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Harnessing mega-analysis in the era of “big data” neuroimaging

Neuropsychopharmacology volume 50, pages 332–334 (2025)Cite this article

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A Correction to this article was published on 19 March 2025

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Associations between the brain and psychiatric symptoms are likely to be small [1]. Detecting these associations reliably requires large sample sizes, beyond the reach of single research sites. Traditionally, meta-analyses of published imaging studies have been used to aggregate data. However, these approaches are vulnerable to publication bias and synthesize heterogeneous group-level differences that achieve significance in small, underpowered samples, which are susceptible to both type-I and type-II errors [1, 2].

A potentially more robust approach to data aggregation is neuroimaging mega-analysis, initially popularized by the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium [3]. This method involves the uniform processing and integration of individual participant-level data across multiple datasets, containing thousands of participants. Initial work from ENIGMA reported smaller subcortical volumes and cortical surface area in youth with ADHD compared with controls [3]. We have applied such mega-analytic methods to data from large publicly available datasets, resulting in a combined dataset of ~9000 youth with or without Attention-Deficit/Hyperactivity Disorder (ADHD). Our findings support prevailing neurobiological models, revealing greater functional connectivity between the default mode network and task-positive networks [1]; greater functional connectivity within subcortico-cortical circuits centered on the caudate [2]; and less fractional anisotropy within long association white matter tracts [4], in youth with ADHD compared to unaffected controls. Similar associations were found with ADHD-traits in the broader population [1, 2, 4].

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Fig. 1: Comparison of neuroimaging meta-analysis and mega-analysis approaches in ADHD research.

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References

  1. Norman LJ, Sudre G, Price J, Shastri GG, Shaw P. Evidence from “big data” for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples. Neuropsychopharmacology. 2023;48:281–89.

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  4. Sudre G, Norman L, Bouyssi-Kobar M, Price J, Shastri GG, Shaw P. A mega-analytic study of white matter microstructural differences across 5 cohorts of youths with attention-deficit/hyperactivity disorder. Biol Psychiatry. 2023;94:18–28.

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Funding

Funded by the NIMH Intramural Research Program and the National Human Genome Research Institute (grant ZIAHG200378 to Dr. Shaw).

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Authors and Affiliations

  1. Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Luke J. Norman & Philip Shaw

  2. Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Luke J. Norman & Philip Shaw

  3. Kings Maudsley Partnership for Children and Young People, Pears Maudsley Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Philip Shaw

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  1. Luke J. Norman
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  2. Philip Shaw
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LJN and PS wrote the paper.

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Correspondence to Luke J. Norman.

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Norman, L.J., Shaw, P. Harnessing mega-analysis in the era of “big data” neuroimaging. Neuropsychopharmacol. 50, 332–334 (2025). https://doi.org/10.1038/s41386-024-01964-6

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