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ADHD symptoms map onto noise-driven structure–function decoupling between hub and peripheral brain regions

Abstract

Adults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure–function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure–function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure–function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure–function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure–function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure–function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.

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References

  1. 1.

    Asherson P, Buitelaar J, Faraone SV, Rohde LA. Adult attention-deficit hyperactivity disorder: key conceptual issues. Lancet Psychiatry. 2016;3:568–78.

    Google Scholar 

  2. 2.

    Sudre G, Mangalmurti A, Shaw P. Growing out of attention deficit hyperactivity disorder: Insights from the ‘remitted’ brain. Neurosci Biobehav Rev. 2018;94:198–209.

    Google Scholar 

  3. 3.

    Aoki Y, Cortese S, Castellanos FX. Research review: diffusion tensor imaging studies of attention-deficit/hyperactivity disorder: meta-analyses and reflections on head motion. J Child Psychol Psychiatry. 2018;59:193–202.

    Google Scholar 

  4. 4.

    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;68:838–47.

    Google Scholar 

  5. 5.

    van Ewijk H, Heslenfeld DJ, Zwiers MP, Buitelaar JK, Oosterlaan J. Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2012;36:1093–106.

    Google Scholar 

  6. 6.

    Castellanos FX, Aoki Y. Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: a science in development. Biol Psychiatry. 2016;1:253–61.

    Google Scholar 

  7. 7.

    Cocchi L, Bramati IE, Zalesky A, Furukawa E, Fontenelle LF, Moll J, et al. Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder. J Neurosci. 2012;32:17753–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Samea F, Soluki S, Nejati V, Zarei M, Cortese S, Eickhoff SB, et al. Brain alterations in children/adolescents with ADHD revisited: a neuroimaging meta-analysis of 96 structural and functional studies. Neurosci Biobehav Rev. 2019;100:1–8.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Aoki Y, Yoncheva YN, Chen B, Nath T, Sharp D, Lazar M, et al. Association of white matter structure with autism spectrum disorder and attention-deficit/hyperactivity disorder. JAMA Psychiatry. 2017;74:1120–8.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Rubia K, Alegria A, Brinson H. Imaging the ADHD brain: disorder-specificity, medication effects and clinical translation. Expert Rev Neurotherapeutics. 2014;14:519–38.

    CAS  Google Scholar 

  11. 11.

    Lin H-Y, Cocchi L, Zalesky A, Lv J, Perry A, Tseng W-YI, et al. Brain–behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder. Psychological Med. 2018;48:2399–408.

    Google Scholar 

  12. 12.

    Deco G, Jirsa VK, McIntosh AR. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci. 2011;12:43–56.

    CAS  Google Scholar 

  13. 13.

    Honey CJ, Kötter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS. 2007;104:10240–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Hauser TU, Fiore VG, Moutoussis M, Dolan RJ. Computational psychiatry of ADHD: neural gain impairments across marrian levels of analysis. Trends Neurosci. 2016;39:63–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Pertermann M, Bluschke A, Roessner V, Beste C. The modulation of neural noise underlies the effectiveness of methylphenidate treatment in attention deficit/hyperactivity disorder. BPS: CNNI. 2019;0:743–50. https://www.biologicalpsychiatrycnni.org/article/S2451-9022(19)30080-1/abstract.

  16. 16.

    Faisal AA, Selen LPJ, Wolpert DM. Noise in the nervous system. Nat Rev Neurosci. 2008;9:292–303.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Castellanos FX, Tannock R. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci. 2002;3:617.

    CAS  Google Scholar 

  18. 18.

    Kofler MJ, Rapport MD, Sarver DE, Raiker JS, Orban SA, Friedman LM, et al. Reaction time variability in ADHD: a meta-analytic review of 319 studies. Clin Psychol Rev. 2013;33:795–811.

    Google Scholar 

  19. 19.

    Eldar E, Cohen JD, Niv Y. The effects of neural gain on attention and learning. Nat Neurosci. 2013;16:1146–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Garrett DD, Samanez-Larkin GR, MacDonald SW, Lindenberger U, McIntosh AR, Grady CL. Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci Biobehav Rev. 2013;37:610–24.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hancock R, Pugh KR, Hoeft F. Neural noise hypothesis of developmental dyslexia. Trends Cogn Sci. 2017;21:434–48.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Depue BE, Burgess GC, Willcutt EG, Bidwell LC, Ruzic L, Banich MT. Symptom-correlated brain regions in young adults with combined-type ADHD: Their organization, variability, and relation to behavioral performance. Psychiatry Res. 2010;182:96–102.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Nomi JS, Schettini E, Voorhies W, Bolt TS, Heller AS, Uddin LQ. Resting-state brain signal variability in prefrontal cortex is associated with ADHD symptom severity in children. Front Hum Neurosci. 2018;12:90.

  24. 24.

    Sørensen L, Eichele T, Van Wageningen H, Plessen KJ, Stevens MC. Amplitude variability over trials in hemodynamic responses in adolescents with ADHD: The role of the anterior default mode network and the non-specific role of the striatum. NeuroImage. 2016;12:397–404.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Gonen-Yaacovi G, Arazi A, Shahar N, Karmon A, Haar S, Meiran N, et al. Increased ongoing neural variability in ADHD. Cortex. 2016;81:50–63.

    Google Scholar 

  26. 26.

    del Campo N, Chamberlain SR, Sahakian BJ, Robbins TW. The roles of dopamine and noradrenaline in the pathophysiology and treatment of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2011;69:e145–57.

    Google Scholar 

  27. 27.

    Faraone SV. The pharmacology of amphetamine and methylphenidate: relevance to the neurobiology of attention-deficit/hyperactivity disorder and other psychiatric comorbidities. Neurosci Biobehav Rev. 2018;87:255–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Frank MJ, Santamaria A, O’Reilly RC, Willcutt E. Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder. Neuropsychopharmacology. 2007;32:1583.

    CAS  Google Scholar 

  29. 29.

    Kroener S, Chandler LJ, Phillips PEM, Seamans JK. Dopamine modulates persistent synaptic activity and enhances the signal-to-noise ratio in the prefrontal cortex. PLoS One. 2009;4:e6507. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715878/.

  30. 30.

    Gamo NJ, Wang M, Arnsten AFT. Methylphenidate and atomoxetine enhance prefrontal function through α2-adrenergic and dopamine D1 receptors. J Am Acad Child Adolesc Psychiatry. 2010;49:1011–23.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain. 2014;137:2382–95.

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RCW, Cahn W, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry. 2013;70:783.

    Google Scholar 

  33. 33.

    Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron. 2014;84:892–905.

    CAS  Google Scholar 

  34. 34.

    Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex. 2018;28:3095–114.

    Google Scholar 

  35. 35.

    Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6:e159. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2443193/.

  36. 36.

    Betzel RF, Byrge L, He Y, Goñi J, Zuo X-N, Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage. 2014;102:345–57.

    Google Scholar 

  37. 37.

    Perry A, Wen W, Lord A, Thalamuthu A, Roberts G, Mitchell PB, et al. The organisation of the elderly connectome. NeuroImage. 2015;114:414–26.

    CAS  Google Scholar 

  38. 38.

    Heuvel MP, van den, Kahn RS, Goñi J, Sporns O. High-cost, high-capacity backbone for global brain communication. PNAS. 2012;109:11372–7.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Van der Waerden BL. Order tests for the two-sample problem and their power. In Indagationes Mathematicae (Proceedings) (Vol. 55, pp. 453–458). North-Holland.

  40. 40.

    Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA. 2009;106:2035–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Coghill D, Sonuga‐Barke EJS. Annual research review: categories versus dimensions in the classification and conceptualisation of child and adolescent mental disorders—implications of recent empirical study. J Child Psychol Psychiatry. 2012;53:469–89.

    Google Scholar 

  42. 42.

    Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63.

    CAS  Google Scholar 

  43. 43.

    Gau SS-F, Shang C-Y, Liu S-K, Lin C-H, Swanson JM, Liu Y-C, et al. Psychometric properties of the Chinese version of the Swanson, Nolan, and Pelham, version IV scale—parent form. Int J Methods Psychiatr Res. 2008;17:35–44.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Yeh C-B, Gau SS-F, Kessler RC, Wu Y-Y. Psychometric properties of the Chinese version of the adult ADHD Self-report Scale. Int J Methods Psychiatr Res. 2008;17:45–54.

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207.

    Google Scholar 

  46. 46.

    Rosenthal R, Rosnow RL. Essentials of behavioral research: methods and data analysis. New York: McGraw-Hill; 1991.

    Google Scholar 

  47. 47.

    Barnett L, Buckley CL, Bullock S. Neural complexity and structural connectivity. Phys Rev E. 2009;79:051914.

    CAS  Google Scholar 

  48. 48.

    Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci. 2013;33:11239–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Saggio ML, Ritter P, Jirsa VK. Analytical operations relate structural and functional connectivity in the brain. PLOS ONE. 2016;11:e0157292.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273–302.

    CAS  Google Scholar 

  51. 51.

    Ziegler S, Pedersen ML, Mowinckel AM, Biele G. Modelling ADHD: a review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neurosci Biobehav Rev. 2016;71:633–56.

    Google Scholar 

  52. 52.

    Arnsten AFT. Catecholamine influences on dorsolateral prefrontal cortical networks. Biol Psychiatry. 2011;69:e89–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Volkow ND, Fowler JS, Wang G-J, Telang F, Logan J, Wong C, et al. Methylphenidate decreased the amount of glucose needed by the brain to perform a cognitive task. PLOS ONE. 2008;3:e2017.

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Gollo LL, Roberts JA, Cropley VL, Di Biase MA, Pantelis C, Zalesky A, et al. Fragility and volatility of structural hubs in the human connectome. Nat Neurosci. 2018;21:1107.

    CAS  Google Scholar 

  55. 55.

    Cocchi L, Harding IH, Lord A, Pantelis C, Yucel M, Zalesky A. Disruption of structure–function coupling in the schizophrenia connectome. NeuroImage. 2014;4:779–87.

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry. 2010;68:61–9.

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Cole MW, Schneider W. The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage. 2007;37:343–60.

    Google Scholar 

  58. 58.

    Weissman DH, Roberts KC, Visscher KM, Woldorff MG. The neural bases of momentary lapses in attention. Nat Neurosci. 2006;9:971–8.

    CAS  Google Scholar 

  59. 59.

    Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, et al. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 2009;1273:114–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Kelly AMC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. Neuroimage. 2008;39:527–37.

    Google Scholar 

  61. 61.

    Liddle EB, Hollis C, Batty MJ, Groom MJ, Totman JJ, Liotti M, et al. Task-related default mode network modulation and inhibitory control in ADHD: effects of motivation and methylphenidate. J Child Psychol Psychiatry. 2011;52:761–71.

    Google Scholar 

  62. 62.

    Rosenberg MD, Zhang S, Hsu W-T, Scheinost D, Finn ES, Shen X, et al. Methylphenidate modulates functional network connectivity to enhance attention. J Neurosci. 2016;36:9547–57.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Yoo JH, Kim D, Choi J, Jeong B. Treatment effect of methylphenidate on intrinsic functional brain network in medication-naïve ADHD children: a multivariate analysis. Brain Imaging Behav. 2018;12:518–31.

  64. 64.

    Cary RP, Ray S, Grayson DS, Painter J, Carpenter S, Maron L, et al. Network structure among brain systems in adult ADHD is uniquely modified by stimulant administration. Cereb Cortex. 2017;27:3970–9.

    Google Scholar 

  65. 65.

    Lin H-Y, Tseng W-YI, Lai M-C, Matsuo K, Gau SS-F. Altered resting-state frontoparietal control network in children with attention-deficit/hyperactivity disorder. J Int Neuropsychological Soc. 2015;21:271–84.

    Google Scholar 

  66. 66.

    Regev M, Simony E, Lee K, Tan KM, Chen J, Hasson U. Propagation of information along the cortical hierarchy as a function of attention while reading and listening to stories. Cereb Cortex. 2018;29:4017–34.

  67. 67.

    Stephan KE, Baldeweg T, Friston KJ. Synaptic plasticity and dysconnection in schizophrenia. Biol Psychiatry. 2006;59:929–39.

    CAS  Google Scholar 

  68. 68.

    Bos DJ, Oranje B, Achterberg M, Vlaskamp C, Ambrosino S, de Reus MA, et al. Structural and functional connectivity in children and adolescents with and without attention deficit/hyperactivity disorder. J Child Psychol Psychiatry. 2017;58:810–8.

    Google Scholar 

  69. 69.

    Yendiki A, Koldewyn K, Kakunoori S, Kanwisher N, Fischl B. Spurious group differences due to head motion in a diffusion MRI study. NeuroImage. 2014;88:79–90.

    Google Scholar 

  70. 70.

    de Luis-García R, Cabús-Piñol G, Imaz-Roncero C, Argibay-Quiñones D, Barrio-Arranz G, Aja-Fernández S, et al. Attention deficit/hyperactivity disorder and medication with stimulants in young children: a DTI study. Prog Neuro-Psychopharmacol Biol Psychiatry. 2015;57:176–84.

    Google Scholar 

  71. 71.

    Adisetiyo V, Tabesh A, Martino AD, Falangola MF, Castellanos FX, Jensen JH, et al. Attention-deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development. Hum Brain Mapp. 2014;35:2148–62.

    Google Scholar 

  72. 72.

    Greene DJ, Black KJ, Schlaggar BL. Considerations for MRI study design and implementation in pediatric and clinical populations. Developmental Cogn Neurosci. 2016;18:101–12.

    Google Scholar 

  73. 73.

    Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome open research. 2019;4. https://wellcomeopenresearch.org/articles/4-63.

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Acknowledgements

This work was supported by the Ministry of Technology and Science, Taiwan (MOST103-2314-B-002-021-MY3), the National Health Research Institutes, Taiwan (NHRI-EX103-10008PI), Chen-Yung Foundation, and National Taiwan University Hospital (NTUH103-S2458, NTUH104-S2761). LC and JAR are supported by the Australian National Health Medical Research Council (LC, 1099082 and 1138711; JAR, 1145168 and 1144936).

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Hearne, L.J., Lin, HY., Sanz-Leon, P. et al. ADHD symptoms map onto noise-driven structure–function decoupling between hub and peripheral brain regions. Mol Psychiatry 26, 4036–4045 (2021). https://doi.org/10.1038/s41380-019-0554-6

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