• Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wagner, A. D. et al. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281, 1188–1191 (1998).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Buckner, R. L. et al. Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging. Proc. Natl Acad. Sci. USA 93, 14878–14883 (1996).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Szucs, D. & Ioannidis, J. P. Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals. Neuroimage 221, 117164 (2020).

    PubMed 

    Google Scholar
     

  • Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • Yarkoni, T. Big correlations in little studies: inflated fMRI correlations reflect low statistical power—Commentary on Vul et al. (2009). Perspect. Psychol. Sci. 4, 294–298 (2009).

    PubMed 

    Google Scholar
     

  • Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Masouleh, S. K., Eickhoff, S. B., Hoffstaedter, F. & Genon, S., Alzheimer’s Disease Neuroimaging Initiative. Empirical examination of the replicability of associations between brain structure and psychological variables. eLife 8, e43464 (2019).


    Google Scholar
     

  • Dinga, R. et al. Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin 22, 101796 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boekel, W. et al. A purely confirmatory replication study of structural brain–behavior correlations. Cortex 66, 115–133 (2015).

    PubMed 

    Google Scholar
     

  • Nosek, B. A., Cohoon, J., Kidwell, M. & Spies, J. R. Estimating the reproducibility of psychological science. Preprint at https://doi.org/10.31219/osf.io/447b3 (2016).

  • Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Begley, C. G. & Ellis, L. M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    ADS 
    CAS 

    Google Scholar
     

  • Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nuzzo, R. Scientific method: Statistical errors. Nature 506, 150–152 (2014).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hawkins, D. M. The problem of overfitting. J. Chem. Inf. Comput. Sci. 44, 1–12 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • Bishop, D. How scientists can stop fooling themselves over statistics. Nature 584, 9 (2020).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schönbrodt, F. D. & Perugini, M. At what sample size do correlations stabilize? J. Res. Pers. 47, 609–612 (2013).


    Google Scholar
     

  • Varoquaux, G. Cross-validation failure: small sample sizes lead to large error bars. Neuroimage 180, 68–77 (2018).

    PubMed 

    Google Scholar
     

  • Yarkoni, T. Big correlations in little studies: Inflated fMRI correlations reflect low statistical power—Commentary on Vul et al. (2009). Perspect. Psychol. Sci. 4, 294–298 (2009).

    PubMed 

    Google Scholar
     

  • Button, K. S. et al. Confidence and precision increase with high statistical power. Nat. Rev. Neurosci. 14, 585–586 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).

    PubMed 

    Google Scholar
     

  • Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Greene, A. S., Gao, S., Scheinost, D. & Constable, R. T. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9, 2807 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chaarani, B. et al. Baseline brain function in the preadolescents of the ABCD Study. Nat. Neurosci. 24, 1176–1186 (2021).

    CAS 
    PubMed 

    Google Scholar
     

  • Heaton, R. K. et al. Reliability and validity of composite scores from the NIH Toolbox Cognition Battery in adults. J. Int. Neuropsychol. Soc. 20, 588–598 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Achenbach, T. M. & Rescorla, L. Manual for the ASEBA School-age Forms & Profiles: An Integrated System of Multi-informant Assessment (ASEBA, 2001).

  • Kharabian Masouleh, S. et al. Influence of processing pipeline on cortical thickness measurement. Cereb. Cortex 30, 5014–5027 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Erdfelder, E., Faul, F. & Buchner, A. GPOWER: A general power analysis program. Behav. Res. Methods Instrum. Comput. 28, 1–11 (1996).


    Google Scholar
     

  • Ioannidis, J. P. A., Munafò, M. R., Fusar-Poli, P., Nosek, B. A. & David, S. P. Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention. Trends Cogn. Sci. 18, 235–241 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174–187 (2017).

    PubMed 

    Google Scholar
     

  • Dosenbach, N. U. F. et al. Real-time motion analytics during brain MRI improve data quality and reduce costs. Neuroimage 161, 80–93 (2017).

    PubMed 

    Google Scholar
     

  • Kanwisher, N., Stanley, D. & Harris, A. The fusiform face area is selective for faces not animals. NeuroReport 10, 183–187 (1999).

    CAS 
    PubMed 

    Google Scholar
     

  • Pritschet, L. et al. Functional reorganization of brain networks across the human menstrual cycle. Neuroimage 220, 117091 (2020).

    PubMed 

    Google Scholar
     

  • Laumann, T. O. et al. Brain network reorganisation in an adolescent after bilateral perinatal strokes. Lancet Neurol. 20, 255–256 (2021).

    PubMed 

    Google Scholar
     

  • Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Newbold, D. J. et al. Plasticity and spontaneous activity pulses in disused human brain circuits. Neuron 107, 580–589.e6 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smith, S. M. & Nichols, T. E. Statistical challenges in ‘big data’ human neuroimaging. Neuron 97, 263–268 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • Border, R. et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am. J. Psychiatry 176, 376–387 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kundu, P., Inati, S. J., Evans, J. W., Luh, W.-M. & Bandettini, P. A. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60, 1759–1770 (2012).

    PubMed 

    Google Scholar
     

  • Vizioli, L. et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat. Commun. 12, 5181 (2021).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shiffman, S., Stone, A. A. & Hufford, M. R. Ecological momentary assessment. Ann. Rev. Clin. Psychol. 4, 1–32 (2008).


    Google Scholar
     

  • NOT-OD-07-088: policy for sharing of data obtained in NIH supported or conducted genome-wide association studies (GWAS). National Institutes of Health https://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html (2007).

  • Buzsáki, G. The brain–cognitive behavior problem: a retrospective. eNeuro 7, ENEURO.0069–20.2020 (2020).


    Google Scholar
     

  • Errington, T. M., Denis, A., Perfito, N., Iorns, E. & Nosek, B. A. Challenges for assessing replicability in preclinical cancer biology. eLife 10, e67995 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Patil, P., Peng, R. D. & Leek, J. T. What should researchers expect when they replicate studies? A statistical view of replicability in psychological science. Perspect. Psychol. Sci. 11, 539–544 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Feczko, E. et al. Adolescent Brain Cognitive Development (ABCD) community MRI collection and utilities. Preprint at https://doi.org/10.1101/2021.07.09.451638 (2021).

  • Volkow, N. D. et al. The conception of the ABCD Study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018).

    PubMed 

    Google Scholar
     

  • Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fair, D. A. et al. Correction of respiratory artifacts in MRI head motion estimates. Neuroimage 208, 116400 (2020).

    PubMed 

    Google Scholar
     

  • Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).

    PubMed 

    Google Scholar
     

  • Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. NeuroImage 9, 179–194 (1999).

    CAS 
    PubMed 

    Google Scholar
     

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).

    PubMed 

    Google Scholar
     

  • Marcus, D. S. et al. Informatics and data mining tools and strategies for the human connectome project. Front. Neuroinform. 5, 4 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hallquist, M. N., Hwang, K. & Luna, B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208–225 (2013).

    PubMed 

    Google Scholar
     

  • Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

    PubMed 

    Google Scholar
     

  • Seitzman, B. A. et al. A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum. Neuroimage 206, 116290 (2020).

    PubMed 

    Google Scholar
     

  • Marek, S. et al. Identifying reproducible individual differences in childhood functional brain networks: an ABCD Study. Dev. Cogn. Neurosci. 40, 100706 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barch, D. M. et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev. Cogn. Neurosci. 32, 55–66 (2018).

    PubMed 

    Google Scholar
     

  • Rothman, K. Modern Epidemiology (Lippincott Williams & Wilkins, 2016).

  • Gratton, C. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439–452.e5 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 62, 2222–2231 (2012).

    PubMed 

    Google Scholar
     

  • Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: raitonale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

    PubMed 

    Google Scholar
     



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