Cited by 252Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsStatistical Methods in Clinical Trials

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation

Open access

Mumford, Jeanette A., Nichols, Thomas E.·2008·NeuroImage

DOI 10.1016/j.neuroimage.2007.07.061PMC PMC2423281

Open this paper in CoChat — free account

Abstract

When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80% power is not as simple as finding the smallest sample size capable of achieving 80% power, since smaller sample sizes require each subject to be scanned longer.

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

Crossref · 10.1016/j.neuroimage.2007.07.061

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001) · NeuroImage · doi:10.1006/nimg.2001.0931 · Cited by 2,859
General multilevel linear modeling for group analysis in FMRI
Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00435-x · Cited by 1,464
Estimating sample size in functional MRI (fMRI) neuroimaging studies: Statistical power analyses
Desmond, J. E., & Glover, G. H. (2002) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(02)00121-8 · Cited by 716
Classical and Bayesian Inference in Neuroimaging: Applications
Friston, K. J., Glaser, D. E., Henson, R. N., Kiebel, S. J., Phillips, C. L. M., & Ashburner, J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1091 · Cited by 709
Empirical Analyses of BOLD fMRI Statistics
Zarahn, E., Aguirre, G. K., & D’Esposito, M. (1997) · NeuroImage · doi:10.1006/nimg.1997.0263 · Cited by 695
Stochastic Designs in Event-Related fMRI
Friston, K. J., Zarahn, E., Josephs, O., Henson, R. N., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1999.0498 · Cited by 634
Classical and Bayesian Inference in Neuroimaging: Theory
Friston, K. J., Penny, W. D., Phillips, C. L. M., Kiebel, S. J., Hinton, G. E., & Ashburner, J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1090 · Cited by 597
Empirical Analyses of BOLD fMRI Statistics
Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1997) · NeuroImage · doi:10.1006/nimg.1997.0264 · Cited by 474
Optimization of experimental design in fMRI: a general framework using a genetic algorithm
Wager, T. D., & Nichols, T. E. (2003) · NeuroImage · doi:10.1016/s1053-8119(02)00046-0 · Cited by 458
Self-citation
Event-related functional magnetic resonance imaging: modelling, inference and optimization
Josephs, O., & Henson, R. N. A. (1999) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1999.0475 · Cited by 425

Show all 22 references

Sorted by citations

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
Self-citation
Best practices in data analysis and sharing in neuroimaging using MRI
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4500 · Cited by 714
Self-citation
Psychedelic drugs: neurobiology and potential for treatment of psychiatric disorders
Vollenweider, F. X., & Preller, K. H. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0367-2 · Cited by 570
How reliable are the results from functional magnetic resonance imaging?
Bennett, C. M., & Miller, M. B. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2010.05446.x · Cited by 579
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 463
Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals
Szucs, D., & Ioannidis, J. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117164 · Cited by 453
Convergence of spoken and written language processing in the superior temporal sulcus
Wilson, S. M., Bautista, A., & McCarron, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.068 · Cited by 402
Small sample sizes reduce the replicability of task-based fMRI studies
Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018) · Communications Biology · doi:10.1038/s42003-018-0073-z · Cited by 420
Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach
Monti, M. M. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00028 · Cited by 343
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315
Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions
Hagen, E. V. D., Stoyanova, R. S., Baron‐Cohen, S., & Calder, A. J. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss053 · Cited by 315
Neuroplasticity Associated With Anterior Cruciate Ligament Reconstruction
Grooms, D. R., Page, S. J., Nichols‐Larsen, D. S., Chaudhari, A. M. W., White, S., & Oñate, J. A. (2016) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2017.7003 · Cited by 293
A 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 304
Neural systems subserving valence and arousal during the experience of induced emotions.
Colibazzi, T., Posner, J. E., Wang, Z., Gorman, D. A., Gerber, A. J., Yu, S., Hongtu Zhu, B., Kangarlu, A., Duan, Y., Russell, J. A., et al. (2010) · Emotion · doi:10.1037/a0018484 · Cited by 281
The neurophysiological bases of emotion: An fMRI study of the affective circumplex using emotion‐denoting words
Posner, J. E., Russell, J. A., Gerber, A. J., Gorman, D. A., Colibazzi, T., Yu, S., Wang, Z., Kangarlu, A., Hongtu Zhu, B., & Peterson, B. S. (2008) · Human Brain Mapping · doi:10.1002/hbm.20553 · Cited by 232
A power calculation guide for fMRI studies
Mumford, J. A. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss059 · Cited by 171
Self-citation
Short-Term Consumption of Sucralose with, but Not without, Carbohydrate Impairs Neural and Metabolic Sensitivity to Sugar in Humans
Dalenberg, J. R., Patel, B. P., Denis, R., Veldhuizen, M. G., Nakamura, Y., Vinke, P. C., Luquet, S. H., & Small, D. M. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.01.014 · Cited by 163
The “Task B problem” and other considerations in developmental functional neuroimaging
Church, J. A., Petersen, S. E., & Schlaggar, B. L. (2010) · Human Brain Mapping · doi:10.1002/hbm.21036 · Cited by 148
Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML
Cusack, R., Vicente-Grabovetsky, A., Mitchell, D. J., Wild, C. J., Auer, T., Linke, A. C., & Peelle, J. E. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00090 · Cited by 144
Influence of sample size and analytic approach on stability and interpretation of brain‐behavior correlations in task‐related fMRI data
Grady, C. L., Rieck, J. R., Nichol, D., Rodrigue, K. M., & Kennedy, K. M. (2020) · Human Brain Mapping · doi:10.1002/hbm.25217 · Cited by 143
Neuroanatomical abnormalities in chronic tinnitus in the human brain
Adjamian, P., Hall, D. A., Palmer, A., Allan, T. W., & Langers, D. (2014) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.013 · Cited by 100
The structural and functional brain networks that support human social networks
Noonan, M. P., Mars, R. B., Sallet, J., Dunbar, R. I. M., & Fellows, L. K. (2018) · Behavioural Brain Research · doi:10.1016/j.bbr.2018.02.019 · Cited by 131
How to Conduct a Functional Magnetic Resonance (fMRI) Study in Social Science Research1
Dimoka, A. (2012) · MIS Quarterly · doi:10.2307/41703482 · Cited by 128
Cognitive Neuroscience Approaches to Individual Differences in Working Memory and Executive Control: Conceptual and Methodological Issues
Yarkoni, T., & Braver, T. S. (2010) · Plenum series on human exceptionality · doi:10.1007/978-1-4419-1210-7_6 · Cited by 145
A shift in perspective: Decentering through mindful attention to imagined stressful events
Lebois, L. A. M., Papies, E. K., Gopinath, K., Cabanban, R., Quigley, K. S., Krishnamurthy, V., Barrett, L. F., & Barsalou, L. W. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.05.030 · Cited by 114
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
Connectivity analysis is essential to understand neurological disorders
Rowe, J. B. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00144 · Cited by 92
Opportunities for increased reproducibility and replicability of developmental neuroimaging
Klapwijk, E. T., van den Bos, W., Tamnes, C. K., Raschle, N. M., & Mills, K. L. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100902 · Cited by 87
fMRI replicability depends upon sufficient individual-level data
Nee, D. E. (2019) · Communications Biology · doi:10.1038/s42003-019-0378-6 · Cited by 96
Longer scans boost prediction and cut costs in brain-wide association studies
Ooi, L. Q. R., Orbán, C., Zhang, S., Nichols, T. E., Tan, T. W. K., Kong, R., Marek, S. A., Dosenbach, N. U. F., Laumann, T. O., Gordon, E. M., et al. (2025) · Nature · doi:10.1038/s41586-025-09250-1 · Cited by 94
Self-citation
Social working memory and its distinctive link to social cognitive ability: an fMRI study
Meyer, M. L., Taylor, S. E., & Lieberman, M. D. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv065 · Cited by 88
Multisite reliability of cognitive BOLD data
Brown, G. G., Mathalon, D. H., Stern, H. S., Ford, J. M., Mueller, B. A., Greve, D. N., McCarthy, G., Voyvodic, J. T., Glover, G. H., Diaz, M. T., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.076 · Cited by 83
The empirical replicability of task-based fMRI as a function of sample size
Bossier, H., Roels, S. P., Seurinck, R., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Quinlan, E. B., Desrivières, S., Flor, H., Grigis, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116601 · Cited by 79
Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing
Lombardo, M., Auyeung, B., Holt, R., Waldman, J., Ruigrok, A., Mooney, N., Bullmore, E., Baron-Cohen, S., & Kundu, P. (2015) · bioRxiv · doi:10.1016/j.neuroimage.2016.07.022 · Cited by 99
Neural Competition for Conscious Representation across Time: An fMRI Study
Slagter, H. A., Johnstone, T., Beets, I. A. M., & Davidson, R. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010556 · Cited by 72
Power and sample size calculations for fMRI studies based on the prevalence of active peaksPreprint
Durnez, J., Degryse, J., Moerkerke, B., Seurinck, R., Sochat, V., Poldrack, R. A., & Nichols, T. E. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/049429 · Cited by 69
Self-citation
Commonalities and Asymmetries in the Neurobiological Infrastructure for Language Production and Comprehension
Giglio, L., Ostarek, M., Weber, K., & Peter Hagoort, A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab287 · Cited by 66
Extraordinary Altruists Exhibit Enhanced Self–Other Overlap in Neural Responses to Distress
Brethel-Haurwitz, K. M., Cardinale, E. M., Vekaria, K. M., Robertson, E., Walitt, B. T., VanMeter, J. W., & Marsh, A. A. (2018) · Psychological Science · doi:10.1177/0956797618779590 · Cited by 62
Rule-Selection and Action-Selection have a Shared Neuroanatomical Basis in the Human Prefrontal and Parietal Cortex
Rowe, J. B., Hughes, L. E., Eckstein, D., & Owen, A. M. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm249 · Cited by 67
Lateralization of cervical spinal cord activity during an isometric upper extremity motor task with functional magnetic resonance imaging
Weber, K. A., II, Chen, Y. J., Wang, X., Kahnt, T., & Parrish, T. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.014 · Cited by 61
Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia
Oliva, V., Hartley-Davies, R., Moran, R., Pickering, A. E., & Brooks, J. C. (2022) · eLife · doi:10.7554/elife.71877 · Cited by 61
Longitudinal Brain Development of Numerical Skills in Typically Developing Children and Children with Developmental Dyscalculia
McCaskey, U., von Aster, M., Maurer, U., Martin, E., Tuura, R., & Kucian, K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00629 · Cited by 60
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
Self-citation
Effect of Citalopram on Emotion Processing in Humans: A Combined 5-HT1A [11C]CUMI-101 PET and Functional MRI Study
Selvaraj, S., Walker, C. P. R., Arnone, D., Cao, B., Faulkner, P., Cowen, P. J., Roiser, J. P., & Howes, O. D. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.166 · Cited by 57
Feedback-Driven Trial-by-Trial Learning in Autism Spectrum Disorders
Solomon, M., Frank, M. J., Ragland, J. D., Smith, A. C., Niendam, T. A., Lesh, T. A., Grayson, D. S., Beck, J. S., Matter, J. C., & Carter, C. S. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14010036 · Cited by 60
Neuroimaging reward, craving, learning, and cognitive control in substance use disorders: review and implications for treatment
Tanabe, J., Regner, M. F., Sakai, J. T., Martínez, D., & Gowin, J. L. (2019) · British Journal of Radiology · doi:10.1259/bjr.20180942 · Cited by 58
Neural correlates of video game empathy training in adolescents: a randomized trial
Kral, T. R. A., Stodola, D. E., Birn, R. M., Mumford, J. A., Solis, E., Flook, L., Patsenko, E. G., Anderson, C. G., Steinkuehler, C. A., & Davidson, R. J. (2018) · npj Science of Learning · doi:10.1038/s41539-018-0029-6 · Cited by 61
Self-citation
Unforgettable Ultimatums? Expectation violations promote enhanced social memory following economic bargaining
Chang, L. J. (2009) · Frontiers in Behavioral Neuroscience · doi:10.3389/neuro.08.036.2009 · Cited by 57
The diminishing dominance of the dominant hemisphere: Language fMRI in focal epilepsy
Tailby, C., Abbott, D. F., & Jackson, G. D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.01.011 · Cited by 49
Slow Down: Behavioural and Physiological Effects of Reducing Eating Rate
Hawton, K., Ferriday, D., Rogers, P. J., Toner, P., Brooks, J. C. W., Holly, J. M. P., Biernacka, K. M., Hamilton-Shield, J. P., & Hinton, E. C. (2018) · Nutrients · doi:10.3390/nu11010050 · Cited by 58
Human Taste-Perception: Brain Computer Interface (BCI) and Its Application as an Engineering Tool for Taste-Driven Sensory Studies
Rajan, A., Gómez-Carmona, D., & Radhakrishnan, M. (2022) · Food Engineering Reviews · doi:10.1007/s12393-022-09308-0 · Cited by 52
Neural correlates of effort-based valuation with prospective choices
Aridan, N., Malecek, N. J., Poldrack, R. A., & Schönberg, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.051 · Cited by 54
Baseline Predictors of Health-Related Quality of Life After Anterior Cruciate Ligament Reconstruction
Dunn, W. R., Wolf, B. R., Harrell, F. E., Reinke, E. K., Huston, L. J., MOON Knee Group, & Spindler, K. P. (2015) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.n.00248 · Cited by 47
Power calculations for multicenter imaging studies controlled by the false discovery rate
Suckling, J., Barnes, A., Job, D. E., Brenan, D., Lymer, K., Dazzan, P., Marques, T. R., Mackay, C. E., McKie, S., Williams, S. R., et al. (2010) · Human Brain Mapping · doi:10.1002/hbm.20927 · Cited by 48
Mapping language with resting‐state functional magnetic resonance imaging: A study on the functional profile of the language network
Branco, P., Seixas, D., & Castro, S. L. (2019) · Human Brain Mapping · doi:10.1002/hbm.24821 · Cited by 47
Brainstem processing of cough sensory inputs in chronic cough hypersensitivity
Moe, A. A. K., Singh, N., Dimmock, M., Cox, K., McGarvey, L. P., Chung, K. F., McGovern, A. E., McMahon, M., Richards, A. L., Farrell, M. J., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.104976 · Cited by 45
Functional Magnetic Resonance Imaging of Cognitive Control following Traumatic Brain Injury
Scheibel, R. S. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00352 · Cited by 43
Using Brain Imaging Measures in Studies of Procognitive Pharmacologic Agents in Schizophrenia: Psychometric and Quality Assurance Considerations
Barch, D. M., & Mathalon, D. H. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.01.004 · Cited by 41
Functional magnetic resonance imaging (fMRI) in design studies: Methodological considerations, challenges, and recommendations
Hay, L., Duffy, A. H. B., Gilbert, S. J., & Grealy, M. (2021) · Design Studies · doi:10.1016/j.destud.2021.101078 · Cited by 44
Neurodesign: Optimal Experimental Designs for Task fMRIPreprint
Durnez, J., Blair, R. W., & Poldrack, R. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/119594 · Cited by 41
Estimating and testing variance components in a multi-level GLM
Lindquist, M. A., Spicer, J., Asllani, I., & Wager, T. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.077 · Cited by 42
Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements
Wajid, A., Raziq, M. M., Ahmed, Q. M., & Ahmad, M. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102373 · Cited by 40
Sense or sensibility? The neuro-functional basis of the structural matching effect in persuasion
Aquino, A., Alparone, F. R., Pagliaro, S., Haddock, G., Maio, G. R., Perrucci, M. G., & Ebisch, S. J. H. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00784-7 · Cited by 42
Overview of the clinical implementation of a study exploring social withdrawal in patients with schizophrenia and Alzheimer’s disease
Bilderbeck, A. C., Penninx, B. W. J. H., Arango, C., van der Wee, N. J. A., Kahn, R. S., Rossum, I. W.-V., Hayen, A., Kas, M. J., Post, A., & Dawson, G. R. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.019 · Cited by 37
Poor supplementary motor area activation differentiates auditory verbal hallucination from imagining the hallucination
Raij, T. T., & Riekki, T. J. J. (2012) · NeuroImage Clinical · doi:10.1016/j.nicl.2012.09.007 · Cited by 38
A systematic review of the reporting of sample size calculations and corresponding data components in observational functional magnetic resonance imaging studies
Guo, Q., Thabane, L., Hall, G. B., McKinnon, M. C., Goeree, R. A., & Pullenayegum, E. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.012 · Cited by 38
Effects of motivation on reward and attentional networks: an fMRI study
Ivanov, I., Liu, X., Clerkin, S. M., Schulz, K. P., Friston, K. J., Newcorn, J. H., & Fan, J. (2012) · Brain and Behavior · doi:10.1002/brb3.80 · Cited by 35
Dysfunctions within limbic–motor networks in amyotrophic lateral sclerosis
Passamonti, L., Fera, F. S., Tessitore, A., Russo, A., Cerasa, A., Gioia, C. M., Monsurrò, M. R., Migliaccio, R., Tedeschi, G., & Quattrone, A. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.05.016 · Cited by 37
Combined mnemonic strategy training and high‐definition transcranial direct current stimulation for memory deficits in mild cognitive impairment
Hampstead, B. M., Sathian, K., Bikson, M., & Stringer, A. Y. (2017) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.04.008 · Cited by 36
Frontocingular Dysfunction in Bulimia Nervosa when Confronted with Disease‐specific Stimuli
Joos, A. A. B., Saum, B., Zeeck, A., Perlov, E., Glauche, V., Hartmann, A., Freyer, T., Sandholz, A., Unterbrink, T., van Elst, L. T., et al. (2011) · European Eating Disorders Review · doi:10.1002/erv.1150 · Cited by 35
Absence of change in the gray matter volume of patients with ulcerative colitis in remission: a voxel based morphometry study
Agostini, A., Campieri, M., Bertani, A., Scarcelli, A., Ballotta, D., Calabrese, C., Rizzello, F., Gionchetti, P., Nichelli, P., & Benuzzi, F. (2015) · BioPsychoSocial Medicine · doi:10.1186/s13030-014-0028-7 · Cited by 35
Development of PowerMap: a Software Package for Statistical Power Calculation in Neuroimaging Studies
Joyce, K. E., & Hayasaka, S. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9152-3 · Cited by 34
Testing the Sensitivity of Tract-Based Spatial Statistics to Simulated Treatment Effects in Preterm Neonates
Ball, G., Boardman, J. P., Arichi, T., Merchant, N. M., Rueckert, D., Edwards, A. D., & Counsell, S. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067706 · Cited by 34
Aberrant Cerebellar–Cerebral Functional Connectivity in Children and Adolescents With Autism Spectrum Disorder
Hanaie, R., Mohri, I., Kagitani‐Shimono, K., Tachibana, M., Matsuzaki, J., Hirata, I., Nagatani, F., Watanabe, Y., Katayama, T., & Taniike, M. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00454 · Cited by 34
Effect of acupuncture on brain regions modulation of mild cognitive impairment: A meta-analysis of functional magnetic resonance imaging studies
Ma, S., Huang, H., Zhong, Z., Zheng, H., Li, M., Yao, L., Yu, B., & Wang, H. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.914049 · Cited by 34
Linear association between social anxiety symptoms and neural activations to angry faces: from subclinical to clinical levels
Carré, A., Gierski, F., Lemogne, C., Tran, E., Raucher‐Chéné, D., Bera‐Potelle, C., Portefaix, C., Kaladjian, A., Pierot, L., Besche‐Richard, C., et al. (2013) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst061 · Cited by 35
Power1D: a Python toolbox for numerical power estimates in experiments involving one-dimensional continua
Pataky, T. C. (2017) · PeerJ Computer Science · doi:10.7717/peerj-cs.125 · Cited by 33
Vulnerability for new episodes in recurrent major depressive disorder: protocol for the longitudinal DELTA-neuroimaging cohort study
Mocking, R. J. T., Figueroa, C. A., Rive, M. M., Geugies, H., Servaas, M. N., Assies, J., Koeter, M. W. J., Vaz, F. M., Wichers, M., van Straalen, J. P., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-009510 · Cited by 32
Multimodal evidence for delayed threat extinction learning in adolescence and young adulthood
Morriss, J., Christakou, A., & van Reekum, C. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-44150-1 · Cited by 32
Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation
Darda, K. M., Butler, E. E., & Ramsey, R. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01261 · Cited by 31
“To this end, a power analysis was performed using the fMRIpower software package (fMRIpower.org; Mumford & Nichols, 2008).”
Method
Post-hoc power estimation for topological inference in fMRI
Durnez, J., Moerkerke, B., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.072 · Cited by 36
Self-citation
Revealing the Neuroimaging Mechanism of Acupuncture for Poststroke Aphasia: A Systematic Review
Li, B., Deng, S., Sang, B., Zhu, W., Zhuo, B., Zhang, M., Qin, C., Lyu, Y., Du, Y., & Meng, Z. (2022) · Neural Plasticity · doi:10.1155/2022/5635596 · Cited by 32
The Reporting of Observational Clinical Functional Magnetic Resonance Imaging Studies: A Systematic Review
Guo, Q., Parlar, M. E., Truong, W., Hall, G. B., Thabane, L., McKinnon, M. C., Goeree, R. A., & Pullenayegum, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094412 · Cited by 31
Statistical testing and power analysis for brain-wide association study
Gong, W., Wan, L., Lu, W., Ma, L., Cheng, F., Cheng, W., Grünewald, S., & Feng, J. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.03.014 · Cited by 31
Characterizing habit learning in the human brain at the individual and group levels: A multi-modal MRI study
Gera, R., Or, M. B., Tavor, I., Roll, D., Cockburn, J., Barak, S., Tricomi, E. M., O’Doherty, J. P., & Schönberg, T. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120002 · Cited by 36
Social Working Memory Predicts Social Network Size in Humans
Krol, S. A., Meyer, M. L., Lieberman, M. D., & Bartz, J. A. (2018) · Adaptive Human Behavior and Physiology · doi:10.1007/s40750-018-0100-9 · Cited by 29
Functional Magnetic Resonance Imaging of Cerebral Hemodynamic Responses to Pain Following Thoracic Thrust Manipulation in Individuals With Neck Pain: A Randomized Trial
Sparks, C. L., Liu, W. C., Cleland, J. A., Kelly, J. P., Dyer, S. J., Szetela, K. M., & Elliott, J. M. (2017) · Journal of Manipulative and Physiological Therapeutics · doi:10.1016/j.jmpt.2017.07.010 · Cited by 27
Specificity, reliability and sensitivity of social brain responses during spontaneous mentalizing
Moessnang, C., Schäfer, A., Bilek, E., Roux, P., Otto, K., Baumeister, S., Hohmann, S., Poustka, L., Brandeis, D., Banaschewski, T., et al. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw098 · Cited by 25
Analysis of task-based functional MRI data preprocessed with fMRIPrepPreprint
Estéban, O., Ćirić, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPré, E., Gomez, D. E. P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/694364 · Cited by 24
The Neural Bases of Action-Outcome Learning in Humans
Morris, R. W., Dezfouli, A., Griffiths, K. R., Pelley, M. E. L., & Balleine, B. W. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1079-21.2022 · Cited by 23
Fronto-parietal homotopy in resting-state functional connectivity predicts task-switching performance
Vallesi, A., Visalli, A., Gracia-Tabuenca, Z., Tarantino, V., Capizzi, M., Alcauter, S., Mantini, D., & Pini, L. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02312-w · Cited by 24
Mid age APOE ε4 carriers show memory-related functional differences and disrupted structure-function relationships in hippocampal regions
Evans, S., Dowell, N. G., Prowse, F., Tabet, N., King, S. L., & Rusted, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-59272-0 · Cited by 22
An Iterative Jackknife Approach for Assessing Reliability and Power of fMRI Group Analyses
Wilke, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035578 · Cited by 21
The fronto‐parietal network is not a flexible hub during naturalistic cognition
Caldinelli, C., & Cusack, R. (2021) · Human Brain Mapping · doi:10.1002/hbm.25684 · Cited by 21
Replicability in Brain Imaging
Kelly, R. E., & Hoptman, M. J. (2022) · Brain Sciences · doi:10.3390/brainsci12030397 · Cited by 24
Neuroimaging Epigenetics: Challenges and Recommendations for Best Practices
Lancaster, K., Morris, J. P., & Connelly, J. J. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.08.004 · Cited by 23
The association between cognitive reserve and performance-related brain activity during episodic encoding and retrieval across the adult lifespan
Elshiekh, A., Subramaniapillai, S., Rajagopal, S., Pasvanis, S., Ankudowich, E., & Rajah, M. N. (2020) · Cortex · doi:10.1016/j.cortex.2020.05.003 · Cited by 18
A methodological assessment of studies that use voxel-based morphometry to study neural changes in tinnitus patients
Scott‐Wittenborn, N., Karadaghy, O. A., Piccirillo, J. F., & Peelle, J. E. (2017) · Hearing Research · doi:10.1016/j.heares.2017.09.002 · Cited by 20
Logical and Methodological Issues Affecting Genetic Studies of Humans Reported in Top Neuroscience Journals
Grabitz, C. R., Button, K. S., Munafo, M. R., Newbury, D. F., Pernet, C. R., Thompson, P. A., & Bishop, D. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01192 · Cited by 18
Plantar stimulation alters brain connectivity in idiopathic Parkinson's disease
Pagnussat, A. S., Salazar, A. P., Pinto, C., Marchese, R. R., Rieder, C. R. D. M., Filho, J. O. A., Franco, A. R., & Kleiner, A. F. R. (2020) · Acta Neurologica Scandinavica · doi:10.1111/ane.13253 · Cited by 18

Authors: Mumford, J. A., & Nichols, T. E.
Affiliations: University of California, Los Angeles (US)
Hammersmith Hospital (GB)
Corresponding author: Mumford, Jeanette A.
ORCID: Mumford, Jeanette A.
Nichols, Thomas E.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2008-01
Volume: 39
Issue: 1
Pages: 261-268
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2007.07.061
PMC: PMC2423281
OpenAlex: W1967252112
Cited by: 252
References: 22
Citations by year: 2026: 10 · 2025: 22 · 2024: 8 · 2023: 12 · 2022: 18 · 2021: 17 · 2020: 29 · 2019: 21 · 2018: 20 · 2017: 19 · 2016: 11 · 2015: 10 · 2014: 7 · 2013: 17 · 2012: 10
FWCI: 1.79
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (R01 DA15410, R01 EB004346-01A1)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: statistical power analysis, sample size determination, temporal autocorrelation, research design, neuroimaging
MeSH terms: Adult, Algorithms, Brain Mapping, Computer Simulation, Evoked Potentials, Auditory, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Sensitivity and Specificity, Speech Perception, Statistics as Topic, Reproducibility of Results

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

Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks