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W4310291372 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics 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 Decision letter Reviews on Sciety eLife's review process 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 Download asset Open asset 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 Download asset Open asset 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 Download asset Open asset 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 Download asset Open asset 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 Download asset Open asset 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 Download asset Open asset 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. Figure 7 Download asset Open asset 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 Download asset Open asset 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" @default.
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W4310291372 title "Author response: Attention-deficit hyperactivity disorder symptoms and brain morphology: Examining confounding bias" @default.
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