The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Potential Reporting Bias in fMRI Studies of the Brain
Open accessAbstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants. METHODOLOGY: After extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher's z transformation. PRINCIPAL FINDINGS: There was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥ 45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies. CONCLUSIONS: These results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“may avoid reporting biases that are likely to affect certain brain regions, especially smaller subcortical regions that are poorly represented in commonly used stereotaxic atlases of the human brain.15,16”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rucker, G., Harbord, R. M., Schmid, C. H., et al. (2011) · BMJ · doi:10.1136/bmj.d4002 · Cited by 7,709
Self-citationPower failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 1×
“Moreover, a recent paper by authors of the present paper (MM & JPAI) reported power analyses of single studies included in 49 metaanalyses and found that almost 50% of studies had an average power lower than 20% [24].”
ResultSelf-citationPublication bias in clinical research
Easterbrook, P. J., Gopalan, R., Berlin, J. A., & Matthews, D. R. (1991) · The Lancet · doi:10.1016/0140-6736(91)90201-y · Cited by 3,109 · cited 1×
“While it is reasonable that particular regions of the brain are indeed activated during a given task, it is also possible that positive findings are more likely to be reported, either because investigators are more likely to report them or because journals are more likely to publish positive findings [14,15,16].”
BackgroundEickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009) · Human Brain Mapping · doi:10.1002/hbm.20718 · Cited by 1,924
The case of the misleading funnel plot
Lau, J., Ioannidis, J. P. A., Terrin, N., Schmid, C. H., & Olkin, I. (2006) · BMJ · doi:10.1136/bmj.333.7568.597 · Cited by 1,766
Self-citationMeta-analysis of the functional neuroanatomy of single-word reading: method and validation
Turkeltaub, P. E., Eden, G. F., Jones, K. M., & Zeffiro, T. A. (2002) · NeuroImage · doi:10.1006/nimg.2002.1131 · Cited by 1,604
Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias
Dwan, K., Altman, D. G., Arnaiz, J. A., Bloom, J., Chan, A.-W., Cronin, E., Decullier, E., Easterbrook, P. J., Von Elm, E., Gamble, C., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003081 · Cited by 1,546
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Fractionating theory of mind: a meta-analysis of functional brain imaging studies
Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.01.009 · Cited by 1,656
Scanning the horizon: towards transparent and reproducible neuroimaging research
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.167 · Cited by 1,410
Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies
Fullana, M. A., Harrison, B. J., Soriano-Mas, C., Vervliet, B., Cardoner, N., Àvila-Parcet, A., & Radua, J. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.88 · Cited by 702
Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention
Ioannidis, J. P. A., Munafò, M. R., Fusar-Poli, P., Nosek, B. A., & David, S. P. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.02.010 · Cited by 471
Self-citationSzucs, D., & Ioannidis, J. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117164 · Cited by 453
Self-citationMaking big data open: data sharing in neuroimaging
Poldrack, R. A., & Gorgolewski, K. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3818 · Cited by 429
Okon-Singer, H., Hendler, T., Pessoa, L., & Shackman, A. J. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00058 · Cited by 371
Ventral Striatal Activation During Reward Processing in Psychosis
Raduà, J., Schmidt, A., Borgwardt, S., Heinz, A., Schlagenhauf, F., McGuire, P. K., & Fusar‐Poli, P. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.2196 · Cited by 357
Self-citationKoch, S. B. J., van Zuiden, M., Nawijn, L., Frijling, J. L., Veltman, D. J., & Olff, M. (2016) · Depression and Anxiety · doi:10.1002/da.22478 · Cited by 334
Revisiting the role of the insula in addiction
Droutman, V., Read, S. J., & Bechara, A. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.05.005 · Cited by 317
The practical and principled problems with educational neuroscience.
Bowers, J. S. (2016) · Psychological Review · doi:10.1037/rev0000025 · Cited by 258
Lack of evidence to favor specific preventive interventions in psychosis: a network meta‐analysis
Davies, C., Cipriani, A., Ioannidis, J. P. A., Radua, J., Stahl, D., Provenzani, U., McGuire, P., & Fusar‐Poli, P. (2018) · World Psychiatry · doi:10.1002/wps.20526 · Cited by 229
Self-citationDispositional negativity: An integrative psychological and neurobiological perspective.
Shackman, A. J., Tromp, D., Stockbridge, M. D., Kaplan, C. M., Tillman, R. M., & Fox, A. S. (2016) · Psychological Bulletin · doi:10.1037/bul0000073 · Cited by 249
Schmucker, C. M., Blümle, A., Schell, L. K., Schwarzer, G., Oeller, P., Cabrera, L., von Elm, E. B., Briel, M., & Meerpohl, J. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176210 · Cited by 236
Cannabis-associated psychosis: Neural substrate and clinical impact
Murray, R. M., Englund, A., Abi‐Dargham, A., Lewis, D. A., Forti, M. D., Davies, C., Sherif, M. A., McGuire, P. K., & D’Souza, D. C. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.06.018 · Cited by 210
BOLD hemodynamic response function changes significantly with healthy aging
West, K. L., Zuppichini, M. D., Turner, M. P., Sivakolundu, D. K., Zhao, Y., Abdelkarim, D., Spence, J. S., & Bart Rypma, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.012 · Cited by 166
Acar, F., Seurinck, R., Eickhoff, S. B., & Moerkerke, B. (2018) · PLOS ONE · doi:10.1371/journal.pone.0208177 · Cited by 154
“In neuroimaging literature, it has been shown that there is an excess of positive findings in literature [24, 25].”
BackgroundThe neural correlates of visual imagery: A co-ordinate-based meta-analysis
Winlove, C. I. P., Milton, F., Ranson, J., Fulford, J., MacKisack, M., Macpherson, F., & Zeman, A. (2018) · Cortex · doi:10.1016/j.cortex.2017.12.014 · Cited by 150
Potential Reporting Bias in Neuroimaging Studies of Sex Differences
David, S. P., Naudet, F., Laude, J. R., Raduà, J., Fusar‐Poli, P., Chu, I. M., Stefanick, M. L., & Ioannidis, J. P. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-23976-1 · Cited by 140
Self-citationFibromyalgia Is Associated with Decreased Connectivity Between Pain- and Sensorimotor Brain Areas
Flodin, P., Martinsen, S., Löfgren, M., Bilevičiūtė-Ljungar, I., Kosek, E., & Fransson, P. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0274 · Cited by 124
A systematic review of structural and functional MRI studies on pain catastrophizing
Galambos, A., Szabó, E., Nagy, Z., Édes, A. E., Kocsel, N., Juhász, G., & Kökönyei, G. (2019) · Journal of Pain Research · doi:10.2147/jpr.s192246 · Cited by 99
How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI
Pajula, J., & Tohka, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/2094601 · Cited by 93
Progress toward openness, transparency, and reproducibility in cognitive neuroscience
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13325 · Cited by 95
Towards a neural model of infant cry perception
Witteman, J., Van IJzendoorn, M. H., Rilling, J. K., Bos, P. A., Schiller, N. O., & Bakermans-Kranenburg, M. J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.026 · Cited by 94
Functional magnetic resonance imaging (fMRI) item analysis of empathy and theory of mind
Tholen, M. G., Trautwein, F., Böckler, A., Singer, T., & Kanske, P. (2020) · Human Brain Mapping · doi:10.1002/hbm.24966 · Cited by 100
LOOKING UNDER THE HOOD OF THE DOT-PROBE TASK: AN fMRI STUDY IN ANXIOUS YOUTH
Price, R. B., Siegle, G. J., Silk, J. S., Ladouceur, C. D., McFarland, A. M., Dahl, R. E., & Ryan, N. D. (2014) · Depression and Anxiety · doi:10.1002/da.22255 · Cited by 88
The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings
Lorca‐Puls, D. L., Gajardo‐Vidal, A., White, J., Seghier, M. L., Leff, A. P., Green, D. W., Crinion, J., Ludersdorfer, P., Hope, T. M. H., Bowman, H., et al. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.03.014 · Cited by 86
Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers
Borghi, J., & Gulick, A. E. V. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200562 · Cited by 84
Morriss, J., Gell, M., & van Reekum, C. M. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.013 · Cited by 76
McGrath, L. M., & Stoodley, C. J. (2019) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-019-9287-8 · Cited by 87
Neurophysiology of action anticipation in athletes: A systematic review
Smith, D. M. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.11.007 · Cited by 75
Hillebrandt, H. F., Friston, K. J., & Blakemore, S. (2014) · Scientific Reports · doi:10.1038/srep06240 · Cited by 66
The role of fMRI in drug development
Carmichael, O. T., Schwarz, A. J., Chatham, C. H., Scott, D., Turner, J. A., Upadhyay, J., Coimbra, A., Goodman, J. A., Baumgartner, R., English, B. A., et al. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.11.012 · Cited by 60
The effect of coloured overlays and lenses on reading: a systematic review of the literature
Griffiths, P. G., Taylor, R. H., Henderson, L. M., & Barrett, B. T. (2016) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12316 · Cited by 68
Scanning the Horizon: Towards transparent and reproducible neuroimaging researchPreprint
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafo, M. R., Nichols, T. E., Poline, J., Vul, E., & Yarkoni, T. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/059188 · Cited by 74
Sherman, L. E., Steinberg, L. D., & Chein, J. M. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.05.007 · Cited by 55
The Coordinate-Based Meta-Analysis of Neuroimaging Data
Samartsidis, P., Montagna, S., Johnson, T. D., & Nichols, T. E. (2017) · Statistical Science · doi:10.1214/17-sts624 · Cited by 58
Fusar-Poli, P., Radua, J., Frascarelli, M., Mechelli, A., Borgwardt, S., Di Fabio, F., Biondi, M., Ioannidis, J. P. A., & David, S. P. (2014) · Human Brain Mapping · doi:10.1002/hbm.22384 · Cited by 56
“A weak or null relationship could indicate potential reporting biases affecting the smaller studies more than the larger studies [David et al., 2013].”
ResultSelf-citationEstimating the prevalence of missing experiments in a neuroimaging meta-analysis
Samartsidis, P., Montagna, S., Laird, A., Fox, P., Johnson, T., & Nichols, T. E. (2020) · Research Synthesis Methods · doi:10.1002/jrsm.1448 · Cited by 47
Neural underpinnings of individual differences in emotion regulation: A systematic review
Morawetz, C., & Basten, U. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105727 · Cited by 62
Breakspear, M. J., Roberts, G. M. P., Green, M. J., Nguyen, V. T., Frankland, A., Lévy, F., Lenroot, R., & Mitchell, P. B. (2015) · Brain · doi:10.1093/brain/awv261 · Cited by 51
Mueller, K. F., Meerpohl, J. J., Briel, M., Antes, G., von Elm, E., Lang, B., Motschall, E., Schwarzer, G., & Bassler, D. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.04.015 · Cited by 48
Riem, M., Bakermans-Kranenburg, M., & van IJzendoorn, M. H. (2016) · Attachment & Human Development · doi:10.1080/14616734.2016.1149872 · Cited by 30
de Goede, J., Reeuwijk, K. G., Braun, K. P. J., le Cessie, S., Durston, S., Engels, R. C. M. E., Goudriaan, A. E., Moons, K. G. M., Vollebergh, W., Vries, T. J. D., et al. (2020) · Advances in Nutrition · doi:10.1093/advances/nmaa170 · Cited by 56
Parsing the components of forgiveness: Psychological and neural mechanisms
Fourie, M. M., Hortensius, R., & Decety, J. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.02.020 · Cited by 53
Berry, M. P., Lutz, J., Schuman-Olivier, Z., Germer, C., Pollak, S., Edwards, R. R., Gardiner, P., Desbordes, G., & Napadow, V. (2020) · Pain Medicine · doi:10.1093/pm/pnaa178 · Cited by 45
Querné, L., Fall, S., Moing, A. G. L., Bourel‐Ponchel, E., Delignières, A., Simonnot, A., de Broca, A., Gondry‐Jouet, C., Boucart, M., & Berquin, P. C. (2014) · Journal of Attention Disorders · doi:10.1177/1087054713517542 · Cited by 46
Fava, N. M., Trucco, E. M., Martz, M. E., Cope, L. M., Jester, J. M., Zucker, R. A., & Heitzeg, M. M. (2018) · Development and Psychopathology · doi:10.1017/s0954579418001025 · Cited by 44
Daamen, M., Bäuml, J., Scheef, L., Sorg, C., Busch, B., Baumann, N., Bartmann, P., Dieter Wolke, C., Wohlschläger, A. M., & Boecker, H. (2014) · Human Brain Mapping · doi:10.1002/hbm.22691 · Cited by 47
Jamadar, S. D. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107446 · Cited by 45
Pain Neuroimaging in Humans: A Primer for Beginners and Non-Imagers
Moayedi, M., Salomons, T. V., & Atlas, L. Y. (2018) · Journal of Pain · doi:10.1016/j.jpain.2018.03.011 · Cited by 38
Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders
Bednarz, H. M., & Kana, R. K. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.025 · Cited by 36
Neural correlates of compassion – An integrative systematic review
Novák, L., Maliňáková, K., Mikoška, P., van Dijk, J. P., & Tavel, P. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.12.004 · Cited by 43
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Kilroy, E., Cermak, S. A., & Aziz‐Zadeh, L. (2019) · Brain Sciences · doi:10.3390/brainsci9040075 · Cited by 34
Beyond romance: Neural and genetic correlates of altruism in pair-bonds.
Acevedo, B. P., Poulin, M. J., & Brown, L. L. (2019) · Behavioral Neuroscience · doi:10.1037/bne0000293 · Cited by 35
Functional Neuroimaging in Dissociative Disorders: A Systematic Review
Modesti, M. N., Rapisarda, L., Capriotti, G., & CASALE, A. D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091405 · Cited by 38
Neural correlates of executive attention in adults born very preterm
Daamen, M., Bäuml, J., Scheef, L., Meng, C., Jurcoane, A., Jaekel, J., Sorg, C., Busch, B., Baumann, N., Bartmann, P., et al. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.09.002 · Cited by 25
Dumas-Mallet, E., Button, K. S., Boraud, T., Munafo, M. R., & Gonon, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158064 · Cited by 30
Neural correlates of taste reward value across eating disorders
Olsavsky, A. K., Shott, M. E., DeGuzman, M. C., & Frank, G. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.08.010 · Cited by 28
Oboh, G., Ogunsuyi, O. B., Ojelade, M. T., & Akomolafe, S. F. (2018) · Food Science & Nutrition · doi:10.1002/fsn3.782 · Cited by 26
Nichols, B. N., & Pohl, K. M. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9293-x · Cited by 25
Arioli, M., Cattaneo, Z., Parimbelli, S., & Canessa, N. (2023) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsad003 · Cited by 27
Effects of Anthocyanins on Cognition and Vascular Function: A Systematic Review
Ellis, L. R., Boesch, C., & Dye, L. (2024) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202300502 · Cited by 23
Cwik, J. C., Vahle, N. M., Woud, M. L., Potthoff, D., Kessler, H., Sartory, G., & Seitz, R. J. (2019) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-019-01011-2 · Cited by 27
Emotion regulation: From neural circuits to a transdiagnostic perspective
Morawetz, C., Hemetsberger, F. J., Laird, A. R., & Kohn, N. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105960 · Cited by 27
Simeon, G., Piella, G., Cámara, Ó., & Pareto, D. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.769274 · Cited by 19
Čolić, L., Sankar, A., Goldman, D. A., Kim, J. A., & Blumberg, H. P. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02758-4 · Cited by 23
Yeung, A. W. K., Wong, N. S. M., & Eickhoff, S. B. (2020) · Human Brain Mapping · doi:10.1002/hbm.24957 · Cited by 19
Barlow, S. J. (2016) · Somatosensory & Motor Research · doi:10.1080/08990220.2016.1197114 · Cited by 19
Funch Uhre, V., Melissa Larsen, K., Marc Herz, D., Baaré, W., Katrine Pagsberg, A., & Roman Siebner, H. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103268 · Cited by 18
Externalizing proneness and brain response during pre-cuing and viewing of emotional pictures
Foell, J., Brislin, S. J., Strickland, C. M., Seo, D., Sabatinelli, D., & Patrick, C. J. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv080 · Cited by 18
Clarke, S., Rogers, R., Wanigasekera, V., Fardo, F., Pia, H., Nochi, Z., Macian, N., Leray, V., Finnerup, N. B., Pickering, G. P., et al. (2024) · European Journal of Pain · doi:10.1002/ejp.2251 · Cited by 17
Kulik, V., Reyes, L. D., & Sherwood, C. C. (2022) · Progress in brain research · doi:10.1016/bs.pbr.2022.10.003 · Cited by 14
A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging
de Ridder, M., Klein, K., & Kim, J. (2018) · Brain Informatics · doi:10.1186/s40708-018-0083-0 · Cited by 14
Yang, Q., Zhang, X., Song, Y., Liu, F., Qin, W., Yu, C., & Liang, M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25373 · Cited by 14
BIARAM: A process for analyzing correlated brain regions using association rule mining
Kalgotra, P., & Sharda, R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.001 · Cited by 12
Clustering the Brain With “CluB”: A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data
Berlingeri, M., Devoto, F., Gasparini, F., Saibene, A., Corchs, S., Clemente, L., Danelli, L., Gallucci, M., Borgoni, R., Borghese, N. A., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01037 · Cited by 12
Is the Psychopathic Brain an Artifact of Coding Bias? A Systematic Review
Jalava, J., Griffiths, S. Y., Larsen, R. R., & Alcott, B. E. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.654336 · Cited by 11
Ito, A., Yoshida, K., Takeda, K., Sawamura, D., Murakami, Y., Hasegawa, A., Sakai, S., & Izuma, K. (2020) · Human Brain Mapping · doi:10.1002/hbm.24996 · Cited by 8
Estimating the prevalence of missing experiments in a neuroimaging meta-analysisPreprint
Samartsidis, P., Montagna, S., Laird, A. R., Fox, P. T., Johnson, T. D., & Nichols, T. E. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/225425 · Cited by 9
Progress toward openness, transparency, and reproducibility in cognitive neurosciencePreprint
Gilmore, R. O., Diaz, M. T., Wyble, B., & Yarkoni, T. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5veew · Cited by 11
Gao, D., Liang, X., Ting, Q., Nichols, E., Bai, Z., Xu, C., Cai, M., & Liu, L. (2024) · Human Brain Mapping · doi:10.1002/hbm.26713 · Cited by 6
A New Editor-in-Chief for Nicotine & Tobacco Research
Munafo, M. R. (2015) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntu222 · Cited by 8
Presti, S. L., Origlia, S., Gianelli, C., & Canessa, N. (2023) · Human Brain Mapping · doi:10.1002/hbm.26312 · Cited by 7
Guan, M., Xie, Y., Li, C., Zhang, T., Ma, C., Wang, Z., Ma, Z., Wang, H., & Fang, P. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103546 · Cited by 6
“Hazy” or “jumbled”? Putting together the pieces of the bilingual puzzle
García‐Pentón, L., García, Y. F., Costello, B., Duñabeitia, J. A., & Carreiras, M. (2016) · Language Cognition and Neuroscience · doi:10.1080/23273798.2015.1135247 · Cited by 7
Guan, Z., Meng, L., Wu, Q., Wu, J., Chen, K., Han, H., Chui, D., Zhang, X., & Li, C. (2023) · Brain Science Advances · doi:10.26599/bsa.2023.9050008 · Cited by 6
Bakker, J. (2024) · Hormones and Behavior · doi:10.1016/j.yhbeh.2024.105601 · Cited by 6
Presti, S. L., Bonavita, M., Piga, V., Lozito, S., Doricchi, F., & Lasaponara, S. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106153 · Cited by 5
Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics
Dall’Aglio, L., Urbina-Johanson, S., Mallard, T. T., Lamballais, S., Delaney, S., Smoller, J. W., Muetzel, R. L., & Tiemeier, H. W. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101443 · Cited by 8
Tamm, L., Dudley, J. A., Karalunas, S. L., Simon, J. O., Maloney, T. C., Atluri, G., & Epstein, J. N. (2025) · NeuroImage: Reports · doi:10.1016/j.ynirp.2025.100263 · Cited by 4
Lin, C., & Wu, S.-Y. (2025) · BDJ Open · doi:10.1038/s41405-025-00339-3 · Cited by 4
Distinct prefrontal lateralization in placebo and reappraisal mechanisms: An ALE meta-analysis
Monachesi, B., Pisanu, E., Chiffi, D., Rumiati, R., & Grecucci, A. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121459 · Cited by 3
Muhr, P. (2022) · transcript Verlag eBooks · doi:10.14361/9783839461761 · Cited by 6
Cristea, I. A., Georgescu, R., & Ioannidis, J. P. A. (2021) · Clinical Psychological Science · doi:10.1177/21677026211049366 · Cited by 4
Self-citationResting-state network analysis of suicide attempt history in the UK Biobank
Thompson, M. F., Ghahramanlou‐Holloway, M., Murphy, M. A., Perera, K. U., Benca‐Bachman, C. E., Palmer, R. H. C., & Gray, J. C. (2023) · Psychological Medicine · doi:10.1017/s0033291723001356 · Cited by 4
Taschereau‐Dumouchel, V., Chiba, T., Koizumi, A., Kawato, M., & Lau, H. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.03.021956 · Cited by 4
Ethical and legal implications of the methodological crisis in neuroimagingPreprint
Kellmeyer, P. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/2gehg · Cited by 4
Comstock, L. B. (2024) · Brain and Language · doi:10.1016/j.bandl.2023.105377 · Cited by 7
Metadata
- Authors
- David, S. P., Ware, J. J., Chu, I. M., Loftus, P. D., Fusar-Poli, P., Radua, J., Munafò, M. R., & Ioannidis, J. P. A.
- Affiliations
- SRI International (US)
- University of Bristol (GB)
- Stanford Medicine (US)
- Corresponding author
- David, Sean P.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-07-25
- Volume
- 8
- Issue
- 7
- Pages
- e70104
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3723634
- OpenAlex
- W1975996475
- Cited by
- 130
- References
- 38
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 14 · 2023: 7 · 2022: 6 · 2021: 6 · 2020: 12 · 2019: 9 · 2018: 15 · 2017: 11 · 2016: 15 · 2015: 11 · 2014: 9 · 2013: 1
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- United Kingdom Clinical Research Collaboration
- Cancer Research UK
- National Institute for Health and Care Research
- British Heart Foundation
- Medical Research Council
- Economic and Social Research Council
- National Institute on Drug Abuse (DA-027331, R21DA027331, DA-017441, R01DA017441)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Education › Academic integrity and plagiarism
- Keywords
- reporting bias, functional MRI, sample size, meta-analysis, publication bias, negative results, brain activation, statistical significance thresholds
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Research Design, Statistics as Topic, Databases, Bibliographic, Publication Bias, Sample Size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
