Attention-deficit hyperactivity disorder symptoms and brain morphology: Examining confounding bias

  1. Lorenza Dall'Aglio
  2. Hannah H Kim
  3. Sander Lamballais
  4. Jeremy Labrecque
  5. Ryan L Muetzel
  6. Henning Tiemeier  Is a corresponding author
  1. Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam-Sophia Children’s Hospital, Netherlands
  2. The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Netherlands
  3. Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, United States
  4. Department of Clinical Genetics, Erasmus MC, Netherlands
  5. Department of Epidemiology, Erasmus MC, Netherlands

Abstract

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 the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

Methods:

Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness 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 further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.

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.

Editor's evaluation

This study provides important and useful information to researchers in brain morphology and ADHD. The strength of the evidence presented is convincing and solid.

https://doi.org/10.7554/eLife.78002.sa0

Introduction

Large strides have been made in the identification of neuroanatomical correlates of psychiatric problems, with attention-deficit/hyperactivity disorder (ADHD) being a prominent example. ADHD is the most prevalent neurodevelopmental disorder in children worldwide and is characterized by atypical levels of inattention, hyperactivity, and/or impulsivity (American Psychiatric Association, 2013). Structural magnetic resonance imaging studies have highlighted that children with ADHD show widespread morphological differences, such as in the basal ganglia (Nakao et al., 2011), subcortical areas (Hoogman et al., 2017), and frontal, cingulate, and temporal cortices, compared to children without the disorder (Hoogman et al., 2019; Shaw et al., 2013).

Consistently identifying the neuroanatomical substrate of ADHD, however, remains challenging. A recent meta-analysis did not find convergence across the literature on brain differences in children and adolescents with ADHD (Samea et al., 2019). One possible explanation for this inconsistency is the multifaceted nature of ADHD, in which children with the disorder have heterogeneous presentations on several cognitive and emotional domains, which could stem from distinct brain structural substrates. Other explanations regard study design. If suboptimal, it may lead to biased estimates and lack of generalizability, thus potentially concealing robust and replicable relations of brain morphology with ADHD. The present study focuses on confounding, a common source of bias in etiological studies.

Confounding bias arises when a third variable affects both the determinant (independent variable) and outcome (dependent variable) of interest (i.e., is a common cause) (VanderWeele, 2019). Confounding leads to over- or underestimation of the true effect between determinant and outcome and can even change the direction of an association. To minimize confounding bias, appropriate confounder control is paramount, although it is challenging, especially in observational studies like most neuroimaging studies of ADHD. Previous literature and expert knowledge can guide the identification of potential confounders (Hernan and Robins, 2020), which can then be appropriately adjusted for in regression models or using methods such as restriction, standardization, or propensity scores.

Within neuroimaging studies of ADHD, except for a few large investigations (Hoogman et al., 2017; Mous et al., 2014; Bernanke et al., 2022), studies have generally matched or adjusted for a few demographic variables (e.g., age and sex) and neuroimaging metrics or parameters. Of the 19 studies included in a systematic review of neuroimaging studies on ADHD (Saad et al., 2020), 17 adjusted or matched for age in their analyses, 14 for sex, 9 for neuroimaging-related variables like head motion during scanning, and 8 for the intelligence quotient (IQ) (Supplementary file 1a). Further potential confounders should, however, be considered. For instance, socioeconomic status (SES) is related to both higher risk for ADHD and variation in cortical brain structure (Russell et al., 2016; Noble et al., 2015). Thus, it is likely a confounder. Lack of adjustment for SES may have therefore concealed key relations between ADHD and brain structure. Adjustment choices are dependent on the availability of large samples with data on a wide variety of covariates, which has to date been limited for psychiatric neuroimaging studies. Yet, this is rapidly changing with the advent of population neuroscience, which entails large-scale studies with neurobiological data. This lends new opportunities for further confounder adjustments to be considered in neuroimaging studies of ADHD. Conversely, previous studies have adjusted for IQ and head motion, which may not be confounders in the association between ADHD symptoms and the brain, and may thus have led to further bias in the results (Dennis et al., 2009).

In this study, we examined the association between brain structure and ADHD symptoms and how the selection and control for potential confounders may affect results (aim 1). Moreover, we discussed the complex role of IQ and head motion in brain structure–ADHD associations and the potential consequences of adjusting for them (aim 2). We leveraged two large, population-based cohorts: the Adolescent Brain Cognitive Development (ABCD) and the Generation R Studies. In line with most neuroimaging studies, we adopted a cross-sectional design.

Results

Associations between ADHD symptoms and brain morphology are widespread

We analyzed data from 10-year-old children from the ABCD (N = 7722, multisite) and Generation R (N = 2531, single-site) Studies (Supplementary file 1b). ADHD symptoms were measured with the Child Behavioral Checklist (CBCL). T1-weighted images were obtained with 3T scanners (Casey et al., 2018; Kooijman et al., 2016). We ran vertex-wise linear regression models for ADHD with cortical surface area, volume, and thickness. Results for surface area, which constituted the main findings here, are presented in-text, while findings for volume and thickness in the figure supplements. We adjusted for demographic and study characteristics which have been generally considered by previous literature (Supplementary file 1a): age, sex, ethnicity, and study site (ABCD only). We refer to this model as model 1, as further adjustments for confounders are outlined in subsequent steps.

We found that higher ADHD symptoms were associated with less bilateral surface area in both cohorts. As shown in Figure 1, associations were widespread, as the clusters of association covered 1165.7 cm2 of the cerebral cortex in the ABCD Study, and 446.1 cm2 in the Generation R Study. Across both cohorts, we consistently identified clusters for surface area in the lateral occipital, postcentral, rostral middle and superior frontal, and superior parietal cortices. For cortical thickness, we found two small frontal clusters in the ABCD Study (16.1 cm2) and no clusters in the Generation R Study, which suggests that cortical thickness does not relate or does not relate strongly to ADHD, in line with prior literature (Hoogman et al., 2019; Figure 1—figure supplement 1).

Figure 1 with 2 supplements see all
Significant clusters in the association of attention-deficit/hyperactivity disorder (ADHD) symptoms with cortical surface area based on the Adolescent Brain Cognitive Development (ABCD) and Generation R Studies, for model 1.

Note. Rows represent the results for the ABCD or Generation R Studies, and the columns represent the left and right hemispheres. Regions in red represent significant clusters from model 1 (adjusted for sex, age, race/ethnicity, and site [ABCD only]).

Confounder selection: socioeconomic and maternal behavioral factors

Next, we considered factors that have been previously linked to ADHD and brain structure in the literature, and are thus potential confounders. To illustrate this background knowledge and the assumptions about relations between variables, we used Directed Acyclic Graphs (DAGs), a type of causal diagram (Hernan and Robins, 2020). These guide the identification (and dismissal) of covariates that may act as confounders. Of note, while assumptions may not hold, this theoretical approach is preferred to methods selecting confounders based on model statistics (Lee, 2014). The DAGs are depicted in Figure 2 and Figure 2—figure supplements 1 and 2, and the rationales for variable inclusion are explained below and in the Methods.

Figure 2 with 2 supplements see all
Directed Acyclic Graphs (DAGs) for brain structure and attention-deficit/hyperactivity disorder (ADHD) symptoms (simplified).

Note. DAGs illustrating potential confounders in the association between brain structure and ADHD symptoms for three sequential models. Model 1 included demographic and study characteristics: sex, age, ethnicity, and study site (Adolescent Brain Cognitive Development [ABCD] only) (in blue). Model 2 additionally included socioeconomic status factors: family income, maternal education, and maternal age at childbirth (in red). Model 3 additionally incorporated postnatal maternal psychopathology and maternal substance use during pregnancy (in green).

Based on the literature, lower SES is associated with a higher risk for ADHD (Russell et al., 2016) and with variation in cortical brain structure (Noble et al., 2015). Thus, confounding by socioeconomic factors in the relation between ADHD and brain morphology is likely. We therefore additionally adjusted for a second set of confounders (model 2) related to SES: household income, maternal education, and maternal age at childbirth.

Moreover, several factors concerning maternal behavior, pre- and postnatally, have been associated with both ADHD and brain morphology. For instance, prenatal exposure to substances is known to increase the risk of developing ADHD symptoms and has been associated with variation in cerebral volume and surface area (Eilertsen et al., 2017; Lees et al., 2020). Postnatal maternal psychopathology has been linked to higher child ADHD symptoms (Clavarino et al., 2010) and smaller brain volume in children (Zou et al., 2019). Thus, in model 3 we additionally adjusted for prenatal exposure to substance use (tobacco and cannabis), and postnatal maternal psychopathology.

Adjusting for additional confounders led to reductions in the clusters of association

Adjustments for SES (model 2) led to reductions in the spatial extent of the clusters for surface area and volume in both cohorts (Figure 3). For surface area, cluster sizes for ADHD symptoms reduced from 1165.7 cm2 in model 1 to 952.8 cm2 in model 2 (=−18%) in the ABCD Study, and from 446.1 to 229.6 cm2 (=−49%) in the Generation R Study. Similar reductions were observed for volume and thickness (Figure 3—figure supplement 1). After adjusting for the confounders added in model 3, across both cohorts, we consistently identified clusters for surface area in the cuneus, precuneus, fusiform, inferior parietal, isthmus of the cingulate, pericalcarine, pre- and postcentral, rostral middle and superior frontal, superior temporal and supramarginal cortices.

Figure 3 with 2 supplements see all
Significant clusters in the association of attention-deficit/hyperactivity disorder (ADHD) symptoms with cortical surface area based on the Adolescent Brain Cognitive Development (ABCD) and Generation R Studies, for models 1–3.

Note. Rows represent the results for the ABCD or Generation R Studies, and the columns represent the left and right hemispheres. The colors denote the different models. Regions in red represent significant clusters from model 1 (sex, age, race/ethnicity, and site [ABCD only]), orange from model 2 (model 1 + family income, maternal education, and maternal age at childbirth), and yellow from model 3 (model 2 + maternal smoking, substance use during pregnancy, psychopathology).

Similar results were observed for ADHD diagnosis

To explore whether the results observed for associations between brain morphology and ADHD symptoms applied to children with an ADHD diagnosis, we repeated the primary analysis using the ADHD diagnostic data from the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS) in the ABCD Study. In line with our primary results, ADHD diagnosis was associated with less bilateral surface area and volume. Compared to clusters for ADHD symptoms, those associated with ADHD diagnosis were smaller, but overlapping (Figure 1—figure supplement 2). We observed similar patterns of reduction in the spatial extent of the clusters after adjusting for each set of confounders (Figure 3—figure supplement 2). For surface area, cluster sizes for ADHD symptoms covered 234.4 cm2 in model 1 and reduced to 199.5 cm2 in model 2 (=−15%), and 55.5 cm2 in model 3 (=−72%, compared to model 2).

Beta coefficients generally decreased after confounder adjustments, but may also increase

Surface-based studies generally focus on the spatial extent of cortical clusters associated with the phenotype, but, in this study, we also explored how confounding adjustments affected the regression coefficients for ADHD symptoms (Figure 4).

Figure 4 with 5 supplements see all
Region-based average regression coefficients for surface area in the Adolescent Brain Cognitive Development (ABCD) and Generation R Studies.

Note. The colors denote the different models, and the circles denote the average of all the betas within that region. The regions are based on the Desikan–Killiany atlas. Results for the ABCD and Generation R Studies are, respectively, shown on the top and bottom.

At a vertex-wise level, adjusting for socioeconomic and maternal factors (model 3) led to reductions in the beta coefficients, across the brain, for both cohorts (Figure 4—figure supplement 1). Of note, some beta coefficients also showed increases. As confounding bias may lead to under- or overestimation, it is not surprising to observe both decreases and increases in the average beta coefficients after adjustments.

At an anatomical region level, where estimates of vertices within a given Desikan–Killiany region were averaged, beta coefficients for surface area tended to decrease from model 1 to 2 by approximately 15% (Figure 4, Figure 4—figure supplement 2). Further adjustments from model 2 to 3 led to decreases in the average beta coefficients of certain regions and increases in others. Similar patterns were found for volume (Figure 4—figure supplements 3 and 4). The average beta coefficients per region correlated moderately to strongly between the ABCD and Generation R Studies for surface area (Spearman rM1 = 0.84, rM2 = 0.83, rM3 = 0.83) and volume (Spearman rM1 = 0.57, rM2 = 0.57, rM3 = 0.70) (Figure 4—figure supplement 5).

IQ may be a confounder, mediator, or collider in neuroanatomical studies of ADHD

We considered one additional scenario which included IQ, a factor that is often adjusted for in previous studies (Supplementary file 1a). However, based on prior literature, it holds an ambiguous role in structural anatomy–ADHD relations. Previous studies found that children with ADHD scored lower on IQ than children without ADHD (Bridgett and Walker, 2006). Differential brain structure with levels of IQ has also been shown (Mcdaniel, 2005). However, the directions of causation between these variables remain unclear (Gallo and Posner, 2016). IQ may therefore be a confounder, collider, and/or mediator in the relation between brain structure and ADHD, as depicted in the DAGs in Figure 5 and Figure 5—figure supplement 1.

Figure 5 with 1 supplement see all
Directed Acyclic Graphs (DAGs) for intelligence quotient (IQ), brain structure, and attention-deficit/hyperactivity disorder (ADHD) symptoms.

Note. (A) DAG for IQ as a confounder. In this case, adjustments are needed as the backdoor path from brain structure to ADHD symptoms through IQ is open. By adjusting (box around IQ), the path gets closed. (B) DAG for IQ as a mediator. Adjustments are not needed to estimate the total effect of brain structure on ADHD symptoms. (C) DAG for IQ as a collider. The backdoor path through IQ is already closed. Adjustments would open the path and lead to collider bias.

First, it could be argued that IQ is partly innate and precedes brain development and ADHD, making it a confounder (Figure 5A). Second, IQ may lie in the pathway between brain structure and ADHD and therefore act as a mediator (Figure 5B). It is conceivable that cognitive differences, as a consequence of subtle neurodevelopmental differences (Lee et al., 2019), could underlie ADHD. Adjusting for a mediator would lead to bias when estimating the total association between brain structure and ADHD (VanderWeele, 2016). Third, brain structure may impact intelligence scores (Lee et al., 2019), and ADHD symptoms may affect IQ test performance (Jepsen et al., 2009; Figure 5C). A variable that is independently caused by the outcome and the determinant is also known as a collider, and adjusting for it leads to (collider) bias. Here, we explored the impact of adjusting for IQ when examining the relation between brain morphology and ADHD (model 4).

Adjustments for IQ led to further cluster reductions

After additionally adjusting for IQ, the spatial extent of the clusters associated with ADHD symptoms reduced further in both cohorts (Figure 6). For surface area, compared to model 3, clusters reduced from 760.2 to 605.1 cm2 (=−20%) for the ABCD Study, and from 208.6 to 93.1 cm2 for the Generation R Study (=−55%). Clusters of association for surface area in model 4 were located in the fusiform, inferior parietal, insula, lateral occipital, middle temporal, pericalcarine, pre- and postcentral, precuneus, rostral middle, and superior frontal, superior parietal and temporal, and supramarginal cortices. Findings for volume and thickness are shown in Figure 6—figure supplement 1.

Figure 6 with 1 supplement see all
Significant clusters in the association of attention-deficit/hyperactivity disorder (ADHD) symptoms with cortical surface area based on the Adolescent Brain Cognitive Development (ABCD) and Generation R Studies, after additional adjustment for intelligence quotient (IQ).

Note. Rows represent the results for the ABCD or Generation R Studies, and the columns represent the left and right hemispheres. The colors denote the different models, with red vertices being significant only in model 3, orange ones in both model 3 and after adjustment for IQ, and yellow ones only after adjusting for IQ.

Head motion does not induce confounding bias, but information bias

A final scenario was also included, to reflect the commonly used adjustments for head motion during scanning (Supplementary file 1a). Motion can be a large source of bias in neuroimaging studies which is important to address. While it does not meet the criteria for confounding as it is not a common cause of ADHD problems and brain morphology (Hernan and Robins, 2020), head motion can induce measurement error of brain morphology (Van de Walle et al., 1997; Figure 7). This is also referred to as information bias and can distort estimates from their true value.

Information bias for brain structure, attention-deficit/hyperactivity disorder (ADHD) symptoms, and head motion.

Note. From the bottom up: We aim to measure the ‘true’ values of brain structure and ADHD symptoms. However, we actually measure both brain structure and ADHD symptoms imperfectly, at the MRI and through self-reports, respectively. What we assess is therefore affected by measurement errors. Error in the MRI measurement is determined, in part, by excessive motion during scanning. Higher ADHD symptoms likely cause higher motion (dotted red path). This leads to differential information bias and creates a non-causal path from ADHD symptoms to brain structure through motion.

The amount of measurement error in brain morphology may differ across children with versus without ADHD. In fact, children with impulsivity and inattention have been shown to move more during MRI scanning (Thomson et al., 2021; Kong et al., 2014), determining different levels of error in the brain morphology assessments (Figure 7, path from ADHD symptoms to motion to error in MRI measurement). In this scenario, adjusting for motion might lead to two situations. On one hand, since motion is a consequence of the outcome (ADHD), adjustments would lead to bias (Westreich, 2012). On the other hand, not adjusting for motion would also lead to bias because part of the observed relation between ADHD symptoms and brain structure would be due to the higher head motion (and thus the underestimation of the cortical values) of children with ADHD. In this study, we explored the effect of adjusting for motion during scanning in the relation between brain morphology and ADHD (model 5).

Adjustments for head motion led to increases in clusters

After additional adjustments for head motion, the spatial extent of the clusters generally increased. For surface area, compared to model 3, clusters increased from 760.2 to 936.4 cm2 (=+23.2%) for the ABCD Study and from 208.6 to 239.7 cm2 (=+14.9%) for the Generation R Study (Figure 8). Clusters of associations consistently found across cohorts were highly similar to the ones identified in model 3. Results for cortical volume and thickness are shown in Figure 8—figure supplement 1.

Figure 8 with 1 supplement see all
Significant clusters in the association of attention-deficit/hyperactivity disorder (ADHD) symptoms with cortical surface area based on the Adolescent Brain Cognitive Development (ABCD) and Generation R Studies, after additional adjustment for motion.

Note. Rows represent the results for the ABCD or Generation R Studies, and the columns represent the left and right hemispheres. The colors denote the different models, with red vertices being significant only in model 3, orange ones in both model 3 and after adjustment for motion, and yellow ones only after adjusting for motion.

Discussion

By leveraging two large population-based studies and adopting a literature- and DAG-informed approach to address confounding, we showed that (1) associations between brain structure and ADHD symptoms, which were initially widespread, reduced when adjusting for socioeconomic and maternal behavioral confounders, and that (2) careful considerations are needed when including IQ and/or head motion due to their complex relation with ADHD and brain morphology.

Adjustments for confounders highlighted key regions of association, observed across two large cohorts

Widespread associations between surface area and ADHD symptoms were initially identified, with higher symptoms relating to smaller brain structures, in line with previous research (Hoogman et al., 2019; Gehricke et al., 2017). After adjustments for potential confounders typically overlooked by prior literature (socioeconomic and maternal behavioral factors), approximately half of the associations remained, and considerable effect size changes were observed in both the ABCD and Generation R Studies and for all cortical measures. We observed similar patterns of cluster reductions for ADHD diagnosis in the ABCD Study.

Regions that remained associated after adjustments and which were consistently identified across cohorts were the precuneus, isthmus of the cingulate, supramarginal, pre- and postcentral, and inferior parietal cortices for both area and volume. Most of these regions (e.g., supramarginal) have been previously implicated in ADHD in clinical samples (Saad et al., 2017; Lei et al., 2014; Solanto et al., 2009). However, many different brain areas have been detected in association with the disorder (Saad et al., 2020), which may have hampered prior meta-analytic efforts to identify consistent neuroanatomical correlates for ADHD.

Of note, some inconsistencies between the ABCD and Generation R Studies, both in size of the clusters and the exact location, were observed. While we used the same processing pipelines and similar quality control procedures and measures across cohorts, potential reasons for discrepancies in results must be discussed. First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas. Second, the multisite structure of the ABCD Study may have introduced noise in the results (e.g., by different scanners, demographic differences), and determined the identification of associations which are not replicable in the Generation R Study. Third, the two studies include children from different populations. While both are very diverse samples, the ABCD Study is comprised of a more heterogeneous sample from the US population, which, for instance, is characterized by a wider variety of ethnicities and cultures, potentially permitting the discovery of more associations. Nevertheless, there was considerable overlap in the findings from the ABCD and Generation R Studies, with consistencies across cohorts indicating the most robust and generalizable associations.

Here, we discerned associated areas likely subject to confounding bias from areas robust to socioeconomic and maternal behavioral factors, and replicable across two large cohorts. Comparisons with prior findings should be made with caution due to differences in study design, samples (clinical vs. population-based), and analytical methods. Importantly, we highlighted the opportunity for future studies to include covariates that go beyond age and sex, can help refine associations, and can be readily collected. Future studies may want to consider other confounding factors, depending on their research question, design, and assumed causal relations.

Adjustments for IQ are often unnecessary when examining the relation between brain structure and ADHD

Avoiding bias from adjusting for variables that are not confounders is as important as identifying sources of confounding. Adjusting for mediators or colliders of the ADHD–brain structure relation would induce bias. Here, when adjusting for IQ, which plays an unclear role in brain structure–ADHD associations, cluster sizes reduced considerably in both the ABCD and Generation R Studies. This could indicate that IQ is a confounder, in which case adjustments would be necessary, or that IQ is a mediator or collider, in which case adjustments must be avoided.

First, based on previous literature and this study, the association between ADHD and IQ is relatively weak (Dennis et al., 2009) (rABCD = −0.11, rGENR = −0.14), but this does not necessarily make it a weak confounder as the strength of confounding is due to a variable’s relation with the exposure and outcome. Second, if brain structure and ADHD symptoms both cause cognitive changes, adjusting for IQ could induce collider bias, although this is also dependent on when IQ is measured relative to the exposure and outcome (Hernan and Robins, 2020). Third, if brain structure determines cognitive functioning, which in turn affects ADHD symptoms (mediation by IQ), adjustments would also induce bias (VanderWeele, 2016).

Given these scenarios, we recommend moving away from routinely adjusting for IQ in ADHD neuroimaging studies, and we highlight the need to carefully consider the causal model for a specific research question to determine whether IQ may confound associations.

There is no easy fix for dealing with head motion in brain morphology–ADHD associations

Adjustments for neuroimaging covariates, such as head motion, are often run to reduce confounding bias. However, head motion, rather than inducing confounding bias, creates measurement error (information bias). When adjusting for head motion during scanning, we observed increases in the spatial extent of the clusters. This might indicate a reduction or an increase in bias. First, bias might have been reduced by adjusting for the fact that children with ADHD will have more error in their cortical measures. Second, bias might have also been increased because we conditioned for head motion, which is a consequence of ADHD.

Overall, the role of head motion in the relation between brain structure and ADHD is complex and warrants the utmost care. Adjusting or not would both lead to bias, meaning that considering which bias might be strongest is necessary. Moreover, methods aiming to reduce information bias should be leveraged (Lash et al., 2021); however, further developments are needed for their application to the neuroimaging field.

Similar considerations should be applied to other neuroimaging parameters which often are indicators of information bias rather than confounders in brain structure–ADHD relations (e.g., time of the day, scanner). For instance, time of the day has been shown to influence morphometric values (Nakamura et al., 2015; Trefler et al., 2016), leading to information bias. If we expect for ADHD symptoms to influence the time of the day in which children with ADHD versus without come to the scanner (differential information bias), the same considerations for head motion would apply. If instead, children with ADHD and without come to the MRI at similar times of the day (non-differential information bias), then adjustments for time of the day would be appropriate and reduce the measurement error.

Generalization to psychiatric neuroimaging studies

Our considerations on confounding likely generalize to the psychiatric neuroimaging field, as several confounders considered here (e.g., SES) also relate to brain function and other psychiatric disorders (Biazoli et al., 2020; Kivimäki et al., 2020; Apter et al., 2017). Similarly, other psychiatric disorders are also characterized by complex relations with IQ (Der et al., 2009). Moreover, mental health problems featuring state anxiety, like internalizing and externalizing symptoms, have also been related to increased head motion during scanning (Eijlers et al., 2021).

Confounding control is paramount to studies examining determinants of a phenotype, like ADHD. However, even in these studies, one may be tempted to conduct correlational research with limited confounding adjustments, and then speculate about biological causal mechanisms (Hernán, 2018; Grosz et al., 2020). Rather, we suggest leveraging prior literature and expert knowledge to identify and adjust for key confounders. This can help eliminate the influence of alternative mechanisms (to the ones hypothesized) on the relation of interest (Hernan and Robins, 2020). Charting the assumed (causal) structures to identify confounders can be done through the use of tools such as DAGs (Hernan and Robins, 2020). Naturally, the plausibility of such assumptions should be evaluated. To facilitate the minimization of confounding bias in psychiatric neuroimaging, we propose a workflow in Figure 9.

Suggestions for minimizing confounding bias: a workflow.

Note. In this workflow, we suggest different actions that can be taken throughout the research process to minimize confounding bias in psychiatric neuroimaging studies.

Limitations of the present study and suggestions for future research

Despite leveraging two large samples with similar characteristics and assessments, this study presents several limitations. First, there is always potential for residual confounding through unmeasured confounders and misclassification of measured confounders. For example, given that genetic factors influence both ADHD and brain morphology and that there is a genetic correlation between ADHD risk and intracranial volume (Jansen et al., 2015; Klein et al., 2019; Klein et al., 2017), certain genetic risk variants may be unmeasured confounders. However, we aimed to illustrate plausible confounding bias scenarios for ADHD and brain structure, and not to provide an exhaustive list of potential confounders, which may vary depending on the study population and research question. Future studies should also consider bias analyses to assess the impact that residual confounding may have on the study results (Lash et al., 2021). Bias analyses can help understand the minimum association strength an unmeasured confounder needs to have with the determinant and outcome to fully explain away the findings (VanderWeele and Ding, 2017). Developments may be needed, however, for their adaptation to the neuroimaging field.

Second, due to our cross-sectional design, deliberately chosen to correspond to most neuroimaging studies, we must assume all confounders precede our determinant and outcome. This is a plausible assumption for the Generation R Study as, being a prospective birth cohort, we could ensure that the confounders here considered temporally preceded both ADHD and neuroanatomical assessments. However, this was not possible for the ABCD Study, which started sampling at child ages 9–10 years. Future research on the temporal relations between potential confounders, ADHD, and brain structure will aid the minimization of confounding bias when investigating the structural substrates of ADHD.

Third, while we leveraged both symptom-level and diagnostic data for ADHD, this was done within population-based studies. Our results cannot, therefore, be generalized to a clinical population. Future research could examine the extent to which associations between brain structure and ADHD change after adjustments for likely confounders in clinical samples.

In conclusion, leveraging an empirical example from two large studies on neuroanatomy and ADHD symptoms, we highlighted the opportunity for future studies to consider further key confounders. These can be identified based on prior literature and causal diagrams as well as be readily collected, offering a feasible venue for future research. Adjusting for these potential confounders helped identify more refined cortical associations with ADHD symptoms, robust to the influence of demographic and socioeconomic factors, pregnancy exposures, and maternal psychopathology. We also evaluated the potential role of IQ, which could be a mediator, collider, and/or confounder. While adjusting for IQ led to reductions in associations, these would, however, likely not be attributable to reduced confounding bias. Lastly, we explored how head motion reflects information, rather than confounding bias. We discussed the generalizability of these considerations on confounding bias to psychiatric neuroimaging, and suggest a workflow that can be followed to minimize confounding bias in future studies.

Materials and methods

Participants

We analyzed data from two independent population-based cohorts: the ABCD Study and the Generation R Study. The ABCD Study is conducted across 21 study sites in the US and recruited since 2015 children aged 9–10 at baseline (Garavan et al., 2018). The Generation R Study is based in Rotterdam, the Netherlands, with data collection spanning from fetal life until early adulthood, and started in 2002 (Kooijman et al., 2016). Details of the sampling rationale, recruitment, methods, and procedures have been described elsewhere (Kooijman et al., 2016; Garavan et al., 2018). Research protocols for the ABCD Study were approved by the institutional review board of the University of California, San Diego (#160091), and the institutional review boards of the 21 data collection sites, while the design of the Generation R Study was approved by the Medical Ethics Committee of the Erasmus MC (METC-2012-165). For both studies, written informed consent and assent from the primary caregiver or child were obtained.

In this cross-sectional study, we leveraged data from the baseline assessment of the ABCD Study (release 2.0.1) and the 10-year assessment of the Generation R Study. Both waves included behavioral and neuroimaging measures. We included children with data on ADHD symptoms and T1-weighted MRI images. Participants were excluded if (1) they had dental braces, (2) incidental findings, (3) their brain scans failed processing or quality assurance procedures, or (4) they were twins or triplets. Of note, excluding children with dental braces is unlikely to determine selection bias by SES in either the ABCD or the Generation R Study as the former cohort covered the costs of dental braces removal for all children who enrolled, while dental care is insured for all children in the Netherlands. Within the Generation R Study, a small set of participants were additionally excluded because they had a different scan sequence. Finally, for each non-twin sibling set, one was randomly included to minimize shared method variance bias. Flowcharts for participant inclusion and exclusion are available in Figure 10. The final samples consisted of 7722 and 2531 children from the ABCD and Generation R Studies, respectively.

Flowcharts of participant inclusion and exclusion for Adolescent Brain Cognitive Development (ABCD) (panel A) and Generation R (panel B).

Note. (A) In the ABCD Study, of the 11,875 participants enrolled in the study, 7722 met our inclusion and exclusion criteria. (B) In the Generation R Study, of the 8548 participants invited to the age 9–10 assessment, 2531 met our inclusion and exclusion criteria.

Measures

ADHD symptoms

Request a detailed protocol

Children’s ADHD symptoms, reported by the primary caregiver, were measured with the CBCL (school-age version) (Achenbach, 2001), an inventory widely used for parent reports of children’s emotional and behavioral problems. The attention problem syndrome scale (20 items) measures inattention, hyperactivity, and impulsivity and has been previously shown to have clinical utility and to discriminate between ADHD cases and controls (Eiraldi et al., 2000). Attention problems were analyzed on a discrete scale (range 0–19). For the ABCD Study, we repeated the analysis using present ADHD diagnosis from a parent-reported and computerized version of the KSADS-5. This is a dimensional and categorical assessment used to diagnose current and past psychiatric disorders according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (Kaufman et al., 1997; Kobak, 2020).

Image acquisition

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T1-weighted data were obtained on multiple 3T scanners in the ABCD Study (Siemens Prisma, General Electric (GE) 750 and Philips) and one scanner in the Generation R Study (GE 750). Standard adult-sized coils were used for the ABCD Study and an eight-channel receive-only head coil for the Generation R Study. To acquire T1-weighted structural images, the ABCD Study used an inversion prepared RF-spoiled gradient echo scan with prospective motion correction while the Generation R Study used an inversion recovery fast spoiled gradient recalled sequence (GE option = BRAVO, TR = 8.77 ms, TE = 3.4 ms, TI = 600 ms, flip angle = 10°, matrix size = 220 × 220, field of view = 220 × 220 mm, slice thickness = 1 mm, number of slices = 230, ARC acceleration factor = 2). More details can be found elsewhere (Casey et al., 2018; Hagler et al., 2019; White et al., 2018). Of note, in the ABCD Study, a technical mistake occurred at one collection site, causing the hemisphere data to be flipped. This was fixed before processing.

Image processing

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FreeSurfer (version 6.0.0) was used for both cohorts for image processing, which was run in-house to maximize comparability across cohorts. Processing involved (1) removal of non-brain tissue, (2) correction of voxel intensities for B1 field inhomogeneities, (3) tissue segmentation, and (4) cortical surface-based reconstruction. Cortical surface maps were smoothed with a full width of a half-maximum Gaussian kernel of 10 mm. Within the ABCD Study, quality assessment was based on manual and automated quality control procedures and recommended inclusion criteria for structural data from the ABCD team (Hagler et al., 2019). Within the Generation R Study, quality assurance was manually performed by visually inspecting all images by trained raters, as previously described in the literature (Muetzel et al., 2019). Poor quality reconstructions were excluded.

Covariate assessment

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The ABCD Study
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All data were collected at baseline (child age 9–10 years). Age and sex were recorded at intake. Child race/ethnicity was reported by the primary caregiver and was categorized as White, Black, Hispanic, Asian, Other by the ABCD team. Household combined net income (<$50,000, ≥$50,000 and <$100,000, ≥$100,000) and highest parental education (<high school, high school diploma/GED, some college, bachelor degree, postgraduate degree) were self-reported by the primary caregiver in the Parent Demographics Survey. Maternal age at childbirth was measured in the Developmental History Questionnaire. Tobacco and cannabis use during pregnancy were retrospectively reported by the mother (yes, no, I do not know) in the Developmental History Questionnaire. Caregiver psychopathology was obtained from the Total Problems Adult Self Report Syndrome Scale. The Wechsler Intelligence Scale for Children-6 Matrix Reasoning total scaled score was used as a proxy for IQ. We used the Euler number obtained from FreeSurfer as a proxy for head motion during the acquisition. The Euler number quantifies the topological complexity of the reconstructed cortical surface, with holes in the reconstructed surface leading to lower Euler numbers.

The Generation R Study
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Age and sex were measured based on medical records obtained at birth. Child ethnicity (western and non-western) was assessed based on the parents’ birth country, in line with the Statistics Netherlands bureau. Maternal age at childbirth was prospectively measured. Family income and highest maternal education were obtained through prospective self-reports by the mother and/or father at child age 5 years. Maternal education was coded into low (no/primary education), intermediate (secondary school, vocational training), and high (Bachelor’s degree/University). Household net monthly income was classified as low (<2000 euros), middle (2000–3200 euros), and high (>3200 euros). Maternal postnatal psychopathology, measured at child age 6 months, was prospectively reported by the mother based on the Brief Symptom Inventory questionnaire global severity index. Mothers prospectively reported smoking (never used, used) and cannabis use during pregnancy (no use vs. use during pregnancy). Non-verbal child IQ was measured at child age 5 years, based on the Snijders-Oomen Niet-Verbale Intelligentie Test (Tellegen and Laros, 1998), a validated Dutch non-verbal intelligence test. The Euler number was used as a proxy for head motion during scanning.

Covariate selection

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Similar covariates were grouped into confounding sets to minimize the number of tested models while including relevant confounders. Factors included in model 1 related to demographic and study characteristics (age, sex, ethnicity, and study site [for ABCD only]). Age and sex were selected as these have been previously adjusted for in previous neuroimaging studies of ADHD (Supplementary file 1a). Ethnicity was used as a proxy for differential health risk exposure among people of different ethnic groups. The study site was incorporated to account for location and scanner differences in the ABCD Study.

Further potential confounders were selected based on previous literature and with the aid of DAGs, as described in the Results. In model 2, variables indicating socioeconomic factors were included (parental education, household income, maternal age at childbirth). Household income and parental education are generally considered to measure childhood SES in health research (American Psychological Association, 2021). Maternal age at childbirth can additionally inform on the SES of the child by capturing part of the variance unexplained by income and education (e.g., younger mothers facing higher occupational challenges, highly educated mothers delaying childbirth Heck et al., 1997). In model 3, maternal factors from the prenatal and postnatal period were grouped (tobacco and cannabis use during pregnancy and maternal psychopathology) to measure early life exposures which may impact a child’s brain and psychiatric development.

Statistical analyses

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The R statistical software (version 4.1.0) was used for all analyses. Missing data on covariates were imputed with chained equations using the mice R package (Buuren and Groothuis-Oudshoorn, 2011). Linear vertex-wise analyses were performed with the QDECR R package (Lamballais and Muetzel, 2021), with surface area/volume/thickness and ADHD symptoms as variables of interest. Correction for multiple testing was applied by using cluster-wise corrections based on Monte Carlo simulations with a cluster forming threshold of 0.001, which yields false-positive rates similar to full permutation testing (Greve and Fischl, 2018). A Bonferroni correction was applied to adjust for analyzing both hemispheres separately (i.e., p < 0.025 cluster-wise).

Our analyses for aim 1 involved three vertex-wise linear regression models, which progressively expanded to adjust for additional confounding factors. The first model focused on demographic covariates, the second on socioeconomic ones, and the third on maternal behavioral variables related to psychopathology and pregnancy exposures. These models were run for ADHD symptoms (in both the ABCD and Generation R Studies) and ADHD diagnosis (in the ABCD Study only, as sensitivity analysis). For the results, we primarily reported cluster sizes, that is, the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cm2.

Two additional models, building upon model 3, were run to address aim 2, to illustrate the consequences of adjusting for factors with a complex and unclear relation with brain structure and ADHD symptoms: IQ and head motion (analyses ran separately). Of note, given that IQ and ADHD were weakly correlated (rABCD = −0.11, rGENR = −0.14), multicollinearity was not expected.

Code availability

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Code used to conduct this project is publicly available at https://github.com/LorenzaDA/ADHD_brainmorphology_confounding (Copy archived at swh:1:rev:95c01381fc7fad9bedb3b5918fb80b02b1dcbdfa) (Dall’Aglio, 2022) under CC by 4.0.

Data availability

All datasets for this article are not automatically publicly available due to legal and informed consent restrictions. Reasonable requests to access the datasets should be directed to the Director of the Generation R Study, Vincent Jaddoe (generationr@erasmusmc.nl), in accordance with the local, national, and European Union regulations. Data for The ABCD Study are already open and available in the NIMH Data Archive (NDA) (nda.nih.gov) to eligible researchers within NIH-verified institutions. Data can be accessed following a data request to the NIH data access committee (https://nda.nih.gov/), which should include information on the planned topic of study. The request is valid for 1 year. Data use should be in line with the NDA Data Use Certification. The code used for this study is publicly available at https://github.com/LorenzaDA/ADHD_brainmorphology_confounding (Copy archived at swh:1:rev:95c01381fc7fad9bedb3b5918fb80b02b1dcbdfa).

The following data sets were generated
    1. Tiemeier H
    2. Muetzel R
    3. Dall'Aglio L
    4. Kim HH
    5. Lamballais S
    6. Labrecque J
    7. Muetzel RL
    8. Tiemeier H
    (2022) NIMH Data Archive
    Attention-Deficit/Hyperactivity Disorder symptoms and Brain Morphology: Examining Confounding Bias #1311.
    https://doi.org/10.15154/1523058

References

  1. Book
    1. Achenbach R
    (2001)
    Manual for the ASEBA School-Age Forms and Profiles
    Burlington: University of Vermont, Reseach Center for Children, Youth, and Families.
    1. Hagler DJ
    2. Hatton S
    3. Cornejo MD
    4. Makowski C
    5. Fair DA
    6. Dick AS
    7. Sutherland MT
    8. Casey BJ
    9. Barch DM
    10. Harms MP
    11. Watts R
    12. Bjork JM
    13. Garavan HP
    14. Hilmer L
    15. Pung CJ
    16. Sicat CS
    17. Kuperman J
    18. Bartsch H
    19. Xue F
    20. Heitzeg MM
    21. Laird AR
    22. Trinh TT
    23. Gonzalez R
    24. Tapert SF
    25. Riedel MC
    26. Squeglia LM
    27. Hyde LW
    28. Rosenberg MD
    29. Earl EA
    30. Howlett KD
    31. Baker FC
    32. Soules M
    33. Diaz J
    34. de Leon OR
    35. Thompson WK
    36. Neale MC
    37. Herting M
    38. Sowell ER
    39. Alvarez RP
    40. Hawes SW
    41. Sanchez M
    42. Bodurka J
    43. Breslin FJ
    44. Morris AS
    45. Paulus MP
    46. Simmons WK
    47. Polimeni JR
    48. van der Kouwe A
    49. Nencka AS
    50. Gray KM
    51. Pierpaoli C
    52. Matochik JA
    53. Noronha A
    54. Aklin WM
    55. Conway K
    56. Glantz M
    57. Hoffman E
    58. Little R
    59. Lopez M
    60. Pariyadath V
    61. Weiss SR
    62. Wolff-Hughes DL
    63. DelCarmen-Wiggins R
    64. Feldstein Ewing SW
    65. Miranda-Dominguez O
    66. Nagel BJ
    67. Perrone AJ
    68. Sturgeon DT
    69. Goldstone A
    70. Pfefferbaum A
    71. Pohl KM
    72. Prouty D
    73. Uban K
    74. Bookheimer SY
    75. Dapretto M
    76. Galvan A
    77. Bagot K
    78. Giedd J
    79. Infante MA
    80. Jacobus J
    81. Patrick K
    82. Shilling PD
    83. Desikan R
    84. Li Y
    85. Sugrue L
    86. Banich MT
    87. Friedman N
    88. Hewitt JK
    89. Hopfer C
    90. Sakai J
    91. Tanabe J
    92. Cottler LB
    93. Nixon SJ
    94. Chang L
    95. Cloak C
    96. Ernst T
    97. Reeves G
    98. Kennedy DN
    99. Heeringa S
    100. Peltier S
    101. Schulenberg J
    102. Sripada C
    103. Zucker RA
    104. Iacono WG
    105. Luciana M
    106. Calabro FJ
    107. Clark DB
    108. Lewis DA
    109. Luna B
    110. Schirda C
    111. Brima T
    112. Foxe JJ
    113. Freedman EG
    114. Mruzek DW
    115. Mason MJ
    116. Huber R
    117. McGlade E
    118. Prescot A
    119. Renshaw PF
    120. Yurgelun-Todd DA
    121. Allgaier NA
    122. Dumas JA
    123. Ivanova M
    124. Potter A
    125. Florsheim P
    126. Larson C
    127. Lisdahl K
    128. Charness ME
    129. Fuemmeler B
    130. Hettema JM
    131. Maes HH
    132. Steinberg J
    133. Anokhin AP
    134. Glaser P
    135. Heath AC
    136. Madden PA
    137. Baskin-Sommers A
    138. Constable RT
    139. Grant SJ
    140. Dowling GJ
    141. Brown SA
    142. Jernigan TL
    143. Dale AM
    (2019) Image processing and analysis methods for the adolescent brain cognitive development study
    NeuroImage 202:116091.
    https://doi.org/10.1016/j.neuroimage.2019.116091
  2. Book
    1. Hernan MA
    2. Robins JM
    (2020)
    Causal Inference What If?
    CRC press Taylor & Francis Group.
    1. Hoogman M
    2. Muetzel R
    3. Guimaraes JP
    4. Shumskaya E
    5. Mennes M
    6. Zwiers MP
    7. Jahanshad N
    8. Sudre G
    9. Wolfers T
    10. Earl EA
    11. Soliva Vila JC
    12. Vives-Gilabert Y
    13. Khadka S
    14. Novotny SE
    15. Hartman CA
    16. Heslenfeld DJ
    17. Schweren LJS
    18. Ambrosino S
    19. Oranje B
    20. de Zeeuw P
    21. Chaim-Avancini TM
    22. Rosa PGP
    23. Zanetti MV
    24. Malpas CB
    25. Kohls G
    26. von Polier GG
    27. Seitz J
    28. Biederman J
    29. Doyle AE
    30. Dale AM
    31. van Erp TGM
    32. Epstein JN
    33. Jernigan TL
    34. Baur-Streubel R
    35. Ziegler GC
    36. Zierhut KC
    37. Schrantee A
    38. Høvik MF
    39. Lundervold AJ
    40. Kelly C
    41. McCarthy H
    42. Skokauskas N
    43. O’Gorman Tuura RL
    44. Calvo A
    45. Lera-Miguel S
    46. Nicolau R
    47. Chantiluke KC
    48. Christakou A
    49. Vance A
    50. Cercignani M
    51. Gabel MC
    52. Asherson P
    53. Baumeister S
    54. Brandeis D
    55. Hohmann S
    56. Bramati IE
    57. Tovar-Moll F
    58. Fallgatter AJ
    59. Kardatzki B
    60. Schwarz L
    61. Anikin A
    62. Baranov A
    63. Gogberashvili T
    64. Kapilushniy D
    65. Solovieva A
    66. El Marroun H
    67. White T
    68. Karkashadze G
    69. Namazova-Baranova L
    70. Ethofer T
    71. Mattos P
    72. Banaschewski T
    73. Coghill D
    74. Plessen KJ
    75. Kuntsi J
    76. Mehta MA
    77. Paloyelis Y
    78. Harrison NA
    79. Bellgrove MA
    80. Silk TJ
    81. Cubillo AI
    82. Rubia K
    83. Lazaro L
    84. Brem S
    85. Walitza S
    86. Frodl T
    87. Zentis M
    88. Castellanos FX
    89. Yoncheva YN
    90. Haavik J
    91. Reneman L
    92. Conzelmann A
    93. Lesch K-P
    94. Pauli P
    95. Reif A
    96. Tamm L
    97. Konrad K
    98. Oberwelland Weiss E
    99. Busatto GF
    100. Louza MR
    101. Durston S
    102. Hoekstra PJ
    103. Oosterlaan J
    104. Stevens MC
    105. Ramos-Quiroga JA
    106. Vilarroya O
    107. Fair DA
    108. Nigg JT
    109. Thompson PM
    110. Buitelaar JK
    111. Faraone SV
    112. Shaw P
    113. Tiemeier H
    114. Bralten J
    115. Franke B
    (2019) Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples
    The American Journal of Psychiatry 176:531–542.
    https://doi.org/10.1176/appi.ajp.2019.18091033
    1. Van de Walle R
    2. Lemahieu I
    3. Achten E
    (1997)
    Magnetic resonance imaging and the reduction of motion artifacts: review of the principles
    Technology and Health Care 5:419–435.

Decision letter

  1. Juan Helen Zhou
    Reviewing Editor; National University of Singapore, Singapore
  2. Jonathan Roiser
    Senior Editor; University College London, United Kingdom
  3. Francisco Xavier Catellanos
    Reviewer; New York University Langone Medical Center, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Attention Deficit Hyperactivity Disorder Symptoms and Brain Morphology: Examining Confounding Bias" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jonathan Roiser as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Francisco Xavier Catellanos (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

In this study, the authors examined the associations between brain morphology and ADHD symptoms and how the adjustments for confounders change these associations. The reviewers were generally supportive, but raised several points that should be addressed:

1) The discrepancy between the two datasets needs to be validated/discussed further.

2) Given the nature of ADHD, it is important to evaluate the influence of preprocessing pipeline (quality control steps), motion parameters, and even time of day. The authors should perform the analyses with comparable preprocessing and motion quality between the two datasets to validate the findings.

3) The authors should include cortical thickness on top of the other two measures, and clarify the unit of outcome measures.

Reviewer #1 (Recommendations for the authors):

One major concern relates to the choice of outcome measures. The authors focus on vertex-wise surface area and volume and do not include cortical thickness. They note the latter was uninformative in a prior analysis. However, cortical volume is the product of area and thickness, so it's unclear what is gained by including it as if it were an independent measure. In fact, all the maps of differences in volume appear to be reduced versions of the surface area difference maps.

It's also completely unclear why both surface area and volume measures are given in units of cm2. That unit is appropriate for area, but volume is a 3-dimensional measure, so it should be reported in cm3 (or mm3).

These two issues made it difficult to truly address the value of the manuscript as currently submitted. The surface area measures appear to be informative – and they support the authors' contention that potentially confounding variables must be carefully attended to. I am confused and unconvinced regarding the value of the volume.

Reviewer #2 (Recommendations for the authors):

Two large datasets were used for replication to strengthen the confidence in the results. However, the significant clusters were different (ABCD showed more widespread regions while GenR showed more localized regions). Is this due to different preprocessing pipelines, motion parameters, or scanner acquisition parameters? Please discuss the possible reasons.

Although the authors have discussed why this study did not consider head motion, it is interesting to see how the scanner acquisition protocol processing parameters and head motion confounds affect the brain morphology-ADHD associations for these two datasets. In addition, from prior literature, time of day also modulates tissue volume metrics (Nakamura et at., 2015, Trefler et al., 2016); therefore it would be interesting to consider time-related confounds as well.

https://doi.org/10.7554/eLife.78002.sa1

Author response

Essential revisions:

In this study, the authors examined the associations between brain morphology and ADHD symptoms and how the adjustments for confounders change these associations. The reviewers were generally supportive, but raised several points that should be addressed:

1) The discrepancy between the two datasets needs to be validated/discussed further.

To address this comment, we have now expanded on the discrepancies across the datasets in the Discussion:

Discussion (pages 11-12):

Of note, some inconsistencies between the results from the ABCD and the Generation R studies, both in size of the clusters as well as the exact location, were observed. While we used the same processing pipelines and similar quality control procedures and measures across cohorts, potential reasons for discrepancies of results must be discussed. First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas. Second, the multi-site structure of the ABCD Study may have introduced noise in the results (e.g., by different scanners, demographic differences), and determined the identification of associations which are not replicable in the Generation R Study. Third, the two studies include children from different populations. While both are very diverse samples, the ABCD Study sample is comprised of a more heterogeneous sample from the U.S. population, which, for instance, is characterized by a wider variety of ethnicities and cultures, potentially permitting the discovery of more associations. Nevertheless, there was considerable overlap in the findings from the ABCD and Generation R Studies, with consistencies across cohorts indicating the most robust and generalizable associations.

2) Given the nature of ADHD, it is important to evaluate the influence of preprocessing pipeline (quality control steps), motion parameters, and even time of day. The authors should perform the analyses with comparable preprocessing and motion quality between the two datasets to validate the findings.

Thank you for this comment. To address it, we further specified the similarities and differences in the pipelines across the two cohorts. First, we clarified the text to better emphasize that many of the processing steps, including the manual quality control, were performed similarly between the two cohorts, with the processing in FreeSurfer being identical. Second, we further aligned the steps in the procedures that we were able to, i.e., applying the inclusion/exclusion criteria in the same order and adding an additional step to exclude incidental findings consistently across the two studies. Third, we mentioned the potential influence of neuroimaging related information, such as head motion and time of the day, in the Discussion section (for in-depth information see Reviewer #2, comment 2).

Changes are shown below:

Methods (page 19):

“FreeSurfer (version 6.0.0) was used for both cohorts for image processing, which was run in-house to maximize comparability across cohorts.”

Methods (page 19):

“Within the ABCD Study, quality assessment was based on manual and automated quality control procedures and recommended inclusion criteria for structural data from the ABCD team (1). Within the Generation R Study, quality assurance was manually performed by visually inspecting all images by trained raters, as previously described in the literature (2).”

3) The authors should include cortical thickness on top of the other two measures, and clarify the unit of outcome measures.

We have now included analyses of cortical thickness. We have also clarified why all findings are reported as cm2 (for in-depth information see Reviewer #1, comment 2). Manuscript changes are shown below, in-text and in the Supplement. Overall, few clusters for cortical thickness were related to ADHD symptoms but had similar patterns of reductions with covariate adjustments to clusters for surface area and volume.

Methods (page 21):

“Linear vertex-wise analyses were performed with the QDECR R package (3), with surface area/volume/thickness and ADHD symptoms as variables of interest.”

Methods (page 21):

“For the results we primarily reported cluster sizes, i.e., the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cm2.”

Results (page 5):

“We ran vertex-wise linear regression models for ADHD with cortical surface area, volume, and thickness. Results for surface area, which constituted the main findings here, are presented in-text, while findings for volume and thickness in the Supplementary Figures.”

Results (page 5):

“For cortical thickness, we found two small frontal clusters in the ABCD Study (16.1 cm2) and no clusters in the Generation R Study, which suggests that cortical thickness does not relate or does not relate strongly to ADHD, in line with prior literature (4) (Figure 1 – Supplement 1).”

Results (page 6):

“Similar reductions were observed for volume and thickness (Figure 3 – Supplement 1).”

Results (page 7):

Compared to clusters for ADHD symptoms, those associated with ADHD diagnosis were smaller, but overlapping (Figure 1 – Supplement 2). We observed similar patterns of reduction in the spatial extent of the clusters after adjusting for each set of confounders (Figure 3 – Supplement 2).”

Results (page 7):

“At a vertex-wise level, adjusting for socioeconomic and maternal factors (model 3) led to reductions in the β coefficients, across the brain, for both cohorts (Figure 4 – Supplement 1). Of note, some β coefficients also showed increases. As confounding bias may lead to under- or over-estimation, it is not surprising to observe both decreases and increases in the average β coefficients after adjustments.”

Results (page 9):

“Findings for volume and thickness are shown in Figure 6 – Supplement 1.”

Results (page 10):

“Results for cortical volume and thickness are shown in Figure 8 – Supplement 1.”

Reviewer #1 (Recommendations for the authors):

One major concern relates to the choice of outcome measures. The authors focus on vertex-wise surface area and volume and do not include cortical thickness. They note the latter was uninformative in a prior analysis. However, cortical volume is the product of area and thickness, so it's unclear what is gained by including it as if it were an independent measure. In fact, all the maps of differences in volume appear to be reduced versions of the surface area difference maps.

We thank the Reviewer for this comment. To address it, we have focused on surface area, which, as the Reviewer rightly pointed out, provides the main finding of this project. More specifically, the main text is now built around the results of the analyses of surface area. At the same time, results for volume and thickness (analyses run to address Essential revisions, comment 3) are also described, partly in-text and in the Supplementary Figures.

It's also completely unclear why both surface area and volume measures are given in units of cm2. That unit is appropriate for area, but volume is a 3-dimensional measure, so it should be reported in cm3 (or mm3).

We agree with the Reviewer that this comes across as incorrect. For completeness: The clusters relate to the surface of the cerebral cortex, and the clusters reflect the surface that associates with ADHD symptoms. That surface is expressed in cm2. At the level of the vertex we indeed consider different characteristics, such as thickness (mm/cm), area (mm2/cm2), and volume (mm3/cm3). The vertices that reach statistical significance cover a certain area, and it is this area that we report in the results. We have clarified the text of the Methods section and the Results section (to explicitly state it at first occurrence):

Methods (page 21):

“For the results we primarily reported cluster sizes, i.e., the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cm2.”

Results (page 5):

“As shown in Figure 1, associations were widespread, as the clusters of association covered 1,165.7 cm2 of the cerebral cortex in the ABCD Study, and 446.1 cm2 in the Generation R Study.”

These two issues made it difficult to truly address the value of the manuscript as currently submitted. The surface area measures appear to be informative – and they support the authors' contention that potentially confounding variables must be carefully attended to. I am confused and unconvinced regarding the value of the volume.

We hope that we have now sufficiently addressed the Reviewer’s comments and rendered the manuscript more informative.

Reviewer #2 (Recommendations for the authors):

Two large datasets were used for replication to strengthen the confidence in the results. However, the significant clusters were different (ABCD showed more widespread regions while GenR showed more localized regions). Is this due to different preprocessing pipelines, motion parameters, or scanner acquisition parameters? Please discuss the possible reasons.

We addressed this important comment by specifying the use of the same processing pipelines and similar motion quality control as well as including a paragraph in the Discussion section highlighting potential reasons for inconsistencies across cohorts. Such changes have been aforementioned in Essential Revisions, comment 1. Furthermore, the differences in widespread versus localized findings could reflect greater power in the ABCD Study (due to large sample size) to detect smaller effects. On top of the aforementioned changes (Essential Revisions, comment 1), we added a sentence about power as a possible explanation for the differences in the Discussion section, as shown below.

Discussion (pages 21-22):

“First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas.”

Although the authors have discussed why this study did not consider head motion, it is interesting to see how the scanner acquisition protocol processing parameters and head motion confounds affect the brain morphology-ADHD associations for these two datasets. In addition, from prior literature, time of day also modulates tissue volume metrics (Nakamura et at., 2015, Trefler et al., 2016); therefore it would be interesting to consider time-related confounds as well.

We agree with the Reviewer that neuroimaging confounds needed further discussion in our manuscript. We have therefore examined the potential role of head motion in the relations between brain structure and ADHD by using causal diagrams and have additionally adjusted for it in another model. Briefly, head motion, rather than inducing confounding bias, induces measurement error (information bias) in the brain morphology values. Head motion during scanning often results from ADHD symptoms, determining a non-causal path from ADHD to brain structure through motion. In this case, adjustments for head motion can cause both increases or decreases in bias. Increases in bias can occur because head motion is a consequence of ADHD, and consequences of outcomes should not be adjusted for. Decreases in bias can occur because the non-causal path through head motion would be closed with adjustments. Further information is shown in-text and reported below:

Introduction (page 4):

“Of the 19 studies included in a systematic review of neuroimaging studies on ADHD (5), 17 adjusted or matched for age in their analyses, 14 for sex, 9 for neuroimaging-related variables like head motion during scanning, and 8 for the intelligence quotient (IQ)

(Supplementary File 1a).

[…]

Conversely, previous studies have adjusted for IQ and head motion, which may not be confounders in the association between ADHD symptoms and the brain, and may thus have led to further bias in the results (6).

In this study, we examined the association between brain structure and ADHD symptoms and how the selection and control for potential confounders may affect results (aim 1). Moreover, we discussed the complex role of IQ and head motion in brain structure – ADHD associations and the potential consequences of adjusting for them (aim 2).”

Results (pages 9-10):

Head motion does not induce confounding bias, but information bias

A final scenario was also included, to reflect the commonly used adjustments for head motion during scanning (Supplementary File 1a). Motion can be a large source of bias in neuroimaging studies which is important to address. While it does not meet the criteria for confounding as it is not a common cause of ADHD problems and brain morphology (7), head motion can induce measurement error of brain morphology (8) (Figure 7). This is also referred to as information bias and can distort estimates from their true value.

The amount of measurement error in brain morphology may differ across children with vs without ADHD. In fact, children with impulsivity and inattention have been shown to move more during MRI scanning (9,10), determining different levels of error in the brain morphology assessments (Figure 7, path from ADHD symptoms to motion to error in MRI measurement). In this scenario, adjusting for motion might lead to two situations. On one hand, since motion is a consequence of the outcome (ADHD), adjustments would lead to bias (11). On the other hand, not adjusting for motion would also lead to bias because part of the observed relation between ADHD symptoms and brain structure would be due to the higher head motion (and thus the underestimation of the cortical values) of children with ADHD. In this study, we explored the effect of adjusting for motion during scanning in the relation between brain morphology and ADHD (model 5).

Adjustments for motion led to increases in clusters

After additional adjustments for head motion, the spatial extent of the clusters generally increased. For surface area, compared to model 3, clusters increased from 760.2 cm2 to 936.4 cm2 ( = +23.2%) for the ABCD Study and from 208.6 cm2 to 239.7 cm2 ( = +14.9%) for the Generation R Study (Figure 8). Clusters of associations consistently found across cohorts were highly similar to the ones identified in model 3. Results for cortical volume and thickness are shown in Figure 8 – Supplement 1.

Discussion (pages 13-14):

There is no easy fix for dealing with head motion in brain morphology – ADHD associations

Adjustments for neuroimaging covariates, such as head motion, are often run to reduce confounding bias. However, head motion, rather than inducing confounding bias, creates measurement error (information bias). When adjusting for head motion during scanning, we observed increases in the spatial extent of the clusters. This might indicate a reduction or an increase in bias. First, bias might have been reduced by adjusting for the fact that children with ADHD will have more error in their cortical measures. Second, bias might have also been increased because we conditioned for head motion, which is a consequence of ADHD.

Overall, the role of head motion in the relation between brain structure and ADHD is complex and warrants the utmost care. Adjusting or not could both lead to bias, meaning that considering which bias might be strongest is necessary. Moreover, methods aiming to reduce information bias should be leveraged (12); however, further developments are needed for their application to the neuroimaging field.

Similar considerations should be applied to other neuroimaging parameters which often are indicators of information bias rather than confounders in brain structure – ADHD relations (e.g., time of the day, scanner). For instance, time of the day has been shown to influence morphometric values (13,14), leading to information bias. If we expect for ADHD symptoms to influence the time of the day in which children with ADHD vs. without come to the scanner (differential information bias), the same considerations as for head motion would apply. If instead, children with ADHD and without will come to the MRI at similar times of the day (non-differential information bias), then adjustments for time of the day would be appropriate and reduce the measurement error.”

Discussion (page 14):

“Our considerations on confounding likely generalize to the psychiatric neuroimaging field, as several confounders considered here (e.g., SES) also relate to brain function and other psychiatric disorders (15–17). Similarly, other psychiatric disorders are also characterized by complex relations with IQ (18). Moreover, mental health problems featuring state anxiety, like internalizing and externalizing symptoms, have also been related to increased head motion during scanning (19).”

Methods (pages 19-20):

For The ABCD Study:

“We used the Euler number obtained from FreeSurfer as a proxy for head motion during the acquisition. The Euler number quantifies the topological complexity of the reconstructed cortical surface, with holes in the reconstructed surface leading to lower Euler numbers.” […]

For The Generation R Study:

“The Euler number was used as a proxy for head motion during scanning.”

References:

1. Hagler DJ, Hatton SeanN, Cornejo MD, Makowski C, Fair DA, Dick AS, et al. (2019): Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 202: 116091.

2. Muetzel RL, Mulder RH, Lamballais S, Cortes Hidalgo AP, Jansen P, Güroğlu B, et al. (2019): Frequent Bullying Involvement and Brain Morphology in Children. Front Psychiatry 10. https://doi.org/10.3389/fpsyt.2019.00696

3. Lamballais S, Muetzel RL (2021): QDECR: A Flexible, Extensible Vertex-Wise Analysis Framework in R. Front Neuroinformatics 15. https://doi.org/10.3389/fninf.2021.561689

4. Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, et al. (2019): Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am J Psychiatry 176: 531–542.

5. Saad JF, Griffiths KR, Korgaonkar MS (2020): A Systematic Review of Imaging Studies in the Combined and Inattentive Subtypes of Attention Deficit Hyperactivity Disorder. Front Integr Neurosci 14: 31.

6. Dennis M, Francis DJ, Cirino PT, Schachar R, Barnes MA, Fletcher JM (2009): Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. J Int Neuropsychol Soc JINS 15: 331–343.

7. Hernan MA, Robins JM (2020): Causal Inference What If? CRC press Taylor and Francis Group.

8. Van de Walle R, Lemahieu I, Achten E (1997): Magnetic resonance imaging and the reduction of motion artifacts: review of the principles. Technol Health Care 5: 419–435.

9. Thomson P, Johnson KA, Malpas CB, Efron D, Sciberras E, Silk TJ (2021): Head Motion During MRI Predicted by out-of-Scanner Sustained Attention Performance in Attention-Deficit/Hyperactivity Disorder. J Atten Disord 25: 1429–1440.

10. Kong X, Zhen Z, Li X, Lu H, Wang R, Liu L, et al. (2014): Individual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging. PLOS ONE 9: e104989.

11. Westreich D (2012): Berkson’s bias, selection bias, and missing data. Epidemiol Camb Mass 23: 159–164.

12. Lash T, Fox M, MacLehose R (2021): Applying Quantitative Bias Analysis to Epidemiologic Data. Springer International Publishing. Retrieved February 16, 2022, from https://link.springer.com/book/9783030826727

13. Nakamura K, Brown RA, Narayanan S, Collins DL, Arnold DL, Alzheimer’s Disease Neuroimaging Initiative (2015): Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations. NeuroImage 118: 126–132.

14. Trefler A, Sadeghi N, Thomas AG, Pierpaoli C, Baker CI, Thomas C (2016): Impact of time-of-day on brain morphometric measures derived from T1-weighted magnetic resonance imaging. NeuroImage 133: 41–52.

15. Biazoli CE, Salum GA, Gadelha A, Rebello K, Moura LM, Pan PM, et al. (2020): Socioeconomic status in children is associated with spontaneous activity in right superior temporal gyrus. Brain Imaging Behav 14: 961–970.

16. Kivimäki M, Batty GD, Pentti J, Shipley MJ, Sipilä PN, Nyberg ST, et al. (2020): Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health 5: e140–e149.

17. Apter G, Bobin A, Genet M-C, Gratier M, Devouche E (2017): Update on Mental Health of Infants and Children of Parents Affected With Mental Health Issues. Curr Psychiatry Rep 19: 72.

18. Der G, Batty GD, Deary IJ (2009): The association between IQ in adolescence and a range of health outcomes at 40 in the 1979 US National Longitudinal Study of Youth. Intelligence 37: 573–580.

19. Eijlers R, Blok E, White T, Utens EMWJ, Tiemeier H, Staals LM, et al. (2021, August 11): Internalizing and externalizing behaviors in school-aged children are related to state anxiety during magnetic resonance imaging. medRxiv, p 2021.08.11.21261892.

https://doi.org/10.7554/eLife.78002.sa2

Article and author information

Author details

  1. Lorenza Dall'Aglio

    1. Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam-Sophia Children’s Hospital, Rotterdam, Netherlands
    2. The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    Contributed equally with
    Hannah H Kim and Sander Lamballais
    Competing interests
    No competing interests declared
  2. Hannah H Kim

    Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Lorenza Dall'Aglio and Sander Lamballais
    Competing interests
    No competing interests declared
  3. Sander Lamballais

    Department of Clinical Genetics, Erasmus MC, Rotterdam, Netherlands
    Contribution
    Conceptualization, Data curation, Software, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Lorenza Dall'Aglio and Hannah H Kim
    Competing interests
    No competing interests declared
  4. Jeremy Labrecque

    Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Ryan L Muetzel

    Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam-Sophia Children’s Hospital, Rotterdam, Netherlands
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    Additional information
    Ryan L. Muetzel and Henning Tiemeier are co-last authors.
  6. Henning Tiemeier

    1. Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam-Sophia Children’s Hospital, Rotterdam, Netherlands
    2. Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    For correspondence
    tiemeier@hsph.harvard.edu
    Competing interests
    No competing interests declared
    Additional information
    Ryan L. Muetzel and Henning Tiemeier are co-last authors.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4395-1397

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO-ZonMW: 016.VICI.170.200)

  • Henning Tiemeier

Erasmus Medisch Centrum (Sophia Foundation S18-20)

  • Ryan L Muetzel

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. All supporters are mentioned at https://abcdstudy.org/federal-partners.html. Participating sites and study investigators are shown at https://abcdstudy.org/consortium_members/. While ABCD investigators designed, implemented the study, and/or provided data, they did not participate in this manuscript. This work reflects the authors’ views, and may not reflect those of the NIH or ABCD investigators. The Generation R Study is supported by Erasmus MC, Erasmus University Rotterdam, the Rotterdam Homecare Foundation, the Municipal Health Service Rotterdam area, the Stichting Trombosedienst & Artsenlaboratorium Rijnmond, the Netherlands Organization for Health Research and Development (ZonMw), and the Ministry of Health, Welfare and Sport. Neuroimaging data acquisition was funded by the European Community’s 7th Framework Program (FP7/2008-2013, 212652, Nutrimenthe). Netherlands Organization for Scientific Research (Exacte Wetenschappen) and SURFsara (Cartesius Compute Cluster, https://www.surfsara.nl/) supported the Supercomputing resources. 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. We thank the participants, general practitioners, hospitals, midwives, and pharmacies in Rotterdam who contributed to the study.

Ethics

Research protocols for the ABCD study were approved by the institutional review board of the University of California, San Diego (#160091), and the institutional review boards of the 21 data collection sites, while the design of the Generation R study was approved by the Medical Ethics Committee of the Erasmus MC (METC-2012-165). For both studies, written informed consent and assent from the primary caregiver or child were obtained.

Senior Editor

  1. Jonathan Roiser, University College London, United Kingdom

Reviewing Editor

  1. Juan Helen Zhou, National University of Singapore, Singapore

Reviewer

  1. Francisco Xavier Catellanos, New York University Langone Medical Center, United States

Publication history

  1. Received: February 18, 2022
  2. Preprint posted: May 3, 2022 (view preprint)
  3. Accepted: November 6, 2022
  4. Accepted Manuscript published: November 9, 2022 (version 1)
  5. Version of Record published: November 29, 2022 (version 2)

Copyright

© 2022, Dall'Aglio, Kim, Lamballais et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Lorenza Dall'Aglio
  2. Hannah H Kim
  3. Sander Lamballais
  4. Jeremy Labrecque
  5. Ryan L Muetzel
  6. Henning Tiemeier
(2022)
Attention-deficit hyperactivity disorder symptoms and brain morphology: Examining confounding bias
eLife 11:e78002.
https://doi.org/10.7554/eLife.78002

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