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Task-Based Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Systematic Review

Open AccessPublished:October 27, 2021DOI:https://doi.org/10.1016/j.bpsgos.2021.10.006

      Abstract

      Altered neurocognitive functioning is a key feature of attention-deficit/hyperactivity disorder (ADHD), and increasing numbers of studies assess task-based functional connectivity in the disorder. We systematically reviewed and critically appraised functional magnetic resonance imaging (fMRI) task-based functional connectivity studies in ADHD. A systematic search conducted up to September 2020 found 34 studies, including 51 comparisons. Comparisons were divided into investigations of ADHD neuropathology (37 comparing ADHD and typical development, 2 comparing individuals with ADHD and their nonsymptomatic siblings, 2 comparing remitted and persistent ADHD, and 1 exploring ADHD symptom severity) and the effects of interventions (8 investigations of stimulant effects and 1 study of fMRI neurofeedback). Large heterogeneity in study methodologies prevented a meta-analysis; thus, the data were summarized as a narrative synthesis. Across cognitive domains, functional connectivity in the cingulo-opercular, sensorimotor, visual, subcortical, and executive control networks in ADHD consistently differed from neurotypical populations. Furthermore, literature comparing individuals with ADHD and their nonsymptomatic siblings as well as adults with ADHD and their remitted peers showed ADHD-related abnormalities in similar sensorimotor and subcortical (primarily striatal) networks. Interventions modulated those dysfunctional networks, with the most consistent action on functional connections with the striatum, anterior cingulate cortex, occipital regions, and midline default mode network structures. Although methodological issues limited many of the reviewed studies, the use of task-based functional connectivity approaches has the potential to broaden the understanding of the neural underpinnings of ADHD and the mechanisms of action of ADHD treatments.

      Keywords

      Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder defined by age-inappropriate levels of hyperactivity, impulsivity, and/or inattention (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      ). ADHD is associated with impairments in various “hot” and “cool” executive functions (
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      The neurocognitive profile of attention-deficit/hyperactivity disorder: A review of meta-analyses.
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      • Pennington B.F.
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      ,
      • Noreika V.
      • Falter C.M.
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      Timing deficits in attention-deficit/hyperactivity disorder (ADHD): Evidence from neurocognitive and neuroimaging studies.
      ). The neural underpinnings of these behavioral problems include hypoactivation in frontostriatal and temporoparietal domain-relevant regions (
      • Cortese S.
      • Kelly C.
      • Chabernaud C.
      • Proal E.
      • Di Martino A.
      • Milham M.P.
      • Castellanos F.X.
      Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies.
      ,
      • Hart H.
      • Radua J.
      • Nakao T.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder.
      ,
      • Hart H.
      • Radua J.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD).
      ,
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • McCarthy H.
      • Skokauskas N.
      • Frodl T.
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      ,
      • Norman L.
      • Carlisi C.
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      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder.
      ,
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      • Bannon K.
      • Castellanos F.X.
      • Milham M.P.
      The neural correlates of attention deficit hyperactivity disorder: An ALE meta-analysis.
      ), which have been associated with disorder severity (
      • van Rooij D.
      • Hoekstra P.J.
      • Mennes M.
      • von Rhein D.
      • Thissen A.J.
      • Heslenfeld D.
      • et al.
      Neural activation patterns during response inhibition distinguish adolescents with ADHD, their unaffected siblings, and healthy controls.
      ,
      • Rubia K.
      • Smith A.B.
      • Brammer M.
      • Toone B.
      • Taylor E.
      Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD.
      ,
      • Cubillo A.I.
      • Halari R.
      • Ecker C.
      • Giampietro V.
      • Taylor E.
      • Rubia K.
      Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
      ), cognitive performance (
      • van Rooij D.
      • Hoekstra P.J.
      • Mennes M.
      • von Rhein D.
      • Thissen A.J.
      • Heslenfeld D.
      • et al.
      Neural activation patterns during response inhibition distinguish adolescents with ADHD, their unaffected siblings, and healthy controls.
      ,
      • Vaidya C.J.
      • Bunge S.A.
      • Dudukovic N.M.
      • Zalecki C.A.
      • Elliott G.R.
      • Gabrieli J.D.
      Altered neural substrates of cognitive control in childhood ADHD: Evidence from functional magnetic resonance imaging.
      ), and symptomatic improvement with treatment (
      • Schulz K.P.
      • Fan J.
      • Bédard A.C.
      • Clerkin S.M.
      • Ivanov I.
      • Tang C.Y.
      • et al.
      Common and unique therapeutic mechanisms of stimulant and nonstimulant treatments for attention-deficit/hyperactivity disorder.
      ,
      • Schulz K.P.
      • Bédard A.V.
      • Fan J.
      • Hildebrandt T.B.
      • Stein M.A.
      • Ivanov I.
      • et al.
      Striatal activation predicts differential therapeutic responses to methylphenidate and atomoxetine.
      ) and can be modulated with pharmacotherapy (
      • Rubia K.
      • Alegria A.A.
      • Cubillo A.I.
      • Smith A.B.
      • Brammer M.
      • Radua J.
      Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: A systematic review and meta-analysis.
      ). However, a recent meta-analysis highlighted the lack of convergence of brain activation alterations in ADHD (
      • Samea F.
      • Soluki S.
      • Nejati V.
      • Zarei M.
      • Cortese S.
      • Eickhoff S.B.
      • et al.
      Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies.
      ), perhaps reflecting a failure to consider the interconnected nature of neural processing.
      As most complex cognitive functions depend on information processing in multiple regions, studying regional interactions is crucial in characterizing brain function. Furthermore, given the large-scale neural reorganization in youth, investigations of functional connectivity may provide a better understanding of neurodevelopmental disorders (
      • Stevens M.C.
      The developmental cognitive neuroscience of functional connectivity.
      ,
      • Vink M.
      • Zandbelt B.B.
      • Gladwin T.
      • Hillegers M.
      • Hoogendam J.M.
      • van den Wildenberg W.P.M.
      • et al.
      Frontostriatal activity and connectivity increase during proactive inhibition across adolescence and early adulthood.
      ,
      • Wang H.
      • Fan L.
      • Song M.
      • Liu B.
      • Wu D.
      • Jiang R.
      • et al.
      Functional connectivity predicts individual development of inhibitory control during adolescence.
      ). Consequently, many studies in ADHD focused on network-wide alterations in resting-state connectivity to characterize domain-independent neural function (
      • Gao Y.
      • Shuai D.
      • Bu X.
      • Hu X.
      • Tang S.
      • Zhang L.
      • et al.
      Impairments of large-scale functional networks in attention-deficit/hyperactivity disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Cortese S.
      • Aoki Y.Y.
      • Itahashi T.
      • Castellanos F.X.
      • Eickhoff S.B.
      Systematic review and meta-analysis: Resting state functional magnetic resonance imaging studies of attention-deficit/hyperactivity disorder.
      ,
      • Sutcubasi B.
      • Metin B.
      • Kurban M.K.
      • Metin Z.E.
      • Beser B.
      • Sonuga-Barke E.
      Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis.
      ). Assessments of task-based functional connectivity, however, allow these findings to be extended by investigating functional connections specific to distinct cognitive processes (
      • Stevens M.C.
      The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation.
      ). Given the presence of discrete cognitive deficits in ADHD, studies of task-based connectivity in ADHD are becoming increasingly common.
      Several systematic reviews and meta-analyses examined differences in cognition-related activation (
      • Cortese S.
      • Kelly C.
      • Chabernaud C.
      • Proal E.
      • Di Martino A.
      • Milham M.P.
      • Castellanos F.X.
      Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies.
      ,
      • Hart H.
      • Radua J.
      • Nakao T.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder.
      ,
      • Hart H.
      • Radua J.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD).
      ,
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • McCarthy H.
      • Skokauskas N.
      • Frodl T.
      Identifying a consistent pattern of neural function in attention deficit hyperactivity disorder: A meta-analysis.
      ,
      • Norman L.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder.
      ,
      • Dickstein S.G.
      • Bannon K.
      • Castellanos F.X.
      • Milham M.P.
      The neural correlates of attention deficit hyperactivity disorder: An ALE meta-analysis.
      ,
      • Samea F.
      • Soluki S.
      • Nejati V.
      • Zarei M.
      • Cortese S.
      • Eickhoff S.B.
      • et al.
      Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies.
      ,
      • Lei D.
      • Du M.
      • Wu M.
      • Chen T.
      • Huang X.
      • Du X.
      • et al.
      Functional MRI reveals different response inhibition between adults and children with ADHD.
      ,
      • Plichta M.M.
      • Scheres A.
      Ventral-striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature.
      ) and connectivity during resting-state paradigms in ADHD (
      • Gao Y.
      • Shuai D.
      • Bu X.
      • Hu X.
      • Tang S.
      • Zhang L.
      • et al.
      Impairments of large-scale functional networks in attention-deficit/hyperactivity disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Cortese S.
      • Aoki Y.Y.
      • Itahashi T.
      • Castellanos F.X.
      • Eickhoff S.B.
      Systematic review and meta-analysis: Resting state functional magnetic resonance imaging studies of attention-deficit/hyperactivity disorder.
      ,
      • Sutcubasi B.
      • Metin B.
      • Kurban M.K.
      • Metin Z.E.
      • Beser B.
      • Sonuga-Barke E.
      Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis.
      ). Although reviews of functional connectivity have been published (
      • Posner J.
      • Park C.
      • Wang Z.
      Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder.
      ,
      • De La Fuente A.
      • Xia S.
      • Branch C.
      • Li X.
      A review of attention-deficit/hyperactivity disorder from the perspective of brain networks.
      ,
      • Konrad K.
      • Eickhoff S.B.
      Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder.
      ,
      • Cao M.
      • Shu N.
      • Cao Q.
      • Wang Y.
      • He Y.
      Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder.
      ,
      • Rubia K.
      Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation.
      ), there have been no systematic evaluations of task-based functional connectivity literature of ADHD or its quality. Consequently, this review focused on functional networks in ADHD aiming to provide a framework for considering the neural correlates of the disorder accommodating context-dependent, correlated activity across brain regions and its modulation with interventions. Furthermore, given the recent advances in understanding the limitations of functional magnetic resonance imaging (fMRI), this review aimed to appraise the quality of studies and reporting practices in the field.

      Methods and Materials

      This preregistered review (https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=205500) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (
      • Moher D.
      • Liberati A.
      • Tetzlaff J.
      • Altman D.G.
      Prisma Group
      Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement.
      ).

      Information Sources and Search Strategy

      A systematic search was conducted using the Cochrane Library, Embase, PubMed/MEDLINE, PsycINFO, and Web of Science Core Collection identifying fMRI studies of task-based functional connectivity in ADHD. The search was undertaken by one investigator (OSK) with keywords approved by the study team. The search string included (functional connectivity or connecti∗) and (ADHD or attention deficit hyperactivity disorder or attention deficit disorder or hyperkinetic) and (functional magnetic resonance imaging or fMRI or BOLD or blood oxygen level dependent). The search was limited to articles published in English between January 1990 and September 2020. Additionally, reference lists of past reviews focusing on functional connectivity in ADHD (
      • Posner J.
      • Park C.
      • Wang Z.
      Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder.
      ,
      • De La Fuente A.
      • Xia S.
      • Branch C.
      • Li X.
      A review of attention-deficit/hyperactivity disorder from the perspective of brain networks.
      ,
      • Konrad K.
      • Eickhoff S.B.
      Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder.
      ,
      • Cao M.
      • Shu N.
      • Cao Q.
      • Wang Y.
      • He Y.
      Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder.
      ,
      • Rubia K.
      Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation.
      ) were screened for relevant publications.

      Study Selection Criteria

      The identified citations were uploaded onto CADIMA (
      • Kohl C.
      • McIntosh E.J.
      • Unger S.
      • Haddaway N.R.
      • Kecke S.
      • Schiemann J.
      • Wilhelm R.
      Online tools supporting the conduct and reporting of systematic reviews and systematic maps: A case study on CADIMA and review of existing tools [published correction appears in Environ Evid 2018; 7:12].
      ). Duplicates were removed semiautomatically using CADIMA’s inbuilt function and reviewed manually by one investigator (OSK). Titles and abstracts and subsequently full texts of surviving records were screened for eligibility in parallel by two investigators (OSK and MC). A screening exercise was conducted on 20 randomly selected records ensuring good reliability between investigators [κ = 0.63, calculated according to measuring agreement of Cochrane Version 5.1 (
      • Higgins J.P.T.
      • Green S.
      The Cochrane Collaboration
      Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011].
      )]. Only peer-reviewed fMRI studies of task-based functional connectivity in patients of all ages, sexes, and races/ethnicities where ADHD (per DSM or ICD) was the primary diagnosis were retained. Discrepancies were resolved by consensus.

      Exclusion Criteria

      Studies were excluded if they did not assess fMRI task-based functional connectivity, did not present primary data, or were not published in a peer-reviewed journal. Studies comparing ADHD solely with other psychiatric/neurodevelopmental disorders, including participants without a formal ADHD diagnosis, recruiting only ADHD remitters, or including participants for whom ADHD was not the primary diagnosis were excluded.

      Data Extraction and Critical Appraisal

      Data were extracted by two investigators (OSK and MC). Records were divided into two equal-sized batches, one for each investigator. The investigators independently extracted data from their allocated studies and cross-checked the accuracy of the other investigator’s extraction. Data pertaining to 1) the study sample (sample size, age, sex, medication history, ADHD presentation, comorbidities); 2) study methods (connectivity estimation method, motion correction [method and exclusion criteria], drug washout period, task, case-control matching criteria); and 3) functional connectivity findings (changes of connectivity [increases/decreases] and their manuscript-defined location in the brain and justification of method used [e.g., choice of seed region]) were extracted and critically appraised. We defined decreased or increased functional connectivity if a hub/network was found in the group contrast in at least two comparisons. Findings were defined as mixed when the hub/network was observed in increased and decreased connectivity.
      Additionally, risk of bias in intervention studies was examined using the Cochrane Collaboration risk of bias tool (
      • Higgins J.P.T.
      • Green S.
      The Cochrane Collaboration
      Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011].
      ) across selection, performance, detection, attrition, and reporting biases. Two investigators (OSK and MC) independently conducted critical appraisal. Records were divided into two equal-sized batches, each assigned to one investigator.

      Results

      Study Selection

      The search yielded 946 unique records, of which 802 were excluded during title and abstract screening. A further 110 were excluded after full-text screening for one or more of the following reasons: 1) not measuring fMRI task-based connectivity (n = 87), 2) no peer review (n = 39), 3) not assessing individuals with current primary ADHD diagnosis (n = 25), 4) not presenting an empirical investigation (n = 20), and 5) no available full text (n = 1). Following the selection process, 34 studies remained (Figure 1; Supplement lists included studies).
      Figure thumbnail gr1
      Figure 1Study selection flow chart. ADHD, attention-deficit/hyperactivity disorder; fMRI, functional magnetic resonance imaging.
      These 34 studies included 51 comparisons. Of these, 37 investigated differences between ADHD and neurotypical groups, 9 tested effects of interventions in patients, 2 compared individuals with ADHD and their nonsymptomatic siblings, 2 compared remitted and persistent ADHD, and 1 explored ADHD symptom severity (investigations of siblings, remitters, and disorder severity are described in the Supplement and Table 2). Across all studies, this review included 981 individuals with ADHD, 38 ADHD remitters, 134 nonsymptomatic siblings of individuals with ADHD, and 774 neurotypical controls.
      The heterogeneity of methodologies of this literature prevented a meta-analysis. Consequently, the comparisons were summarized as a narrative synthesis.

      Functional Connectivity in ADHD

      The differences in connectivity between ADHD and neurotypical groups were investigated in 37 comparisons (youths = 23, adults = 14) (Table 1). Based on the collective descriptions in the literature (
      • Coghill D.R.
      • Toplak M.
      • Rhodes S.
      • Adamo N.
      Cognitive functioning in ADHD: Inhibition, memory, temporal discounting, decision-making, timing and reaction time variability.
      ), the following cognitive domains emerged: attention (n = 4) (
      • Li X.
      • Sroubek A.
      • Kelly M.S.
      • Lesser I.
      • Sussman E.
      • He Y.
      • et al.
      Atypical pulvinar-cortical pathways during sustained attention performance in children with attention-deficit/hyperactivity disorder.
      ,
      • Luo Y.
      • Schulz K.P.
      • Alvarez T.L.
      • Halperin J.M.
      • Li X.
      Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD.
      ,
      • Rubia K.
      • Halari R.
      • Cubillo A.I.
      • Mohammad A.
      • Brammer M.
      • Taylor E.
      Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a rewarded continuous performance task.
      ,
      • Xia S.
      • Foxe J.J.
      • Sroubek A.E.
      • Branch C.
      • Li X.
      Topological organization of the “small-world” visual attention network in children with attention deficit/hyperactivity disorder (ADHD).
      ), cognitive control (n = 6) (
      • Cubillo A.I.
      • Halari R.
      • Ecker C.
      • Giampietro V.
      • Taylor E.
      • Rubia K.
      Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
      ,
      • Hwang S.
      • White S.F.
      • Nolan Z.T.
      • Craig Williams W.
      • Sinclair S.
      • Blair R.J.
      Executive attention control and emotional responding in attention-deficit/hyperactivity disorder—a functional MRI study.
      ,
      • Querne L.
      • Fall S.
      • Le Moing A.G.
      • Bourel-Ponchel E.
      • Delignieres A.
      • Simonnot A.
      • et al.
      Effects of methylphenidate on default-mode network/task-positive network synchronization in children with ADHD.
      ,
      • Plessen K.J.
      • Allen E.A.
      • Eichele H.
      • van Wageningen H.
      • Hovik M.F.
      • Sorensen L.
      • et al.
      Reduced error signalling in medication-naive children with ADHD: Associations with behavioural variability and post-error adaptations.
      ,
      • Vloet T.D.
      • Gilsbach S.
      • Neufang S.
      • Fink G.R.
      • Herpertz-Dahlmann B.
      • Konrad K.
      Neural mechanisms of interference control and time discrimination in attention-deficit/hyperactivity disorder.
      ,
      • Zamorano F.
      • Billeke P.
      • Kausel L.
      • Larrain J.
      • Stecher X.
      • Hurtado J.M.
      • et al.
      Lateral prefrontal activity as a compensatory strategy for deficits of cortical processing in attention deficit hyperactivity disorder.
      ), response inhibition (n = 5) (
      • Cubillo A.I.
      • Halari R.
      • Ecker C.
      • Giampietro V.
      • Taylor E.
      • Rubia K.
      Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
      ,
      • Cai W.
      • Griffiths K.
      • Korgaonkar M.S.
      • Williams L.M.
      • Menon V.
      Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD.
      ,
      • Massat I.
      • Slama H.
      • Villemonteix T.
      • Mary A.
      • Baijot S.
      • Albajara Sáenz A.
      • et al.
      Hyperactivity in motor response inhibition networks in unmedicated children with attention deficit-hyperactivity disorder.
      ,
      • Mulder M.J.
      • van Belle J.
      • van Engeland H.
      • Durston S.
      Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD.
      ,
      • van Rooij D.
      • Hartman C.A.
      • Mennes M.
      • Oosterlaan J.
      • Franke B.
      • Rommelse N.
      • et al.
      Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings.
      ), reward processing (n = 5) (
      • Ceceli A.O.
      • Natsheh J.Y.
      • Cruz D.
      • Tricomi E.
      The neurobehavioral mechanisms of motivational control in attention-deficit/hyperactivity disorder.
      ,
      • Ma I.
      • van Holstein M.
      • Mies G.W.
      • Mennes M.
      • Buitelaar J.
      • Cools R.
      • et al.
      Ventral striatal hyperconnectivity during rewarded interference control in adolescents with ADHD.
      ,
      • Mowinckel A.M.
      • Alnaes D.
      • Pedersen M.L.
      • Ziegler S.
      • Fredriksen M.
      • Kaufmann T.
      • et al.
      Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD.
      ,
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      ,
      • von Rhein D.
      • Beckmann C.F.
      • Franke B.
      • Oosterlaan J.
      • Heslenfeld D.J.
      • Hoekstra P.J.
      • et al.
      Network-level assessment of reward-related activation in patients with ADHD and healthy individuals.
      ), working memory (n = 5) (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      ,
      • Bédard A.C.
      • Newcorn J.H.
      • Clerkin S.M.
      • Krone B.
      • Fan J.
      • Halperin J.M.
      • Schulz K.P.
      Reduced prefrontal efficiency for visuospatial working memory in attention-deficit/hyperactivity disorder.
      ,
      • Massat I.
      • Slama H.
      • Kavec M.
      • Linotte S.
      • Mary A.
      • Baleriaux D.
      • et al.
      Working memory-related functional brain patterns in never medicated children with ADHD.
      ,
      • Wolf R.C.
      • Plichta M.M.
      • Sambataro F.
      • Fallgatter A.J.
      • Jacob C.
      • Lesch K.P.
      • et al.
      Regional brain activation changes and abnormal functional connectivity of the ventrolateral prefrontal cortex during working memory processing in adults with attention-deficit/hyperactivity disorder.
      ,
      • Wu Z.M.
      • Bralten J.
      • An L.
      • Cao Q.J.
      • Cao X.H.
      • Sun L.
      • et al.
      Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.
      ), and emotion processing (n = 6) (
      • Hwang S.
      • White S.F.
      • Nolan Z.T.
      • Craig Williams W.
      • Sinclair S.
      • Blair R.J.
      Executive attention control and emotional responding in attention-deficit/hyperactivity disorder—a functional MRI study.
      ,
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      ,
      • Hafeman D.
      • Bebko G.
      • Bertocci M.A.
      • Fournier J.C.
      • Chase H.W.
      • Bonar L.
      • et al.
      Amygdala-prefrontal cortical functional connectivity during implicit emotion processing differentiates youth with bipolar spectrum from youth with externalizing disorders.
      ,
      • Posner J.
      • Nagel B.J.
      • Maia T.V.
      • Mechling A.
      • Oh M.
      • Wang Z.
      • Peterson B.S.
      Abnormal amygdalar activation and connectivity in adolescents with attention-deficit/hyperactivity disorder.
      ,
      • Schulz K.P.
      • Bedard A.C.
      • Fan J.
      • Clerkin S.M.
      • Dima D.
      • Newcorn J.H.
      • Halperin J.M.
      Emotional bias of cognitive control in adults with childhood attention-deficit/hyperactivity disorder.
      ,
      • Stoddard J.
      • Tseng W.L.
      • Kim P.
      • Chen G.
      • Yi J.
      • Donahue L.
      • et al.
      Association of irritability and anxiety with the neural mechanisms of implicit face emotion processing in youths with psychopathology.
      ). Additionally, 6 comparisons that could not be classified into the above domains included error monitoring (
      • Chevrier A.
      • Bhaijiwala M.
      • Lipszyc J.
      • Cheyne D.
      • Graham S.
      • Schachar R.
      Disrupted reinforcement learning during post-error slowing in ADHD.
      ), response preparation (
      • Clerkin S.M.
      • Schulz K.P.
      • Berwid O.G.
      • Fan J.
      • Newcorn J.H.
      • Tang C.Y.
      • Halperin J.M.
      Thalamo-cortical activation and connectivity during response preparation in adults with persistent and remitted ADHD.
      ), motor response (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      ), social cognition/relational processing (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      ), and time discrimination (
      • Vloet T.D.
      • Gilsbach S.
      • Neufang S.
      • Fink G.R.
      • Herpertz-Dahlmann B.
      • Konrad K.
      Neural mechanisms of interference control and time discrimination in attention-deficit/hyperactivity disorder.
      ).
      Table 1Studies Investigating fMRI Functional Connectivity Differences Between ADHD and Typical Development Grouped by Cognitive Domain
      StudyAnalysis MethodTask (Contrast)NADHD (% Male)AgeADHD, Years, Mean (SD)Medication HistoryMedication WashoutADHD ComorbiditiesNControl (% Male)AgeControl, Years, Mean (SD)Control > ADHDADHD > Control
      Attention
      Li et al., 2012 (
      • Li X.
      • Sroubek A.
      • Kelly M.S.
      • Lesser I.
      • Sussman E.
      • He Y.
      • et al.
      Atypical pulvinar-cortical pathways during sustained attention performance in children with attention-deficit/hyperactivity disorder.
      )
      SBCCPT (unspecified)22 (55%)11.6 (2.86)Current MPH use (41%); medication-free (59%)48 hoursNone22 (45%)12.1 (2.23)L pulvinar nuclei ↔ R IFC, MFG; R pulvinar nuclei ↔ R PFCR pulvinar nuclei ↔ BIL occipital lobe
      Luo et al., 2018 (
      • Luo Y.
      • Schulz K.P.
      • Alvarez T.L.
      • Halperin J.M.
      • Li X.
      Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD.
      )
      GTTsCued attention task (cues)17 (77%)24.69 (2.1)Current stimulant use (12%); past stimulant use (unspecified)48 hoursNone33 (85%)24.27 (2.2)Acting network hubs in BIL IPL; L IPL ↔ L SFG; degree in R MFG; betweenness centrality in L SFG, MFG, precentral, R IFCActing network hubs in L MFG and precentral
      Rubia et al., 2009 (
      • Rubia K.
      • Halari R.
      • Cubillo A.I.
      • Mohammad A.
      • Brammer M.
      • Taylor E.
      Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a rewarded continuous performance task.
      )
      SBCCPT (targets > nontargets)13 (100%)12.5 (1.3)Medication-naïve (100%)ODD/CD (8%)13 (100%)13 (1.7)L IFC ↔ striatum, cerebellum; R IFC ↔ striatum, BIL cerebellum; BIL thalamus/striatum ↔ striatum, R cerebellum; L striatum ↔ BIL cerebellum, R striatum; ACC ↔ cerebellum, cerebellar vermis; R IPL ↔ cerebellum, L IPL; cerebellar vermis ↔ PCC; R cerebellum ↔ PCC, L IPL; L cerebellum ↔ L IPLNone
      Xia et al., 2014 (
      • Xia S.
      • Foxe J.J.
      • Sroubek A.E.
      • Branch C.
      • Li X.
      Topological organization of the “small-world” visual attention network in children with attention deficit/hyperactivity disorder (ADHD).
      )
      GTTsCPT (unspecified)22 (55%)11.6 (2.86)Current MPH use (41%); medication-free (59%)48 hoursNone22 (45%)12.1 (2.23)Nodal efficiency in L superior OFG and R SOG; degree and betweenness centrality in BIL occipital lobes, R temporal lobe, L paracentral, SMGNodal efficiency in L cuneus; degree and betweenness centrality in ACC
      Cognitive Control
      Cubillo et al., 2010 (
      • Cubillo A.I.
      • Halari R.
      • Ecker C.
      • Giampietro V.
      • Taylor E.
      • Rubia K.
      Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
      )
      SBCSwitch task (unspecified)11 (100%)29 (1)Medication-naïve (100%)Anxiety disorder (9%), mood disorder (27%), CD (9%), substance use disorder (18%)13 (100%)28 (1)NoneNone
      Hwang et al., 2015 (
      • Hwang S.
      • White S.F.
      • Nolan Z.T.
      • Craig Williams W.
      • Sinclair S.
      • Blair R.J.
      Executive attention control and emotional responding in attention-deficit/hyperactivity disorder—a functional MRI study.
      )
      gPPIAFNIAffective Stroop task (incongruent > congruent stimuli)26 (65%)14.53 (unspecified)Current stimulant use (42%); medication-free (58%)>24 hoursODD (4%), substance use disorder (8%)35 (51%)13.91 (unspecified)L DMFG ↔ R lateral frontal, claustrum
      Correction for multiple comparisons not specified.
      L DMFG ↔ L posterior insula
      Querne et al., 2017 (
      • Querne L.
      • Fall S.
      • Le Moing A.G.
      • Bourel-Ponchel E.
      • Delignieres A.
      • Simonnot A.
      • et al.
      Effects of methylphenidate on default-mode network/task-positive network synchronization in children with ADHD.
      )
      ICAFlanker task (unspecified)11 (unspecified)9.8 (1.7)Medication-naïve (100%)None11 (unspecified)10.8 (1.7)Anticorrelation between DMN and frontotemporoparietal regions (direct group comparison not reported)None
      Plessen et al., 2016 (
      • Plessen K.J.
      • Allen E.A.
      • Eichele H.
      • van Wageningen H.
      • Hovik M.F.
      • Sorensen L.
      • et al.
      Reduced error signalling in medication-naive children with ADHD: Associations with behavioural variability and post-error adaptations.
      )
      ICAFlanker task (post-error > post-correct trials)25 (68%)10.75 (1.09)Medication-naïve (100%)ODD (40%), ODD+CD (8%), phobia (16%), tics (4%), separation anxiety disorder (4%), elimination disorder (4%)29 (52%)10.15 (1.04)NoneCingulo-opercular network ↔ VAN
      Correction for multiple comparisons not specified.
      Vloet et al., 2010 (
      • Vloet T.D.
      • Gilsbach S.
      • Neufang S.
      • Fink G.R.
      • Herpertz-Dahlmann B.
      • Konrad K.
      Neural mechanisms of interference control and time discrimination in attention-deficit/hyperactivity disorder.
      )
      PPISPMTime discrimination + stimulus-response compatibility task (stimulus-response compatibility)14 (100%)11.3 (2)Past or current stimulant use (100%)>48 hoursNone14 (100%)11.9 (1.4)L IFC ↔ L SPG; R IFC ↔ R SPGNone
      Zamorano et al., 2017 (
      • Zamorano F.
      • Billeke P.
      • Kausel L.
      • Larrain J.
      • Stecher X.
      • Hurtado J.M.
      • et al.
      Lateral prefrontal activity as a compensatory strategy for deficits of cortical processing in attention deficit hyperactivity disorder.
      )
      PPIFSLMSIT (incongruent > congruent conditions)17 (100%)11.6 (0.86)Current MPH use (100%)Medication not taken on study dayNone17 (100%)11.7 (0.67)Not reportedR MFG + R IFC ↔ BIL OFC, striatum
      Emotion Processing
      Hafeman et al., 2017 (
      • Hafeman D.
      • Bebko G.
      • Bertocci M.A.
      • Fournier J.C.
      • Chase H.W.
      • Bonar L.
      • et al.
      Amygdala-prefrontal cortical functional connectivity during implicit emotion processing differentiates youth with bipolar spectrum from youth with externalizing disorders.
      )
      gPPISPMEmotional dynamic faces task (emotional faces > shapes)30 (67%)14.1 (1.8)Current use of stimulants (43%), antipsychotics (10%), antidepressants (10%)UnspecifiedODD (53%), CD (3%), depressive disorder (53%), anxiety disorder (3%)26 (46%)13.2 (2.2)NoneBIL amygdala ↔ subgenual cingulate; BIL amygdala ↔ R SFG
      Hwang et al., 2015 (
      • Hwang S.
      • White S.F.
      • Nolan Z.T.
      • Craig Williams W.
      • Sinclair S.
      • Blair R.J.
      Executive attention control and emotional responding in attention-deficit/hyperactivity disorder—a functional MRI study.
      )
      gPPISPMAffective Stroop task: 1) positive > neutral stimuli; 2) positive > neutral incongruent stimuli; 3) negative > neutral stimuli)26 (65%)14.53 (unspecified)Current stimulant use (42%); medication-free (58%)>24 hoursODD (4%), substance use disorder (8%)35 (51%)13.91 (unspecified)1) R amygdala ↔ R MOG, L lentiform nucleus; 2) R amygdala ↔ BIL postcentral
      Correction for multiple comparisons not specified.
      ; 3) none
      None
      Park et al., 2016 (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      )
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Emotive faces task (unspecified)34 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)Degree in BIL medial frontal, L ACC, L postcentral, R caudate, L insulaDegree in L MFG, R SMG, R IPL, L MOG, L IOG, R cerebellum
      Posner et al., 2011 (
      • Posner J.
      • Nagel B.J.
      • Maia T.V.
      • Mechling A.
      • Oh M.
      • Wang Z.
      • Peterson B.S.
      Abnormal amygdalar activation and connectivity in adolescents with attention-deficit/hyperactivity disorder.
      )
      DCMFearful faces task with priming (fearful faces)15 (87%)13.5 (1.2)Current stimulant use (100%)>48 hoursODD/CD (% unspecified)15 (87%)13.4 (1.2)NoneR amygdala ↔ R lateral PFC
      Schulz et al., 2014 (
      • Schulz K.P.
      • Bedard A.C.
      • Fan J.
      • Clerkin S.M.
      • Dima D.
      • Newcorn J.H.
      • Halperin J.M.
      Emotional bias of cognitive control in adults with childhood attention-deficit/hyperactivity disorder.
      )
      PPISPMFace emotion GNG (correct no-go > go)14 (100%)23.3 (2.3)Medication-naïve (29%); past stimulant use but medication-free at time of study (71%)Mood disorder (14%), anxiety disorder (14%), substance use disorder (36%)14 (100%)22.8 (2.7)R DLPFC ↔ L IFC, putamen, BIL subgenual cingulateNone
      Stoddard et al., 2017 (
      • Stoddard J.
      • Tseng W.L.
      • Kim P.
      • Chen G.
      • Yi J.
      • Donahue L.
      • et al.
      Association of irritability and anxiety with the neural mechanisms of implicit face emotion processing in youths with psychopathology.
      )
      gPPIAFNIImplicit face emotion processing task (150% intensity across emotions)24 (75%)13.5 (2.9)UnspecifiedUnspecifiedUnspecified22 (41%)14.2 (2.1)NoneL amygdala ↔ L insula
      Response Inhibition
      Cai et al., 2021 (
      • Cai W.
      • Griffiths K.
      • Korgaonkar M.S.
      • Williams L.M.
      • Menon V.
      Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD.
      )
      gPPISPMGNG (correct no-go)27 (78%)13.95 (2.62)Medication-free during testing (100%)>5 half-lives of drugUnspecified30 (73%)13.65 (2.47)R DLPFC ↔ R posterior parietalNone
      Cubillo et al., 2010 (
      • Cubillo A.I.
      • Halari R.
      • Ecker C.
      • Giampietro V.
      • Taylor E.
      • Rubia K.
      Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching.
      )
      SBCSST (unspecified)10 (100%)28 (1)Medication-naïve (100%)Anxiety disorder (10%), mood disorder (30%), CD (10%), substance use disorder (20%)14 (100%)28 (2)R IFC ↔ L IFC, R MFG, ACC, PCC, SMA, thalamus, striatum, BIL parietal/temporal/occipital; R ACC/PCC/SMA ↔ R thalamus, striatumNone
      Massat et al., 2018 (
      • Massat I.
      • Slama H.
      • Villemonteix T.
      • Mary A.
      • Baijot S.
      • Albajara Sáenz A.
      • et al.
      Hyperactivity in motor response inhibition networks in unmedicated children with attention deficit-hyperactivity disorder.
      )
      PPISPMSST (successful > failed stop)18 (44%)10.6 (1.13)Medication-naïve (100%)None19 (47%)10 (1.35)R IFC ↔ R OFC, L MFG, IFCR dorsal caudate ↔ R IPL, SPG, L MFG, middle cingulate, precentral, postcentral
      Mulder et al., 2011 (
      • Mulder M.J.
      • van Belle J.
      • van Engeland H.
      • Durston S.
      Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD.
      )
      SBCGNG (unspecified)Sample 1: 11 (100%)

      Sample 2: 12 (100%)
      Sample 1: 13.97 (3.14) Sample 2: 14.9 (2.3)Sample 1: current stimulant use (55%); medication-free (45%)

      Sample 2: current stimulant use (58%); medication-free (42%)
      >24 hoursSample 1: ODD (27%)

      Sample 2: ODD (33%)
      Sample 1: 11 (100%) Sample 2: 12 (100%)Sample 1: 15.27 (1.92) Sample 2: 15 (2.1)Samples 1 and 2: ACC ↔ cerebellum Sample 1: motor cortex ↔ striatum
      Correction for multiple comparisons not specified.


      Sample 2: not reported
      None
      van Rooij et al., 2015 (
      • van Rooij D.
      • Hartman C.A.
      • Mennes M.
      • Oosterlaan J.
      • Franke B.
      • Rommelse N.
      • et al.
      Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings.
      )
      PPIFSL
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      ,
      Scrubbing regressors included in the model for volumes with excessive motion in addition to standard motion parameters.
      SST: 1) successful stop > go; 2) failed stop > go)185 (70%)17.3 (3.2)Current medication use, class unspecified (77%); medication-free (23%)UnspecifiedODD (30%), CD (7%), reading disability (18%)125 (44%)16.5 (3.3)1) L IFC ↔ R putamen; L SFG ↔ L thalamus, operculum; 2) L IFC ↔ R IFC, BIL SFG/preSMA, L occipital cortex, MTG; L SFG ↔ L IFC1) L IFC ↔ L MTG, cerebellum; L SFG ↔ R ACC, frontal pole, BIL precuneus, L precentral, R cerebellum; 2) L IFC ↔ BIL temporal pole, L cerebellum, R SMG; L SFG ↔ L MTG
      Reward Processing
      Ceceli et al., 2020 (
      • Ceceli A.O.
      • Natsheh J.Y.
      • Cruz D.
      • Tricomi E.
      The neurobehavioral mechanisms of motivational control in attention-deficit/hyperactivity disorder.
      )
      PPIFSLFree operant task with food rewards (late > early phase)25 (56%)22.31 (4.69)Current or previous stimulant use (72%); past stimulant use but medication-free at time of study (16%); medication-naïve (12%)36 hoursNone25 (56%)21.48 (2.92)L posterior putamen ↔ dorsal ACC, medial frontalNone
      Ma et al., 2016 (
      • Ma I.
      • van Holstein M.
      • Mies G.W.
      • Mennes M.
      • Buitelaar J.
      • Cools R.
      • et al.
      Ventral striatal hyperconnectivity during rewarded interference control in adolescents with ADHD.
      )
      gPPISPMRewarded Stroop task (rewarded > neutral Stroop)25 (76%)15.36 (1.08)Current MPH use (60%); medication-free (40%)24 hoursODD and CD (% unspecified)33 (67%)15.3 (1.05)NoneL ventral striatum ↔ R precentral
      Mowinckel et al., 2017 (
      • Mowinckel A.M.
      • Alnaes D.
      • Pedersen M.L.
      • Ziegler S.
      • Fredriksen M.
      • Kaufmann T.
      • et al.
      Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD.
      )
      Bayesian hierarchical mixed modelValue-based decision-making task (unspecified)20 (35%)29.9 (1.41)Current stimulant use (100%)>20 hoursNone27 (30%)27.42 (1.23)Within VIS, FPN, ECN, subcortical network, L VAN; ECN ↔ FPN ↔ sensorimotor network; DAN ↔ sensorimotor network; DAN ↔ VISVAN ↔ DMN; VAN ↔ ECN; DMN ↔ ECN
      Park et al., 2016 (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      )
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Gambling task: 1) gambling reward; 2) gambling punishment34 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)1) Degree in BIL SFG, MTG; 2) degree in R medial frontal, MFG, insula, BIL SFG, L IPL, thalamus, parahippocampal1) Degree in R ACC, L PCC, lingual, thalamus, BIL insula, cerebellum; 2) R precentral, MTG, L postcentral, STG, BIL cerebellum
      von Rhein et al., 2017 (
      • von Rhein D.
      • Beckmann C.F.
      • Franke B.
      • Oosterlaan J.
      • Heslenfeld D.J.
      • Hoekstra P.J.
      • et al.
      Network-level assessment of reward-related activation in patients with ADHD and healthy individuals.
      )
      ICAMID task (unspecified)150 (70%)17.7 (3)Unspecified>48 hoursODD (23%), CD (5%)48 (69%)16.9 (3.2)Within SAL (R ITG), ECN (R IFC, L cerebellum)Within SAL (R cerebellum)
      Working Memory
      Bédard et al., 2014 (
      • Bédard A.C.
      • Newcorn J.H.
      • Clerkin S.M.
      • Krone B.
      • Fan J.
      • Halperin J.M.
      • Schulz K.P.
      Reduced prefrontal efficiency for visuospatial working memory in attention-deficit/hyperactivity disorder.
      )
      PPISPMVisuospatial n-back task: 1) 1-back > 0-back; 2) 2-back > 0-back24 (88%)13.07 (1.93)Current stimulant use (4%); current nonstimulant use (4%); past stimulant/nonstimulant use but medication-free at time of study (29%); medication-naïve (63%)2 weeksODD (8%), CD (4%), anxiety disorder (17%)21 (76%)12.44 (1.95)1) L DLPFC ↔ L PCC; 2) L DLPFC ↔ L midcingulate, PCC1) L DLPFC ↔ BIL posterior insula, R temporal cortex; 2) L DLPFC ↔ L intraparietal sulcus, cerebellum
      Massat et al., 2012 (
      • Massat I.
      • Slama H.
      • Kavec M.
      • Linotte S.
      • Mary A.
      • Baleriaux D.
      • et al.
      Working memory-related functional brain patterns in never medicated children with ADHD.
      )
      gPPISPMVerbal n-back task (2-back > 0-back)19 (47%)10.75 (1.31)Medication-naïve (100%)None14 (57%)10.05 (1.28)NoneR cerebellum ↔ red nucleus, R amygdala
      Correction for multiple comparisons not specified.
      , hippocampus
      Correction for multiple comparisons not specified.
      , lingual
      Correction for multiple comparisons not specified.
      , precuneus
      Correction for multiple comparisons not specified.
      , L IFC
      Correction for multiple comparisons not specified.
      , MFG
      Correction for multiple comparisons not specified.
      , postcentral
      Correction for multiple comparisons not specified.
      , cerebellum
      Correction for multiple comparisons not specified.
      ; L occipital ↔ BIL MFG
      Correction for multiple comparisons not specified.
      , R MTG
      Correction for multiple comparisons not specified.
      , STG
      Correction for multiple comparisons not specified.
      , fusiform
      Correction for multiple comparisons not specified.
      , putamen
      Correction for multiple comparisons not specified.
      , L cerebellum
      Correction for multiple comparisons not specified.
      ; L IPL ↔ BIL IFC
      Correction for multiple comparisons not specified.
      , MFG
      Correction for multiple comparisons not specified.
      , STG
      Correction for multiple comparisons not specified.
      , L ACC
      Correction for multiple comparisons not specified.
      , SMA
      Correction for multiple comparisons not specified.
      ; R caudate ↔ BIL MFG
      Correction for multiple comparisons not specified.
      , R SFG
      Correction for multiple comparisons not specified.
      , putamen
      Correction for multiple comparisons not specified.
      , insula
      Correction for multiple comparisons not specified.
      Park et al., 2016 (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      )
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Visuospatial n-back task (unspecified)34 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)Degree in L precuneus, MTG, cuneus, insulaDegree in L precentral, IPL, cerebellum, R MFG, IFC, STG, BIL SFG, caudate
      Wolf et al., 2009 (
      • Wolf R.C.
      • Plichta M.M.
      • Sambataro F.
      • Fallgatter A.J.
      • Jacob C.
      • Lesch K.P.
      • et al.
      Regional brain activation changes and abnormal functional connectivity of the ventrolateral prefrontal cortex during working memory processing in adults with attention-deficit/hyperactivity disorder.
      )
      ICAVerbal working memory task (unspecified)12 (100%)22.2 (4.4)Current MPH use (50%); past MPH use but medication-free at time of study (50%)72 hoursNone12 (100%)21.6 (4.7)Within BIL IFC, SFG, SPG, cerebellum, L ACC, medial frontalWithin L dorsal cingulate, cuneus, R IFC, SFG
      Wu et al., 2017 (
      • Wu Z.M.
      • Bralten J.
      • An L.
      • Cao Q.J.
      • Cao X.H.
      • Sun L.
      • et al.
      Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.
      )
      ICAVerbal n-back task (2-back > 0-back)22 (100%)12.71 (1.55)Past stimulant use but medication-free at time of study (23%); medication-naïve (77%)>4 weeksODD (18%)30 (100%)11.96 (1.72)Within ECN (L SMG, insula)Within FPN (L postcentral, SPG), auditory network (R cuneus, occipital pole, supracalcarine, intracalcarine, lateral SOG, precuneus)
      Other Cognitive Functions
      Chevrier et al., 2019 (
      • Chevrier A.
      • Bhaijiwala M.
      • Lipszyc J.
      • Cheyne D.
      • Graham S.
      • Schachar R.
      Disrupted reinforcement learning during post-error slowing in ADHD.
      )
      SBCSST: 1) error detection; 2) post-error slowing14 (50%)13.7 (2.1)Current stimulant use (43%); medication-free (57%)24 hoursODD (14%)14 (64%)15.4 (1.6)1) SN ↔ medial septal; 2) LC ↔ L amygdala, L hypothalamus; medial septal nuclei ↔ R amygdala, LC, R hypothalamus; raphe nucleus ↔ R SN/parahippocampal1) Dorsal striatum ↔ R IPL; SN ↔ R hypothalamus; SN ↔ L amygdala, LC, raphe nucleus; 2) ventral pallidum ↔ SN/parahippocampal, R dorsal pallidum, L amygdala; SN ↔ L hypothalamus; LC ↔ R IFC; medial septal nuclei ↔ BIL amygdala, L SN, BIL basal forebrain; raphe nucleus ↔ BIL amygdala, R SN, BIL hypothalamus
      Clerkin et al., 2013 (
      • Clerkin S.M.
      • Schulz K.P.
      • Berwid O.G.
      • Fan J.
      • Newcorn J.H.
      • Tang C.Y.
      • Halperin J.M.
      Thalamo-cortical activation and connectivity during response preparation in adults with persistent and remitted ADHD.
      )
      PPISPMCued reaction time task (cues > noncues)35 (83%)24.6 (2.04)Current stimulant use (6%); past stimulant use but medication-free at time of study (71%)>48 hoursMood disorder (23%), anxiety disorder (23%), substance use disorder (43%)32 (84%)24.38 (2.4)R thalamus ↔ ponsNone
      Park et al., 2016 (
      • Park B.Y.
      • Kim M.
      • Seo J.
      • Lee J.M.
      • Park H.
      Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: A task functional magnetic resonance imaging study.
      )
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Motor task (unspecified)34 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)Degree in R precentral, medial frontal, SMG, L MFG, precuneus, cuneus, parahippocampal, cerebellum, BIL MTG, MOGDegree in BIL SFG, PCC, R MFG, ACC, L postcentral
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Relational processing task (unspecified)34 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)Degree in R medial frontal, SFG, BIL ACC, L lingual, cerebellumDegree in R PCC, cuneus, BIL IPL, STG, L MTG
      GTTs
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      Social cognition task (unspecified)35 (59%)27.88 (3.37)UnspecifiedUnspecifiedUnspecified34 (62%)29.44 (3.57)Degree in BIL SFG, R PCC, L cuneusDegree in L precentral, postcentral, cerebellum, BIL precuneus, R MTG
      Vloet et al., 2010 (
      • Vloet T.D.
      • Gilsbach S.
      • Neufang S.
      • Fink G.R.
      • Herpertz-Dahlmann B.
      • Konrad K.
      Neural mechanisms of interference control and time discrimination in attention-deficit/hyperactivity disorder.
      )
      PPISPMTime discrimination + stimulus-response compatibility task (time discrimination)14 (100%)11.3 (2)Past or current stimulant use (100%)>48 hoursNone14 (100%)11.9 (1.4)R IFC ↔ R cerebellumNone
      ACC, anterior cingulate cortex; ADHD, attention-deficit/hyperactivity disorder; BIL, bilateral; CD, conduct disorder; CPT, continuous performance task; DAN, dorsal attention network; DCM, dynamic causal modeling; DLPFC, dorsolateral prefrontal cortex; DMFG, dorsomedial frontal gyrus; DMN, default mode network; ECN, executive control network; fMRI, functional magnetic resonance imaging; FPN, frontoparietal network; GNG, go/no-go; gPPI, generalized psychophysiological interaction; GTT, graph theoretic technique; ICA, independent component analysis; IFC, inferior frontal cortex; IOG, inferior occipital gyrus; IPL, inferior parietal lobule; ITG, inferior temporal gyrus; L, left; LC, locus coeruleus; MFG, middle frontal gyrus; MID, monetary incentive delay; MOG, middle occipital gyrus; MPH, methylphenidate; MSIT, multi-source interference task; MTG, middle temporal gyrus; ODD, oppositional defiant disorder; OFG, orbitofrontal gyrus; PCC, posterior cingulate cortex; PFC, prefrontal cortex; PPI, psychophysiological interaction; preSMA, pre–supplementary motor area; R, right; SAL, salience network; SBC, seed-based correlation; SFG, superior frontal gyrus; SMA, supplementary motor area; SMG, supramarginal gyrus; SN, substantia nigra; SOG, superior occipital gyrus; SPG, superior parietal gyrus; SST, stop signal task; STG, superior temporal gyrus; VAN, ventral attention network; VIS, visual network.
      a Correction for multiple comparisons not specified.
      b White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      c Scrubbing regressors included in the model for volumes with excessive motion in addition to standard motion parameters.
      Table 2Studies Investigating fMRI Functional Connectivity Differences Between Individuals With ADHD and Nonsymptomatic Siblings and Persisters and Remitters and Exploring the Impact of Symptom Severity
      StudyAnalysis MethodTask (Contrast)NADHD (% Male)AgeADHD, Years, Mean (SD)Medication HistoryMedication WashoutADHD ComorbiditiesComparison GroupNComparison (% Male)AgeComparison, Years, Mean (SD)Comparison > ADHDADHD > Comparison
      Clerkin et al., 2013 (
      • Clerkin S.M.
      • Schulz K.P.
      • Berwid O.G.
      • Fan J.
      • Newcorn J.H.
      • Tang C.Y.
      • Halperin J.M.
      Thalamo-cortical activation and connectivity during response preparation in adults with persistent and remitted ADHD.
      )
      PPISPMCued reaction time task (cues > noncues)16 (75%)24.44 (2.02)Current stimulant use (6%); past stimulant use but medication-free at time of study (71%)>48 hoursMood disorder (23%), anxiety disorder (23%), substance use disorder (43%)Remitters19 (90%)24.74 (2.1)R thalamus ↔ BIL frontal pole, L DLPFCNone
      Kolodny et al., 2020 (
      • Kolodny T.
      • Mevorach C.
      • Stern P.
      • Biderman N.
      • Ankaoua M.
      • Tsafrir S.
      • Shalev L.
      Fronto-parietal engagement in response inhibition is inversely scaled with attention-deficit/hyperactivity disorder symptom severity.
      )
      gPPIFSL
      Scrubbing regressors included in the model for volumes with excessive motion in addition to standard motion parameters.
      GNG (rare no-go > prevalent no-go)37 (41%)26.6 (4)Current stimulant use (84%); medication-free (16%)>24 hoursNoneL IPS ↔ R IFC, postcentral/SPG (negatively related to symptom severity)
      Luo et al., 2018 (
      • Luo Y.
      • Schulz K.P.
      • Alvarez T.L.
      • Halperin J.M.
      • Li X.
      Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD.
      )
      GTTsCued attention task (cues)17 (77%)24.55 (2.2)Current stimulant use (12%); past stimulant use (unspecified)48 hoursNoneRemitters19 (84%)24.79 (2.2)Acting network hubs in R MFG, globus pallidus, putamen; nodal efficiency in BIL MFGActing network hubs in L MFG and precentral
      Mulder et al., 2011 (
      • Mulder M.J.
      • van Belle J.
      • van Engeland H.
      • Durston S.
      Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD.
      )
      SBCGNG (unspecified)Sample 1: 11 (100%)

      Sample 2: 12 (100%)
      Sample 1: 13.97 (3.14)

      Sample 2: 14.9 (2.3)
      Sample 1: current stimulant use (55%); medication-free (45%)

      Sample 2: current stimulant use (58%); medication-free (42%)
      >24 hoursSample 1: ODD (27%)

      Sample 2: ODD (33%)
      Nonsymptomatic siblingsSample 1: 11 (100%)

      Sample 2: 12 (100%)
      Sample 1: 14.45 (2.58)

      Sample 2: 14.1 (2.7)
      Motor cortex ↔ striatumNone
      van Rooij et al., 2015 (
      • van Rooij D.
      • Hartman C.A.
      • Mennes M.
      • Oosterlaan J.
      • Franke B.
      • Rommelse N.
      • et al.
      Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings.
      )
      PPIFSL
      Scrubbing regressors included in the model for volumes with excessive motion in addition to standard motion parameters.
      ,
      White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.
      SST: 1) successful stop > go; 2) failed stop > go185 (70%)17.3 (3.2)Current medication use, class unspecified (77%); medication-free (23%)UnspecifiedODD (30%), CD (7%), reading disability (18%)Nonsymptomatic siblings111 (43%)17.3 (4)1) L IFC ↔ R putamen; L SFG ↔ L thalamus, operculum; 2) L IFC ↔ L occipital cortex, MTG, R IFC, MFG1) L IFC ↔ L cerebellum, precuneus, MTG; L SFG ↔ BIL precentral, precuneus, R frontal pole, ACC, cerebellum; 2) L IFC ↔ R medial frontal, ACC; L SFG ↔ L MTG
      ACC, anterior cingulate cortex; ADHD, attention-deficit/hyperactivity disorder; BIL, bilateral; CD, conduct disorder; DLPFC, dorsolateral prefrontal cortex; fMRI, functional magnetic resonance imaging; GNG, go/no-go; gPPI, generalized psychophysiological interaction; GTT, graph theoretic technique; IFC, inferior frontal cortex; IPS, intraparietal sulcus; L, left; MFG, middle frontal gyrus; MTG, middle temporal gyrus; ODD, oppositional defiant disorder; PPI, psychophysiological interaction; R, right; SBC, seed-based correlation; SFG, superior frontal gyrus; SPG, superior parietal gyrus; SST, stop signal task.
      a Scrubbing regressors included in the model for volumes with excessive motion in addition to standard motion parameters.
      b White matter and cerebrospinal fluid signal regressors included in the model in addition to standard motion parameters.

      Differences Between Individuals With ADHD and Neurotypical Populations by Cognitive Domain

      There was an overall decrease of connectivity in ADHD compared with neurotypical control subjects during attention tasks (74 patients and 90 control subjects across 4 comparisons). The right inferior frontal cortex (IFC) and bilateral inferior parietal lobules (IPLs) were indicated as hubs of connectivity decreases in ADHD, whereas the anterior cingulate cortex (ACC), left middle frontal gyrus (MFG), precentral gyrus, and bilateral occipital lobes showed both increases and decreases of connectivity, all with a 1:1 ratio indicating equal number of increases and decreases.
      The cognitive control results were heterogeneous, not yielding many common case-control differences (104 patients and 119 control subjects across 6 comparisons). Only the right IFC consistently showed abnormalities, with both increases and decreases (1:1 ratio) of functional connectivity.
      ADHD was related to predominantly decreased functional connectivity compared with neurotypical control subjects during response inhibition (263 patients and 211 control subjects across 5 comparisons). The right IFC, supplementary motor complex, and parieto-occipital regions showed decreased connectivity in ADHD, while the left precentral gyrus exhibited increased connectivity. Conversely, the right striatum (2:1 ratio, decreases-to-increases ratio), left IFC (3:1), MFG (2:1), superior frontal gyrus (SFG) (1:1) middle temporal gyrus (1:1), ACC (1:1), and cerebellum (1:1) were hubs of increased and decreased connectivity in patients.
      ADHD was associated with an overall increase in connectivity compared with control subjects during working memory (111 patients and 111 control subjects across 5 comparisons). The right insula, superior temporal gyrus, striatum, and left MFG and IPL showed increased connectivity in patients. Bilateral IFCs, SFG, left insula, cingulate, precuneus, cuneus, and cerebellum showed both increases and decreases of connectivity in ADHD, all with a 1:1 ratio except for the cerebellum, which showed more increases (3:1).
      During reward processing, the medial frontal cortex showed decreased functional connectivity, while the precentral gyrus was a hub of increased connectivity in ADHD (254 patients and 167 control subjects across 5 comparisons). The right insula, middle temporal gyrus, left thalamus, striatum, bilateral ACC, and cerebellum exhibited increases and decreases of connectivity in patients, all with a 1:1 ratio.
      During emotion processing, the left postcentral gyrus showed decreased connectivity in ADHD compared with control subjects (143 patients and 146 control subjects across 6 comparisons). Additionally, the right amygdala, left insula, and ACC formed hubs of increased and decreased connectivity in ADHD, all with a 1:1 ratio.

      Differences Between Individuals With ADHD and Neurotypical Populations by Functional Network

      We also aimed to identify hubs and networks exhibiting common connectivity differences in ADHD across cognitive functions. Regions that formed hubs of connectivity differences between patients and control subjects included the ACC (6:7, decreases-to-increases ratio), IFC (4:3), MFG (3:4), SFG (5:3), insula (3:4), sensorimotor cortex (1:1 ratio), IPL (1:2), striatum (3:1), and cerebellum (3:4). These regions exhibited increases and decreases of connectivity across tasks (Figure 2).
      Figure thumbnail gr2
      Figure 2Regions that formed core hubs of functional connectivity differences between individuals with ADHD and neurotypical control subjects across cognitive domains. ACC, anterior cingulate cortex; Cb., cerebellum; IFC, inferior frontal cortex; Ins., insula; IPL, inferior parietal lobule; MFG, middle frontal gyrus; SFG, superior frontal gyrus; SMC, sensorimotor cortex; Str., striatum. (Figure created with BioRender; https://biorender.com/.)
      Several studies performed formal analyses of established functional networks, often described in resting-state literature (
      • Fox M.D.
      • Snyder A.Z.
      • Vincent J.L.
      • Corbetta M.
      • Van Essen D.C.
      • Raichle M.E.
      The human brain is intrinsically organized into dynamic, anticorrelated functional networks.
      ,
      • Smith S.M.
      • Vidaurre D.
      • Beckmann C.F.
      • Glasser M.F.
      • Jenkinson M.
      • Miller K.L.
      • et al.
      Functional connectomics from resting-state fMRI.
      ), finding both within- and between-network differences. Relative to control subjects, patients showed reduced connectivity in visual (VIS), frontoparietal (FPN), executive control (ECN), ventral attention (VAN), subcortical, and salience networks during reward processing as well as in the ECN during working memory. Individuals with ADHD also showed increased connectivity within the salience network during reward processing and within the FPN and auditory networks during working memory compared with control subjects. Furthermore, ADHD was associated with decreased functional connectivity between the default mode (DMN) and frontotemporoparietal networks during cognitive control as well as between the ECN and both the FPN and the sensorimotor network (SMN), between the dorsal attention network (DAN) and SMN, and between the DAN and VIS during reward processing. Increased functional connectivity in ADHD was observed between the cingulo-opercular network (CON) and VAN in cognitive control and between the VAN and DMN, between the VAN and ECN, and between the DMN and ECN across cognitive domains.
      These studies suggest that the functional network architecture differs in ADHD. Alterations of functional connectivity were observed primarily in SMN, VIS, ECN, DMN, CON, and subcortical networks across cognitive domains. Nonetheless, both increases and decreases of connectivity were observed in ADHD across all implicated networks.

      Effects of Interventions on Functional Connectivity in ADHD

      Nine studies tested the effects of interventions on functional connectivity, with 8 studies investigating stimulants (youths = 6, adults = 2) (
      • Rubia K.
      • Halari R.
      • Cubillo A.I.
      • Mohammad A.
      • Brammer M.
      • Taylor E.
      Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a rewarded continuous performance task.
      ,
      • Querne L.
      • Fall S.
      • Le Moing A.G.
      • Bourel-Ponchel E.
      • Delignieres A.
      • Simonnot A.
      • et al.
      Effects of methylphenidate on default-mode network/task-positive network synchronization in children with ADHD.
      ,
      • Mowinckel A.M.
      • Alnaes D.
      • Pedersen M.L.
      • Ziegler S.
      • Fredriksen M.
      • Kaufmann T.
      • et al.
      Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD.
      ,
      • Wu Z.M.
      • Bralten J.
      • An L.
      • Cao Q.J.
      • Cao X.H.
      • Sun L.
      • et al.
      Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.
      ,
      • Posner J.
      • Nagel B.J.
      • Maia T.V.
      • Mechling A.
      • Oh M.
      • Wang Z.
      • Peterson B.S.
      Abnormal amygdalar activation and connectivity in adolescents with attention-deficit/hyperactivity disorder.
      ,
      • Schulz K.P.
      • Krone B.
      • Adler L.A.
      • Bédard A.V.
      • Duhoux S.
      • Pedraza J.
      • et al.
      Lisdexamfetamine targets amygdala mechanisms that bias cognitive control in attention-deficit/hyperactivity disorder.
      ,
      • Sheridan M.A.
      • Hinshaw S.
      • D’Esposito M.
      Stimulant medication and prefrontal functional connectivity during working memory in ADHD.
      ,
      • Wong C.G.
      • Stevens M.C.
      The effects of stimulant medication on working memory functional connectivity in attention-deficit/hyperactivity disorder.
      ) and 1 study evaluating fMRI neurofeedback of the right IFC (
      • Marcos-Vidal L.
      • Martinez-Garcia M.
      • Pretus C.
      • Garcia-Garcia D.
      • Martinez K.
      • Janssen J.
      • et al.
      Local functional connectivity suggests functional immaturity in children with attention-deficit/hyperactivity disorder.
      ). The intervention studies investigated various cognitive domains, and thus findings were synthesized across cognitive functions and within treatment type (Table 3).
      Table 3Studies Investigating the Impact of Interventions on fMRI Functional Connectivity in ADHD
      StudyAnalysis MethodTask (Contrast)NADHD (% Male)AgeADHD, Years, Mean (SD)Medication HistoryMedication WashoutADHD ComorbiditiesIntervention/ComparisonDesignOn Intervention > Off InterventionOff Intervention > On Intervention
      Mowinckel et al., 2017 (
      • Mowinckel A.M.
      • Alnaes D.
      • Pedersen M.L.
      • Ziegler S.
      • Fredriksen M.
      • Kaufmann T.
      • et al.
      Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD.
      )
      Bayesian hierarchical mixed modelValue-based decision making task (unspecified)20 (35%)29.9 (1.41)Current stimulant use (100%)>20 hoursNoneAcute MPH (10–40 mg of regularly prescribed formulation)/placeboRandomized, double-blind, crossoverAuditory network ↔ ECNWithin DMN and VIS
      Posner et al., 2011 (
      • Posner J.
      • Nagel B.J.
      • Maia T.V.
      • Mechling A.
      • Oh M.
      • Wang Z.
      • Peterson B.S.
      Abnormal amygdalar activation and connectivity in adolescents with attention-deficit/hyperactivity disorder.
      )
      DCMFearful faces task with priming (fearful faces)15 (87%)13.5 (1.2)Current stimulant use (100%)>48 hoursODD/CD (% unspecified)Acute stimulant (regularly prescribed formulation and dose)/off medicationCross-overNoneNone (main group comparison); BIL amygdala ↔ BIL lateral PFC (secondary nonparametric analysis)
      Querne et al., 2017 (
      • Querne L.
      • Fall S.
      • Le Moing A.G.
      • Bourel-Ponchel E.
      • Delignieres A.
      • Simonnot A.
      • et al.
      Effects of methylphenidate on default-mode network/task-positive network synchronization in children with ADHD.
      )
      ICAFlanker task (unspecified)11 (unspecified)9.8 (1.7)Medication-naïve (100%)None4 weeks MPH (20–30 mg extended release)/off medicationCrossover (off medication → MPH)DMN composed of anterior and posterior regions; anticorrelation between DMN and BIL anterior frontal, striatum, dorsal ACC, R occipitoparietal cortex, L cerebellum (direct group comparison not reported)DMN composed posterior regions only (direct group comparison not reported)
      Rubia et al., 2009 (
      • Rubia K.
      • Halari R.
      • Cubillo A.I.
      • Mohammad A.
      • Brammer M.
      • Taylor E.
      Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a rewarded continuous performance task.
      )
      SBCCPT (targets > nontargets)13 (100%)12.5 (1.3)Medication-naïve (100%)ODD/CD (8%)Acute MPH (0.3 mg/kg)/placeboRandomized, double-blind, crossoverL caudate/putamen ↔ R caudate/putamenNone
      Rubia et al., 2019 (
      • Rubia K.
      • Criaud M.
      • Wulff M.
      • Alegria A.
      • Brinson H.
      • Barker G.
      • et al.
      Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD.
      )
      SBCNeurofeedbackActive group: 18 (100%); control group: 13 (100%)Active group: 14 (2); control group: 14 (2)Active group: current use of stimulants (83%), withdrew from medication for duration of study (17%); control group: current stimulant use (69%), withdrew from medication for duration of study (23%), medication-naïve (8%)>7 days for those willing to withdraw from medicationODD/CD (% unspecified)fMRI neurofeedback of R IFC/fMRI neurofeedback of L parahippocampal gyrus11-run parallel groups active control (randomized single-blind control trial)Relative to first run and control: R IFC (BA 45) ↔ R caudate, ACC; R IFC (BA 44) ↔ R ACCRelative to first run: R IFC (BA 45) ↔ L parahippocampal, hippocampus, lingual, BIL PCC, precuneus, calcarine, thalamus, caudate, putamen, pallidum; R IFC (BA 44) ↔ BIL precuneus, PCC, hippocampus, parahippocampal, lingual, thalamus; relative to control: R IFC (BA 45) ↔ BIL PCC, precuneus, calcarine; R IFC (BA 44) ↔ BIL PCC, precuneus, hippocampus, parahippocampal, lingual, thalamus
      Schulz et al., 2018 (
      • Schulz K.P.
      • Krone B.
      • Adler L.A.
      • Bédard A.V.
      • Duhoux S.
      • Pedraza J.
      • et al.
      Lisdexamfetamine targets amygdala mechanisms that bias cognitive control in attention-deficit/hyperactivity disorder.
      )
      PPISPMEmotional GNG (correct go trials cued by sad faces)25 (56%)34.8 (9.8)Current use of medication, class unspecified (8%); past stimulant and/or nonstimulant use but medication-free at time of study (36%); medication-naïve (56%)2 weeksNone5 weeks LDX (30–70 mg)/placeboRandomized, single-blind, crossoverNoneL amygdala ↔ R SPG, L STG; R amygdala ↔ L IFC, STG, R SPG
      Sheridan et al., 2010 (
      • Sheridan M.A.
      • Hinshaw S.
      • D’Esposito M.
      Stimulant medication and prefrontal functional connectivity during working memory in ADHD.
      )
      BSCDelayed match to sample task (encoding)5 (0%)14.8 (2.4)Current stimulant use (60%); current stimulant and nonstimulant use (20%); current stimulant and SSRI use (20%)24 hours (for stimulants only)UnspecifiedAcute stimulant (regularly prescribed formulation and dose)/off medicationCrossoverBIL MFG ↔ cerebellar vermis
      Correction for multiple comparisons not specified.
      BIL MFG ↔ striatum
      Correction for multiple comparisons not specified.
      , L MFG
      Correction for multiple comparisons not specified.
      , medial PFC
      Correction for multiple comparisons not specified.
      , hippocampus
      Correction for multiple comparisons not specified.
      , ITG
      Correction for multiple comparisons not specified.
      , R TPJ
      Correction for multiple comparisons not specified.
      , insula
      Correction for multiple comparisons not specified.
      , lingual
      Correction for multiple comparisons not specified.
      Wong and Stevens, 2012 (
      • Wong C.G.
      • Stevens M.C.
      The effects of stimulant medication on working memory functional connectivity in attention-deficit/hyperactivity disorder.
      )
      ICASternberg item recognition task (unspecified)18 (83%)14.6 (2)Current stimulant use (100%)48 hoursODD (6%)Acute stimulant (regularly prescribed formulation and dose)/placeboRandomized, double-blind, crossoverWithin ACC, medial frontal, PCC, precuneus, cuneus, lingual, SFG, cingulate, R postcentral, precentral, L IFC, SMG, MTG, angular regionsWithin PCC, precuneus
      Wu et al., 2017 (
      • Wu Z.M.
      • Bralten J.
      • An L.
      • Cao Q.J.
      • Cao X.H.
      • Sun L.
      • et al.
      Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.
      )
      ICAVerbal n-back task (2-back > 0-back)22 (100%)12.71 (1.55)Past stimulant use but medication-free at time of study (23%); medication-naïve (77%)>4 weeksODD (18%)Acute MPH (10 mg)/placeboRandomized, double-blind, crossoverWithin ECN (R precuneus, L PCC)None
      ACC, anterior cingulate cortex; ADHD, attention-deficit/hyperactivity disorder; BIL, bilateral; BA, Brodmann area; BSC, beta series correlation; CD, conduct disorder; CPT, continuous performance task; DCM, dynamic causal modeling; DMN, default mode network; ECN, executive control network; fMRI, functional magnetic resonance imaging; GNG, go/no-go; gPPI, generalized psychophysiologic interaction; ICA, independent component analysis; IFC, inferior frontal cortex; ITG, inferior temporal gyrus; L, left; LDX, lisdexamfetamine; MFG, middle frontal gyrus; MPH, methylphenidate; MTG, middle temporal gyrus; ODD, oppositional defiant disorder; PCC, posterior cingulate cortex; PFC, prefrontal cortex; PPI, psychophysiological interaction; R, right; SBC, seed-based correlation; SFG, superior frontal gyrus; SMG, supramarginal gyrus; SPG, superior parietal gyrus; SSRI, selective serotonin reuptake inhibitor; STG, superior temporal gyrus; TPJ, temporoparietal junction; VIS, visual network.
      a Correction for multiple comparisons not specified.
      Stimulants increased connectivity of the striatum (although decreases were seen in one study), ACC, and cerebellum across tasks and decreased connectivity of the amygdala in emotion paradigms compared with no intervention/placebo. MFG, IFC, medial frontal cortex, posterior cingulate cortex (PCC), occipital cortex, and precuneus showed both increased and decreased connectivity with stimulants, all with 1:1 ratio. Additionally, network analyses showed decreased connectivity within DMN and VIS with stimulants relative to no treatment/placebo. Stimulants enhanced connections within the ECN and between the ECN and auditory networks.
      The neurofeedback study showed increased functional connectivity between the right IFC and the right striatum and ACC relative to both baseline and control subjects. Additionally, neurofeedback was associated with decreased connectivity between the right IFC and various PCC-occipital, striatothalamic, and hippocampal regions. Overall, interventions modulated functional connectivity of the striatum, ACC, occipital regions, and midline DMN areas.

      Critical Appraisal

      Across all 51 included comparisons, 28 specified a motion cutoff. All comparisons included motion correction, with 36 comparisons applying standard methods (e.g., default software options) and 15 comparisons using more advanced approaches.
      Average sample size of patient groups across all comparisons was 28, with larger samples in case-control than intervention comparisons (31 relative to 16, respectively). Independent samples were tested in 42 comparisons. Within those, studies reported matching groups on age in 40 comparisons, sex in 35 comparisons, handedness in 26 comparisons, motion in 21 comparisons, IQ in 21 comparisons, race/ethnicity in 9 comparisons, socioeconomic status in 7 comparisons, presence of unrelated symptoms in 7 comparisons, education level in 6 comparisons, working memory capacity in 1 comparison, and pubertal status in 1 comparison. Additionally, of all 51 comparisons, 42 reported information about ADHD presentation. On average, 72% of patients had combined ADHD, 22% had inattentive presentation, 3% had hyperactive-impulsive presentation, and 0.5% were classified as ADHD not otherwise specified.
      The reviewed studies used heterogeneous methods to assess connectivity. Of all 51 comparisons, 20 used psychophysiological interaction (psychophysiological interaction = 12, generalized psychophysiological interaction = 8), 9 used seed-based correlations, 9 used graph theoretic techniques, 8 used independent component analysis, 2 used dynamic causal modeling, 2 used Bayesian hierarchical mixed models, and 1 used beta series correlation. Of all comparisons, 41 were seed-based and required definition of seed regions used in analysis. Within those, 21 used seeds defined independently of the dataset studied (based on past research or anatomical atlases), while 20 used seeds based on the same dataset (e.g., regions of peak activation in the same cohort). Furthermore, while most comparisons reported multiple comparisons correction, 6 of all 51 comparisons did not (indicated by a footnote symbol in Tables 1 and 3).
      Of 51 comparisons, 34 recruited samples currently receiving pharmacotherapy, 9 recruited medication-naïve participants, 1 recruited participants who were medication-naïve or had a history of pharmacotherapy, and 7 did not specify medication history. Within the 34 comparisons recruiting currently medicated participants, 31 specified a washout period. Washout periods ranged from 20 hours to 4 weeks (20 hours = 2; 24 hours = 8; 36 hours = 1; 48 hours = 12; 72 hours = 1; 1 week = 1; 2 weeks = 2; 4 weeks = 2). Additionally, 2 comparisons used washout periods without specifying their exact duration (Tables 1 and 2).
      The effects of interventions were tested in 9 comparisons. Selection bias (random sequence generation and allocation concealment) was deemed low in 6 comparisons, unclear in 2 comparisons, and high in 1 comparison. Performance (blinding of participants/personnel) and detection (blinding of outcome assessment) biases were rated low in 3 comparisons, unclear in 3 comparisons, and high in 3 comparisons. Attrition (incomplete outcome data) and reporting (selective reporting) biases were deemed low in all 9 comparisons (Supplement).

      Discussion

      Task-Based Connectivity in ADHD

      Across cognitive domains, changes of functional connectivity were observed in ADHD relative to neurotypical populations, with core hubs of connectivity differences in the ACC, IFC, MFG, SFG, sensorimotor cortex, insula, IPL, striatum, and cerebellum. Although changes of connectivity were observed when cognitive domains were considered individually, inhibition and attention were associated primarily with reductions in connectivity, whereas working memory was related to enhanced connectivity in ADHD relative to typical development.
      Additional differences were observed in between-network connectivity. Across cognitive domains, individuals with ADHD showed stronger connections between VAN and both DMN and ECN as well as between ECN and DMN. During cognitive control, decreased connectivity was observed between DMN and frontotemporoparietal networks, while increased connectivity was seen between CON and VAN. During reward processing, only decreases of connectivity were observed between ECN and both FPN and SMN as well as between DAN and both SMN and VIS. Furthermore, for individuals with ADHD and their nonsymptomatic siblings and adults with ADHD and ADHD remitters, a limited literature showed connectivity differences similar to those seen between ADHD and neurotypical populations, specifically in striatal and sensorimotor regions (Table 2).
      This review compiled findings estimated with several methods. Although these methods have fundamental differences and their outcomes may not represent the same aspects of connectivity, they reflect abnormal functioning of discrete networks in ADHD. This heterogeneity of methods prevents a synthesis yielding mechanistic insight into network-level pathophysiology of ADHD, although there is value in highlighting the cumulative evidence implicating certain neural systems.
      The observations of abnormalities in task-relevant functional networks in ADHD bolster evidence of largely decreased local activation in core executive function–relevant areas, including ventrolateral, dorsolateral, and medial prefrontal, temporoparietal, and striatal regions in meta-analyses of fMRI studies in ADHD (
      • Cortese S.
      • Kelly C.
      • Chabernaud C.
      • Proal E.
      • Di Martino A.
      • Milham M.P.
      • Castellanos F.X.
      Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies.
      ,
      • Hart H.
      • Radua J.
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      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder.
      ,
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      Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD).
      ,
      • Lukito S.
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      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • McCarthy H.
      • Skokauskas N.
      • Frodl T.
      Identifying a consistent pattern of neural function in attention deficit hyperactivity disorder: A meta-analysis.
      ,
      • Norman L.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder.
      ,
      • Dickstein S.G.
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      The neural correlates of attention deficit hyperactivity disorder: An ALE meta-analysis.
      ,
      • Lei D.
      • Du M.
      • Wu M.
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      • Huang X.
      • Du X.
      • et al.
      Functional MRI reveals different response inhibition between adults and children with ADHD.
      ). Consequently, these findings support the presence of abnormalities in core task-positive networks and DMN in ADHD, and the high prevalence of abnormal sensorimotor connectivity resonates with similar observations in resting-state studies (
      • Gao Y.
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      • et al.
      Impairments of large-scale functional networks in attention-deficit/hyperactivity disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Marcos-Vidal L.
      • Martinez-Garcia M.
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      • Martinez K.
      • Janssen J.
      • et al.
      Local functional connectivity suggests functional immaturity in children with attention-deficit/hyperactivity disorder.
      ,
      • Pretus C.
      • Marcos-Vidal L.
      • Martinez-García M.
      • Picado M.
      • Ramos-Quiroga J.A.
      • Richarte V.
      • et al.
      Stepwise functional connectivity reveals altered sensory-multimodal integration in medication-naive adults with attention deficit hyperactivity disorder.
      ,
      • McLeod K.R.
      • Langevin L.M.
      • Goodyear B.G.
      • Dewey D.
      Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder.
      ), which may reflect the previously proposed hypothesis of deviant maturational trajectories within these networks in ADHD (
      • Marcos-Vidal L.
      • Martinez-Garcia M.
      • Pretus C.
      • Garcia-Garcia D.
      • Martinez K.
      • Janssen J.
      • et al.
      Local functional connectivity suggests functional immaturity in children with attention-deficit/hyperactivity disorder.
      ). Nonetheless, the current literature largely focused on pediatric samples, and more exploration of adults and longitudinal cohorts is needed to better characterize the developmental trajectories of ADHD.
      Our review also extends the knowledge base of resting-state connectivity alterations in ADHD in DMN, ECN, DAN, VAN, and salience networks (
      • Gao Y.
      • Shuai D.
      • Bu X.
      • Hu X.
      • Tang S.
      • Zhang L.
      • et al.
      Impairments of large-scale functional networks in attention-deficit/hyperactivity disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Sutcubasi B.
      • Metin B.
      • Kurban M.K.
      • Metin Z.E.
      • Beser B.
      • Sonuga-Barke E.
      Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis.
      ,
      • Posner J.
      • Park C.
      • Wang Z.
      Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder.
      ,
      • Sripada C.
      • Kessler D.
      • Fang Y.
      • Welsh R.C.
      • Prem Kumar K.
      • Angstadt M.
      Disrupted network architecture of the resting brain in attention-deficit/hyperactivity disorder.
      ,
      • Castellanos F.X.
      • Aoki Y.
      Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: A science in development.
      ) in two important ways. First, during different tasks, both increases and decreases of connectivity in ADHD were observed. Relative to connectivity under unconstrained context (resting state), which may reflect underlying anatomical or long-term functional plasticity differences, task-based literature indicates that connectivity alterations in ADHD may reflect differences in adaptability of functional circuits to changing demands. These context-dependent changes may be related to arousal systems that respond differently under distinct tasks (
      • Sergeant J.
      The cognitive-energetic model: An empirical approach to attention-deficit hyperactivity disorder.
      ). Such explanations of ADHD pathophysiology move beyond seeing the brain as a static system and suggest a conceptualization of ADHD as a disorder of dynamic neurocognitive processes.
      Second, the review emphasizes that even within tasks results to date are mixed. With small numbers of studies in some areas, it was not possible to assess whether these mixed findings were due to low power or specific task or patient factors. Although ADHD heterogeneity can contribute to the mixed findings (
      • Ghaderi A.H.
      • Nazari M.A.
      • Shahrokhi H.
      • Darooneh A.H.
      Functional brain connectivity differences between different ADHD presentations: Impaired functional segregation in ADHD-combined presentation but not in ADHD-inattentive presentation.
      ), the association between neurocognitive phenotypes and individual differences is still poorly understood (Supplement). Task factors, however, are supported by a recent study that found that youths with ADHD engage more task-specific than generic networks, showing hypoconnectivity in executive and reward circuits relative to neurotypical control subjects and nonsymptomatic siblings of individuals with ADHD (
      • Chauvin R.J.
      • Buitelaar J.K.
      • Sprooten E.
      • Oldehinkel M.
      • Franke B.
      • Hartman C.
      • et al.
      Task-generic and task-specific connectivity modulations in the ADHD brain: An integrated analysis across multiple tasks.
      ). These findings suggest that the inconsistencies in the literature may reflect inefficient task-specific networks in ADHD, with greater variability in functional connections.
      This review summarizes impairments of functional connectivity in ADHD across several cognitive domains. The included studies used different tasks to elicit specific cognitive processes. However, there is a risk of nonspecificity in tasks. While this review indicates context-specific alterations, efforts have been made to understand the underlying processes key in explaining ADHD pathophysiology, with some investigators proposing executive dysfunction (
      • Willcutt E.G.
      • Doyle A.E.
      • Nigg J.T.
      • Faraone S.
      • Pennington B.F.
      Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review.
      ,
      • Barkley R.A.
      Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD.
      ), while others argue for poor deployment of resources (
      • Sergeant J.
      The cognitive-energetic model: An empirical approach to attention-deficit hyperactivity disorder.
      ,
      • Martella D.
      • Aldunate N.
      • Fuentes L.J.
      • Sánchez-Pérez N.
      Arousal and executive alterations in attention deficit hyperactivity disorder (ADHD).
      ). As yet, the precise neurofunctional manifestation of these explanations is poorly understood in patients. While cross-sectional imaging studies cannot clearly address questions of multifinality or equifinality in ADHD, they demonstrate the context-dependent nature of the dysfunction. How this relates to symptoms, clinical presentation, and treatment effects can help determine the degree to which ADHD is associated with one set of dysfunctions that differentially manifest across patients or whether true biological subtypes exist. Such efforts show promise (
      • Fair D.A.
      • Bathula D.
      • Nikolas M.A.
      • Nigg J.T.
      Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD.
      ,
      • Fair D.A.
      • Nigg J.T.
      • Iyer S.
      • Bathula D.
      • Mills K.L.
      • Dosenbach N.U.F.
      • et al.
      Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data.
      ) but have not yet been applied to context-dependent connectivity.

      Effects of Interventions on Task-Based Connectivity in ADHD

      Most intervention studies investigated stimulant medications, while one addressed the effects of fMRI neurofeedback. All interventions modulated connections of the striatum, ACC, occipital regions, and midline DMN structures. Furthermore, stimulants increased connectivity of cerebellar hubs across task paradigms and decreased amygdala connectivity during emotion processing. Additionally, stimulants led to increases and decreases of connectivity with IFC, MFG, medial frontal cortex, PCC, precuneus, and occipital regions across cognitive functions. Network-wide modulation with stimulants was also observed, with decreased connectivity within DMN and auditory networks and increased connectivity within ECN as well as between ECN and auditory networks.
      Our findings align with individual resting-state studies showing that stimulants modulate spontaneous brain activity in similar ventrolateral frontal, occipital, and cerebellar regions, along with connectivity within ECN, VIS, and DMN (
      • Silk T.J.
      • Malpas C.
      • Vance A.
      • Bellgrove M.A.
      The effect of single-dose methylphenidate on resting-state network functional connectivity in ADHD.
      ,
      • Yoo J.H.
      • Kim D.
      • Choi J.
      • Jeong B.
      Treatment effect of methylphenidate on intrinsic functional brain network in medication-naïve ADHD children: A multivariate analysis.
      ,
      • Picon F.A.
      • Sato J.R.
      • Anes M.
      • Vedolin L.M.
      • Mazzola A.A.
      • Valentini B.B.
      • et al.
      Methylphenidate alters functional connectivity of default mode network in drug-naive male adults with ADHD.
      ,
      • Cary R.P.
      • Ray S.
      • Grayson D.S.
      • Painter J.
      • Carpenter S.
      • Maron L.
      • et al.
      Network structure among brain systems in adult ADHD is uniquely modified by stimulant administration.
      ). Our findings also complement evidence of stimulant-related modulation of activation in areas dysfunctional in ADHD (
      • Hart H.
      • Radua J.
      • Nakao T.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder.
      ,
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • McCarthy H.
      • Skokauskas N.
      • Frodl T.
      Identifying a consistent pattern of neural function in attention deficit hyperactivity disorder: A meta-analysis.
      ,
      • Norman L.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder.
      ,
      • Rubia K.
      • Alegria A.A.
      • Cubillo A.I.
      • Smith A.B.
      • Brammer M.
      • Radua J.
      Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: A systematic review and meta-analysis.
      ). These results highlight that stimulants also act on context-dependent network reorganization, potentially facilitating task performance.
      One study explored the effects of fMRI neurofeedback. The modulation of connectivity of striatal, ventrolateral frontal, cingulate, and occipital regions observed with the intervention mirrored the changes seen with stimulant use, suggesting that neurofeedback of the right IFC may offer similar benefits as stimulants; however, more research is needed.

      Limitations and Recommendations

      Although this review supports the presence of network-wide dysfunction in ADHD and its modulation with treatment, a meta-analysis was not possible owing to the methodological heterogeneity of the literature. Consequently, it is difficult to quantify the degree of convergence across studies. A similar problem was noted in a recent systematic review of pharmacological effects on resting-state connectivity in ADHD (
      • Pereira-Sanchez V.
      • Franco A.R.
      • Vieira D.
      • de Castro-Manglano P.
      • Soutullo C.
      • Milham M.P.
      • Castellanos F.X.
      Systematic review: Medication effects on brain intrinsic functional connectivity in patients with attention-deficit/hyperactivity disorder.
      ). This is particularly relevant as recent task-based activation (
      • Samea F.
      • Soluki S.
      • Nejati V.
      • Zarei M.
      • Cortese S.
      • Eickhoff S.B.
      • et al.
      Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies.
      ) and resting-state (
      • Cortese S.
      • Aoki Y.Y.
      • Itahashi T.
      • Castellanos F.X.
      • Eickhoff S.B.
      Systematic review and meta-analysis: Resting state functional magnetic resonance imaging studies of attention-deficit/hyperactivity disorder.
      ) meta-analyses of ADHD fMRI literature showed no spatial convergence across studies. Within the current review, eight different methods of estimating functional connectivity were used. Although most studies used seed-based methods, these comprised seven distinct approaches and different ways of defining seed regions. Furthermore, only approximately half of the studies used seeds defined independently of the dataset studied, thus avoiding the potential biases of circular analyses (
      • Kriegeskorte N.
      • Simmons W.K.
      • Bellgowan P.S.
      • Baker C.I.
      Circular analysis in systems neuroscience: The dangers of double dipping.
      ). Overall, while diverse methods provide different ways of characterizing the data and avoid potential issues stemming from one specific method, these benefits come at the cost of limiting the quantitative synthesis of findings across studies.
      Past and current medication history represented another source of heterogeneity. Most studies included previously medicated participants. As stimulant use has been associated with structural (
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • Nakao T.
      • Radua J.
      • Rubia K.
      • Mataix-Cols D.
      Gray matter volume abnormalities in ADHD: Voxel-based meta-analysis exploring the effects of age and stimulant medication.
      ,
      • Frodl T.
      • Skokauskas N.
      Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects.
      ), functional (
      • Hart H.
      • Radua J.
      • Nakao T.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder.
      ,
      • Hart H.
      • Radua J.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD).
      ,
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • Norman L.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder.
      ,
      • Konrad K.
      • Neufang S.
      • Fink G.R.
      • Herpertz-Dahlmann B.
      Long-term effects of methylphenidate on neural networks associated with executive attention in children with ADHD: Results from a longitudinal functional MRI study.
      ), and neurochemical changes (
      • Fusar-Poli P.
      • Rubia K.
      • Rossi G.
      • Sartori G.
      • Balottin U.
      Striatal dopamine transporter alterations in ADHD: Pathophysiology or adaptation to psychostimulants? A meta-analysis.
      ), studying neural networks in currently or previously medicated individuals may confound pathophysiology of the disorder with the long-term impact of treatment. Another issue is the variability in the drug washout periods used (20 hours to 1 month). A minimum washout of 5 half-lives of the drug is recommended (
      • Dhariwal K.
      • Jackson A.
      Effect of length of sampling schedule and washout interval on magnitude of drug carryover from period 1 to period 2 in two-period, two-treatment bioequivalence studies and its attendant effects on determination of bioequivalence.
      ); however, discontinuing treatment can lead to withdrawal or rebound effects (
      • Buitelaar J.K.
      • Asherson P.
      • Soutullo C.
      • Colla M.
      • Adams D.H.
      • Tanaka Y.
      • et al.
      Differences in maintenance of response upon discontinuation across medication treatments in attention-deficit/hyperactivity disorder.
      ), and the length of the washout period may influence the level of neural differences between ADHD and neurotypical populations (
      • McCarthy H.
      • Skokauskas N.
      • Frodl T.
      Identifying a consistent pattern of neural function in attention deficit hyperactivity disorder: A meta-analysis.
      ). Therefore, aside from the confounding effects of medication, some of the variability within the observed findings may be attributed to variable washout periods.
      Small sample sizes, particularly in the intervention literature, which are linked to lower replicability of findings (
      • Button K.S.
      • Ioannidis J.P.
      • Mokrysz C.
      • Nosek B.A.
      • Flint J.
      • Robinson E.S.
      • Munafò M.R.
      Power failure: Why small sample size undermines the reliability of neuroscience.
      ,
      • Poldrack R.A.
      • Baker C.I.
      • Durnez J.
      • Gorgolewski K.J.
      • Matthews P.M.
      • Munafò M.R.
      • et al.
      Scanning the horizon: Towards transparent and reproducible neuroimaging research.
      ,
      • Szucs D.
      • Ioannidis J.P.
      Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature.
      ,
      • Thirion B.
      • Pinel P.
      • Mériaux S.
      • Roche A.
      • Dehaene S.
      • Poline J.B.
      Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses.
      ,
      • Turner B.O.
      • Paul E.J.
      • Miller M.B.
      • Barbey A.K.
      Small sample sizes reduce the replicability of task-based fMRI studies.
      ), are a limitation of this literature. Such issues have prompted recommendations such as a minimum sample size of 20 (
      • Thirion B.
      • Pinel P.
      • Mériaux S.
      • Roche A.
      • Dehaene S.
      • Poline J.B.
      Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses.
      ) and development of software allowing power calculations for fMRI studies (
      • Mumford J.A.
      A power calculation guide for fMRI studies.
      ). Consequently, these findings need to be interpreted with caution given that many were likely underpowered.
      Some limitations of the reviewed studies involve the transparency of reporting, data quality assurance, and processing pipelines. For instance, only approximately half of the comparisons specified a motion cutoff. Given that ADHD is characterized by increased movement (
      • Epstein J.N.
      • Casey B.J.
      • Tonev S.T.
      • Davidson M.
      • Reiss A.L.
      • Garrett A.
      • et al.
      Assessment and prevention of head motion during imaging of patients with attention deficit hyperactivity disorder.
      ,
      • Pardoe H.R.
      • Kucharsky Hiess R.
      • Kuzniecky R.
      Motion and morphometry in clinical and nonclinical populations.
      ) and lower tolerability of the scanner environment (
      • Yerys B.E.
      • Jankowski K.F.
      • Shook D.
      • Rosenberger L.R.
      • Barnes K.A.
      • Berl M.M.
      • et al.
      The fMRI success rate of children and adolescents: Typical development, epilepsy, attention deficit/hyperactivity disorder, and autism spectrum disorders.
      ) and that functional connectivity methods are particularly sensitive to motion artifacts (
      • Goto M.
      • Abe O.
      • Miyati T.
      • Yamasue H.
      • Gomi T.
      • Takeda T.
      Head motion and correction methods in resting-state functional MRI.
      ,
      • Spisák T.
      • Jakab A.
      • Kis S.A.
      • Opposits G.
      • Aranyi C.
      • Berényi E.
      • Emri M.
      Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state fMRI.
      ,
      • Satterthwaite T.D.
      • Ciric R.
      • Roalf D.R.
      • Davatzikos C.
      • Bassett D.S.
      • Wolf D.H.
      Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies.
      ), appropriate checks of data quality are essential. Issues with transparent reporting and data processing were also evident in studies not specifying multiple comparisons correction. False-positive rates in fMRI analyses are notorious without adjustment for multiple comparisons (
      • Poldrack R.A.
      • Baker C.I.
      • Durnez J.
      • Gorgolewski K.J.
      • Matthews P.M.
      • Munafò M.R.
      • et al.
      Scanning the horizon: Towards transparent and reproducible neuroimaging research.
      ,
      • Bennett C.M.
      • Wolford G.L.
      • Miller M.B.
      The principled control of false positives in neuroimaging.
      ,
      • Poldrack R.A.
      The future of fMRI in cognitive neuroscience.
      ), and thus publications not reporting application of multiple comparisons correction should be interpreted with caution.
      Further, the reviewed studies differed in general methodology, including study design, acquisition parameters, and data processing. Such heterogeneity further complicates cross-study synthesis of findings. Although these factors are not specific to this field and assessment of their impact was beyond the scope of this review, future studies should carefully consider and outline justification of their methodological choices.
      This literature was also limited by other patient-specific factors frequently present in ADHD research, including male predominance, presence of comorbidities, variability of clinical presentation, and age-related differences (Supplement). Finally, ADHD is an inherently heterogeneous disorder with variable severity and class of symptoms, genetic and environmental risk factors, and profiles of associated pathophysiology (
      • Willcutt E.G.
      • Doyle A.E.
      • Nigg J.T.
      • Faraone S.
      • Pennington B.F.
      Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review.
      ,
      • Faraone S.
      • Asherson P.
      • Banaschewski T.
      • Biederman J.
      • Buitelaar J.
      • Ramos-Quiroga J.
      • et al.
      Attention-deficit/hyperactivity disorder.
      ,
      • Castellanos F.X.
      • Tannock R.
      Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes.
      ,
      • Saad J.F.
      • Griffiths K.R.
      • Korgaonkar M.S.
      A systematic review of imaging studies in the combined and inattentive subtypes of attention deficit hyperactivity disorder.
      ). Consequently, it is likely that the heterogeneity of findings can be partly explained by the interindividual differences of ADHD groups. The impact of these factors should thus be explored further.
      Overall, the limitations of the current literature illustrate the need for improved standards of study methodology and reporting. We propose that researchers prioritize recruiting larger, more diverse, and medication-naïve samples; implement greater control of in-scan motion and motion-related artifacts; use state-of-the-art data processing pipelines; and promote reporting transparency and openness [see Pereira-Sanchez et al. (
      • Pereira-Sanchez V.
      • Franco A.R.
      • Vieira D.
      • de Castro-Manglano P.
      • Soutullo C.
      • Milham M.P.
      • Castellanos F.X.
      Systematic review: Medication effects on brain intrinsic functional connectivity in patients with attention-deficit/hyperactivity disorder.
      ) for an in-depth discussion].

      Conclusions

      This is the first systematic review appraising the task-based functional connectivity literature of ADHD. We reviewed studies describing ADHD and the impact of interventions on task-relevant functional networks involved in the pathophysiology of the disorder. Our review supports the presence of CON, SMN, VIS, subcortical, ECN, and DMN network abnormalities in ADHD and shows that interventions can modulate the functional reorganization of those circuits. Overall, this review highlights the utility of task-based connectivity studies in broadening the understanding of the neural underpinnings of ADHD and in studying the mechanisms of action of ADHD treatments, but advocates for improvements to methodological quality of this line of research.

      Acknowledgments and Disclosures

      This work was supported by the National Institute for Health Research Maudsley Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London, and the Medical Research Council (postdoctoral appointment to MC).
      The views expressed are those of the authors and not necessarily those of the National Health Service, National Institute for Health Research, Medical Research Council, or Department of Health and Social Care.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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