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A mega-analytic study of white matter microstructural differences across five cohorts of youth with attention deficit hyperactivity disorder

Published:September 25, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.09.021

      Abstract

      Background

      While ADHD has been associated with differences in the structural connections formed by the brain’s white matter tracts, studies of such differences have returned inconsistent findings, likely reflecting small sample sizes. Thus, we conducted a mega-analysis on in vivo measures of white matter microstructure obtained through diffusion tensor imaging of over 6000 participants, from five cohorts.

      Methods

      In a mega-analysis, linear mixed models tested for associations between the fractional anisotropy of 42 white matter tracts and ADHD traits and diagnosis. Contrasts were made against measures of mood, anxiety, and other externalizing problems.

      Findings

      Overall, 6993 participants (between ages 6 to 18 years, mean 10.62 [SD 1.99]; 3,368 girls, 3,625 boys; 4146 white, non-Hispanic, 764 African American, 2083 other race/ethnicities) had either measures of ADHD and other emotional/behavioral symptoms (N=6933) and/or enough clinical data to allow a diagnosis of ADHD (N=951) or its absence (N=4884). Both the diagnosis and symptoms of ADHD were associated with lower fractional anisotropy of inferior longitudinal and left uncinate fasciculi (at FDR adjusted p<0.05). Associated effect sizes were small (the strongest association with ADHD traits had an effect size of partial-r=-0.14, while the largest case control difference was associated with an effect size of d=-0.3). Similar microstructural anomalies were not present for anxiety, mood, or externalizing problems. Findings held when ADHD cases and controls were matched on in-scanner motion.

      Interpretation

      While present across cohorts, ADHD-associated microstructural differences had small effects, underscoring the limited clinical utility of this imaging modality in isolation.

      Keywords

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      References

        • Sudre G.
        • Bouyssi-Kobar M.
        • Norman L.
        • Sharp W.
        • Choudhury S.
        • Shaw P.
        Estimating the Heritability of Developmental Change in Neural Connectivity, and Its Association With Changing Symptoms of Attention-Deficit/Hyperactivity Disorder.
        Biological Psychiatry. 2021; 89: 443-450
        • Konrad K.
        • Eickhoff S.B.
        Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder.
        Human Brain Mapping. 2010; 31: 904-916
        • van Ewijk H.
        • Heslenfeld D.J.
        • Zwiers M.P.
        • Buitelaar J.K.
        • Oosterlaan J.
        Diffusion tensor imaging in attention deficit/hyperactivity disorder: A systematic review and meta-analysis.
        Neuroscience & Biobehavioral Reviews. 2012; 36: 1093-1106
        • Aoki Y.
        • Cortese S.
        • Castellanos F.X.
        Research Review: Diffusion tensor imaging studies of attention‐deficit/hyperactivity disorder: meta‐analyses and reflections on head motion.
        Journal of Child Psychology and Psychiatry. 2018; 59: 193-202
        • Chen L.
        • Hu X.
        • Ouyang L.
        • He N.
        • Liao Y.
        • Liu Q.
        • et al.
        A systematic review and meta-analysis of tract-based spatial statistics studies regarding attention-deficit/hyperactivity disorder.
        Neurosci Biobehav Rev. 2016; 68: 838-847
        • Zhao Y.
        • Yang L.
        • Gong G.
        • Cao Q.
        • Liu J.
        Identify aberrant white matter microstructure in ASD, ADHD and other neurodevelopmental disorders: A meta-analysis of diffusion tensor imaging studies.
        Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2022; 113110477
        • Radua J.
        • Mataix-Cols D.
        Voxel-wise meta-analysis of grey matter changes in obsessive–compulsive disorder.
        The British Journal of Psychiatry. 2009; 195: 393-402
        • Eickhoff S.B.
        • Bzdok D.
        • Laird A.R.
        • Kurth F.
        • Fox P.T.
        Activation likelihood estimation meta-analysis revisited.
        Neuroimage. 2012; 59: 2349-2361
        • Button K.S.
        • Ioannidis J.P.
        • Mokrysz C.
        • Nosek B.A.
        • Flint J.
        • Robinson E.S.
        • et al.
        Power failure: why small sample size undermines the reliability of neuroscience.
        Nat Rev Neurosci. 2013; 14: 365-376
        • Marek S.
        • Tervo-Clemmens B.
        • Calabro F.J.
        • Montez D.F.
        • Kay B.P.
        • Hatoum A.S.
        • et al.
        Reproducible brain-wide association studies require thousands of individuals.
        Nature. 2022; 603: 654-660
      1. Achenbach TM, Rescorla LA (2001): ASEBA School Age Forms and Profiles. Burlington, Vt: ASEBA.

        • Achenbach T.M.
        • Ruffle T.M.
        The Child Behavior Checklist and related forms for assessing behavioral/emotional problems and competencies.[see comment].
        Pediatr Rev. 2000; 21: 265-271
        • Hoogman M.
        • Muetzel R.
        • Guimaraes J.P.
        • Shumskaya E.
        • Mennes M.
        • Zwiers M.P.
        • et al.
        Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples.
        American Journal of Psychiatry. 2019; 176: 531-542
        • Alexander L.M.
        • Escalera J.
        • Ai L.
        • Andreotti C.
        • Febre K.
        • Mangone A.
        • et al.
        An open resource for transdiagnostic research in pediatric mental health and learning disorders.
        Scientific data. 2017; 4: 1-26
        • Sudre G.
        • Sharp W.
        • Kundzicz P.
        • Bouyssi-Kobar M.
        • Norman L.
        • Choudhury S.
        • et al.
        Predicting the course of ADHD symptoms through the integration of childhood genomic, neural, and cognitive features.
        Molecular Psychiatry. 2020; : 1-9
        • Casey B.
        • Cannonier T.
        • Conley M.I.
        • Cohen A.O.
        • Barch D.M.
        • Heitzeg M.M.
        • et al.
        The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites.
        Developmental cognitive neuroscience. 2018; 32: 43-54
        • Pfefferbaum A.
        • Rohlfing T.
        • Pohl K.M.
        • Lane B.
        • Chu W.
        • Kwon D.
        • et al.
        Adolescent development of cortical and white matter structure in the NCANDA sample: role of sex, ethnicity, puberty, and alcohol drinking.
        Cerebral cortex. 2016; 26: 4101-4121
        • Somerville L.H.
        • Bookheimer S.Y.
        • Buckner R.L.
        • Burgess G.C.
        • Curtiss S.W.
        • Dapretto M.
        • et al.
        The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds.
        Neuroimage. 2018; 183: 456-468
        • Brown S.A.
        • Brumback T.
        • Tomlinson K.
        • Cummins K.
        • Thompson W.K.
        • Nagel B.J.
        • et al.
        The National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA): a multisite study of adolescent development and substance use.
        Journal of studies on alcohol and drugs. 2015; 76: 895-908
        • Zhang S.
        • Arfanakis K.
        Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: Template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences.
        Neuroimage. 2018; 172: 40-50
        • VanderWeele T.J.
        Principles of confounder selection.
        Eur J Epidemiol. 2019; 34: 211-219
        • Russell A.E.
        • Ford T.
        • Williams R.
        • Russell G.
        The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): a systematic review.
        Child Psychiatry & Human Development. 2016; 47: 440-458
        • Bax A.C.
        • Bard D.E.
        • Cuffe S.P.
        • McKeown R.E.
        • Wolraich M.L.
        The association between race/ethnicity and socioeconomic factors and the diagnosis and treatment of children with attention-deficit hyperactivity disorder.
        J Dev Behav Pediatr. 2019; 40: 81-91
        • Ozernov‐Palchik O.
        • Norton E.S.
        • Wang Y.
        • Beach S.D.
        • Zuk J.
        • Wolf M.
        • et al.
        The relationship between socioeconomic status and white matter microstructure in pre‐reading children: a longitudinal investigation.
        Human brain mapping. 2019; 40: 741-754
      2. Ursache A, Noble KG, Pediatric Imaging N, Study G (2016): Socioeconomic status, white matter, and executive function in children. Brain and behavior. 6:e00531.

        • Miller G.A.
        • Chapman J.P.
        Misunderstanding analysis of covariance.
        J Abnorm Psychol. 2001; 110: 40-48
        • Dennis M.
        • Francis D.J.
        • Cirino P.T.
        • Schachar R.
        • Barnes M.A.
        • Fletcher J.M.
        Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders.
        J Int Neuropsychol Soc. 2009; 15: 331-343
        • Bridgett D.J.
        • Walker M.E.
        Intellectual functioning in adults with ADHD: a meta-analytic examination of full scale IQ differences between adults with and without ADHD.
        Psychol Assess. 2006; 18: 1-14
        • Benjamini Y.
        • Hochberg Y.
        Controlling the false discovery rate: a practical and powerful approach to multiple testing.
        Journal of the Royal Statistical Society, Series B. 1995; 57: 289-300
      3. Fox J, Weisberg S (2019): An R Companion to Applied Regression. Third ed. Thousand Oaks CA: Sage.

        • Meinshausen N.
        • Bühlmann P.
        Stability selection.
        Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010; 72: 417-473
        • Bühlmann P.
        • Yu B.
        Analyzing bagging.
        The annals of Statistics. 2002; 30: 927-961
        • Breiman L.
        Bagging predictors.
        Machine learning. 1996; 24: 123-140
        • Sabourin J.A.
        • Cropp C.D.
        • Sung H.
        • Brody L.C.
        • Bailey‐Wilson J.E.
        • Wilson A.F.
        ComPaSS‐GWAS: A method to reduce type I error in genome‐wide association studies when replication data are not available.
        Genetic epidemiology. 2019; 43: 102-111
        • Ho D.E.
        • Imai K.
        • King G.
        • Stuart E.A.
        MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.
        Journal of Statistical Software. 2011; 42: 1-28
        • Hek K.
        • Demirkan A.
        • Lahti J.
        • Terracciano A.
        • Teumer A.
        • Cornelis M.C.
        • et al.
        A genome-wide association study of depressive symptoms.
        Biological psychiatry. 2013; 73: 667-678
        • Versace A.
        • Jones N.
        • Joseph H.
        • Lindstrom R.
        • Wilson T.
        • Lima Santos J.
        • et al.
        White matter abnormalities associated with ADHD outcomes in adulthood.
        Molecular psychiatry. 2021; : 1-11
        • Nagel B.J.
        • Bathula D.
        • Herting M.
        • Schmitt C.
        • Kroenke C.D.
        • Fair D.
        • et al.
        Altered white matter microstructure in children with attention-deficit/hyperactivity disorder.
        Journal of the American Academy of Child & Adolescent Psychiatry. 2011; 50: 283-292
        • Sudre G.
        • Shaw P.
        • Wharton A.
        • Weingart D.
        • Sharp W.
        • Sarlls J.
        White matter microstructure and the variable adult outcome of childhood Attention Deficit Hyperactivity Disorder.
        Neuropsychopharmacology. 2015; 40: 746-754
        • Thapar A.
        Discoveries on the genetics of ADHD in the 21st century: new findings and their implications.
        American Journal of Psychiatry. 2018; 175: 943-950
        • Faraone S.V.
        • Larsson H.
        Genetics of attention deficit hyperactivity disorder.
        Molecular psychiatry. 2018; 1
        • Bernanke J.
        • Luna A.
        • Chang L.
        • Bruno E.
        • Dworkin J.
        • Posner J.
        Structural brain measures among children with and without ADHD in the Adolescent Brain and Cognitive Development Study cohort: a cross-sectional US population-based study.
        The Lancet Psychiatry. 2022; 9: 222-231
        • Ressel V.
        • Van Hedel H.J.
        • Scheer I.
        • Tuura R.O.G.
        Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods.
        European Radiology Experimental. 2018; 2: 1-12
        • LeWinn K.Z.
        • Sheridan M.A.
        • Keyes K.M.
        • Hamilton A.
        • McLaughlin K.A.
        Sample composition alters associations between age and brain structure.
        Nature Communications. 2017; 8: 1-14
      4. Heeringa SG, Berglund PA (2020): A guide for population-based analysis of the Adolescent Brain Cognitive Development (ABCD) Study baseline data. BioRxiv.

        • Kraemer H.C.
        • Yesavage J.A.
        • Taylor J.L.
        • Kupfer D.
        How can we learn about developmental processes from cross-sectional studies, or can we?.
        American Journal of Psychiatry. 2000; 157: 163-171
        • Muetzel R.L.
        • Blanken L.M.
        • van der Ende J.
        • El Marroun H.
        • Shaw P.
        • Sudre G.
        • et al.
        Tracking brain development and dimensional psychiatric symptoms in children: a longitudinal population-based neuroimaging study.
        American Journal of psychiatry. 2018; 175: 54-62
        • Raffelt D.A.
        • Tournier J.-D.
        • Smith R.E.
        • Vaughan D.N.
        • Jackson G.
        • Ridgway G.R.
        • et al.
        Investigating white matter fibre density and morphology using fixel-based analysis.
        Neuroimage. 2017; 144: 58-73
        • Schilling K.G.
        • Janve V.
        • Gao Y.
        • Stepniewska I.
        • Landman B.A.
        • Anderson A.W.
        Histological validation of diffusion MRI fiber orientation distributions and dispersion.
        Neuroimage. 2018; 165: 200-221
        • Tournier J.D.
        • Calamante F.
        • Connelly A.
        Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging.
        NMR in Biomedicine. 2013; 26: 1775-1786