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Brain–behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder

Published online by Cambridge University Press:  07 February 2018

Hsiang-Yuan Lin
Affiliation:
Department of Psychiatry, National Taiwan University Hospital, and College of Medicine, Taipei, Taiwan Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Luca Cocchi
Affiliation:
Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Andrew Zalesky
Affiliation:
Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
Jinglei Lv
Affiliation:
Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Alistair Perry
Affiliation:
Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Wen-Yih Isaac Tseng
Affiliation:
Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
Prantik Kundu
Affiliation:
Departments of Radiology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Michael Breakspear
Affiliation:
Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia Metro North Mental Health Service, The Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
Susan Shur-Fen Gau*
Affiliation:
Department of Psychiatry, National Taiwan University Hospital, and College of Medicine, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
*
Author for correspondence: Susan Shur-Fen Gau, E-mail: gaushufe@ntu.edu.tw

Abstract

Background

Childhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD.

Methods

We first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the individual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype.

Results

Adults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain–symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD.

Conclusions

Overall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain–symptom mediators.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

*

Equally contributed as co-first authors.

References

Acosta, MT, Castellanos, FX, Bolton, KL, Balog, JZ, Eagen, P, Nee, L et al. (2008) Latent class subtyping of attention-deficit/hyperactivity disorder and comorbid conditions. Journal of the American Academy of Child and Adolescent Psychiatry 47, 797807.Google Scholar
Asherson, P, Buitelaar, J, Faraone, SV and Rohde, LA (2016) Adult attention-deficit hyperactivity disorder: key conceptual issues. Lancet Psychiatry 3, 568578.Google Scholar
Barber, AD, Jacobson, LA, Wexler, JL, Nebel, MB, Caffo, BS, Pekar, JJ et al. (2015) Connectivity supporting attention in children with attention deficit hyperactivity disorder. NeuroImage: Clinical 7, 6881.Google Scholar
Barch, DM (2017) Biotypes: promise and pitfalls. Biological Psychiatry 82, 23.Google Scholar
Birn, RM, Molloy, EK, Patriat, R, Parker, T, Meier, TB, Kirk, GR et al. (2013) The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83, 550558.Google Scholar
Cai, W, Chen, T, Szegletes, L, Supekar, K and Menon, V (2017) Aberrant time-varying cross-network interactions in children with attention-deficit/hyperactivity disorder and its relation to attention deficits. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://www.sciencedirect.com/science/article/pii/S2451902217301969.Google Scholar
Castellanos, FX and Aoki, Y (2016) Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: a science in development. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1, 253261.Google Scholar
Cheung, CH, Rijdijk, F, McLoughlin, G, Faraone, SV, Asherson, P and Kuntsi, J (2015) Childhood predictors of adolescent and young adult outcome in ADHD. Journal of Psychiatric Research 62, 92100.Google Scholar
Clementz, BA, Sweeney, JA, Hamm, JP, Ivleva, EI, Ethridge, LE, Pearlson, GD et al. (2016) Identification of distinct psychosis biotypes using brain-based biomarkers. The American Journal of Psychiatry 173, 373384.Google Scholar
Cocchi, L, Bramati, IE, Zalesky, A, Furukawa, E, Fontenelle, LF, Moll, J et al. (2012) Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder. Journal of Neuroscience 32, 1775317761.Google Scholar
Cole, MW, Ito, T, Bassett, DS and Schultz, DH (2016) Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience 19, 17181726.Google Scholar
Conners, CK, Erhardt, D and Sparrow, E (1999) Conners' Adult ADHD Rating Scales (CAARS). New York: MHS.Google Scholar
Costa Dias, TG, Iyer, SP, Carpenter, SD, Cary, RP, Wilson, VB, Mitchell, SH et al. (2015) Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Developmental Cognitive Neuroscience 11, 155174.Google Scholar
Cuthbert, BN (2015) Research domain criteria: toward future psychiatric nosologies. Dialogues in Clinical Neuroscience 17, 8997.Google Scholar
Demontis, D, Walters, RK, Martin, J, Mattheisen, M, Als, TD, Agerbo, E et al. (2017) Discovery of the first genome-wide significant risk loci for ADHD. bioRxiv, 145581. https://www.biorxiv.org/content/early/2017/06/03/145581.Google Scholar
Drysdale, AT, Grosenick, L, Downar, J, Dunlop, K, Mansouri, F, Meng, Y et al. (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine 23, 2838.Google Scholar
Fair, DA, Bathula, D, Nikolas, MA and Nigg, JT (2012) Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences 109, 67696774.Google Scholar
Faraone, SV and Biederman, J (2016) Can attention-deficit/hyperactivity disorder onset occur in adulthood? JAMA Psychiatry 73, 655656.Google Scholar
Gallo, EF and Posner, J (2016) Moving towards causality in attention-deficit hyperactivity disorder: overview of neural and genetic mechanisms. Lancet Psychiatry 3, 555567.Google Scholar
Gates, KM, Molenaar, PC, Iyer, SP, Nigg, JT and Fair, DA (2014) Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. PLoS ONE 9, e91322.Google Scholar
Hearne, LJ, Mattingley, JB and Cocchi, L (2016) Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports 6, 32328.Google Scholar
Hennig, C, Meila, M, Murtagh, F and Rocci, R (2015) Handbook of Cluster Analysis. Boca Raton, FL, USA: CRC Press/Taylor & Francis.Google Scholar
Hoogman, M, Bralten, J, Hibar, DP, Mennes, M, Zwiers, MP, Schweren, LS et al. (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4, 310319.Google Scholar
Jafri, MJ, Pearlson, GD, Stevens, M and Calhoun, VD (2008) A method for functional network connectivity among spatially independent resting-state components in schizophrenia. NeuroImage 39, 16661681.Google Scholar
Kaczkurkin, AN, Moore, TM, Calkins, ME, Ciric, R, Detre, JA, Elliott, MA et al. (2017) Common and dissociable regional cerebral blood flow differences associate with dimensions of psychopathology across categorical diagnoses. Molecular Psychiatry. doi: 10.1038/mp.2017.174.Google Scholar
Karalunas, SL, Fair, D, Musser, ED, Aykes, K, Iyer, SP and Nigg, JT (2014) Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. JAMA Psychiatry 71, 10151024.Google Scholar
Keyes, KM, Platt, J, Kaufman, AS and McLaughlin, KA (2017) Association of fluid intelligence and psychiatric disorders in a population-representative sample of US adolescents. JAMA Psychiatry 74, 179188.Google Scholar
Krzanowski, W (2000) Principles of Multivariate Analysis. New York, NY, USA: Oxford University Press, Inc.Google Scholar
Kumar, A and Daumé, H (2011) A co-training approach for multi-view spectral clustering. In Getoor, L and Scheffer, T (eds). Proceedings of the 28th International Conference on Machine Learning (ICML-11). Bellevue, Washington, USA: ACM, pp. 393400.Google Scholar
Kundu, P, Inati, SJ, Evans, JW, Luh, WM and Bandettini, PA (2012) Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage 60, 17591770.Google Scholar
Kundu, P, Voon, V, Balchandani, P, Lombardo, MV, Poser, BA and Bandettini, P (2017) Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage 154, 5980.Google Scholar
Lin, HY and Gau, SS (2015) Atomoxetine treatment strengthens an anti-correlated relationship between functional brain networks in medication-naive adults with attention-deficit hyperactivity disorder: a randomized double-blind placebo-controlled clinical trial. The International Journal of Neuropsychopharmacology 19, pyv094.Google Scholar
Lin, YJ, Yang, LK and Gau, SS (2016) Psychiatric comorbidities of adults with early- and late-onset attention-deficit/hyperactivity disorder. Australian & New Zealand Journal of Psychiatry 50, 548556.Google Scholar
Lombardo, MV, Auyeung, B, Holt, RJ, Waldman, J, Ruigrok, AN, Mooney, N et al. (2016) Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing. NeuroImage 142, 5566.Google Scholar
Marcus, DK and Barry, TD (2011) Does attention-deficit/hyperactivity disorder have a dimensional latent structure? A taxometric analysis. Journal of Abnormal Psychology 120, 427442.Google Scholar
Marquand, AF, Wolfers, T, Mennes, M, Buitelaar, J and Beckmann, CF (2016) Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1, 433447.Google Scholar
Menon, V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15, 483506.Google Scholar
Mostert, JC, Hoogman, M, Onnink, AM, van Rooij, D, von Rhein, D, van Hulzen, KJ et al. (2018) Similar subgroups based on cognitive performance parse heterogeneity in adults with ADHD and healthy controls. Journal of Attention Disorders 22, 281292.Google Scholar
Perry, A, Wen, W, Kochan, NA, Thalamuthu, A, Sachdev, PS and Breakspear, M (2017) The independent influences of age and education on functional brain networks and cognition in healthy older adults. Human Brain Mapping 38, 50945114.Google Scholar
Power, JD, Cohen, AL, Nelson, SM, Wig, GS, Barnes, KA, Church, JA et al. (2011) Functional network organization of the human brain. Neuron 72, 665678.Google Scholar
Rommelse, N, van der Kruijs, M, Damhuis, J, Hoek, I, Smeets, S, Antshel, KM et al. (2016) An evidenced-based perspective on the validity of attention-deficit/hyperactivity disorder in the context of high intelligence. Neuroscience & Biobehavioral Reviews 71, 2147.Google Scholar
Schnack, HG and Kahn, RS (2016) Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters. Frontiers in Psychiatry 7, 50.Google Scholar
Smith, SM, Nichols, TE, Vidaurre, D, Winkler, AM, Behrens, TE, Glasser, MF et al. (2015) A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience 18, 15651567.Google Scholar
Tibshirani, R, Walther, G and Hastie, T (2001) Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, 411423.Google Scholar
Van Dijk, KR, Hedden, T, Venkataraman, A, Evans, KC, Lazar, SW and Buckner, RL (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology 103, 297321.Google Scholar
van Rooij, D, Hartman, CA, Mennes, M, Oosterlaan, J, Franke, B, Rommelse, N et al. (2015) Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings. NeuroImage: Clinical 7, 325335.Google Scholar
Wechsler, D (1997) Wechsler Adult Intelligence Scale - Third Edition (WAIS-III). San Antonio, TX: Psychological Corporation.Google Scholar
Willcutt, EG, Nigg, JT, Pennington, BF, Solanto, MV, Rohde, LA, Tannock, R et al. (2012) Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology 121, 9911010.Google Scholar
Yeh, CB, Gau, SS, Kessler, RC and Wu, YY (2008) Psychometric properties of the Chinese version of the adult ADHD self-report scale. International Journal of Methods in Psychiatric Research 17, 4554.Google Scholar
Zalesky, A, Fornito, A and Bullmore, ET (2010) Network-based statistic: identifying differences in brain networks. NeuroImage 53, 11971207.Google Scholar
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