Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations
Open accessAbstract
Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.
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The results reported later are all significant at P < 0.05 (corrected) based on a voxel-wise error of P < 0.001, unless otherwise stated.
“As the latter criterion has been recommended to avoid false positives (Woo et al., 2014), the results reported later are all significant at P < 0.05 (corrected) based on a voxel-wise error of P < 0.001, unless otherwise stated.”
From The brain's conversation with itself: neural substrates of dialogic inner speech (2016) · cites this paper as Woo et al., 2014
A threshold of P < 0.005 and a voxel extent of k = 60 were deemed adequate to maintain a balance between sensitivity and false-positive rates.
“Next, we performed a regression analysis with OXT methylation values entered as the predictor variable and contrast estimates (emotion perspectivetaking > shape match) as the criterion variable using a threshold of P < 0.005, voxel extent of k = 60, which is sufficient to preserve the balance between sensitivity and false-positive rates (55,56).”
From Epigenetic modification of <i>OXT</i> and human sociability (2016) · cites this paper as [56]
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Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,406 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Applied Multivariate Statistical Analysis.
Johnson, A., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,374 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
SMITH, S., & NICHOLS, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.061 · Cited by 5,250 · see citing works
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002) · NeuroImage · doi:10.1006/nimg.2001.1037 · Cited by 4,923 · see citing works
Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910330508 · Cited by 3,202 · see citing works
Applied Multivariate Statistical Analysis
Härdle, W. K., & Simar, L. (2007) · Springer eBooks · doi:10.1007/978-3-540-72244-1 · Cited by 2,103 · see citing works
A three-dimensional statistical analysis for CBF activation studies in human brain
Worsley, K. J., Evans, A. C., Marrett, S., & Neelin, P. (1992) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.1992.127 · Cited by 2,036 · see citing works
Assessing the significance of focal activations using their spatial extent
Friston, K. J., Worsley, K. J., Frackowiak, R. S. J., Mazziotta, J. C., & Evans, A. C. (1994) · Human Brain Mapping · doi:10.1002/hbm.460010306 · Cited by 1,907 · see citing works
A whole brain fMRI atlas generated via spatially constrained spectral clustering
Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.21333 · Cited by 1,636 · see citing works
An fMRI-Based Neurologic Signature of Physical Pain
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C.-W., & Kross, E. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1204471 · Cited by 1,597 · see citing works
Self-citationControlling the familywise error rate in functional neuroimaging: a comparative review
Nichols, T. E., & Hayasaka, S. (2003) · Statistical Methods in Medical Research · doi:10.1191/0962280203sm341ra · Cited by 1,210 · see citing works
Wager, T. D., Phan, K. L., Liberzon, I., & Taylor, S. F. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00078-8 · Cited by 1,150 · see citing works
Self-citationNonstationary cluster-size inference with random field and permutation methods
Hayasaka, S., Phan, K. L., Liberzon, I., Worsley, K. J., & Nichols, T. E. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.041 · Cited by 661 · see citing works
Neuroimaging studies of shifting attention: a meta-analysis
Wager, T. D., Jonides, J., & Reading, S. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.052 · Cited by 654 · see citing works
Self-citationValidating cluster size inference: random field and permutation methods
Hayasaka, S. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.003 · Cited by 491 · see citing works
The principled control of false positives in neuroimaging
Bennett, C. M., Wolford, G. L., & Miller, M. B. (2009) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsp053 · Cited by 486 · see citing works
The secret lives of experiments: Methods reporting in the fMRI literature
Carp, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.004 · Cited by 473 · see citing works
Meta-analysis of functional neuroimaging data: current and future directions
Wager, T. D., Lindquist, M., & Kaplan, L. (2007) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsm015 · Cited by 434 · see citing works
Self-citationWager, T. D., van Ast, V. A., Hughes, B. L., Davidson, M. L., Lindquist, M. A., & Ochsner, K. N. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.044 · Cited by 264 · see citing works
Self-citationFalse Discovery Rates for Spatial Signals
Benjamini, Y., & Heller, R. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000941 · Cited by 224 · see citing works
Multiple testing corrections, nonparametric methods, and random field theory
Nichols, T. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.014 · Cited by 205 · see citing works
Cluster-based analysis of FMRI data
Heller, R., Stanley, D. A., Yekutieli, D., Rubin, N., & Benjamini, Y. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.04.233 · Cited by 202 · see citing works
(2000) · Elsevier eBooks · doi:10.1016/b978-0-12-481460-8.x5000-8 · Cited by 157 · see citing works
False positives in neuroimaging genetics using voxel-based morphometry data
Silver, M., Montana, G., & Nichols, T. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.049 · Cited by 146 · see citing works
Independent component analysis of fMRI data in the complex domain
Calhoun, V. D., Adalı, T., Pearlson, G. D., van Zijl, P. C. M., & Pekar, J. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10202 · Cited by 146 · see citing works
Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes
Kang, J., Johnson, T., Nichols, T. E., & Wager, T. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09735 · Cited by 68 · see citing works
Self-citationExperimental Design and Statistical Issues
Friston, K. J. (2000) · Elsevier eBooks · doi:10.1016/b978-012481460-8/50005-6 · Cited by 26 · see citing works
Flagged first, then by citations
1 of the 31 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging
Yan, C., Wang, X., Zuo, X., & Zang, Y. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9299-4 · Cited by 3,906 · see citing works
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487 · see citing works
Ten simple rules for neuroimaging meta-analysis
Müller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., Tench, C. R., Yarkoni, T., Nichols, T. E., Turkeltaub, P. E., et al. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.012 · Cited by 805 · see citing works
Self-citationEickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D., & Eickhoff, C. R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.072 · Cited by 683 · see citing works
“Searches for studies investigating major brain disorders such as schizophrenia, depression, dementia, stroke or Parkinson’s disease likewise revealed many hundred clinical neuroimaging papers.”
Measuring shared responses across subjects using intersubject correlation
Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz037 · Cited by 419 · see citing works
Deficient approaches to human neuroimaging
Stelzer, J., Lohmann, G., Mueller, K., Buschmann, T., & Turner, R. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00462 · Cited by 413 · see citing works
Child Maltreatment and Neural Systems Underlying Emotion Regulation
McLaughlin, K. A., Peverill, M., Gold, A. L., Alves, S., & Sheridan, M. A. (2015) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2015.06.010 · Cited by 409 · see citing works
Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis
Zhang, R., Geng, X., & Lee, T. M. C. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1443-x · Cited by 398 · see citing works
The relation between statistical power and inference in fMRI
Cremers, H. R., Wager, T. D., & Yarkoni, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184923 · Cited by 336 · see citing works
Self-citationA Hitchhiker's Guide to Functional Magnetic Resonance Imaging
Soares, J. M., Magalhães, R. J. D. S., Moreira, P. S., Sousa, A., Ganz, E., Sampaio, A., Alves, V., Marques, P., & Sousa, N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00515 · Cited by 270 · see citing works
Valk, S. L., Bernhardt, B. C., Trautwein, F.-M., Böckler, A., Kanske, P., Guizard, N., Collins, D. L., & Singer, T. (2017) · Science Advances · doi:10.1126/sciadv.1700489 · Cited by 264 · see citing works
“Given previous discussions in the fMRI community on the impact of cluster-forming thresholds on overall FWE levels and interpretability (106, 107), statistical results were corrected for multiple comparisons by means of random field theory using both typically used (108–111) cluster-defining thresholds for 20-mm FWHM…”
MethodIsmail, R., Parbo, P., Madsen, L. S., Hansen, A. K., Hansen, K. V., Schaldemose, J. L., Kjeldsen, P. L., Stokholm, M. G., Gottrup, H., Eskildsen, S. F., et al. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-01820-6 · Cited by 261 · see citing works
Johnston, J. A. Y., Wang, F., Liu, J., Blond, B. N., Wallace, A., Liu, J., Spencer, L., Cox Lippard, E. T., Purves, K. L., Landeros-Weisenberger, A., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15050652 · Cited by 247 · see citing works
Non‐parametric combination and related permutation tests for neuroimaging
Winkler, A. M., Webster, M., Brooks, J. C. W., Tracey, I., Smith, S. M., & Nichols, T. E. (2016) · Human Brain Mapping · doi:10.1002/hbm.23115 · Cited by 237 · see citing works
False positive rates in surface-based anatomical analysis
Greve, D. N., & Fischl, B. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.072 · Cited by 224 · see citing works
Analysis of family‐wise error rates in statistical parametric mapping using random field theory
Flandin, G., & Friston, K. J. (2017) · Human Brain Mapping · doi:10.1002/hbm.23839 · Cited by 232 · see citing works
Functional Magnetic Resonance Imaging Methods
Chen, J. E., & Glover, G. H. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9294-9 · Cited by 217 · see citing works
Khan, A. J., Nair, A., Keown, C. L., Datko, M., Lincoln, A. J., & Müller, R. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.03.024 · Cited by 206 · see citing works
Overlapping neural systems represent cognitive effort and reward anticipation
Vassena, E., Silvetti, M., Boehler, C. N., Achten, E., Fias, W., & Verguts, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091008 · Cited by 200 · see citing works
Hippocampal structural and functional changes associated with electroconvulsive therapy response
Abbott, C., Jones, T. R., Lemke, N. T., Gallegos, P., McClintock, S. M., Mayer, A. R., Bustillo, J., & Calhoun, V. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.124 · Cited by 207 · see citing works
Tillisch, K., Mayer, E. A., Gupta, A., Gill, Z., Brazeilles, R., Le Nevé, B., van Hylckama Vlieg, J. E. T., Guyonnet, D., Derrien, M., & Labus, J. S. (2017) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000493 · Cited by 185 · see citing works
Combining complex networks and data mining: Why and how
Zanin, M., Papo, D., Sousa, P. A., Menasalvas, E., Nicchi, A., Kubik, E., & Boccaletti, S. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.04.005 · Cited by 185 · see citing works
Zen and the Art of Multiple Comparisons
Lindquist, M. A., & Mejia, A. F. (2015) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000148 · Cited by 177 · see citing works
Evans, J. W., Szczepanik, J., Brutsché, N., Park, L. T., Nugent, A. C., & Zarate, C. A. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.01.027 · Cited by 173 · see citing works
Rehabilitation and neuroplasticity in children with unilateral cerebral palsy
Reid, L. B., Rose, S., & Boyd, R. N. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.97 · Cited by 174 · see citing works
Assessing and mapping language, attention and executive multidimensional deficits in stroke aphasia
Schumacher, R., Halai, A. D., & Ralph, M. A. L. (2019) · Brain · doi:10.1093/brain/awz258 · Cited by 169 · see citing works
Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis
Sütçübaşı, B., Metin, B., Kurban, K., Metin, Z. E., Beşer, B., & Sonuga-Barke, E. J. S. (2020) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2020.1775889 · Cited by 166 · see citing works
Pilgramm, S., de Haas, B., Helm, F., Zentgraf, K., Stark, R. M., Munzert, J., & Krüger, B. (2015) · Human Brain Mapping · doi:10.1002/hbm.23015 · Cited by 161 · see citing works
Feng, C., Hackett, P. D., DeMarco, A. C., Chen, X., Stair, S., Haroon, E., Ditzen, B., Pagnoni, G., & Rilling, J. K. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-014-9333-9 · Cited by 157 · see citing works
Aging Affects Dopaminergic Neural Mechanisms of Cognitive Flexibility
Berry, A. S., Shah, V. D., Baker, S. L., Vogel, J. W., O’Neil, J. P., Janabi, M., Schwimmer, H., Marks, S. M., & Jagust, W. J. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0626-16.2016 · Cited by 155 · see citing works
Neural mediators of changes of mind about perceptual decisions
Fleming, S. M., van der Putten, E. J., & Daw, N. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0104-6 · Cited by 155 · see citing works
Threlkeld, Z. D., Bodien, Y. G., Rosenthal, E. S., Giacino, J. T., Nieto-Castañón, A., Wu, O., Whitfield, S., & Edlow, B. L. (2018) · Cortex · doi:10.1016/j.cortex.2018.05.004 · Cited by 148 · see citing works
Differential tractography as a track-based biomarker for neuronal injury
Yeh, F.-C. F., Zaydan, I., Suski, V. R., Lacomis, D., Richardson, R. M., Maroon, J. C., & Barrios‐Martinez, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116131 · Cited by 142 · see citing works
Exploring the impact of analysis software on task fMRI results
Bowring, A., Maumet, C., & Nichols, T. E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24603 · Cited by 158 · see citing works
AMYGDALA RESPONSE TO NEGATIVE STIMULI PREDICTS PTSD SYMPTOM ONSET FOLLOWING A TERRORIST ATTACK
McLaughlin, K. A., Busso, D. S., Duys, A., Green, J. G., Alves, S., Way, M., & Sheridan, M. A. (2014) · Depression and Anxiety · doi:10.1002/da.22284 · Cited by 146 · see citing works
Palomar‐García, M., Zatorre, R. J., Ventura‐Campos, N., Bueichekú, E., & Ávila, C. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw120 · Cited by 141 · see citing works
Investigating Default Mode and Sensorimotor Network Connectivity in Amyotrophic Lateral Sclerosis
Chenji, S., Jha, S., Lee, D., Brown, M., Seres, P., Mah, D., & Kalra, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157443 · Cited by 134 · see citing works
Ready and waiting: Freezing as active action preparation under threat
Gladwin, T. E., Hashemi, M. M., van Ast, V., & Roelofs, K. (2016) · Neuroscience Letters · doi:10.1016/j.neulet.2016.03.027 · Cited by 133 · see citing works
Chrastil, E. R., Sherrill, K. R., Hasselmo, M. E., & Stern, C. E. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.1209-15.2015 · Cited by 128 · see citing works
Arrondo, G., Segarra, N., Metastasio, A., Ziauddeen, H., Spencer, J., Reinders, N. R., Dudas, R. B., Robbins, T. W., Fletcher, P. C., & Murray, G. K. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01280 · Cited by 130 · see citing works
Mullins, R. J., Mustapić, M., Goetzl, E. J., & Kapogiannis, D. (2017) · Human Brain Mapping · doi:10.1002/hbm.23494 · Cited by 128 · see citing works
Achterberg, M., van Duijvenvoorde, A. C. K., Bakermans-Kranenburg, M. J., & Crone, E. A. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv154 · Cited by 125 · see citing works
BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Larivière, S., Bayrak, Ş., de Wael, R. V., Benkarim, O. M., Herholz, P., Rodríguez‐Cruces, R., Paquola, C., Hong, S., Mišić, B., Evans, A. C., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119807 · Cited by 136 · see citing works
Fibromyalgia 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 · see citing works
Emotion regulation and amygdala-precuneus connectivity: Focusing on attentional deployment
Ferri, J., Schmidt, J., Hajcak, G., & Canli, T. (2016) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-016-0447-y · Cited by 123 · see citing works
Spisák, T., Spisák, Z., Zunhammer, M., Bingel, U., Smith, S. M., Nichols, T. E., & Kincses, Z. T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.078 · Cited by 123 · see citing works
Fallon, N. B., Roberts, C. A., & Stančák, A. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa090 · Cited by 125 · see citing works
Killgore, W. D. S., Vanuk, J. R., Shane, B. R., Weber, M., & Bajaj, S. (2019) · Neurobiology of Disease · doi:10.1016/j.nbd.2019.104679 · Cited by 120 · see citing works
Gray matter correlates of creative potential: A latent variable voxel-based morphometry study
Jauk, E., Neubauer, A. C., Dunst, B., Fink, A., & Benedek, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.002 · Cited by 114 · see citing works
Wilson, S. M., & Schneck, S. M. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00025 · Cited by 115 · see citing works
Layden, E. A., Cacioppo, J. T., Cacioppo, S., Cappa, S. F., Dodich, A., Falini, A., & Canessa, N. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.050 · Cited by 113 · see citing works
Philip, N. S., Barredo, J., Aiken, E., & Carpenter, L. L. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.10.007 · Cited by 111 · see citing works
The default network and the combination of cognitive processes that mediate self-generated thought
Axelrod, V., Rees, G., & Bar, M. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0244-9 · Cited by 111 · see citing works
Huskey, R., Craighead, B., Miller, M. B., & Weber, R. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-0612-6 · Cited by 118 · see citing works
Li, R., Nguyen, T. P., Potter, T. B. H., & Zhang, Y. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.101622 · Cited by 110 · see citing works
Shifting visual perspective during retrieval shapes autobiographical memories
St. Jacques, P. L., Szpunar, K. K., & Schacter, D. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.028 · Cited by 108 · see citing works
Deprez, S., Kesler, S. R., Saykin, A. J., Silverman, D. H. S., de Ruiter, M. B., & McDonald, B. C. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx285 · Cited by 108 · see citing works
Grimm, C., Duss, S. N., Privitera, M., Munn, B., Karalis, N., Frässle, S., Wilhelm, M., Patriarchi, T., Razansky, D., Wenderoth, N., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01755-8 · Cited by 128 · see citing works
Minimum statistical standards for submissions to Neuroimage: Clinical
Roiser, J. P., Linden, D. E. J., Gorno‐Tempini, M., Moran, R., Dickerson, B. C., & Grafton, S. T. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.08.002 · Cited by 106 · see citing works
Dissociating contributions of ACC and vmPFC in reward prediction, outcome, and choice
Vassena, E., Krebs, R. M., Silvetti, M., Fias, W., & Verguts, T. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.04.019 · Cited by 106 · see citing works
Functional connectivity changes in insomnia disorder: A systematic review
Fasiello, E., Gorgoni, M., Scarpelli, S., Alfonsi, V., Ferini‐Strambi, L., & Gennaro, L. D. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101569 · Cited by 106 · see citing works
Tu, Y., Fang, J., Cao, J., Wang, Z., Park, J., Jorgenson, K., Lang, C., Liu, J., Zhang, G., Zhao, Y., et al. (2018) · Brain stimulation · doi:10.1016/j.brs.2018.01.006 · Cited by 104 · see citing works
“05 (cluster-based false discovery rate [FDR] corrected) were applied for the correction of multiple comparisons [32].”
Decoding the contents and strength of imagery before volitional engagement
Koenig‐Robert, R., & Pearson, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-39813-y · Cited by 104 · see citing works
Han, H., & Park, J. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00001 · Cited by 103 · see citing works
Dannlowski, U., Kugel, H., Grotegerd, D., Redlich, R., Opel, N., Dohm, K., Zaremba, D., Grögler, A., Schwieren, J., Suslow, T., et al. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.010 · Cited by 102 · see citing works
Real time and delayed effects of subcortical low intensity focused ultrasound
Cain, J. A., Visagan, S., Johnson, M. A., Crone, J. S., Blades, R. B., Spivak, N. M., Shattuck, D. W., & Monti, M. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-85504-y · Cited by 101 · see citing works
van der Meulen, M., van IJzendoorn, M. H., & Crone, E. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159045 · Cited by 100 · see citing works
van Rooij, D., Hoekstra, P. J., Mennes, M., von Rhein, D., Thissen, A. J. A. M., Heslenfeld, D., Zwiers, M. P., Faraone, S. V., Oosterlaan, J., Franke, B., et al. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.13121635 · Cited by 100 · see citing works
Evidence for exercise-related plasticity in functional and structural neural network connectivity
Won, J., Callow, D. D., Pena, G. S., Gogniat, M. A., Kommula, Y., Arnold-Nedimala, N. A., Jordan, L. S., & Smith, J. C. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.10.013 · Cited by 102 · see citing works
How Human Amygdala and Bed Nucleus of the Stria Terminalis May Drive Distinct Defensive Responses
Klumpers, F., Kroes, M. C. W., Baas, J. M. P., & Fernández, G. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3830-16.2017 · Cited by 95 · see citing works
Gay, C. W., Robinson, M. E., Lai, S., O’Shea, A. M., Craggs, J. G., Price, D. D., & Staud, R. M. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0366 · Cited by 99 · see citing works
Schmitz, T. W., Mur, M., Aghourian, M., Bédard, M., & Spreng, R. N. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.001 · Cited by 98 · see citing works
Masouleh, S. K., Arélin, K., Horstmann, A., Lampe, L., Kipping, J., Luck, T., Riedel-Heller, S. G., Schroeter, M. L., Stümvoll, M., Villringer, A., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.12.020 · Cited by 96 · see citing works
The brain's conversation with itself: neural substrates of dialogic inner speech
Alderson-Day, B., Weis, S., McCarthy-Jones, S., Moseley, P., Smailes, D., & Fernyhough, C. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv094 · Cited by 94 · see citing works
“As the latter criterion has been recommended to avoid false positives (Woo et al., 2014), the results reported later are all significant at P< 0.”
Johnson, S. L., Elliott, M. V., & Carver, C. S. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.08.018 · Cited by 90 · see citing works
Alkozei, A., Smith, R. S., Pisner, D., Vanuk, J. R., Berryhill, S. M., Fridman, A., Shane, B. R., Knight, S. A., & Killgore, W. D. S. (2016) · SLEEP · doi:10.5665/sleep.6090 · Cited by 97 · see citing works
Koyama, M. S., O’Connor, D. A., Shehzad, Z. E., & Milham, M. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-17702-6 · Cited by 97 · see citing works
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 · see citing works
Feng, C., Eickhoff, S. B., Li, T., Wang, L., Becker, B., Camilleri, J. A., Hétu, S., & Luo, Y. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.03.025 · Cited by 96 · see citing works
The interaction of emotion and pain in the insula and secondary somatosensory cortex.
Orenius, T. I., Raij, T. T., Nuortimo, A., Näätänen, P., Lipsanen, J., & Karlsson, H. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.02.047 · Cited by 94 · see citing works
Trait and neurobiological underpinnings of negative emotion regulation in gambling disorder
Navas, J. F., Contreras‐Rodríguez, O., Verdejo‐Román, J., Perandrés‐Gómez, A., Albein‐Urios, N., Verdejo‐García, A., & Perales, J. C. (2017) · Addiction · doi:10.1111/add.13751 · Cited by 93 · see citing works
Epigenetic modification of <i>OXT</i> and human sociability
Haas, B. W., Filkowski, M. M., Cochran, R. N., Denison, L., Ishak, A., Nishitani, S., & Smith, A. K. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602809113 · Cited by 91 · see citing works
Kyle, C. T., Stokes, J. D., Lieberman, J. S., Hassan, A. S., & Ekstrom, A. D. (2015) · eLife · doi:10.7554/elife.10499 · Cited by 92 · see citing works
Wang, J., Wang, Y., Wu, X., Huang, H., Jia, Y., Zhong, S., Wu, X., Zhao, L., He, Y., Huang, L., et al. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9978-x · Cited by 91 · see citing works
Using executive control training to suppress amygdala reactivity to aversive information
Cohen, N., Margulies, D. S., Ashkenazi, S., Schaefer, A., Taubert, M., Henik, A., Villringer, A., & Okon-Singer, H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.069 · Cited by 90 · see citing works
Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking
Heinonen, J., Numminen, J., Hlushchuk, Y., Antell, H., Taatila, V. P., & Suomala, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162234 · Cited by 92 · see citing works
Mapping Human Laryngeal Motor Cortex during Vocalization
Eichert, N., Papp, D., Mars, R. B., & Watkins, K. E. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa182 · Cited by 89 · see citing works
van Rooij, D., Hartman, C. A., Mennes, M., Oosterlaan, J., Franke, B., Rommelse, N., Heslenfeld, D., Faraone, S. V., Buitelaar, J. K., & Hoekstra, P. J. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.01.004 · Cited by 89 · see citing works
Flodin, P., Martinsen, S., Altawil, R., Waldheim, E., Lampa, J., Kosek, E., & Fransson, P. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00107 · Cited by 89 · see citing works
Resting state functional connectivity and cognitive task-related activation of the human claustrum
Krimmel, S. R., White, M. G., Panicker, M. H., Barrett, F. S., Mathur, B. N., & Seminowicz, D. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.075 · Cited by 89 · see citing works
Neuroanatomical basis of concern-based altruism in virtual environment
Patil, I., Zanon, M., Novembre, G., Zangrando, N., Chittaro, L., & Silani, G. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.02.015 · Cited by 89 · see citing works
Cluster failure or power failure? Evaluating sensitivity in cluster-level inference
Noble, S., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116468 · Cited by 83 · see citing works
Eklund, A., Knutsson, H., & Nichols, T. E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24350 · Cited by 82 · see citing works
fMRI study of neural sensitization to hedonic stimuli in long‐term, daily cannabis users
Filbey, F. M., Dunlop, J. P., Ketcherside, A., Baine, J., Rhinehardt, T., Kuhn, B. N., DeWitt, S. J., & Alvi, T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23250 · Cited by 86 · see citing works
Browndyke, J. N., Berger, M., Harshbarger, T. B., Smith, P. J., White, W. D., Bisanar, T. L., Alexander, J. H., Gaca, J. G., Welsh-Bohmer, K. A., Newman, M. F., et al. (2016) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14534 · Cited by 85 · see citing works
van de Haar, H. J., Jansen, J. F. A., Jeukens, C. R. L. P. N., Burgmans, S., van Buchem, M. A., Muller, M., Hofman, P. A. M., Verhey, F. R. J., van Osch, M. J. P., & Backes, W. H. (2017) · Medical Physics · doi:10.1002/mp.12328 · Cited by 85 · see citing works
Toward valid and reliable brain imaging results in eating disorders
Frank, G., Favaro, A., Marsh, R., Ehrlich, S., & Lawson, E. A. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22829 · Cited by 85 · see citing works
Open monitoring meditation reduces the involvement of brain regions related to memory function
Fujino, M., Ueda, Y., Mizuhara, H., Saiki, J., & Nomura, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-28274-4 · Cited by 86 · see citing works
Towards algorithmic analytics for large-scale datasets
Bzdok, D., Nichols, T. E., & Smith, S. M. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0069-5 · Cited by 84 · see citing works
Lopopolo, A., Frank, S. L., van den Bosch, A. P. J., & Willems, R. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177794 · Cited by 78 · see citing works
- Authors
- Woo, C.-W., Krishnan, A., & Wager, T. D.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Wager, Tor D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 91
- Pages
- 412-419
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4214144
- OpenAlex
- W2169463832
- Cited by
- 1,253
- References
- 31
- Citations by year
- 2026: 77 · 2025: 79 · 2024: 81 · 2023: 81 · 2022: 96 · 2021: 110 · 2020: 138 · 2019: 112 · 2018: 158 · 2017: 135 · 2016: 93 · 2015: 77 · 2014: 16
- FWCI
- 59.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH076136)
- National Institute on Drug Abuse (R01DA035484)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- multiple testing correction, spatial specificity, false positives, statistical maps
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, Cluster Analysis, Normal Distribution, Signal-To-Noise Ratio, Neuroimaging
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