Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cortical thinning and hippocampal expansion as brain signatures of attention deficit hyperactivity disorder symptom trajectories

Nature Mental Health volume 4, pages 263–278 (2026)Cite this article

Subjects

Abstract

Clinical heterogeneity in the symptom trajectories of attention deficit hyperactivity disorder (ADHD) is well documented, but their neurodevelopmental mechanisms remain unclear. We used a longitudinal cohort of adolescents (ABCD; n = 7,436) to show that persistent, remitting and emergent ADHD symptom trajectories correlated with persistent, improving and worsening behavioral changes, respectively. Each trajectory had distinct brain signatures: faster cortical thinning (persistence), slower thinning (emergence) and faster subcortical expansion (remission). Slower cortical thinning in the right posterior cingulate was associated with inattention symptom increase, whereas faster hippocampal expansion was associated with inattention symptom decrease. These signatures enhance ADHD symptom prediction at age 13 and generalize to young adults (age 23) in the IMAGEN cohort. The hippocampal signature for remitting symptoms was replicated in IMAGEN and two clinical cohorts (ADHD-200 and ADHD-1000). Given that baseline ADHD medication use was not significantly associated with the remitting trajectory, our findings suggest that current treatments may not facilitate sustained remission, highlighting the potential for new interventions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The distinct trajectories of ADHD symptoms and their differences in functional and genetic profiles.
The alternative text for this image may have been generated using AI.
Fig. 2: Brain signatures of ADHD symptom trajectories.
The alternative text for this image may have been generated using AI.
Fig. 3: Transcriptomic and neurotransmitter findings.
The alternative text for this image may have been generated using AI.
Fig. 4: Subsequent ADHD symptom prediction and validation.
The alternative text for this image may have been generated using AI.
Fig. 5: The updated developmental models of ADHD symptom trajectories.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

Data are publicly released on an annual basis through the NDA (https://nda.nih.gov/abcd). The ABCD Study data are openly available to qualified researchers for free. Access can be requested at https://nda.nih.gov/abcd/request-access. The ABCD data used in this report came from the ABCD Study at https://doi.org/10.15154/z563-zd24. The IMAGEN study data are available upon application: https://www.imagen-project.org/. ADHD-200 data are available from a dedicated database (http://fcon_1000.projects.nitrc.org/indi/adhd200). ADHD-1000 data were downloaded from the https://nda.nih.gov/study.html?id=1938. Summary statistics of genome-wide association studies for ADHD are publicly available and can be downloaded from https://pgc.unc.edu/for-researchers/download-results/. The transcriptomic data were downloaded from the AHBA (https://human.brain-map.org/). Due to participant privacy and institutional data-use restrictions, the ADHD-Shanghai dataset cannot be publicly shared. The data are available from the corresponding author upon reasonable request and subject to institutional approval. Source data are provided with this paper.

Code availability

Code for the replication of analyses conducted in the paper can be retrieved from https://github.com/DanTouHaHa/Neurodevelopmental-Correlates-of-ADHD-Symptom-Trajectories. Brain visualizations were generated by the authors using the ENIGMA Toolbox66 (https://enigma-toolbox.readthedocs.io/).

References

  1. Cortese, S. et al. Incidence, prevalence, and global burden of ADHD from 1990 to 2019 across 204 countries: data, with critical re-analysis, from the Global Burden of Disease study. Mol. Psychiatry 28, 4823–4830 (2023).

    Article  PubMed  Google Scholar 

  2. Faraone, S. V. et al. Attention-deficit/hyperactivity disorder. Nat. Rev. Dis. Prim. 10, 11 (2024).

    Article  PubMed  Google Scholar 

  3. Norman, L. J. et al. Longitudinal trajectories of childhood and adolescent attention deficit hyperactivity disorder diagnoses in three cohorts. EClinicalMedicine 60, 102021 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shaw, P. & Sudre, G. Adolescent attention-deficit/hyperactivity disorder: understanding teenage symptom trajectories. Biol. Psychiatry 89, 152–161 (2021).

    Article  PubMed  Google Scholar 

  5. Shaw, P. et al. Psychostimulant treatment and the developing cortex in attention deficit hyperactivity disorder. Am. J. Psychiatry 166, 58–63 (2009).

    Article  PubMed  Google Scholar 

  6. Shaw, P. et al. Trajectories of cerebral cortical development in childhood and adolescence and adult attention-deficit/hyperactivity disorder. Biol. Psychiatry 74, 599–606 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sudre, G. et al. Predicting the course of ADHD symptoms through the integration of childhood genomic, neural, and cognitive features. Mol. Psychiatry 26, 4046–4054 (2021).

    Article  PubMed  Google Scholar 

  8. Sibley, M. H. et al. Variable patterns of remission from ADHD in the Multimodal Treatment Study of ADHD. Am. J. Psychiatry 179, 142–151 (2022).

    Article  PubMed  Google Scholar 

  9. Shaw, P. et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc. Natl Acad. Sci. USA 104, 19649–19654 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shaw, P. et al. Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch. Gen. Psychiatry 63, 540–549 (2006).

    Article  PubMed  Google Scholar 

  11. Muetzel, R. L. et al. Tracking brain development and dimensional psychiatric symptoms in children: a longitudinal population-based neuroimaging study. Am. J. Psychiatry 175, 54–62 (2017).

    Article  PubMed  Google Scholar 

  12. Wang, Y. et al. The neural and genetic underpinnings of different developmental trajectories of attention-deficit/hyperactivity symptoms in children and adolescents. BMC Med. 22, 223 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jung, T. & Wickrama, K. A. S. An introduction to latent class growth analysis and growth mixture modeling. Soc. Personal. Psychol. Compass 2, 302–317 (2008).

    Article  Google Scholar 

  14. Guerrera, S. et al. Assessment of psychopathological comorbidities in children and adolescents with autism spectrum disorder using the child behavior checklist. Front. Psychiatry https://doi.org/10.3389/fpsyt.2019.00535 (2019).

  15. Chen, J. W. et al. Best practice guidelines for propensity score methods in medical research: consideration on theory, implementation, and reporting. A review. Arthroscopy 38, 632–642 (2022).

    Article  PubMed  Google Scholar 

  16. Shaw, M. et al. A systematic review and analysis of long-term outcomes in attention deficit hyperactivity disorder: effects of treatment and non-treatment. BMC Med. 10, 99 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mechler, K., Banaschewski, T., Hohmann, S. & Häge, A. Evidence-based pharmacological treatment options for ADHD in children and adolescents. Pharmacol. Ther. 230, 107940 (2022).

    Article  PubMed  Google Scholar 

  18. Baboli, R. et al. Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD). Cortex 179, 1–13 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Long, Y. et al. Distinct brain structural abnormalities in attention-deficit/hyperactivity disorder and substance use disorders: a comparative meta-analysis. Transl. Psychiatry 12, 368 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hoogman, M. et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4, 310–319 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bahrami, S. et al. Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders. Nat. Commun. 13, 3436 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Noble, K. G. et al. Hippocampal volume varies with educational attainment across the life-span. Front. Hum. Neurosci. 6, 307 (2012).

  23. Kandola, A., Hendrikse, J., Lucassen, P. J. & Yücel, M. Aerobic exercise as a tool to improve hippocampal plasticity and function in humans: practical implications for mental health treatment. Front. Hum. Neurosci. 10, 373 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Saccaro, L. F. et al. Limbic-sensorimotor tug of war for the hippocampus: dynamic functional connectivity as a transdiagnostic vulnerability marker in offspring of emotion dysregulation patients. Biol. Psychiatry Cogn. Neurosci. Neuroimaging https://doi.org/10.1016/j.bpsc.2025.03.007 (2025).

  25. Russell, V. A. Neurobiology of animal models of attention-deficit hyperactivity disorder. J. Neurosci. Methods 161, 185–198 (2007).

    Article  PubMed  Google Scholar 

  26. Hilton, B. J., Griffin, J. M., Fawcett, J. W. & Bradke, F. Neuronal maturation and axon regeneration: unfixing circuitry to enable repair. Nat. Rev. Neurosci. 25, 649–667 (2024).

    Article  PubMed  Google Scholar 

  27. Anacker, C. & Hen, R. Adult hippocampal neurogenesis and cognitive flexibility—linking memory and mood. Nat. Rev. Neurosci. 18, 335–346 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Shen, C. et al. Neural correlates of the dual-pathway model for ADHD in adolescents. Am. J. Psychiatry 177, 844–854 (2020).

    Article  PubMed  Google Scholar 

  29. Shaw, P., Stringaris, A., Nigg, J. & Leibenluft, E. Emotion dysregulation in attention deficit hyperactivity disorder. Am. J. Psychiatry 171, 276–293 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sakai, J. How synaptic pruning shapes neural wiring during development and, possibly, in disease. Proc. Natl Acad. Sci. USA 117, 16096–16099 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Demontis, D. et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat. Genet. 55, 198–208 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Contreras, D. et al. Methylphenidate restores behavioral and neuroplasticity impairments in the prenatal nicotine exposure mouse model of ADHD: evidence for involvement of AMPA receptor subunit composition and synaptic spine morphology in the hippocampus. Int. J. Mol. Sci. https://doi.org/10.3390/ijms23137099 (2022).

  33. Ginsberg, Y., Quintero, J., Anand, E., Casillas, M. & Upadhyaya, H. P. Underdiagnosis of attention-deficit/hyperactivity disorder in adult patients: a review of the literature. Prim. Care Companion CNS Disord. https://doi.org/10.4088/PCC.13r01600 (2014).

  34. Isaac, V., Lopez, V. & Escobar, M. J. Can attention-deficit/hyperactivity disorder be considered a form of cerebellar dysfunction. Front. Neurosci. 19, 1453025 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang, Y. et al. Association of emotional and behavioral problems with the development of the substantia nigra, subthalamic nucleus, and red nucleus volumes and asymmetries from childhood to adolescence: a longitudinal cohort study. Transl. Psychiatry 14, 117 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128–1139 (2010).

    Article  PubMed  Google Scholar 

  37. Nigg, J. T. et al. The Oregon ADHD-1000: a new longitudinal data resource enriched for clinical cases and multiple levels of analysis. Dev. Cogn. Neurosci. 60, 101222 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  38. DuPaul, G. J., Power, T. J., Anastopoulos, A. D. & Reid, R. ADHD Rating Scale—IV: Checklists, Norms, and Clinical Interpretation. (The Guilford Press, 1998).

  39. Achenbach, T. M. Achenbach System of Empirically Based Assessment (ASEBA). In The Encyclopedia of Clinical Psychology (eds Cautin, R. L. & Lilienfeld, S. O.) 1–8 (Wiley-Blackwell, 2015); https://doi.org/10.1002/9781118625392.wbecp150

  40. Nakamura, B. J., Ebesutani, C., Bernstein, A. & Chorpita, B. F. A psychometric analysis of the Child Behavior Checklist DSM-Oriented Scales. J. Psychopathol. Behav. Assess. 31, 178–189 (2009).

    Article  Google Scholar 

  41. Bernanke, J. et al. 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. Lancet Psychiatry 9, 222–231 (2022).

    Article  PubMed  Google Scholar 

  42. Li, Q. et al. Cognitive predictors of mental health trajectories are mediated by inferior frontal and occipital development during adolescence. Mol. Psychiatry https://doi.org/10.1038/s41380-025-02912-6 (2025).

  43. Bleck, T. P., Nowinski, C. J., Gershon, R. & Koroshetz, W. J. What is the NIH Toolbox, and what will it mean to neurology?. Neurology 80, 874–875 (2013).

    Article  PubMed  Google Scholar 

  44. Acker, W. J. I. J. O. M.-M. S. A computerized approach to psychological screening—The Bexley-Maudsley Automated Psychological Screening and the Bexley-Maudsley Category Sorting Test. Int. J. Man Mach. Stud. 17, 361–369 (1982).

    Article  Google Scholar 

  45. Serrano-Ibáñez, E. R., Ramírez-Maestre, C., Esteve, R. & López-Martínez, A. E. The behavioural inhibition system, behavioural activation system and experiential avoidance as explanatory variables of comorbid chronic pain and posttraumatic stress symptoms. Eur. J. Psychotraumatol. 10, 1581013 (2019).

    Article  PubMed  Google Scholar 

  46. Mujahid, M. S., Diez Roux, A. V., Morenoff, J. D. & Raghunathan, T. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Am. J. Epidemiol. 165, 858–867 (2007).

    Article  PubMed  Google Scholar 

  47. Sanford, K., Bingham, C. R. & Zucker, R. A. Validity issues with the Family Environment Scale: psychometric resolution and research application with alcoholic families. Psychol. Assess. 11, 315–325 (1999).

    Article  Google Scholar 

  48. Arthur, M. W. et al. Measuring risk and protection in communities using the Communities That Care Youth Survey. Eval. Program Plann. 30, 197–211 (2007).

    Article  PubMed  Google Scholar 

  49. Gonzalez, R. et al. An update on the assessment of culture and environment in the ABCD Study: emerging literature and protocol updates over three measurement waves. Dev. Cogn. Neurosci. 52, 101021 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Goodman, R., Meltzer, H. & Bailey, V. The Strengths and Difficulties Questionnaire: a pilot study on the validity of the self-report version. Int. Rev. Psychiatry 15, 173–177 (2003).

    Article  PubMed  Google Scholar 

  51. Ferreira, V. R. et al. Sleep disturbance scale for children: translation, cultural adaptation, and validation. Sleep Med. 10, 457–463 (2009).

    Article  PubMed  Google Scholar 

  52. Bunford, N. et al. The Difficulties in Emotion Regulation Scale-Parent Report: a psychometric investigation examining adolescents with and without ADHD. Assessment 27, 921–940 (2020).

    Article  PubMed  Google Scholar 

  53. Hagler, D. J. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 202, 116091 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  56. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  PubMed  Google Scholar 

  57. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Arnatkevic̆iūtė, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage 189, 353–367 (2019).

    Article  PubMed  Google Scholar 

  59. Dukart, J. et al. JuSpace: a tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum. Brain Mapp. 42, 555–566 (2021).

    Article  PubMed  Google Scholar 

  60. Nigg, J. T. et al. Attention-deficit/hyperactivity disorder (ADHD) and being overweight/obesity: new data and meta-analysis. Clin. Psychol. Rev. 43, 67–79 (2016).

    Article  PubMed  Google Scholar 

  61. Zhao, C.-l. et al. Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain. Cogn. Neurodyn. https://doi.org/10.1007/s11571-023-09954-y (2023).

  62. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  PubMed  Google Scholar 

  63. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Piñero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e214 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Larivière, S. et al. The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nat. Methods 18, 698–700 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was partially supported by grants from the National Key Research and Development Program of China (number 2023YFE0109700) (Q. Luo), the National Natural Science Foundation of China (numbers 82272079 and 32441107) (Q. Luo) and the Program of Shanghai Academic Research Leader (number 23XD1423400) (Q. Luo). Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them for over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A list of participating sites and a complete list of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from the ABCD Study at https://doi.org/10.15154/z563-zd24. This work also received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology; LSHM-CT- 2007-037286), the Horizon 2020-funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders; 695313), Horizon Europe ‘environMENTAL’, grant number 101057429, UK Research and Innovation Horizon Europe funding guarantee (10041392 and 10038599), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the Chinese government via the Ministry of Science and Technology (MOST), the German Center for Mental Health (DZPG), the Bundesministerium für Bildung und Forschung (BMBF grants 01GS08152 and 01EV0711, Forschungsnetz AERIAL 01EE1406A and 01EE1406B and Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265 and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the NIH-funded ENIGMA grants (5U54EB020403-05, 1R56AG058854-01 and U54 EB020403), NIH R01DA049238, the NIH, Science Foundation Ireland (16/ERCD/3797) and NSFC grant 82150710554. Further support was provided by grants from the ANR (ANR-12-SAMA-0004 and AAPG2019 – GeBra), the Eranet Neuron (AF12-NEUR0008-01 – WM2NA and ANR-18-NEUR00002-01 – ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance Publique–Hôpitaux de Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013) and the Fédération pour la Recherche sur le Cerveau.

Author information

Author notes

  1. A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China

    Wenjie Hou, Zixin Gu, Luolong Cao & Qiang Luo

  2. Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education–Shanghai Key Laboratory for Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Wenjie Hou, Qingyang Li & Qiang Luo

  3. Child Health Care Medical Division, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China

    Daqian Zhu

  4. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian & Christelle Langley

  5. Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian & Christelle Langley

  6. Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK

    Samuele Cortese

  7. Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK

    Samuele Cortese

  8. Hampshire and Isle of Wight Healthcare NHS Foundation Trust, Southampton, UK

    Samuele Cortese

  9. Hassenfeld Children’s Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA

    Samuele Cortese

  10. Department of Precision and Regenerative Medicine and Ionian Area, University of Bari ‘Aldo Moro’, Bari, Italy

    Samuele Cortese

  11. Division of Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore

    Lizhu Luo

  12. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Gareth J. Barker

  13. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

    Arun L. W. Bokde

  14. Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany

    Rüdiger Brühl

  15. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Sylvane Desrivières

  16. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Herta Flor

  17. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

    Herta Flor & Dimitri Papadopoulos Orfanos

  18. Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA

    Hugh Garavan

  19. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK

    Penny Gowland

  20. NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

    Antoine Grigis

  21. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany

    Andreas Heinz & Michael N. Smolka

  22. Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany

    Andreas Heinz

  23. German Center for Mental Health (DZPG), Site Tübingen, Tübingen, Germany

    Andreas Heinz

  24. Department of Psychiatry, LabD-Psy, Barthélémy Durand Hospital, Etampes, France

    Jean-Luc Martinot & Eric Artiges

  25. INSERM U1299 “Developmental Trajectories in Psychiatry & Addictology”; University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, Mathematics Department, Centre Borelli CNRS UMR9010, Gif-sur-Yvette, France

    Jean-Luc Martinot, Marie-Laure Paillère Martinot & Eric Artiges

  26. AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France

    Marie-Laure Paillère Martinot

  27. Institute of Medical Psychology, Ludwig-Maximilians-Universität (LMU) in Munich, Munich, Germany

    Frauke Nees

  28. Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany

    Luise Poustka

  29. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Sarah Hohmann

  30. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Nathalie Holz & Tobias Banaschewski

  31. German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Mannheim, Germany

    Nathalie Holz & Tobias Banaschewski

  32. Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany

    Nilakshi Vaidya & Gunter Schumann

  33. Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany

    Henrik Walter

  34. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

    Robert Whelan

  35. Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China

    Gunter Schumann

  36. German Center for Mental Health (DZPG), Site Berlin-Potsdam, Berlin, Germany

    Gunter Schumann

  37. Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), Beijing, China

    Li Yang

  38. Beijing Key Laboratory for Big Data Innovative Application of Child and Adolescent Mental Disorders, Beijing, China

    Li Yang

  39. State Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai, China

    Qiang Luo

  40. Shanghai Research Center of Acupuncture and Meridian, Shanghai, China

    Qiang Luo

Authors
  1. Wenjie Hou
    View author publications

    Search author on:PubMed Google Scholar

  2. Daqian Zhu
    View author publications

    Search author on:PubMed Google Scholar

  3. Barbara J. Sahakian
    View author publications

    Search author on:PubMed Google Scholar

  4. Samuele Cortese
    View author publications

    Search author on:PubMed Google Scholar

  5. Christelle Langley
    View author publications

    Search author on:PubMed Google Scholar

  6. Lizhu Luo
    View author publications

    Search author on:PubMed Google Scholar

  7. Qingyang Li
    View author publications

    Search author on:PubMed Google Scholar

  8. Zixin Gu
    View author publications

    Search author on:PubMed Google Scholar

  9. Luolong Cao
    View author publications

    Search author on:PubMed Google Scholar

  10. Gareth J. Barker
    View author publications

    Search author on:PubMed Google Scholar

  11. Arun L. W. Bokde
    View author publications

    Search author on:PubMed Google Scholar

  12. Rüdiger Brühl
    View author publications

    Search author on:PubMed Google Scholar

  13. Sylvane Desrivières
    View author publications

    Search author on:PubMed Google Scholar

  14. Herta Flor
    View author publications

    Search author on:PubMed Google Scholar

  15. Hugh Garavan
    View author publications

    Search author on:PubMed Google Scholar

  16. Penny Gowland
    View author publications

    Search author on:PubMed Google Scholar

  17. Antoine Grigis
    View author publications

    Search author on:PubMed Google Scholar

  18. Andreas Heinz
    View author publications

    Search author on:PubMed Google Scholar

  19. Jean-Luc Martinot
    View author publications

    Search author on:PubMed Google Scholar

  20. Marie-Laure Paillère Martinot
    View author publications

    Search author on:PubMed Google Scholar

  21. Eric Artiges
    View author publications

    Search author on:PubMed Google Scholar

  22. Frauke Nees
    View author publications

    Search author on:PubMed Google Scholar

  23. Dimitri Papadopoulos Orfanos
    View author publications

    Search author on:PubMed Google Scholar

  24. Luise Poustka
    View author publications

    Search author on:PubMed Google Scholar

  25. Michael N. Smolka
    View author publications

    Search author on:PubMed Google Scholar

  26. Sarah Hohmann
    View author publications

    Search author on:PubMed Google Scholar

  27. Nathalie Holz
    View author publications

    Search author on:PubMed Google Scholar

  28. Nilakshi Vaidya
    View author publications

    Search author on:PubMed Google Scholar

  29. Henrik Walter
    View author publications

    Search author on:PubMed Google Scholar

  30. Robert Whelan
    View author publications

    Search author on:PubMed Google Scholar

  31. Gunter Schumann
    View author publications

    Search author on:PubMed Google Scholar

  32. Li Yang
    View author publications

    Search author on:PubMed Google Scholar

  33. Tobias Banaschewski
    View author publications

    Search author on:PubMed Google Scholar

  34. Qiang Luo
    View author publications

    Search author on:PubMed Google Scholar

Consortia

for the IMAGEN Consortium

  • Gareth J. Barker
  • , Arun L. W. Bokde
  • , Rüdiger Brühl
  • , Sylvane Desrivières
  • , Herta Flor
  • , Hugh Garavan
  • , Penny Gowland
  • , Antoine Grigis
  • , Andreas Heinz
  • , Jean-Luc Martinot
  • , Marie-Laure Paillère Martinot
  • , Eric Artiges
  • , Frauke Nees
  • , Dimitri Papadopoulos Orfanos
  • , Luise Poustka
  • , Michael N. Smolka
  • , Sarah Hohmann
  • , Nathalie Holz
  • , Nilakshi Vaidya
  • , Henrik Walter
  • , Robert Whelan
  • , Gunter Schumann
  •  & Tobias Banaschewski

Contributions

Q. Luo and W.H. had full access to the data. Q. Luo proposed and designed the study. D.Z., L.L., T.B., G.J.B., A.L.W.B., R.B., S.D., H.F., H.G., P.G., A.G., A.H., J.-L.M., M.-L.P.M., E.A., F.N., D.P.O., L.P., M.N.S., S.H., N.V., H.W., R.W. and G.S. acquired the data. W.H. and Q. Luo analyzed the data. W.H. and Q. Luo drafted the paper. L.Y., B.J.S. D.Z., C.L., Z.G., L.C., S.C. and T.B. made critical edits to the paper. All authors contributed to the interpretation of results. All authors contributed to the visualization. All authors read and approved the paper.

Corresponding author

Correspondence to Qiang Luo.

Ethics declarations

Competing interests

S.C. has declared reimbursement for travel and accommodation expenses from the Association for Child and Adolescent Central Health (ACAMH) in relation to lectures delivered for ACAMH, the Canadian AADHD Alliance Resource, the British Association of Psychopharmacology, Healthcare Convention and CCM Group team for educational activity on ADHD, and has received honoraria from Medice. T.B. served in an advisory or consultancy role for Eye Level, Infectopharm, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Takeda. He received conference support or speaker’s fees from Janssen-Cilag, Medice and Takeda. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press. The present work is unrelated to the above grants and relationships. L.P. served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fees from Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships. The other authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Valeria Isaac, Sha Tao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–5, Methods 1–9, Results 1–7 and Tables 1–17.

Reporting Summary (download PDF )

Supplementary Data Table 1 (download XLSX )

Differences in environmental, functional, genetic and neuroimaging features among ADHD symptom trajectory subgroups.

Supplementary Data Table 2 (download XLSX )

Biological processes and neurotransmitter systems associated with ADHD symptom trajectories.

Supplementary Data 1 (download XLSX )

Statistical data for Supplementary Figs. 3–5.

Source data

Source Data Fig. 1 (download XLSX )

Statistical data for Fig. 1.

Source Data Fig. 2 (download XLSX )

Statistical data for Fig. 2.

Source Data Fig. 3 (download XLSX )

Statistical data for Fig. 3.

Source Data Fig. 4 (download XLSX )

Statistical data for Fig. 4.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, W., Zhu, D., Sahakian, B.J. et al. Cortical thinning and hippocampal expansion as brain signatures of attention deficit hyperactivity disorder symptom trajectories. Nat. Mental Health 4, 263–278 (2026). https://doi.org/10.1038/s44220-025-00578-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44220-025-00578-1

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing