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

Bernanke, Joel; Luna, Alex; Chang, Le; Bruno, Elizabeth; Dworkin, Jordan D.; Posner, Jonathan

doi:10.1016/s2215-0366(21)00505-8

Cited by 47 publications

(45 citation statements)

References 36 publications

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“…As such, this study takes a dimensional approach to include the full spectrum of attention problems in the ABCD sample (3, 71). Previous work in the ABCD study indicates that 949 individuals (7.99%) met ADHD diagnostic criteria, but the ABCD study also includes many youth with subclinical symptoms and youth with minimal attention problems (48). Despite these methodological differences, the current findings are consistent with previous work in children with ADHD and generalize to a representative sample of US youth (2).…”

Section: Discussionmentioning

confidence: 99%

“…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 using cross-sectional data at baseline were the information (n=2) or parental income (n=1022); (2) missing trial-wise neuropsychological test data for the Flanker (n=1310), Pattern Comparison Processing Speed (n=1316), Dimensional Change Card Sort (n=1687); or (3) missing psychiatric symptom data at baseline (n =10).…”

Section: Introductionmentioning

confidence: 99%

“…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 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.…”

Section: Introductionmentioning

confidence: 99%

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

Chang¹,

Lenartowicz²,

Hellemann³

et al. 2022

Preprint

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Background Intra-individual variability (IIV) during cognitive task performance is a key behavioral index of attention and 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 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…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 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.…”

Section: Introductionmentioning

confidence: 99%

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

Chang¹,

Lenartowicz²,

Hellemann³

et al. 2022

Preprint

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“…Magnetic resonance imaging (MRI) has become an essential tool for measuring neuroanatomical and functional variation of the human brain. Structural and functional imaging derived features have been used for understanding brain-behavioral associations (Bernanke J et al, 2022), neurodevelopment (Bethlehem RAI et al, 2022), and the purported impact of environmental exposures on the brain (Marshall AT et al, 2020). Although effect sizes and test-retest reliability of MRI features have recently been critically examined (Dick et al 2021; Kennedy JT et al, 2022; Marek S et al, 2022), the internal validity of associations based on neuroimaging features has seldom been discussed.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Population Stratification on the Analysis of Multimodal Neuroimaging Derived Measures

Huang

Loughnan

Thompson

et al. 2022

Preprint

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Magnetic resonance imaging (MRI) studies of the human brain are now attaining larger sample sizes with more diverse samples. However, population stratification, a key factor driving heterogeneity and confounding of associations, is seldom accounted for in neuroimaging analyses. To investigate this issue, we assessed the impact of population stratification on multimodal imaging measures using baseline data from the Adolescent Brain Cognitive Development (ABCD) StudySM (n = 10,748). Given this sociodemographically diverse sample, which broadly reflects the population composition of the United States, we performed a thorough evaluation of the impact of population stratification on derived neuroimaging metrics across five different imaging modalities: task functional MRI (task fMRI), resting state functional MRI (rsMRI), diffusion tensor images (DTI), restricted spectrum images (RSI), and structural T1 MRI (sMRI). We used parental income level as an example to highlight the impact of population stratification in confounding brain-wide associations. We show that derived metrics from structural images have up to three times more signal related to population stratification than do functional images. Controlling for population stratification in statistical models leads to a substantial reduction in the association strength between variables of interests and imaging measures, indicating the scale of potential bias. Moreover, because of unequal access to resources (such as income) across ancestral groups in United States, population stratification effects on imaging features may bias associations between parental income levels and imaging features, as we demonstrate. Our results provide a guide for researchers to critically examine the impact of population stratification and to assist in avoiding spurious brain-behavior associations.

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“…Within neuroimaging studies of ADHD, except for a few large investigations (3,9,10), studies have generally matched or adjusted for a few demographic variables (e.g., age, sex) and neuroimaging metrics or parameters. Of the 19 studies included in a systematic review of neuroimaging studies on ADHD (11), 17 adjusted or matched for age in their analyses, 14 for sex, 9 for precision variables like study site, and 8 for the intelligence quotient (IQ) ( Supplementary Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

Attention Deficit Hyperactivity Disorder Symptoms and Brain Morphology: Examining Confounding Bias

Dall'Aglio

Kim

Lamballais

et al. 2022

Preprint

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Background: Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for IQ, which is typically corrected for but plays an unclear role (aim 2). Methods: Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N=7,961) and Generation R (N=2,531) studies. Cortical area and volume were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.Results: ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes were further reduced when including IQ (aim 2), however, we argue that adjustments could have introduced bias (e.g., by conditioning on a collider).Conclusions: Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.Funding: Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.

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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

Cited by 47 publications

References 36 publications

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

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

The Impact of Population Stratification on the Analysis of Multimodal Neuroimaging Derived Measures

Attention Deficit Hyperactivity Disorder Symptoms and Brain Morphology: Examining Confounding Bias

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