that higher IIV robustly identifies ADHD vs. TD youth (
      • Castellanos F.X.
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      Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes.
      ,
      • Kofler M.J.
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      ) - a binary approach to attention problems (
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      ). However, continuous approaches are closer to the phenomena of attention and its underlying neurobiological processes (
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      • et al.
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      ). As such, this study takes a dimensional approach to include the full spectrum of attention problems in the ABCD sample (
      • Kofler M.J.
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      • Raiker J.S.
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      ,
      • Tamm L.
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      ). The current findings are consistent with previous work in children with ADHD and generalize to a representative sample of US youth (
      • Castellanos F.X.
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      Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes.
      ).
      The current results are the first to show that IIV assessed at age 9-10 is a consistent, proximal marker of worsening attention during early adolescence (1, 2, and 3 years later). These results align with two longitudinal, developmental studies (non-diverse, smaller samples) that controlled for attention symptoms at baseline and used IIV to predict overall functioning and symptoms of inattention (
      • van Lieshout M.
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      ). By controlling for attention problems at baseline, the current results highlight the relationship between a lab-based measure of attentional variability in early adolescence and future worsening of attention symptoms, over and above early parent-reported attention deficits. In early adolescence, worsening attention problems may be a broad indicator of functional impairment, regardless of specific etiology or diagnoses that may emerge later on (
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      ). Research on other neurodevelopmental disorders with onset in late adolescence (i.e, psychotic disorders) indicates that early intervention can be key for improving future symptom trajectories (
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      • Bearden C.E.
      • Sabb F.W.
      • Stone W.S.
      • Torous J.
      • Cornblatt B.A.
      • et al.
      Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit.
      ,
      • Csillag C.
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      ,
      • Malla A.
      • McGorry P.
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      ). Since ABCD will continue to collect data prospectively on participants through late adolescence, future work could examine IIV as a predictor of altered trajectories of cognitive development and mental health outcomes (
      • Solmi M.
      • Radua J.
      • Olivola M.
      • Croce E.
      • Soardo L.
      • Salazar de Pablo G.
      • et al.
      Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies.
      ).
      While we found that greater DMN-DAN anticorrelation was linked with lower contemporaneous attention problems, we did not find an association between DMN-DAN connectivity and future attention symptoms. The cross-sectional DMN-DAN FC -attention symptom association aligns with work using a previous release of the ABCD data (
      • Owens M.M.
      • Yuan D.
      • Hahn S.
      • Albaugh M.
      • Allgaier N.
      • Chaarani B.
      • et al.
      Investigation of Psychiatric and Neuropsychological Correlates of Default Mode Network and Dorsal Attention Network Anticorrelation in Children.
      ). However, this work also stands in contrast with previous studies which have found no association between anticorrelated networks and attention symptoms, albeit in much smaller adult samples (
      • Sidlauskaite J.
      • Sonuga-Barke E.
      • Roeyers H.
      • Wiersema J.R.
      Altered intrinsic organisation of brain networks implicated in attentional processes in adult attention-deficit/hyperactivity disorder: A resting-state study of attention, default mode and salience network connectivity.
      ,
      • McCarthy H.
      • Skokauskas N.
      • Mulligan A.
      • Donohoe G.
      • Mullins D.
      • Kelly J.
      • et al.
      Attention network hypoconnectivity with default and affective network hyperconnectivity in adults diagnosed with attention-deficit/hyperactivity disorder in childhood.
      ). Using multivariate approaches may improve prediction abilities (
      • Yoo K.
      • Rosenberg M.D.
      • Noble S.
      • Scheinost D.
      • Constable R.T.
      • Chun M.M.
      Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors.
      ). Notably, Rosenberg et al. 2020 used a connectome-based predictive model to predict future sustained attention in adults (87). Therefore, other metrics of RSFC may be important neural predictors of symptom onset or progression.
      In exploratory analyses, we evaluated whether additional task-positive networks were anticorrelated with the DMN and determined that only the DMN and CON were significantly anticorrelated. Because DMN-CON anticorrelation was significantly associated with Gaussian and ex-Gaussian measures of variability for the Dimensional Change Card Sort but not the other two tasks, these findings may suggest that this circuitry specifically supports fluctuation in cognitive flexibility, as opposed to variability in cognitive performance broadly. In addition to attention problems, we found that more severe externalizing symptoms were cross-sectionally associated with greater IIV and weaker DMN-DAN anticorrelation. Further, greater IIV at baseline predicted more severe externalizing symptoms at the following year, after controlling for externalizing symptoms at baseline. These results give credence to the possibility that attention is an important feature in self-regulation. Focusing on the task at hand, while suppressing internally-directed cognition, may be critical for controlling one’s behavior in relation to current and future goals (
      • Rueda M.R.
      • Posner M.I.
      • Rothbart M.K.
      Attentional control and self-regulation.
      ,
      • Berger A.
      • Kofman O.
      • Livneh U.
      • Henik A.
      Multidisciplinary perspectives on attention and the development of self-regulation.
      ,
      • Posner M.I.
      • Rothbart M.K.
      Toward a physical basis of attention and self regulation.
      ).

      Limitations and Future Directions

      This study leverages the largest imaging and behavior study of adolescence to date to examine a key developmental shift in attentional variability and its neural correlates; however, some limitations should be noted. Global signal regression (GSR) is included in the standard ABCD data analysis pipeline, used in the current analyses. While GSR is a powerful tool to remove spurious artifacts due to motion, respiration, and physiological noise that is prominent in developmental populations (
      • Power J.D.
      • Mitra A.
      • Laumann T.O.
      • Snyder A.Z.
      • Schlaggar B.L.
      • Petersen S.E.
      Methods to detect, characterize, and remove motion artifact in resting state fMRI.
      ), it also can introduce negative correlations into the data. This has called into question whether DMN-DAN anticorrelation may be an artifact of GSR (
      • Murphy K.
      • Birn R.M.
      • Handwerker D.A.
      • Jones T.B.
      • Bandettini P.A.
      The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?.
      ,
      • Fox M.D.
      • Zhang D.
      • Snyder A.Z.
      • Raichle M.E.
      The global signal and observed anticorrelated resting state brain networks.
      ). However, anticorrelation between the default mode and task-positive networks has been detected in studies both with and without GSR, indicating that that anticorrelation is not a result of GSR (
      • Fox M.D.
      • Zhang D.
      • Snyder A.Z.
      • Raichle M.E.
      The global signal and observed anticorrelated resting state brain networks.
      ,
      • Beckmann C.F.
      • DeLuca M.
      • Devlin J.T.
      • Smith S.M.
      Investigations into resting-state connectivity using independent component analysis.
      ,
      • Chang C.
      • Glover G.H.
      Effects of model-based physiological noise correction on default mode network anti-correlations and correlations.
      ). Because of the consistency of anticorrelated networks, whether GSR is included or not, we have chosen to replicate and extend the majority of studies using GSR that show (i) development of anticorrelated networks or (ii) that anticorrelation is linked with superior cognitive performance (
      • Kelly A.M.C.
      • Uddin L.Q.
      • Biswal B.B.
      • Castellanos F.X.
      • Milham M.P.
      Competition between functional brain networks mediates behavioral variability.
      ,
      • Gao W.
      • Gilmore J.H.
      • Shen D.
      • Smith J.K.
      • Zhu H.
      • Lin W.
      The synchronization within and interaction between the default and dorsal attention networks in early infancy.
      ,
      • Chai X.J.
      • Ofen N.
      • Gabrieli J.D.E.
      • others
      Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.
      ,
      • Spreng R.N.
      • Schacter D.L.
      Default network modulation and large-scale network interactivity in healthy young and old adults.
      ,
      • Barber A.D.
      • Jacobson L.A.
      • Wexler J.L.
      • Nebel M.B.
      • Caffo B.S.
      • Pekar J.J.
      • Mostofsky S.H.
      Connectivity supporting attention in children with attention deficit hyperactivity disorder.
      ,
      • O’Halloran L.
      • Cao Z.
      • Ruddy K.
      • Jollans L.
      • Albaugh M.D.
      • Aleni A.
      • et al.
      Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology.
      ). Furthermore, Nomi et al. 2022 discovered complex linear and quadratic associations between GSR and age, with different brain regions and networks (

      Nomi, J. S., Bzdok, D., Li, J., Bolt, T., Kornfeld, S., Goodman, Z. T., et al. (2022). Global signal topography in the human brain differs systematically across the lifespan (p. 2022.07.27.501804). bioRxiv.

      ). Though the impact of GSR is minimal on the linear and quadratic associations of the DMN-DAN anticorrelation with age, this question warrants future investigation in lifespan studies of functional connectivity. Lastly, we note that although large-scale brain networks undergo development during adolescence, no adolescent-specific brain parcellations exist to date. Ongoing work addresses this issue by creating age-specific parcellations (
      • Uddin L.Q.
      • Betzel R.F.
      • Cohen J.R.
      • Damoiseaux J.S.
      • Brigard F.D.
      • Eickhoff S.B.
      • et al.
      Controversies and current progress on large-scale brain network nomenclature from OHBM WHATNET: Workgroup for HArmonized Taxonomy of NETworks.
      ).
      With regard to capturing attention problems, the CBCL inattention scale includes a variety of attention symptoms, including both hyperactivity and inattentiveness, which may warrant separate investigation in future studies (

      Achenbach, T. M. The Child Behavior Checklist and related instruments. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment., 2nd Ed., 2(1999), 429–466.

      ). As a parent-report measure, the CBCL may be biased due to rater; multiple informants including teachers would be ideal to capture inattentive behavior across contexts. Though ABCD used a structured interview to assess categorical psychiatric disorders, ADHD diagnoses were not available in the current Data Release, due to errors in the programming algorithm. Regardless, our current work focuses on dimensional phenotypes of attention dysfunction. In the future, we will investigate these questions in the context of ADHD when accurate diagnoses are available.
      The results of the current study demonstrate the functional significance of lab-based measures of attentional variability, linking them to attentional impairment in early adolescence. Increased intra-individual variability across three goal-directed behavioral tasks was robustly linked with more severe attention deficits at baseline as well as 1-, 2-, and 3- years later. While higher IIV is a well-known hallmark of current ADHD (
      • Castellanos F.X.
      • Tannock R.
      Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes.
      ,
      • Kofler M.J.
      • Rapport M.D.
      • Sarver D.E.
      • Raiker J.S.
      • Orban S.A.
      • Friedman L.M.
      • Kolomeyer E.G.
      Reaction time variability in ADHD: a meta-analytic review of 319 studies.
      ), these results show that IIV may be a useful index to identify worsening attention symptoms in the general population. Further, we have shown that stronger negative anticorrelation between the DMN and DAN is cross-sectionally associated with lower IIV across multiple tasks, better attention, and older age. These results suggest that the segregation of brain networks is associated with developmental improvements in cognition. In future studies, focusing on altered longitudinal patterns of DMN-DAN anticorrelation may be informative for predicting functional outcome.

      Uncited reference

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      • Chang L.
      • Bruno E.
      • Dworkin J.
      • Posner J.
      Structural brain measures among children with and without ADHD in the Adolescent Brain and Cognitive Development Study cohort: A cross-sectional US population-based study.
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      Functional connectivity predicts changes in attention observed across minutes, days, and months.
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      Acknowledgements and Disclosures

      We thank Drs. Andrew Fuligni, Mirella Dapretto, and Adriana Galván for their feedback on these analyses and for their mentorship through the T32 for Brain and Behavioral Development during Adolescence. This work was supported by 1T32HD091059-01A1 (to SEC) and U01MH124639 (to CEB). A preprint of this manuscript was previously posted to bioRxiv.

      Supplementary Material

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