The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Open accessAbstract
Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([ 1996 ]: J Cereb Blood Flow Metab 16:7–22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel‐by‐voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi‐subject PET/SPECT or f MRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and f MRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices. Hum. Brain Mapping 15:1–25, 2001. © 2001 Wiley‐Liss, Inc.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Statistical significance of decoding performance was assessed via permutation test, randomly permuting the sample labels and repeating the decoding analysis 1,000 times, corrected for multiple comparisons using a cluster-based correction (cluster mass inference with cluster forming threshold at P < 0.05)105,106.”
From Adversarial testing of global neuronal workspace and integrated information theories of consciousness (2025) · cites this paper as [105]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The permutation p-value in Equation ( 10) is super-uniform under H 0 (i.e., P H0 (p ≤ α) ≤ α for all α ∈ [0, 1]), a standard consequence of randomization inference (Nichols & Holmes, 2002;Phipson & Smyth, 2010;Good, 2005)”
From Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026) · cites this paper as Nichols & Holmes, 2002
The cited source says what the claim attributes to it.
In the citing paper
“While resampling-based “maxT” procedures (Westfall & Young, 1993;Nichols & Holmes, 2002) can calibrate such aggregates, this has not yet been applied in representational similarity studies.”
From Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026) · cites this paper as Nichols & Holmes, 2002
In the citing paper
“To find the true type I error rate, we performed Monte Carlo simulations that permute the sign of the whole-brain maps from individual subjects58.”
From Anchoring the neural compass: coding of local spatial reference frames in human medial parietal lobe (2014) · cites this paper as [58]
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
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Self-citationKendall, M. G. (1957) · Biometrika · doi:10.2307/2333282 · Cited by 6,442
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
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
Analysis of fMRI Time-Series Revisited
Friston, K. J., Holmes, A. P., Poline, J., Grasby, P. M., Williams, S., Frackowiak, R. S. J., & Turner, R. (1995) · NeuroImage · doi:10.1006/nimg.1995.1007 · Cited by 2,504
Self-citationBevan, J. M., & Kendall, M. G. (1971) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2986801 · Cited by 2,458
Multiple Comparison Procedures
Hochberg, Y., & Tamhane, A. C. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316672 · Cited by 2,389
Analysis of fMRI Time-Series Revisited—Again
Worsley, K. J., & Friston, K. J. (1995) · NeuroImage · doi:10.1006/nimg.1995.1023 · Cited by 2,198
Multiple Comparison Procedures
Hahs-Vaughn, D., & Lomax, R. (2013) · Routledge eBooks · doi:10.4324/9780203137819-17 · Cited by 2,132
Edgington, E. S., Edgington, E., & Onghena, P. (1964) · Springer eBooks · doi:10.1002/0471667196.ess2169 · Cited by 2,052
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- 10.1080/00223980.1964.9916711 · DOI
- Fisher RA, 1990, Bennett JH
- HolmesAP(1994): Statistical issues in functional brain mapping PhD thesis. University of Glasgow.http://www.fil.ion.ucl.ac.uk/spm/papers/APH_thesis
- Pitman EJG, 1937, Significance tests which may be applied to samples from any population. II. The correlation coefficient test, J R Stat Soc, 4, 224
- Worsley KJ, 1995, A unified statistical approach for determining significant signals in images of cerebral activation, Hum Brain Mapp, 4, 58, 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O · DOI
Cited by
Sorted by citations
Items 301–400 of 1,112
Krogsrud, S. K., Fjell, A. M., Tamnes, C. K., Grydeland, H., Mork, L., Due-Tønnessen, P., Bjørnerud, A., Sampaio-Baptista, C., Andersson, J., Johansen-Berg, H., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.017 · Cited by 190
Mindfulness-induced selflessness: a MEG neurophenomenological study
Dor-Ziderman, Y., Berkovich-Ohana, A., Glicksohn, J., & Goldstein, A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00582 · Cited by 205
Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask
Kriegeskorte, N., Lindquist, M. A., Nichols, T. E., Poldrack, R. A., & Vul, E. (2010) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2010.86 · Cited by 220
Self-citationMechanisms underlying fatigue: a voxel-based morphometric study of chronic fatigue syndrome
Okada, T., Tanaka, M., Kuratsune, H., Watanabe, Y., & Sadato, N. (2004) · BMC Neurology · doi:10.1186/1471-2377-4-14 · Cited by 214
Longitudinal stability of MRI for mapping brain change using tensor-based morphometry
Leow, A., Klunder, A. D., Jack, C., Toga, A., Dale, A., Bernstein, M., Britson, P. J., Gunter, J., Ward, C. P., Whitwell, J., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.013 · Cited by 237
Effects of age and gender on white matter integrity
Inano, S., Takao, H., Hayashi, N., Abe, O., & Ohtomo, K. (2011) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2785 · Cited by 213
Functional Magnetic Resonance Imaging Methods
Chen, J. E., & Glover, G. H. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9294-9 · Cited by 249
Neural mechanisms supporting maladaptive food choices in anorexia nervosa
Foerde, K., STEINGLASS, J. E., Daphna Shohamy, A., & Walsh, B. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4136 · Cited by 218
A Microsaccadic Rhythm Modulates Gamma-Band Synchronization and Behavior
Bosman, C. A., Womelsdorf, T., Desimone, R., & Fries, P. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1193-09.2009 · Cited by 212
Out‐of‐synchrony speech entrainment in developmental dyslexia
Molinaro, N., Lizarazu, M., Lallier, M., Bourguignon, M., & Carreiras, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23206 · Cited by 225
Bjørnebekk, A., Fjell, A. M., Walhovd, K. B., Grydeland, H., Torgersen, S. O., & Westlye, L. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.009 · Cited by 224
Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG
McMenamin, B. W., Shackman, A. J., Maxwell, J. S., Bachhuber, D. R. W., Koppenhaver, A. M., Greischar, L. L., & Davidson, R. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.010 · Cited by 214
From novel to familiar: Tuning the brain for metaphors
Cardillo, E. R., Watson, C. E., Schmidt, G. L., Kranjec, A., & Chatterjee, A. K. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.079 · Cited by 223
Burst and Tonic Spinal Cord Stimulation: Different and Common Brain Mechanisms
Ridder, D. D., & Vanneste, S. (2015) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.12368 · Cited by 215
Gweon, H., Dodell-Feder, D., Bedny, M., & Saxe, R. (2012) · Child Development · doi:10.1111/j.1467-8624.2012.01829.x · Cited by 206
When ‘go’ and ‘nogo’ are equally frequent: ERP components and cortical tomography
Lavric, A., Pizzagalli, D. A., & Forstmeier, S. (2004) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2004.03683.x · Cited by 219
Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., Madsen, S. K., Parikshak, N., Hua, X., Toga, A. W., Jack, C. R., et al. (2009) · Human Brain Mapping · doi:10.1002/hbm.20708 · Cited by 217
Wu, X., Li, R., Fleisher, A., Reiman, E. M., Guan, X., Zhang, Y., Chen, K., & Yao, L. (2011) · Human Brain Mapping · doi:10.1002/hbm.21153 · Cited by 222
Leow, A., Yanovsky, I., Chiang, M.-C., Lee, A. D., Klunder, A. D., Lu, A. P., Becker, J. T., Davis, S. W., Toga, A. W., & Thompson, P. M. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892646 · Cited by 209
DeMarco, A. T., & Turkeltaub, P. E. (2018) · Human Brain Mapping · doi:10.1002/hbm.24289 · Cited by 203
Top-Down Beta Enhances Bottom-Up Gamma
Richter, C. G., Thompson, W. H., Bosman, C. A., & Fries, P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3771-16.2017 · Cited by 195
Palacios, E. M., Owen, J. P., Yuh, E. L., Wang, M. B., Vassar, M. J., Ferguson, A. R., Diaz-Arrastia, R., Giacino, J. T., Okonkwo, D. O., Robertson, C. S., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz6892 · Cited by 194
Coding of navigational affordances in the human visual system
Bonner, M., & Epstein, R. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618228114 · Cited by 209
Gaetz, W. C., & Cheyne, D. O. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.009 · Cited by 206
Emotional imagery: Assessing pleasure and arousal in the brain's reward circuitry
Costa, V. D., Lang, P. J., Sabatinelli, D., Versace, F., & Bradley, M. M. (2010) · Human Brain Mapping · doi:10.1002/hbm.20948 · Cited by 202
Wright, I. C., Sham, P. C., Murray, R. M., Weinberger, D. R., & Bullmore, E. T. (2002) · NeuroImage · doi:10.1006/nimg.2002.1163 · Cited by 205
Supramammillary glutamate neurons are a key node of the arousal system
Pedersen, N. P., Ferrari, L., Venner, A., Wang, J. L., Abbott, S. B. G., Vujovic, N., Arrigoni, E., Saper, C. B., & Fuller, P. M. (2017) · Nature Communications · doi:10.1038/s41467-017-01004-6 · Cited by 203
Distances between real-world locations are represented in the human hippocampus
Morgan, L. K., MacEvoy, S. P., Aguirre, G. K., & Epstein, R. A. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4667-10.2011 · Cited by 203
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
Self-citationDevereux, B. J., Clarke, A., Marouchos, A., & TYLER, L. K. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3809-13.2013 · Cited by 215
3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry
Chiang, M.-C., Reiss, A. L., Lee, A. D., Bellugi, U., Galaburda, A. M., Korenberg, J. R., Mills, D. L., Toga, A. W., & Thompson, P. M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.024 · Cited by 203
Bergmann, T. O., Mölle, M., Schmidt, M. A., Lindner, C., Marshall, L., Jan Born, A., & Siebner, H. R. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4792-11.2012 · Cited by 211
Canuet, L., Ishii, R., Pascual-Marqui, R. D., Iwase, M., Kurimoto, R., Aoki, Y., Ikeda, S., Takahashi, H., Nakahachi, T., & Takeda, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027863 · Cited by 204
García-Cordero, I., Sedeño, L., de la Fuente, L., Slachevsky, A., Forno, G., Klein, F., Lillo, P., Ferrari, J., Rodriguez, C., Bustin, J., et al. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0006 · Cited by 199
Sleep-Dependent Improvement in Visuomotor Learning: A Causal Role for Slow Waves
Crupi, D., Hulse, B. K., Peterson, M. J., Huber, R., Ansari, H., Coen, M., Cirelli, C., Benca, R. M., Ghilardi, M. F., & Tononi, G. (2009) · Sleep · doi:10.1093/sleep/32.10.1273 · Cited by 228
Vernooij, M. W., de Groot, M., van der Lugt, A., Ikram, M. A., Krestin, G. P., Albert Hofman, D., Niessen, W. J., & Breteler, M. M. B. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.07.052 · Cited by 204
Models of Brain Function in Neuroimaging
Friston, K. J. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.56.091103.070311 · Cited by 202
Alterations of the salience network in obesity: A resting‐state fMRI study
García‐García, I., Jurado, M. Á., Garolera, M., Segura, B., Sala‐Llonch, R., Marqués‐Iturria, I., Pueyo, R., Sender‐Palacios, M. J., Vernet‐Vernet, M., Narberhaus, A., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22104 · Cited by 200
Cortical Thinning in Cingulate and Occipital Cortices in First Episode Schizophrenia
Narr, K. L., Toga, A. W., Szeszko, P. R., Thompson, P. M., Woods, R. P., Robinson, D. G., Sevy, S. M., Wang, Y.-P., Schrock, K., & Bilder, R. M. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.03.043 · Cited by 203
Adaptive analysis of fMRI data
Friman, O., Borga, M., Lundberg, P., & Knutsson, H. E. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00077-6 · Cited by 200
Shine, J. M., Koyejo, S., & Poldrack, R. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604898113 · Cited by 195
Koster, R., Chadwick, M. J., Chen, Y., Berron, D., Banino, A., Düzel, E., Hassabis, D., & Kumaran, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.009 · Cited by 191
The Statistical Analysis of Functional MRI Data
Lazar, N. A. (2008) · Statistics in the health sciences · doi:10.1007/978-0-387-78191-4 · Cited by 197
Balthazar, M. L. F., Pereira, F., Lopes, T. M., da Silva, E. L., Coan, A. C., de Campos, B. M., Duncan, N. W., Stella, F., Northoff, G., Damasceno, B. P., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22248 · Cited by 207
Emotional context modulates response inhibition: Neural and behavioral data
Albert, J., López‐Martín, S., & Carretié, L. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.045 · Cited by 194
Kauppi (2010) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2010.00005 · Cited by 197
A multi-layer network approach to MEG connectivity analysis
Brookes, M. J., Tewarie, P. K. B., Hunt, B. A. E., Robson, S. E., Gascoyne, L. E., Liddle, E. B., Liddle, P. F., & Morris, P. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.045 · Cited by 197
Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets
Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., & Poline, J. (2005) · Human Brain Mapping · doi:10.1002/hbm.20210 · Cited by 194
Multivoxel Pattern Analysis for fMRI Data: A Review
Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., & Brovelli, A. (2012) · Computational and Mathematical Methods in Medicine · doi:10.1155/2012/961257 · Cited by 206
Inferior frontal gyrus activity triggers anterior insula response to emotional facial expressions.
Jabbi, M., & Keysers, C. (2008) · Emotion · doi:10.1037/a0014194 · Cited by 190
Structural and functional abnormalities in migraine patients without aura
Jin, C., Yuan, K., Zhao, L., Zhao, L., Yu, D., von Deneen, K. M., Zhang, M., Qin, W., Sun, W., & Tian, J. (2012) · NMR in Biomedicine · doi:10.1002/nbm.2819 · Cited by 198
A comparison of random field theory and permutation methods for the statistical analysis of MEG data
Pantazis, D., Nichols, T. E., Baillet, S., & Leahy, R. M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.09.040 · Cited by 196
Self-citationPenadés, R., Pujol, N., Catalán, R., Massana, G., Rametti, G., García‐Rizo, C., Bargalló, N., Gastó, C., Bernardo, M., & Junqué, C. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.01.017 · Cited by 208
Medendorp, W. P., Kramer, G., Jensen, O. B., Oostenveld, R., Schoffelen, J., & Fries, P. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhl145 · Cited by 193
Confidence drives a neural confirmation bias
Rollwage, M., Loosen, A. M., Hauser, T. U., Moran, R., Dolan, R. J., & Fleming, S. M. (2020) · Nature Communications · doi:10.1038/s41467-020-16278-6 · Cited by 213
Pattern-information analysis: From stimulus decoding to computational-model testing
Kriegeskorte, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.061 · Cited by 197
A Method to Determine the Presence of Averaged Event-Related Fields Using Randomization Tests
Koenig, T. W., & Melie‐García, L. (2010) · Brain Topography · doi:10.1007/s10548-010-0142-1 · Cited by 194
Gender Differences in White Matter Microstructure
Kanaan, R., Allin, M. P. G., Picchioni, M., Barker, G. J., Daly, E., Shergill, S. S., Woolley, J., & McGuire, P. K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038272 · Cited by 204
Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling
Quinn, A. J., Vidaurre, D., Abeysuriya, R., Becker, R., Nobre, A. C., & Woolrich, M. W. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00603 · Cited by 188
Deo, A. K., Borson, S., Link, J. M., Domino, K., Eary, J. F., Ke, B., Richards, T. L., Mankoff, D. A., Minoshima, S., O’Sullivan, F., et al. (2014) · Journal of Nuclear Medicine · doi:10.2967/jnumed.113.130161 · Cited by 193
Independence in ROI analysis: where is the voodoo?
Poldrack, R. A., & Mumford, J. A. (2009) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsp011 · Cited by 192
Tamnes, C. K., Fjell, A. M., Westlye, L. T., Østby, Y., & Walhovd, K. B. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4779-11.2012 · Cited by 193
Fast and accurate modelling of longitudinal and repeated measures neuroimaging data
Guillaume, B., Hua, X., Thompson, P. M., Waldorp, L., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.029 · Cited by 187
Self-citationStress Potentiates Early and Attenuates Late Stages of Visual Processing
Shackman, A. J., Maxwell, J. S., McMenamin, B. W., Greischar, L. L., & Davidson, R. J. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3384-10.2011 · Cited by 191
Altered prefrontal and insular cortical thickness in adolescent marijuana users
Lopez-Larson, M. P., Bogorodzki, P., Rogowska, J., McGlade, E., King, J. B., Terry, J., & Yurgelun-Todd, D. (2011) · Behavioural Brain Research · doi:10.1016/j.bbr.2011.02.001 · Cited by 187
Lulé, D. E., Noirhomme, Q., Kleih, S. C., Chatelle, C., Halder, S., Athena Demertzi, A., Bruno, M., Gosseries, O., Vanhaudenhuyse, A., Caroline Schnakers, A., et al. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.04.030 · Cited by 191
Reorganization of hippocampal functional connectivity with transition to chronic back pain
Mutso, A., Petre, B., Huang, L. Q., Baliki, M. N., Torbey, S., Herrmann, K. M., Schnitzer, T. J., & Apkarian, A. V. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00611.2013 · Cited by 188
Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning
Assaneo, M. F., Ripollés, P., Orpella, J., Lin, W. M., de Diego‐Balaguer, R., & Poeppel, D. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0353-z · Cited by 199
Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations
Pustina, D., Avants, B., Faseyitan, O. K., Medaglia, J. D., & Coslett, H. B. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.08.027 · Cited by 192
Vestergaard, M., Madsen, K. S., Baaré, W. F. C., Skimminge, A., Ejersbo, L. R., Ramsøy, T. Z., Gerlach, C., Åkeson, P., Paulson, O. B., & Jernigan, T. L. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21592 · Cited by 181
Unger, M. M., Belke, M., Menzler, K., Heverhagen, J. T., Keil, B. R., Stiasny‐Kolster, K., Rosenow, F., Diederich, N. J., Mayer, G., Möller, J. C., et al. (2010) · SLEEP · doi:10.1093/sleep/33.6.767 · Cited by 197
Eikenes, L., Løhaugen, G. C. C., Brubakk, A., Skranes, J. S., & Haberg, A. K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.037 · Cited by 187
The Role of Corpus Callosum Development in Functional Connectivity and Cognitive Processing
Hinkley, L. B. N., Marco, E. J., Findlay, A. M., Honma, S. M., Jeremy, R. J., Strominger, Z. A., Bukshpun, P., Wakahiro, M. L. J., Brown, W. S., Paul, L. K., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039804 · Cited by 198
Intrinsic Brain Network Abnormalities in Migraines without Aura Revealed in Resting-State fMRI
Xue, T., Yuan, K., Zhao, L., Yu, D., Zhao, L., Dong, T., Cheng, P., von Deneen, K. M., Qin, W., & Tian, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052927 · Cited by 194
Jimura, K., & Poldrack, R. A. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.11.007 · Cited by 185
White matter integrity in small vessel disease is related to cognition
Tuladhar, A. M., van Norden, A. G. W., de Laat, K. F., Zwiers, M. P., van Dijk, E. J., Norris, D. G., & de Leeuw, F.-E. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.02.003 · Cited by 182
Doppelmayr, M. M., Finkenzeller, T., & Sauseng, P. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.12.026 · Cited by 190
Hecht, E. E., Gutman, D. A., Khreisheh, N., Taylor, S. V., Kilner, J. M., Faisal, A. A., Bradley, B. A., Chaminade, T., & Stout, D. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0789-6 · Cited by 188
Tu, S., Wong, S., Hodges, J. R., Irish, M., Piguet, O., & Hornberger, M. (2015) · Cortex · doi:10.1016/j.cortex.2015.03.016 · Cited by 180
Factors underlying prefrontal and insula structural alterations in smokers
Zhang, X., Salmeron, B. J., Ross, T. J., Geng, X., Yang, Y., & Stein, E. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.008 · Cited by 188
Hellyer, P. J., Scott, G., Shanahan, M., Sharp, D. J., & Leech, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4648-14.2015 · Cited by 187
Ibarretxe‐Bilbao, N., Junqué, C., TOLOSA, E. S., Martı́, M. J., Valldeoriola, F., Bargalló, N., & Zarei, M. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06892.x · Cited by 187
Seeber, M., Scherer, R., Wagner, J., Solis‐Escalante, T., & Müller-Putz, G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.045 · Cited by 195
Jobert, M., Wilson, F. J., Ruigt, G. S. F., Brunovský, M., Prichep, L. S., & Drinkenburg, W. H. I. M. (2012) · Neuropsychobiology · doi:10.1159/000343478 · Cited by 186
The neural dynamics of hierarchical Bayesian causal inference in multisensory perception
Rohe, T., Ehlis, A.-C., & Noppeney, U. (2019) · Nature Communications · doi:10.1038/s41467-019-09664-2 · Cited by 187
Imaging the where and when of tic generation and resting state networks in adult Tourette patients
Neuner, I., Werner, C. J., Arrubla, J., Stöcker, T., Ehlen, C., Wegener, H. P., Schneider, F. W., & Shah, N. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00362 · Cited by 198
Hsu, J., Chen, Y., Leu, J.-G., Jaw, F.-S., Lee, C.-H., Tsai, Y., Hsu, C., Bai, C., & Leemans, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.041 · Cited by 180
Symmetry of Cortical Folding Abnormalities in Williams Syndrome Revealed by Surface-Based Analyses
Essen, D. C. V., Dierker, D. L., Snyder, A. Z., Raichle, M. E., Reiss, A. L., & Korenberg, J. R. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.4154-05.2006 · Cited by 183
Gamma-Band Synchronization in the Macaque Hippocampus and Memory Formation
Jutras, M. J., Fries, P., & Buffalo, E. A. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0640-09.2009 · Cited by 193
White matter damage in frontotemporal lobar degeneration spectrum
Agosta, F., Scola, E., Canu, E., Marcone, A., Magnani, G., Sarro, L., Copetti, M., Caso, F., Cerami, C., Comi, G., et al. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr288 · Cited by 180
Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula
Geuter, S., Boll, S., Eippert, F., & Büchel, C. (2017) · eLife · doi:10.7554/elife.24770 · Cited by 184
Mining the posterior cingulate: Segregation between memory and pain components
Nielsen, F. Å., Balslev, D., & Hansen, L. K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.04.034 · Cited by 185
Group analysis of DTI fiber tract statistics with application to neurodevelopment
Goodlett, C., Fletcher, P. T., Gilmore, J. H., & Gerig, G. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.060 · Cited by 187
The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states
Munn, B., Müller, E. J., Wainstein, G., & Shine, J. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26268-x · Cited by 188
Intrinsic organization of the anesthetized brain
Liang, Z., King, J., & Zhang, N. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.1020-12.2012 · Cited by 168
Gordon, B. A., Friedrichsen, K., Brier, M., Blazey, T., Su, Y., Christensen, J., Aldea, P., McConathy, J., Holtzman, D. M., Cairns, N. J., et al. (2016) · Brain · doi:10.1093/brain/aww139 · Cited by 184
Takamiya, A., Hirano, J., Yamagata, B., Takei, S., Kishimoto, T., & Mimura, M. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00001 · Cited by 184
A comprehensive study of whole-brain functional connectivity in children and young adults
Jolles, D. D., van Buchem, M. A., Crone, E. A., & Rombouts, S. A. R. B. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq104 · Cited by 181
A theta rhythm in macaque visual cortex and its attentional modulation
Spyropoulos, G., Bosman, C. A., & Fries, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719433115 · Cited by 187
The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson’s disease
Biondetti, E., Santin, M. D., Valabrègue, R., Mangone, G., Gaurav, R., Pyatigorskaya, N., Hutchison, M., Yahia‐Cherif, L., Villain, N., Habert, M., et al. (2021) · Brain · doi:10.1093/brain/awab191 · Cited by 186
Metadata
- Authors
- Nichols, T. E., & Holmes, A. P.
- Affiliations
- University of Michigan (US)
- UCL Queen Square Institute of Neurology (GB)
- ORCID
- Nichols, Thomas E.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2001-10-15
- Volume
- 15
- Issue
- 1
- Pages
- 1-25
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6871862
- OpenAlex
- W2138790588
- Cited by
- 6,256
- References
- 56
- Citations by year
- 2026: 223 · 2025: 264 · 2024: 255 · 2023: 275 · 2022: 321 · 2021: 350 · 2020: 392 · 2019: 368 · 2018: 381 · 2017: 368 · 2016: 343 · 2015: 385 · 2014: 365 · 2013: 356 · 2012: 314
- FWCI
- 16.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- Wellcome Trust
- 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 › Medical Imaging Techniques and Applications
- Keywords
- permutation tests, functional neuroimaging, multiple testing correction, statistical parametric mapping, random field theory, PET, functional MRI, population effects, MATLAB software
- MeSH terms
- Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Signal Processing, Computer-Assisted, Tomography, Emission-Computed, Statistical Distributions
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
