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Smaller total brain volume but not subcortical structure volume related to common genetic risk for ADHD

Published online by Cambridge University Press:  24 January 2020

Michael A. Mooney
Affiliation:
Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA OHSU Knight Cancer Institute, Portland, Oregon, USA
Priya Bhatt
Affiliation:
Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, USA
Robert J. M. Hermosillo
Affiliation:
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
Peter Ryabinin
Affiliation:
Oregon Clinical and Translational Research Institute, Portland, Oregon, USA
Molly Nikolas
Affiliation:
Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, Iowa, USA
Stephen V. Faraone
Affiliation:
Departments of Psychiatry and Neuroscience & Physiology, State University of New York Upstate Medical University, Syracuse, New York, USA
Damien A. Fair
Affiliation:
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA Advanced Imaging Research Center, OHSU, Portland, Oregon, USA
Beth Wilmot
Affiliation:
Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA Oregon Clinical and Translational Research Institute, Portland, Oregon, USA
Joel T. Nigg*
Affiliation:
Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, USA Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
*
Author for correspondence: Joel Nigg, E-mail: [email protected]

Abstract

Background

Mechanistic endophenotypes can inform process models of psychopathology and aid interpretation of genetic risk factors. Smaller total brain and subcortical volumes are associated with attention-deficit hyperactivity disorder (ADHD) and provide clues to its development. This study evaluates whether common genetic risk for ADHD is associated with total brain volume (TBV) and hypothesized subcortical structures in children.

Methods

Children 7–15 years old were recruited for a case–control study (N = 312, N = 199 ADHD). Children were assessed with a multi-informant, best-estimate diagnostic procedure and motion-corrected MRI measured brain volumes. Polygenic scores were computed based on discovery data from the Psychiatric Genomics Consortium (N = 19 099 ADHD, N = 34 194 controls) and the ENIGMA + CHARGE consortium (N = 26 577).

Results

ADHD was associated with smaller TBV, and altered volumes of caudate, cerebellum, putamen, and thalamus after adjustment for TBV; however, effects were larger and statistically reliable only in boys. TBV was associated with an ADHD polygenic score [β = −0.147 (−0.27 to −0.03)], and mediated a small proportion of the effect of polygenic risk on ADHD diagnosis (average ACME = 0.0087, p = 0.012). This finding was stronger in boys (average ACME = 0.019, p = 0.008). In addition, we confirm genetic variation associated with whole brain volume, via an intracranial volume polygenic score.

Conclusion

Common genetic risk for ADHD is not expressed primarily as developmental alterations in subcortical brain volumes, but appears to alter brain development in other ways, as evidenced by TBV differences. This is among the first demonstrations of this effect using molecular genetic data. Potential sex differences in these effects warrant further examination.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2020

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References

Adams, H. H., Hibar, D. P., Chouraki, V., Stein, J. L., Nyquist, P. A., Rentería, M. E., … Rundek, T. (2016). Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nature Neuroscience, 19, 15691582.CrossRefGoogle ScholarPubMed
Alemany, S., Jansen, P. R., Muetzel, R. L., Marques, N., El Marroun, H., Jaddoe, V. W., … White, T. (2019). Common polygenic variations for psychiatric disorders and cognition in relation to brain morphology in the general pediatric population. Journal of the American Academy of Child & Adolescent Psychiatry, 58(6), 600607.CrossRefGoogle ScholarPubMed
Blokland, G. A. M., de Zubicaray, G. I., McMahon, K. L., & Wright, M. J. (2012). Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies. Twin Research and Human Genetics, 15, 351371.CrossRefGoogle ScholarPubMed
Castellanos, F. X., Lee, P. P., Sharp, W., Jeffries, N. O., Greenstein, D. K., Clasen, L. S., … Rapoport, J. L. (2002). Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA, 288, 17401748.CrossRefGoogle ScholarPubMed
Costa Dias, T. G., Iyer, S. P., Carpenter, S. D., Cary, R. P., Wilson, V. B., Mitchell, S. H., … Fair, D. A. (2015). Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Developmental Cognitive Neuroscience, 11, 155174.CrossRefGoogle ScholarPubMed
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., … Neale, B. M. (2019). Discovery of the first genome-wide significant risk loci for ADHD. Nature Genetics, 51, 63.CrossRefGoogle Scholar
Dosenbach, N. U. F., Koller, J. M., Earl, E. A., Miranda-Dominguez, O., Klein, R. L., Van, A. N.Fair, D. A. (2017). Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage, 161, 8093.CrossRefGoogle ScholarPubMed
Ellison-Wright, I., Ellison-Wright, Z., & Bullmore, E. (2008). Structural brain change in attention deficit hyperactivity disorder identified by meta-analysis. BMC Psychiatry, 8, 51.CrossRefGoogle ScholarPubMed
Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T. (2012). Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences of the USA, 109, 67696774.CrossRefGoogle ScholarPubMed
Faraone, S. V., & Larsson, H. (2019). Genetics of attention deficit hyperactivity disorder. Molecular Psychiatry, 24, 562.CrossRefGoogle ScholarPubMed
Fischl, B. (2012). Freesurfer. NeuroImage, 62, 774781.CrossRefGoogle ScholarPubMed
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C. Collins, D. L., & Brain Development Cooperative Group (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54, 313327.CrossRefGoogle ScholarPubMed
Frodl, T., & Skokauskas, N. (2012). Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica, 125, 114126.CrossRefGoogle ScholarPubMed
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48, 6372.CrossRefGoogle ScholarPubMed
Greven, C. U., Bralten, J., Mennes, M., O'Dwyer, L., van Hulzen, K. J. E., Rommelse, N., … Buitelaar, J. K. (2015). Developmentally stable whole-brain volume reductions and developmentally sensitive caudate and putamen volume alterations in those with attention-deficit/hyperactivity disorder and their unaffected siblings. JAMA Psychiatry, 72, 490499.CrossRefGoogle ScholarPubMed
Hess, J. L., Akutagava-Martins, G. C., Patak, J. D., Glatt, S. J., & Faraone, S. V. (2018). Why is there selective subcortical vulnerability in ADHD? Clues from postmortem brain gene expression data. Molecular Psychiatry, 23, 17871793.CrossRefGoogle ScholarPubMed
Hibar, D. P., Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., Jahanshad, N., … Corvin, A. (2015). Common genetic variants influence human subcortical brain structures. Nature, 520, 224229.CrossRefGoogle ScholarPubMed
Hinshaw, S. P. (2018). Attention deficit hyperactivity disorder (ADHD): Controversy, developmental mechanisms, and multiple levels of analysis. Annual Review of Clinical Psychology, 14, 291316.CrossRefGoogle ScholarPubMed
Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., Schweren, L. S. J., … Franke, B. (2017). Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. The Lancet. Psychiatry, 4, 310319.CrossRefGoogle ScholarPubMed
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62, 782790.CrossRefGoogle ScholarPubMed
Király, A., Szabó, N., Tóth, E., Csete, G., Faragó, P., Kocsis, K., … Kincses, Z. T. (2016). Male brain ages faster: The age and gender dependence of subcortical volumes. Brain Imaging and Behavior, 10, 901910.CrossRefGoogle ScholarPubMed
Klein, M., Walters, R. K., Demontis, D., Stein, J. L., Hibar, D. P., Adams, H. H., … Franke, B. (2019). Genetic markers of ADHD-related variations in intracranial volume. American Journal of Psychiatry, 176, 228238.CrossRefGoogle ScholarPubMed
Martel, M. M. (2013). Sexual selection and sex differences in the prevalence of childhood externalizing and adolescent internalizing disorders. Psychological Bulletin, 139, 12211259.CrossRefGoogle ScholarPubMed
Martin, J., Walters, R. K., Demontis, D., Mattheisen, M., Lee, S. H., Robinson, E., … Neale, B. M. (2018). A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder. Biological Psychiatry, 83, 10441053.CrossRefGoogle ScholarPubMed
Mills, B. D., Miranda-Dominguez, O., Mills, K. L., Earl, E., Cordova, M., Painter, J., … Fair, D. A. (2017). ADHD and attentional control: Impaired segregation of task positive and task negative brain networks. Network Neuroscience, 2, 200217.CrossRefGoogle Scholar
Miranda-Dominguez, O., Feczko, E., Grayson, D. S., Walum, H., Nigg, J. T., & Fair, D. A. (2017). Heritability of the human connectome: A connectotyping study. Network Neuroscience, 2, 175199.CrossRefGoogle Scholar
Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews of the Cambridge Philosophical Society, 82, 591605.CrossRefGoogle ScholarPubMed
Nakao, T., Radua, J., Rubia, K., & Mataix-Cols, D. (2011). Gray matter volume abnormalities in ADHD: Voxel-based meta-analysis exploring the effects of age and stimulant medication. The American Journal of Psychiatry, 168, 11541163.CrossRefGoogle ScholarPubMed
Ni, G., Moser, G., Wray, N. R., & Lee, S. H. (2018). Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood. American Journal of Human Genetics, 102, 11851194.CrossRefGoogle ScholarPubMed
Nigg, J. T., Gustafsson, H. C., Karalunas, S. L., Ryabinin, P., McWeeney, S. K., Faraone, S. V., … Wilmot, B. (2018). Working memory and vigilance as multivariate endophenotypes related to common genetic risk for attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 57, 175182.CrossRefGoogle ScholarPubMed
Norman, L. J., Carlisi, C., Lukito, S., Hart, H., Mataix-Cols, D., Radua, J., & Rubia, K. (2016). Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiatry, 73, 815825.CrossRefGoogle ScholarPubMed
Qiu, A., Crocetti, D., Adler, M., Mahone, E. M., Denckla, M. B., Miller, M. I., & Mostofsky, S. H. (2009). Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. American Journal of Psychiatry, 166, 7482.CrossRefGoogle ScholarPubMed
Riglin, L., Collishaw, S., Thapar, A. K., Dalsgaard, S., Langley, K., Smith, G. D., … Thapar, A. (2016). Association of genetic risk variants with attention-deficit/hyperactivity disorder trajectories in the general population. JAMA Psychiatry, 73, 12851292.CrossRefGoogle ScholarPubMed
Savalia, N. K., Agres, P. F., Chan, M. Y., Feczko, E. J., Kennedy, K. M., & Wig, G. S. (2017). Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion. Human Brain Mapping, 38, 472492.CrossRefGoogle ScholarPubMed
Shaw, P., Ishii-Takahashi, A., Park, M. T., Devenyi, G. A., Zibman, C., Kasparek, S., … White, T. (2018). A multicohort, longitudinal study of cerebellar development in attention deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 59, 11141123.CrossRefGoogle ScholarPubMed
Stojanovski, S., Felsky, D., Viviano, J. D., Shahab, S., Bangali, R., Burton, C. L., … Ameis, S. (2019). Polygenic risk and neural substrates of attention-deficit/hyperactivity disorder symptoms in youths with a history of mild traumatic brain injury. Biological Psychiatry, 85(5), 408416.CrossRefGoogle ScholarPubMed
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 138.CrossRefGoogle Scholar
Valera, E. M., Faraone, S. V., Murray, K. E., & Seidman, L. J. (2007). Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biological Psychiatry, 61, 13611369.CrossRefGoogle ScholarPubMed
Visser, S. N., Blumberg, S. J., Danielson, M. L., Bitsko, R. H., & Kogan, M. D. (2013). State-based and demographic variation in parent-reported medication rates for attention-deficit/hyperactivity disorder, 2007–2008. Preventing Chronic Disease, 10, 20073.CrossRefGoogle ScholarPubMed
Wang, T., Zhang, X., Li, A., Zhu, M., Liu, S., Qin, W., … Liu, B. (2017). Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. NeuroImage. Clinical, 14, 441449.10.1016/j.nicl.2017.02.011CrossRefGoogle ScholarPubMed
Wang, Y., Xu, Q., Li, S., Li, G., Zuo, C., Liao, S., … Joshi, R. M. (2018). Gender differences in anomalous subcortical morphology for children with ADHD. Neuroscience Letters, 665, 176181.CrossRefGoogle ScholarPubMed
Whalley, H. C., Hall, L., Romaniuk, L., Macdonald, A., Lawrie, S. M., Sussmann, J. E., & McIntosh, A. M. (2015). Impact of cross-disorder polygenic risk on frontal brain activation with specific effect of schizophrenia risk. Schizophrenia Research, 161, 484489.CrossRefGoogle ScholarPubMed
Wyciszkiewicz, A., Pawlak, M. A., & Krawiec, K. (2017). Cerebellar volume in children with attention-deficit hyperactivity disorder (ADHD). Journal of Child Neurology, 32, 215221.CrossRefGoogle Scholar
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