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The Relationship between Inhibitory Control of Attention and fMRI Functional Connectivity in Children with and without ADHD

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Title: The Relationship between Inhibitory Control of Attention and fMRI Functional Connectivity in Children with and without ADHD
Language: English
Authors: Kelsey Harkness (ORCID 0000-0002-5770-9964), Matthias Wilms, Kate J. Godfrey (ORCID 0000-0002-2578-2382), Signe Bray, Kara Murias
Source: Journal of Attention Disorders. 2026 30(6):784-794.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 11
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Inhibition, Attention Control, Attention Deficit Hyperactivity Disorder, Brain, Diagnostic Tests, Preadolescents, Behavior, Interaction, Correlation
DOI: 10.1177/10870547261419585
ISSN: 1087-0547
1557-1246
Abstract: Introduction: Attention abilities can be represented within the population as a spectrum from low to high ability. Attention deficits are present in a number of neurodevelopmental disorders, including as a primary symptom of ADHD. When evaluating the relationship between brain networks and attention abilities, it is important to know whether this relationship is mediated by diagnosis to understand processes that contribute to disability and to determine if attention can appropriately be studied transdiagnostically. Functional connectivity (FC) within the brain has been studied in association with inhibitory attention and ADHD diagnosis separately, but it is unclear whether the relationship between inhibitory attention and FC is altered in individuals with ADHD. Methods: We evaluated whether the relationship between inhibitory attention, as measured by the Flanker Inhibitory Control and Attention test, and FC was impacted by ADHD diagnostic status in children age 9 to 10 using the Adolescent Brain and Cognitive Development (ABCD) database. Results: We found that, although there were significant associations between FC and both ADHD diagnosis and attention, the interaction between attention and diagnostic group was not significantly associated with functional connectivity. Conclusion: These results support that the relationship between attention and FC is not mediated by ADHD diagnosis and thus provides evidence for a transdiagnostic-dimensional relationship between FC and inhibitory attention.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1504203
Database: ERIC
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  Value: <anid>AN0193250482;gs001jun.26;2026Apr28.02:16;v2.2.500</anid> <title id="AN0193250482-1">The Relationship Between Inhibitory Control of Attention and fMRI Functional Connectivity in Children With and Without ADHD </title> <p>Introduction: Attention abilities can be represented within the population as a spectrum from low to high ability. Attention deficits are present in a number of neurodevelopmental disorders, including as a primary symptom of ADHD. When evaluating the relationship between brain networks and attention abilities, it is important to know whether this relationship is mediated by diagnosis to understand processes that contribute to disability and to determine if attention can appropriately be studied transdiagnostically. Functional connectivity (FC) within the brain has been studied in association with inhibitory attention and ADHD diagnosis separately, but it is unclear whether the relationship between inhibitory attention and FC is altered in individuals with ADHD. Methods: We evaluated whether the relationship between inhibitory attention, as measured by the Flanker Inhibitory Control and Attention test, and FC was impacted by ADHD diagnostic status in children age 9 to 10 using the Adolescent Brain and Cognitive Development (ABCD) database. Results: We found that, although there were significant associations between FC and both ADHD diagnosis and attention, the interaction between attention and diagnostic group was not significantly associated with functional connectivity. Conclusion: These results support that the relationship between attention and FC is not mediated by ADHD diagnosis and thus provides evidence for a transdiagnostic-dimensional relationship between FC and inhibitory attention.</p> <p>Keywords: attention; ADHD; mixed effects models; functional connectivity</p> <hd id="AN0193250482-2">Introduction</hd> <p>Inhibitory control of attention (or inhibitory attention) is defined as the ability to attend to a target stimulus while inhibiting non-target stimuli ([<reflink idref="bib42" id="ref1">42</reflink>]), and is commonly dysregulated in individuals with neurodevelopmental or neurological diagnoses. Deficits of inhibitory control have been associated with several diagnostic groups including ADHD; [<reflink idref="bib46" id="ref2">46</reflink>]; [<reflink idref="bib48" id="ref3">48</reflink>]), Autism Spectrum Disorder (ASD; [<reflink idref="bib45" id="ref4">45</reflink>]), intellectual disability (ID; [<reflink idref="bib6" id="ref5">6</reflink>]), and mood disorders ([<reflink idref="bib26" id="ref6">26</reflink>]; [<reflink idref="bib33" id="ref7">33</reflink>]). Inhibitory attention skill presents on a spectrum of ability across neurodiverse and typically developing populations ([<reflink idref="bib29" id="ref8">29</reflink>]), and its dysregulation has been associated with poorer academic, occupational, and personal outcomes ([<reflink idref="bib8" id="ref9">8</reflink>]). Assessing the relationship between brain measures and inhibitory attention in individuals can provide insight into the neurophysiology supporting this important skill. It is important to understand whether the relationship between the brain and inhibitory attention is influenced by diagnostic status given the breadth of diagnoses associated with inhibition deficits. Furthermore, analysing associations between brain measures and ability across different diagnostic groups provides a foundation for understanding whether inhibitory attention difficulties manifest differently in clinical groups such as ADHD.</p> <p>Functional connectivity (FC) is a measure of the relatedness of the activation patterns of different brain regions and can be measured using functional MRI (fMRI). FC has been employed to predict cognitive traits ([<reflink idref="bib20" id="ref10">20</reflink>]; [<reflink idref="bib53" id="ref11">53</reflink>]), including inhibitory attention ([<reflink idref="bib10" id="ref12">10</reflink>]). However, it is unclear whether the brain-behaviour relationships that have been established in typically developing samples extend to individuals with neurodevelopmental diagnoses such as ADHD ([<reflink idref="bib40" id="ref13">40</reflink>]; [<reflink idref="bib53" id="ref14">53</reflink>]), which needs to be understood if the brain-behaviour relationship is to be evaluated transdiagnostically. Previous studies utilising fMRI to look at FC differences between individuals with and without ADHD and have demonstrated differences in connectivity between and within the default mode, sensorimotor, and fronto-parietal networks ([<reflink idref="bib37" id="ref15">37</reflink>]). Further, stimulant medications, which are commonly used to treat symptoms of ADHD, like inattention, have also been associated with default mode and task positive network inter- and intra-connectivity ([<reflink idref="bib50" id="ref16">50</reflink>]).</p> <p>Given the high rates of variability in symptom presentation and treatment response in ADHD, assessing the association between brain biomarkers (such as FC) and symptom-level traits may increase efficiency of ADHD treatment by providing information to personalise treatment. This is reflected in the growing interest in investigations of individual traits across different conditions or samples. The Research Domain Criteria (RDoC) approach conceives of behavioural traits, like inhibition, transdiagnostically across a continuum of expression ([<reflink idref="bib16" id="ref17">16</reflink>]) and has been used to investigate mental health outcomes including suicide risk ([<reflink idref="bib21" id="ref18">21</reflink>]), anxiety, and mood disorders ([<reflink idref="bib32" id="ref19">32</reflink>]). However, to use a transdiagnostic approach to evaluate brain-behaviour relationships it must first be demonstrated that the brain-behaviour relationship does not differ between diagnostic groups. This is particularly important within domains central to diagnosis, such as inhibitory attention in ADHD. Previous studies evaluating the relationship between functional connectivity and an indirect measure of attention and demonstrated that there was a relationship between FC and attention particularly between default mode and dorsal attention network ([<reflink idref="bib18" id="ref20">18</reflink>]). However, this study did not evaluate whether ADHD diagnosis impacted this relationship.</p> <p>Inhibitory attention can be measured using a variety of assessments ([<reflink idref="bib2" id="ref21">2</reflink>]; [<reflink idref="bib54" id="ref22">54</reflink>]; [<reflink idref="bib59" id="ref23">59</reflink>]), including the Flanker Inhibitory Control and Attention test ([<reflink idref="bib62" id="ref24">62</reflink>]). The Flanker assessment is a direct measure of inhibitory attention. Although inhibitory control is a central deficit of ADHD, it is not sufficient or required for ADHD diagnosis, there are many other symptoms associated with ADHD like hyperactivity, and impulsivity. Inhibitory control also sometimes considered a 'core' or 'base' executive function that contributes, in varying degrees, to 'higher-order' executive functions like planning and executive control ([<reflink idref="bib5" id="ref25">5</reflink>]). Performance on Flanker tasks have been linked to underlying functional brain activity in the DMN ([<reflink idref="bib4" id="ref26">4</reflink>]; [<reflink idref="bib29" id="ref27">29</reflink>]; [<reflink idref="bib57" id="ref28">57</reflink>]), salience network, and dorsal attention networks ([<reflink idref="bib4" id="ref29">4</reflink>]; [<reflink idref="bib29" id="ref30">29</reflink>]; [<reflink idref="bib57" id="ref31">57</reflink>]). However, direct measures of attention do not always directly correlate with behavioural outcomes (e.g., as measured using questionnaires; [<reflink idref="bib25" id="ref32">25</reflink>]).</p> <p>Functional Magnetic Resonance Imaging (fMRI) studies have consistently shown increased BOLD signal in the anterior cingulate cortex (ACC), a key salience network node, during inhibitory attention tasks ([<reflink idref="bib4" id="ref33">4</reflink>]; [<reflink idref="bib38" id="ref34">38</reflink>]; [<reflink idref="bib57" id="ref35">57</reflink>]). Additionally, [<reflink idref="bib29" id="ref36">29</reflink>] reported that the strength of the anti-correlation between the default mode and task-positive networks (including the salience network) during completion of a Flanker task, was associated with inhibitory attention abilities. While these studies help to elucidate the neural circuitry involved during performance of inhibitory attention tasks, we know relatively less about brain features that associate with trait-level abilities ([<reflink idref="bib51" id="ref37">51</reflink>]; [<reflink idref="bib53" id="ref38">53</reflink>]). Task-based fMRI can be limited by performance confounds ([<reflink idref="bib47" id="ref39">47</reflink>]), prompting a focus on task-free resting paradigms to assess brain-behaviour relationships. Despite advances in understanding the neural correlates of cognitive ability broadly ([<reflink idref="bib51" id="ref40">51</reflink>]; [<reflink idref="bib53" id="ref41">53</reflink>]), and inhibitory attention specifically ([<reflink idref="bib9" id="ref42">9</reflink>]; [<reflink idref="bib13" id="ref43">13</reflink>]; [<reflink idref="bib35" id="ref44">35</reflink>]), we know relatively little about how resting FC associates with the trait of inhibitory attention in children.</p> <p>ADHD is a neurodevelopmental disorder that presents with a high level of variability in symptoms and severity ([<reflink idref="bib12" id="ref45">12</reflink>]). Task-based FC studies have shown FC differences between individuals with and without ADHD in visual, ventral attention, default mode, salience, and fronto-parietal networks, with some inconsistencies that could be due to differences in task ([<reflink idref="bib30" id="ref46">30</reflink>]; [<reflink idref="bib39" id="ref47">39</reflink>]). Resting state fMRI studies have shown that FC in regions of the default mode, salience, frontoparietal, and cingulo-opercular networks associate with ADHD diagnosis ([<reflink idref="bib14" id="ref48">14</reflink>]). However, previous meta-analysis of resting state fMRI correlates of ADHD indicated no spatial convergence in the brain regions associated with ADHD between studies ([<reflink idref="bib15" id="ref49">15</reflink>]). On the other hand, investigations of a whole brain FC relationship with a composite dimensional measure of ADHD symptom severity in the large, multi-site Adolescent Brain and Cognitive Development (ABCD) database, indicated associations were widely distributed throughout the brain and had small effect sizes ([<reflink idref="bib43" id="ref50">43</reflink>]). These findings were further validated in an independent sample ([<reflink idref="bib43" id="ref51">43</reflink>]). Together, prior studies on the resting-state FC correlates of ADHD suggest that investigating FC associations with dimensional measures, such as symptom severity, may demonstrate a more robust relationship to FC when compared to diagnostic categories.</p> <p>This paper aimed to assess whether the relationship between FC and inhibitory attention is different in individuals with and without ADHD, or if individuals with ADHD exist along the same continuum of brain and behaviour association as unaffected children. To achieve this, data was accessed from the ABCD database. We hypothesised that the relationship between network FC and inhibitory attention is not different between groups. We first demonstrated a relationship between FC and inhibitory attention (as measured by the Flanker task) and between FC and ADHD diagnosis. We then assessed whether the interaction between inhibitory attention and ADHD diagnosis was significantly associated with FC. A non-significant interaction term suggests that the relationship between inhibitory attention and FC is consistent across individuals with and without ADHD, which would allow for transdiagnostic evaluation of traits across the spectrum of ability.</p> <hd id="AN0193250482-3">Methods</hd> <p></p> <hd id="AN0193250482-4">ABCD Database</hd> <p>Data was acquired from the ABCD database, a longitudinal database focussing on collecting comprehensive neuroimaging, cognitive, behavioural, demographic, and physiological data from children across 21 study sites in the United States. (The ABCD data used in this report came from NIMH Data Archive Digital Object Identifier [10.15154/1503209] full information in Supplemental A.) The ABCD database targeted groups traditionally underrepresented in other neuroimaging cohort studies, including low socioeconomic status, with the intention of having a sample that is representative of the larger population of the United States ([<reflink idref="bib28" id="ref52">28</reflink>]).</p> <p>The ABCD database includes pre-processed network-level FC data from resting state fMRI (details below), scores from the Flanker Inhibitory Control and Attention test, ADHD diagnosis (as measured by the Kiddie Score for Affective Disorders and Schizophrenia; KSADS), motion during the fMRI scan (mean relative motion in mm), and demographic information including age, sex, scanning site, and parental education (highest level of education obtained by the parent attending the research appointment). In addition to database exclusion criteria ([<reflink idref="bib28" id="ref53">28</reflink>]), we excluded individuals with substance abuse disorder (<emph>n</emph> = 412) and schizophrenia (<emph>n</emph> = 25) due to known differences in FC ([<reflink idref="bib61" id="ref54">61</reflink>]; [<reflink idref="bib64" id="ref55">64</reflink>]), and autism (<emph>n</emph> = 94) due to a lack of independent diagnostic confirmation. Participants with less than 10 mins of retained fMRI data (<emph>n</emph> = 1,568; following motion exclusion; details below) were also excluded. We split the remaining participants (aged 9–10 years old; <emph>n</emph> = 7,030) into groups based on whether they had a current ADHD diagnosis (based on the KSADs; <emph>n</emph> = 494) or did not have an ADHD diagnosis (<emph>n</emph> = 6,536).</p> <hd id="AN0193250482-5">Inhibitory Attention</hd> <p>The Flanker assessment included in the NIH Toolbox ([<reflink idref="bib62" id="ref56">62</reflink>]) is a measure of inhibitory attention that requires an individual to indicate the direction of a central arrow that is surrounded, or flanked, by arrows either pointing the same direction as the target (congruent trials), or the opposite direction of the target (incongruent trials; [<reflink idref="bib60" id="ref57">60</reflink>]; [<reflink idref="bib62" id="ref58">62</reflink>]). Following a brief practice to ensure understanding, participants completed 2 blocks of 25 presentations (16 congruent and 9 incongruent). Flanker scores are determined based on the number of correct trials (only including the first 20 trails from each block to account for fatigue) combined with reaction time when accuracy is above 80% to produce final scores ([<reflink idref="bib36" id="ref59">36</reflink>]; [<reflink idref="bib62" id="ref60">62</reflink>]). Uncorrected standard scores were used for this analysis, with no correction for age or sex.</p> <hd id="AN0193250482-6">Functional Connectivity</hd> <p>This analysis utilised FC measures that were provided in the ABCD release 4.0 (https://nda.nih.gov/abcd/abcd-annual-releases). The ABCD study collected two 10-min runs of high temporal and spatial resolution multiband EPI resting-state fMRI (rs-fMRI) images at each study site with additional runs added for some individuals with excessive motion ([<reflink idref="bib11" id="ref61">11</reflink>]). Standard fMRI preprocessing steps were applied, which are fully described in [<reflink idref="bib23" id="ref62">23</reflink>]. In brief, preprocessing consisted of head motion correction to the first volume using AFNI's 3dvolreg function, B<subs>0</subs> distortion correction, realignment with a reference from the middle of each scan run, and rigid registration to a study specific atlas. Following preprocessing any image volumes were censored if they had greater than 0.2 mm of framewise displacement, less than 5 contiguous volumes between censored volumes, or if they had spatial variation greater than 3 standard deviations from the mean of the whole scan. Band pass filtering (0.009–0.08 Hz) was performed. Time series were extracted from 422 regions using the [<reflink idref="bib22" id="ref63">22</reflink>] atlas. FC was calculated by Pearson-correlation of time series between pairs of regions and subsequently Fischer-transforming correlation values to <emph>z</emph> scores. These values were then averaged within each network to get network-level FC estimates and provided for access through the ABCD database.</p> <p>The FC measures used in this analysis were interconnectivity and intraconnectivity (between and within respectively) for twelve brain networks (auditory, cingulo-opercular, cingulo-parietal, default mode, dorsal attention, frontoparietal, retrosplenial temporal, sensorimotor hand, sensorimotor mouth, salience, ventral attention, and visual), as defined by the parcellation in [<reflink idref="bib22" id="ref64">22</reflink>]. Interconnectivity was calculated by averaging the correlation from each region in one network to every other region in the other network. Intraconnectivity was calculated by averaging all the correlations within one network. This resulted in the upper triangle of a 12 × 12 connectivity matrix, which was used in all subsequent analyses.</p> <hd id="AN0193250482-7">Analyses</hd> <p></p> <hd id="AN0193250482-8">Demographics</hd> <p>Group level comparisons (Table 1 and Supplemental Table B1) were conducted between individuals with (<emph>n</emph> = 492) and without (<emph>n</emph> = 6536) a diagnosis of ADHD. Continuous variables were evaluated using a <emph>t</emph>-test (age in months, fMRI movement, and Flanker scores), and categorical variables were evaluated using a chi square test (sex, parental education, co-occurring disorders, medication usage, race, and site). Group level demographics (Table 1 and Supplemental Table B1) indicated significant differences between diagnostic groups in sex (<emph>p</emph> < 0.00001), age (<emph>p</emph> =.019), in scanner motion (<emph>p</emph> =.0035), and Flanker scores (<emph>p</emph> =.00011). These confounds were included in all subsequent models. Number of ADHD symptoms present (based on the KSADs) within the ADHD group and control group (e.g., inattentive, hyperactive, and impulsive symptoms) are summarised in supplemental Table B2.</p> <p>Table 1. Demographic Information About Study Population.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Demographics</th><th align="center" colspan="3">ADHD</th><th align="center" colspan="3">Controls</th><th align="center" colspan="2">Statistics</th></tr><tr><th align="center">Average</th><th align="center"><italic>SD</italic></th><th align="center">Range</th><th align="center">Average</th><th align="center"><italic>SD</italic></th><th align="center">Range</th><th align="center">X<sup>2</sup>/<italic>t</italic> Value</th><th align="center"><italic>p</italic>-value</th></tr></thead><tbody><tr><td><italic>n</italic></td><td>492</td><td /><td /><td>6,536</td><td /><td /><td /><td /></tr><tr><td>Age (years)</td><td>9.87</td><td>7.49</td><td>23</td><td>9.94</td><td>7.41</td><td>23</td><td>−2.36</td><td>.019</td></tr><tr><td>Sex (M/F)</td><td>319/173</td><td /><td /><td>3,137/3,399</td><td /><td /><td>51.93</td><td>5.80E−13</td></tr><tr><td colspan="9">Parental education</td></tr><tr><td> Did not graduate</td><td>9</td><td /><td /><td>285</td><td /><td /><td rowspan="9">25.83</td><td rowspan="9">.1</td></tr><tr><td> High school graduate/GED</td><td>36</td><td /><td /><td>614</td><td /><td /></tr><tr><td> Some college</td><td>86</td><td /><td /><td>1,055</td><td /><td /></tr><tr><td> Associate degree</td><td>85</td><td /><td /><td>810</td><td /><td /></tr><tr><td> Bachelor's degree</td><td>147</td><td /><td /><td>1,951</td><td /><td /></tr><tr><td> Master's degree</td><td>86</td><td /><td /><td>1,343</td><td /><td /></tr><tr><td> Professional school degree</td><td>14</td><td /><td /><td>196</td><td /><td /></tr><tr><td> Doctoral degree</td><td>24</td><td /><td /><td>223</td><td /><td /></tr><tr><td> Refused to answer</td><td>0</td><td /><td /><td>5</td><td /><td /></tr><tr><td colspan="9">Motion correction in fMRI</td></tr><tr><td> Average framewise displacement (mm)</td><td>0.21</td><td>0.16</td><td>0.9</td><td>0.19</td><td>0.15</td><td>1.79</td><td>2.94</td><td>.0035</td></tr><tr><td colspan="9">Attention measure</td></tr><tr><td> Flanker (uncorrected standard score)</td><td>93.16</td><td>9.18</td><td>57</td><td>94.83</td><td>8.61</td><td>62</td><td>−3.91</td><td>.00011</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. Group comparisons were calculated using chi square tests for categorical variables and <emph>t</emph>-tests for continuous variables.</p> <hd id="AN0193250482-9">Functional Connectivity</hd> <p>We first examined the relation between network-level FC and the main effect of Flanker scores and ADHD diagnosis. Each was considered independently in separate linear mixed effects models (i.e., just the Flanker score term or just the ADHD diagnosis term) controlling for the fixed effects of age, sex, parental education, and in-scanner movement (mean motion mm) and the nested random effects of site and family group. We additionally predicted network-level FC in mixed effects models that included main effects of both Flanker scores and ADHD diagnosis, and an interaction between Flanker scores and ADHD diagnosis, while controlling for the same covariates as above. For completeness, we re-ran all models that included ADHD diagnosis with a dimensional ADHD symptom score rather than the binary diagnostic group. We utilised the dimensional symptom score derived by [<reflink idref="bib43" id="ref65">43</reflink>], which is calculated from symptom presence in the KSADs and the inattention subscore of the CBCL. Additionally, given the differences in sample size and head motion between the diagnostic groups, we replicated the analysis using both: (<reflink idref="bib1" id="ref66">1</reflink>) a randomly selected subsample of the control group the same size as the ADHD group (i.e., <emph>n</emph> = 494) and; (<reflink idref="bib2" id="ref67">2</reflink>) a subsample of the control group the same size as the ADHD group that was matched for head motion (Supplemental Tables C1–C8).</p> <p>We built models using each intra- and inter-network FC value as the outcome, totaling 78 models per hypothesis. Bonferroni correction was therefore applied to account for 78 comparisons (corrected alpha =.00065). As Bonferroni correction is conservative, we also conducted inference using a multivariate version of the constrained network-based statistic (mv-cNBS) to consider effects at the whole-brain level with elevated sensitivity ([<reflink idref="bib49" id="ref68">49</reflink>]). The mv-cNBS statistic creates a null distribution via permutations and, at the whole-brain level, considers the Euclidian distance across network-level effects ([<reflink idref="bib49" id="ref69">49</reflink>]). We calculated a mv-cNBS statistic for all models.</p> <hd id="AN0193250482-10">Results</hd> <p></p> <hd id="AN0193250482-11">Flanker Scores</hd> <p>Flanker scores were significantly associated with FC between the retrosplenial-temporal network and several networks including the cingulo-parietal, sensorimotor-mouth, visual, and default mode networks. We also observed significant associations within the cingulo-parietal network, in addition to associations between the sensorimotor-mouth network and both the fronto-parietal and sensorimotor-hand networks (full results in Figure 1(A); Supplemental Table B3). Within the multivariate whole brain analysis (mv-cNBS), we found a significant effect of Flanker scores (Euclidean Distance: 14.56587; <emph>p</emph> =.001).</p> <p>Graph: Figure 1. Panel A, B, and C depict beta coefficients from Linear Mixed Effects Models (LMEMs) utilising Flanker scores (Panel A), categorical ADHD diagnosis (Panel B), or a dimensional ADHD symptom score (Panel C) while accounting for covariates (age, sex, site, parental education, and head motion). Coefficients that are bolded, underlined, and indicated with in asterix (*) indicate significant coefficients at the Bonferroni corrected alpha threshold (corrected α =.00065). Colours are related to the value of the coefficient as indicated within the colour bars on the right.</p> <hd id="AN0193250482-12">ADHD Diagnosis</hd> <p>ADHD diagnosis was significantly associated with FC between the auditory and salience networks; cingulo-opercular and ventral attention networks; and auditory and ventral attention networks (full results in Figure 1(B); Supplemental Table B4). Within the multivariate whole brain analysis (mv-cNBS), we found a significant association between FC and ADHD diagnosis (Euclidean Distance: 13.86607; <emph>p</emph> <.0001). Similar results were observed when utilising a total dimensional ADHD symptom composite score, with significant FC identified between the visual and cingulo-parietal networks; default mode and cingulo-parietal networks; and dorsal attention and default mode networks (full models in Figure 1(C), Supplemental Tables B6 and B7). Total dimensional ADHD symptoms were also significant in the multivariate whole brain model (Euclidean Distance: 16.79214; <emph>p</emph> =.001).</p> <hd id="AN0193250482-13">Interaction between Flanker Scores and ADHD Diagnosis</hd> <p>We found no significant interactions between ADHD diagnosis and Flanker scores across FC features (model information in Supplemental Table B5). This was confirmed with the multivariate whole brain calculation (mv-cNBS), which also did not show a significant interaction between Flanker scores and ADHD diagnosis at the whole brain level (Euclidean Distance: 7.183449; <emph>p</emph> =.907). This was also observed when utilising the dimensional ADHD symptom score (results in Supplemental Table B7; Euclidean Distance: 8.270318; <emph>p</emph> =.618).</p> <p>The results for the sample-size and motion matched samples (control <emph>n</emph> = 494) were largely the same and did not change the implications derived from the results. The results including the full sample are presented here and the additional analyses are available in the Supplemental (C1–C8).</p> <hd id="AN0193250482-14">Discussion</hd> <p>We aimed to analyse if the relationship between inhibitory attention and FC was altered in children with an ADHD diagnosis compared to those without. In a large sample from the ABCD database, we found that network-level FC is significantly associated with both inhibitory attention and ADHD diagnosis but not the interaction between the two. This supports the hypothesis that neural correlates of inhibitory attention abilities in ADHD lie along a spectrum that includes typical development ([<reflink idref="bib56" id="ref70">56</reflink>]). More specifically, it supports the hypothesis that there is a spectrum of inhibitory attention ability with similar neural correlates in children with or without an ADHD diagnosis giving evidence to support the ability to assess cognitive traits across diagnostic groups ([<reflink idref="bib29" id="ref71">29</reflink>]). Lack of spatial congruence in FC patterns associated with inhibitory attention and diagnosis may also suggest that FC difference in individuals with and without ADHD could be driven by differences related to processes other than inhibitory control of attention.</p> <p>Our finding that the relationship between FC and inhibitory attention is not impacted by diagnostic status is discordant with some prior work. Other studies have indicated differences in the relationship between resting state FC and inhibitory attention in individuals with and without ADHD ([<reflink idref="bib40" id="ref72">40</reflink>]; [<reflink idref="bib63" id="ref73">63</reflink>]), though these observations may be influenced by small sample sizes (<emph>n</emph> = 17 in each diagnostic group in [<reflink idref="bib40" id="ref74">40</reflink>]), or other participant factors that differ between groups (e.g., motion). Our findings that trait-level inhibitory attention shows consistent brain-behaviour relationships across diagnostic groups supports the use of transdiagnostic research approaches such as Research Domain Criteria (RDoC). While RDoC has largely been applied to mental health outcomes ([<reflink idref="bib21" id="ref75">21</reflink>]), ([<reflink idref="bib32" id="ref76">32</reflink>]), it has also been proposed to have utility for investigating neurodevelopmental disorders ([<reflink idref="bib41" id="ref77">41</reflink>]), which have previously been conceived as separate entities that are unrelated to typical development or each other ([<reflink idref="bib44" id="ref78">44</reflink>]; [<reflink idref="bib56" id="ref79">56</reflink>]).</p> <p>Categorical diagnostic groups promote clear communication between practitioners, patients, families, and other community and governmental services ([<reflink idref="bib16" id="ref80">16</reflink>]). This is most clearly reflected in research on medication efficacy where treatment protocols and approvals are tested within specific clinical (i.e., diagnostic) groups ([<reflink idref="bib55" id="ref81">55</reflink>]). A categorical approach assumes that groups with and without a diagnosis are distinct, however there is evidence to suggest that individuals with NDDs express skills or traits across a spectrum rather than as discrete groups ([<reflink idref="bib31" id="ref82">31</reflink>]). For example, those with sub-threshold symptoms (i.e., those just failing to meet diagnostic criteria) express similar levels of deficits in behavioural measures (such as the child behaviour checklist [CBCL]) and have similar co-occurring disorders when compared to their diagnosed peers ([<reflink idref="bib7" id="ref83">7</reflink>]). Reducing the complexity of individual presentations to binary diagnostic groups may limit the understanding of the whole spectrum of ability and thus individual deficits. Dimensional variables are better able to represent the variability that exists within diagnostic groups and may, therefore, be more appropriate than diagnostic categories when investigating physiology of transdiagnostic traits.</p> <p>Our analysis identified a number of networks associated with Flanker scores, with connections involving the retrosplenial-temporal and the sensorimotor mouth networks being the most significant. We found some network connections that were consistent with the literature including contribution from the dorsal attention and the fronto-parietal networks ([<reflink idref="bib57" id="ref84">57</reflink>]); however, the salience network was not significant, despite reported associations with inhibitory attention in the literature ([<reflink idref="bib10" id="ref85">10</reflink>]; [<reflink idref="bib29" id="ref86">29</reflink>]). These conflicting findings may be due to the use of different assessments for inhibitory attention, which may differentially involve discrete neural functions ([<reflink idref="bib51" id="ref87">51</reflink>]). [<reflink idref="bib51" id="ref88">51</reflink>] suggested that there are different functional correlates for each aspect of attention – alerting, orienting, and executive function – and that these processes are engaged in different proportions for different attention assessments which is likely also true across different assessments of attentional inhibition. This could help explain the differences in FC correlates to performance on different assessment tasks.</p> <p>ADHD diagnostic status was associated primarily with FC involving the ventral attention, and auditory networks, but also the salience and cingulo-opercular networks. In previous literature, associations between resting state FC and ADHD diagnosis have shown very inconsistent results with meta-analysis showing no consistency ([<reflink idref="bib15" id="ref89">15</reflink>]). We found that salience and ventral attention networks, which have previously been associated with inhibitory attention ([<reflink idref="bib29" id="ref90">29</reflink>]), were associated with ADHD diagnosis despite lack of association with inhibitory attention. Among the previously recognised attention networks, our analysis found that the ventral attention network was associated with ADHD diagnosis, while the dorsal attention network was associated with inhibitory attention. This is consistent with the literature indicating that the dorsal attention network is associated with the conscious direction of attention and the ventral attention network is associated more with alerting and orienting of attention ([<reflink idref="bib58" id="ref91">58</reflink>]) with alerting (i.e., appropriate redirection of attention) having been shown to be impaired in ADHD ([<reflink idref="bib1" id="ref92">1</reflink>]).</p> <p>[<reflink idref="bib43" id="ref93">43</reflink>] also utilised the ABCD database to investigate the relationship between edgewise resting state FC and ADHD symptomology and demonstrated that ADHD symptom severity was associated with diffuse FC patterns across the brain as opposed to specific brain region differences. Given the use of the same database, this may appear to be in contradiction to the relatively localised networks that were found to associate with ADHD diagnosis in our study. However, this discrepancy most likely arises because [<reflink idref="bib43" id="ref94">43</reflink>] used a different fMRI processing and statistical approach, while we elected to use network level connectivity while they maintained a finer grained parcellation. In the present analysis we elected to use network level connectivity which could explain why we were able to find significant results given the summary nature of the metric. Investigating network associations with a composite ADHD symptom severity score could result in associations with networks widely distributed throughout the brain, perhaps representing different symptoms or traits. When associations are drawn with diagnostic categories, small effects that are associated with traits that are variable in the different groups may not be detectable. By studying the FC associations with specific symptoms or cognitive impairment measures, we can improve our understanding of the different brain networks that contribute to outcomes in individuals.</p> <p>We found no overlap in the networks associated with inhibitory attention and ADHD diagnosis. This suggests that differences in FC in children with ADHD are influenced by factors other than inhibitory attention ability. FC differences between groups could be due to other impairments or traits ([<reflink idref="bib51" id="ref95">51</reflink>]; [<reflink idref="bib52" id="ref96">52</reflink>]) or they could be attributed to different environments or exposures (e.g., socioeconomic status) that differentially affect children with ADHD ([<reflink idref="bib19" id="ref97">19</reflink>]). Our findings broadly concur with [<reflink idref="bib53" id="ref98">53</reflink>] who found that FC of cortical areas associated with sustained attention did not overlap with the cortical areas that related to ADHD diagnosis ([<reflink idref="bib53" id="ref99">53</reflink>]). Although previous literature has shown some overlap in the networks associated with inhibitory attention and ADHD diagnosis, particularly the default mode network ([<reflink idref="bib29" id="ref100">29</reflink>]; [<reflink idref="bib39" id="ref101">39</reflink>]), these differences could be due to methodological differences or certain design choices in the analyses, such as different assessment measures, the use of task-based fMRI, different brain parcellations, and different approaches to FC analysis.</p> <hd id="AN0193250482-15">Strengths and Limitations</hd> <p>The major methodological strength of the present study is the use of a large sample from the ABCD database allowing for a well-powered statistical analysis. However, by only including 9- and 10-year olds, generalisability of our results to other age groups is limited and would have to be confirmed in follow-up work. Future studies should evaluate whether ADHD diagnosis influences the relationship between FC and cognitive ability across development. Given that ADHD is a neurodevelopmental disorder, affecting development, this is an important step to understanding the dimensionality of the relationship between FC and attention. A previous meta-analysis of resting state fMRI in individuals with ADHD compared to controls found small between-group differences which changed from childhood to adulthood ([<reflink idref="bib34" id="ref102">34</reflink>]), suggesting possible developmental effects that could not be examined here. Our study is also limited in the use of a single measure of inhibitory attention, the Flanker task. Evaluations of the test-retest correlation between baseline and follow-up data for the Flanker task within the ABCD database has shown limited (<emph>r</emph> =.44) correlation ([<reflink idref="bib3" id="ref103">3</reflink>]). While the Flanker task has clinical relevance, as it has been used as an outcome measure in clinical trials evaluating ADHD treatment effectiveness ([<reflink idref="bib17" id="ref104">17</reflink>]; [<reflink idref="bib24" id="ref105">24</reflink>]), the use of multiple tasks, or aggregate measures in future work may boost reliability. Finally, network-level FC metrics do not allow for assessment of individual brain regions, including subcortical structures like thalamus and the striatum. FC of the thalamus and the striatum have been previously associated with stimulant medication use, which targets inhibitory control deficits in children with ADHD ([<reflink idref="bib27" id="ref106">27</reflink>]). Thus, it would be of interest for future studies to evaluate the brain at a finer level while including subcortical structures.</p> <hd id="AN0193250482-16">Conclusions</hd> <p>We demonstrated no significant interaction between inhibitory attention and ADHD diagnostic status in predicting FC. By showing lack of significant interaction, this work suggests that analysis evaluating the relationship between FC and inhibitory attention can be performed independent of ADHD diagnosis to evaluate the full spectrum of ability, including those with a deficit. This has implications for understanding of the full spectrum of inhibitory attention ability, as well as the brain correlates of ADHD diagnostic features. Better understanding of how attention impairments relate to brain differences has potential to lead to treatment more targeted to individual deficits and, therefore, more improvement in long-term outcomes in ADHD.</p> <hd id="AN0193250482-17">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-jad-10.1177_10870547261419585 for The Relationship Between Inhibitory Control of Attention and fMRI Functional Connectivity in Children With and Without ADHD by Kelsey Harkness, Matthias Wilms, Kate J. Godfrey, Signe Bray and Kara Murias in Journal of Attention Disorders</p> <ref id="AN0193250482-18"> <title> References </title> <blist> <bibl id="bib1" idref="ref66" type="bt">1</bibl> <bibtext> Abramov D. M., Cunha C. Q., Galhanone P. R., Alvim R. J., de Oliveira A. M., Lazarev V. V. (2019). Neurophysiological and behavioral correlates of alertness impairment and compensatory processes in ADHD evidenced by the Attention Network Test. 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Godfrey</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-2578-2382</bibtext> </blist> <blist> <bibtext> Kelsey Harkness: Conceptualisation, Methodology, Formal Analysis, Data Curation, Writing – Original Draft, Writing – Review & Editing, and Visualisation. Mathias Wilms: Writing – Review & Editing. Kate Godfrey: Writing – Review & Editing. Signe Bray: Conceptualisation, Methodology, Writing – Review & Editing, and Supervision. Kara Murias: Conceptualisation, Methodology, Resources, Writing – Review & Editing, and Supervision.</bibtext> </blist> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: No funding was directly allocated to this project, but lab funding was obtained through the Alberta Children's Hospital Research Institute and the Owerko Centre.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Kelsey Harkness; Matthias Wilms; Kate J. Godfrey; Signe Bray and Kara Murias</p> <p>Reported by Author; Author; Author; Author; Author</p> <p></p> <p>Kelsey Harkness, PhD is a researcher at the Alberta Children's Hospital Research Institute (ACHRI) at the University of Calgary. She uses big data to evaluate the relationship bewteen the brain, behaviour, and neurodevelopmental disorders.</p> <p>Mathias Wilms, PhD is an assistant professor at the University of Michigan Medical Schoowho does research centering around the development of machine learning solutions for various medical image analysis applications.</p> <p>Kate J. Godfrey, PhD is a postdoctoral researcher at the University of Minnesota who does research focusing on precision, high reliability, fMRI and quantitative psychology.</p> <p>Signe Bray, PhD is a Professor of Radiology in the Cumming School of Medicine at the University of Calgary. She uses neuroimaging to study brain development, neurodevelopment and mental health.</p> <p>Kara Murias, MD, PhD, FRCPC is an Assistant Professor in the Cumming School of Medicine at the University of Calgary. She does research to better understand the underlying processes (both dysfunction and plasticity) that contribute to the cognitive and behavioural outcomes of children with developmental and neurological conditions.</p> </aug> <nolink nlid="nl1" bibid="bib42" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib46" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib48" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib45" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib26" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib33" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib29" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib20" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib53" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib10" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib40" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib37" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib50" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib16" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib21" firstref="ref18"></nolink> <nolink nlid="nl16" bibid="bib32" firstref="ref19"></nolink> <nolink nlid="nl17" bibid="bib18" firstref="ref20"></nolink> <nolink nlid="nl18" bibid="bib54" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib59" firstref="ref23"></nolink> <nolink nlid="nl20" bibid="bib62" firstref="ref24"></nolink> <nolink nlid="nl21" bibid="bib57" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib25" firstref="ref32"></nolink> <nolink nlid="nl23" bibid="bib38" firstref="ref34"></nolink> <nolink nlid="nl24" bibid="bib51" firstref="ref37"></nolink> <nolink nlid="nl25" bibid="bib47" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib13" firstref="ref43"></nolink> <nolink nlid="nl27" bibid="bib35" firstref="ref44"></nolink> <nolink nlid="nl28" bibid="bib12" firstref="ref45"></nolink> <nolink nlid="nl29" bibid="bib30" firstref="ref46"></nolink> <nolink nlid="nl30" bibid="bib39" firstref="ref47"></nolink> <nolink nlid="nl31" bibid="bib14" firstref="ref48"></nolink> <nolink nlid="nl32" bibid="bib15" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib43" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib28" firstref="ref52"></nolink> <nolink nlid="nl35" bibid="bib61" firstref="ref54"></nolink> <nolink nlid="nl36" bibid="bib64" firstref="ref55"></nolink> <nolink nlid="nl37" bibid="bib60" firstref="ref57"></nolink> <nolink nlid="nl38" bibid="bib36" firstref="ref59"></nolink> <nolink nlid="nl39" bibid="bib11" firstref="ref61"></nolink> <nolink nlid="nl40" bibid="bib23" firstref="ref62"></nolink> <nolink nlid="nl41" bibid="bib22" firstref="ref63"></nolink> <nolink nlid="nl42" bibid="bib49" firstref="ref68"></nolink> <nolink nlid="nl43" bibid="bib56" firstref="ref70"></nolink> <nolink nlid="nl44" bibid="bib63" firstref="ref73"></nolink> <nolink nlid="nl45" bibid="bib41" firstref="ref77"></nolink> <nolink nlid="nl46" bibid="bib44" firstref="ref78"></nolink> <nolink nlid="nl47" bibid="bib55" firstref="ref81"></nolink> <nolink nlid="nl48" bibid="bib31" firstref="ref82"></nolink> <nolink nlid="nl49" bibid="bib58" firstref="ref91"></nolink> <nolink nlid="nl50" bibid="bib52" firstref="ref96"></nolink> <nolink nlid="nl51" bibid="bib19" firstref="ref97"></nolink> <nolink nlid="nl52" bibid="bib34" firstref="ref102"></nolink> <nolink nlid="nl53" bibid="bib17" firstref="ref104"></nolink> <nolink nlid="nl54" bibid="bib24" firstref="ref105"></nolink> <nolink nlid="nl55" bibid="bib27" firstref="ref106"></nolink>
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  Data: The Relationship between Inhibitory Control of Attention and fMRI Functional Connectivity in Children with and without ADHD
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  Data: English
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kelsey+Harkness%22">Kelsey Harkness</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5770-9964">0000-0002-5770-9964</externalLink>)<br /><searchLink fieldCode="AR" term="%22Matthias+Wilms%22">Matthias Wilms</searchLink><br /><searchLink fieldCode="AR" term="%22Kate+J%2E+Godfrey%22">Kate J. Godfrey</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2578-2382">0000-0002-2578-2382</externalLink>)<br /><searchLink fieldCode="AR" term="%22Signe+Bray%22">Signe Bray</searchLink><br /><searchLink fieldCode="AR" term="%22Kara+Murias%22">Kara Murias</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. 2026 30(6):784-794.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 11
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  Label: Publication Date
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  Data: 2026
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  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Inhibition%22">Inhibition</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Control%22">Attention Control</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+Tests%22">Diagnostic Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Preadolescents%22">Preadolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior%22">Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction%22">Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
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  Data: 10.1177/10870547261419585
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  Data: 1087-0547<br />1557-1246
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Introduction: Attention abilities can be represented within the population as a spectrum from low to high ability. Attention deficits are present in a number of neurodevelopmental disorders, including as a primary symptom of ADHD. When evaluating the relationship between brain networks and attention abilities, it is important to know whether this relationship is mediated by diagnosis to understand processes that contribute to disability and to determine if attention can appropriately be studied transdiagnostically. Functional connectivity (FC) within the brain has been studied in association with inhibitory attention and ADHD diagnosis separately, but it is unclear whether the relationship between inhibitory attention and FC is altered in individuals with ADHD. Methods: We evaluated whether the relationship between inhibitory attention, as measured by the Flanker Inhibitory Control and Attention test, and FC was impacted by ADHD diagnostic status in children age 9 to 10 using the Adolescent Brain and Cognitive Development (ABCD) database. Results: We found that, although there were significant associations between FC and both ADHD diagnosis and attention, the interaction between attention and diagnostic group was not significantly associated with functional connectivity. Conclusion: These results support that the relationship between attention and FC is not mediated by ADHD diagnosis and thus provides evidence for a transdiagnostic-dimensional relationship between FC and inhibitory attention.
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  Data: 2026
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        PageCount: 11
        StartPage: 784
    Subjects:
      – SubjectFull: Inhibition
        Type: general
      – SubjectFull: Attention Control
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
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      – SubjectFull: Brain
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      – SubjectFull: Diagnostic Tests
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      – SubjectFull: Preadolescents
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      – SubjectFull: Behavior
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      – TitleFull: The Relationship between Inhibitory Control of Attention and fMRI Functional Connectivity in Children with and without ADHD
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