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Evidence from “big data” for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples

Abstract

We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen’s d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.

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Fig. 1: Forest plots for brain network metrics that differed significantly (corrected p < 0.05) between N = 1301 ADHD cases and N = 1301 non-ADHD controls.
Fig. 2: Associations between ADHD diagnosis and connectivity within and between brain networks.
Fig. 3: Forest plots for significant associations (corrected p < 0.05) between ADHD-traits and resting-state connectivity (N = 10,113).

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Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (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 to 10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health 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 listing of participating sites and a complete listing 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 analysis or writing of this report. The ABCD data repository grows and changes over time. The ABCD data used in this report came from DOI: 10.15154/1519007. The DOIs can be found at https://doi.org/10.15154/1519007 and from the fast-track data release (raw neuroimaging data). The raw data are available at https://nda.nih.gov/edit_collection.html?id=2573. Instructions on how to create an NDA study are available at https://nda.nih.gov/training/modules/study.html). Data and/or research tools used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Dataset identifier (s): #2573, #2846 and #3165. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA (https://doi.org/10.15154/1527788). The Human Connectome Project-Development was supported by the NIMH under Award Number U01MH109589. This manuscript included data from a limited access dataset obtained from the Child Mind Institute Biobank, the HBN study (http://www.healthybrainnetwork.org). Data collection as for the ADHD200-NYU cohort was funded by the NIMH (R01MH083246), Autism Speaks, The Stavros Niarchos Foundation, The Leon Levy Foundation, and an endowment provided by Phyllis Green and Randolph Cōwen. Data collection for the ADHD200-Peking cohort was supported by The Commonwealth Sciences Foundation, Ministry of Health, China (200802073), The National Foundation, Ministry of Science and Technology, China (2007BAI17B03), The National Natural Sciences Foundation, China (30970802), The Funds for International Cooperation of the National Natural Science Foundation of China (81020108022), The National Natural Science Foundation of China (8100059), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning. Data collection and sharing for this project was provided by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), which is funded by the U.S. National Institute on Alcohol Abuse and Alcoholism with co-funding from the National Institute on Drug Abuse, the National Institute of Mental Health, the National Institute of Child Health and Human Development, and the National Institute of Health Office of the Director (AA021695 (MPI: SA Brown, SF Tapert), AA021697 (MPI: A Pfefferbaum, KM Pohl), AA021692 (PI: SF Tapert), AA021681 (PI: MD DeBellis), AA021690 (PI: DB Clark), AA021691 (PI: B Nagel), AA021696 (MPI: IM Colrain, FC Baker), AA021697-04S1 (PI: KM Pohl)). NCANDA data are disseminated by the Center for Health Sciences, SRI International. We used data from NCANDA_PUBLIC_4Y_REDCAP_V01 (https://doi.org/10.7303/syn22216455), NCANDA_PUBLIC_BASE_RESTINGSTATE_V01 (https://doi.org/10.7303/syn11605291) and NCANDA_PUBLIC_BASE_STRUCTURAL_V01 (https://doi.org/10.7303/syn11541569). This study reflects the views of the authors and may not reflect the opinions or views of other individuals or institutions including the NIH, the ABCD, NCANDA, HCP or ADHD200 consortium investigators, the Child Mind Institute, SRI International, or other funding agencies. Image processing was conducted using the high-performance computing capabilities of the NIH Biowulf cluster. The authors thank the NIMH Data Science and Sharing Team for help with accessing and processing the ABCD-BIDS dataset.

Funding

Funded by the intramural research program of the National Institute of Mental Health and the National Human Genome Research Institute (ZIAHG200378 to PS). This funding supported data collection for the NCR cohort (ClinicalTrials.gov identifier: NCT01721720).

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Study concept and design: LJN. Acquisition, analysis, or interpretation of data: all authors. Drafting of the manuscript: LJN, PS. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: LJN, GS. Administrative, technical, or material support: LJN, GS, PS. Study supervision: PS. Obtained funding: PS.

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Correspondence to Luke J. Norman.

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Norman, L.J., Sudre, G., Price, J. et al. Evidence from “big data” for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples. Neuropsychopharmacol. (2022). https://doi.org/10.1038/s41386-022-01408-z

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