Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes
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Abstract
Concerns regarding reproducibility of resting‐state functional magnetic resonance imaging (R‐fMRI) findings have been raised. Little is known about how to operationally define R‐fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test–retest reliability and replicability, on widely used R‐fMRI metrics in both between‐subject contrasts of sex differences and within‐subject comparisons of eyes‐open and eyes‐closed (EOEC) conditions. We noted permutation test with Threshold‐Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family‐wise error rate (under 5%) and test–retest reliability/replicability (e.g., 0.68 for test–retest reliability and 0.25 for replicability of amplitude of low‐frequency fluctuations (ALFF) for between‐subject sex differences, 0.49 for replicability of ALFF for within‐subject EOEC differences). Although R‐fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between‐subject sex differences, < 0.5 for within‐subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect “true” effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R‐fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300–318, 2018 . © 2017 Wiley Periodicals, Inc.
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The multiple comparison problem was accounted for by using threshold-free cluster enhancement, except for graph measures where false discovery rate with p < 0.05 was used.
“The multiple comparison problem was accounted for by using a threshold-free cluster enhancement (TFCE) method (Chen et al., 2018; Smith & Nichols, 2009), except for the graph measures, where we used the false discovery rate (FDR) method with p < 0.05.”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Chen et al., 2018
TFCE can assess statistical significance at the voxel and ROI level without requiring an arbitrary threshold, thus providing unbiased results.
“TFCE’s corrections were employed for raw and ROI-to-ROI connectivity because they can assess statistical significance at the voxel and ROI level without requiring one to set an arbitrary threshold, thus providing unbiased results (Chen et al., 2018; Smith & Nichols, 2009).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Chen et al., 2018
TFCE is more sensitive to both focal and peripheral effects than classical correction methods, reaching the best balance between family-wise error rates and replicability.
“Additionally, TFCE is more sensitive to both focal and peripheral effects than classical correction methods, reaching the best balance between family-wise error (FWE) rates and replicability (Chen et al., 2018).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Chen et al., 2018
Cites
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 23,433 · see citing works
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553 · see citing works
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515 · see citing works
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · see citing works
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 21× · see citing works
“Here, we define findings that are reproducible in many datasets as the “gold standard”, which makes it possible to quantify sensitivity and PPV as a function of sample size.”
BackgroundMethodInfluentialAshburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507 · see citing works
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700 · 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
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
Self-citationMovement‐Related effects in fMRI time‐series
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350312 · Cited by 3,774 · 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
DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI
Yan (2010) · Frontiers in System Neuroscience · doi:10.3389/fnsys.2010.00013 · Cited by 3,475 · see citing works
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · see citing works
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862 · see citing works
Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI
Yu-Feng, Z., Yong, H., Chao-Zhe, Z., Qing-Jiu, C., Man-Qiu, S., Meng, L., Li-Xia, T., Tian-Zi, J., & Yu-Feng, W. (2007) · Brain and Development · doi:10.1016/j.braindev.2006.07.002 · Cited by 2,660 · see citing works
Regional homogeneity approach to fMRI data analysis.
Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.030 · Cited by 2,614 · see citing works
The role of the posterior cingulate cortex in cognition and disease
Leech, R., & Sharp, D. J. (2014) · Brain · doi:10.1093/brain/awt162 · Cited by 2,247 · see citing works
Zou, Q., Zhu, C.-Z., Yang, Y., Zuo, X., Long, X., Cao, Q., Wang, Y., & Zang, Y. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.04.012 · Cited by 2,169 · see citing works
Statistics Notes: Diagnostic tests 1: sensitivity and specificity
Altman, D. G., & Bland, J. M. (1994) · BMJ · doi:10.1136/bmj.308.6943.1552 · Cited by 2,074 · 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
Satterthwaite, T., Elliott, M., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M., Eickhoff, S., Hakonarson, H., Gur, R., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,900 · see citing works
Believe it or not: how much can we rely on published data on potential drug targets?
Prinz, F., Schlange, T., & Asadullah, K. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3439-c1 · Cited by 1,888 · see citing works
Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461 · see citing works
Self-citationThe oscillating brain: Complex and reliable
Zuo, X., Martino, A. D., Kelly, C. A. M., Shehzad, Z. E., Gee, D. G., Klein, D. F., Castellanos, F. X., Biswal, B. B., & Milham, M. P. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.037 · Cited by 1,460 · see citing works
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 · see citing works
A Baseline for the Multivariate Comparison of Resting-State Networks
Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W. E., Segall, J. M., Silva, R. F., Havlíček, M., Rachakonda, S., Fries, J. F., Kalyanam, R., et al. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00002 · Cited by 1,286 · see citing works
Fransson, P., & Marrelec, G. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.059 · Cited by 1,213 · cited 1× · see citing works
“It has been suggested that the PCC is associated with self-referential thoughts, emotions relating to others, remembering the past and thinking about the future [Fransson and Marrelec, 2008; Leech and Sharp, 2014; Maddock et al., 2001], thus our results are consistent r R-fMRI Reproducibility: on the Impact of Correct…”
ResultControlling 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
Network Centrality in the Human Functional Connectome
Zuo, X., Ehmke, R. C., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., & Milham, M. P. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr269 · Cited by 1,168 · see citing works
Towards a consensus regarding global signal regression for resting state functional connectivity MRI
Murphy, K., & Fox, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.052 · Cited by 1,043 · see citing works
The Resting Brain: Unconstrained yet Reliable
Shehzad, Z. E., Kelly, C. A. M., Reiss, P. T., Gee, D. G., Gotimer, K., Uddin, L. Q., Lee, S. H., Margulies, D. S., Roy, A. K., Biswal, B. B., et al. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn256 · Cited by 913 · see citing works
FMRI Clustering in AFNI: False-Positive Rates Redux
Cox, R., Chen, G., Glen, D., Reynolds, R., & Taylor, P. (2017) · Brain Connectivity · doi:10.1089/brain.2016.0475 · Cited by 792 · see citing works
Hallquist, M. N., Hwang, K., & Luna, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.116 · Cited by 753 · see citing works
Zuo, X., Kelly, C. A. M., Martino, A. D., Mennes, M., Margulies, D. S., Bangaru, S. D., Grzadzinski, R., Evans, A. C., Zang, Y., Castellanos, F. X., et al. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2612-10.2010 · Cited by 741 · see citing works
Zuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 657 · see citing works
Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI
Yang, H., Long, X., Yang, Y., Yan, H., Zhu, C.-Z., Zhou, X.-P., Zang, Y., & Gong, Q. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.054 · Cited by 613 · see citing works
Gorgolewski, K. J., Varoquaux, G. P., Rivera, G., Schwarz, Y., Ghosh, S., Maumet, C., Sochat, V., Nichols, T. E., Poldrack, R. A., Poline, J., et al. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00008 · Cited by 554 · see citing works
Remembering familiar people: the posterior cingulate cortex and autobiographical memory retrieval
Maddock, R. J., Garrett, A. S., & Buonocore, M. H. (2001) · Neuroscience · doi:10.1016/s0306-4522(01)00108-7 · Cited by 551 · see citing works
Zuo, X., Xu, T., Jiang, L., Yang, Z., Cao, X., Yong He, A., Zang, Y., Castellanos, F. X., & Milham, M. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.017 · Cited by 506 · 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
An open science resource for establishing reliability and reproducibility in functional connectomics
Zuo, X., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.49 · Cited by 441 · see citing works
Self-citationYan, C.-G., Craddock, R. C., Zuo, X.-N., Zang, Y.-F., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.081 · Cited by 440 · see citing works
Self-citationDecreased interhemispheric functional connectivity in autism
Anderson, J. S., Druzgal, T. J., Froehlich, A., DuBray, M. B., Lange, N., Alexander, A. L., Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq190 · Cited by 438 · see citing works
On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments
Carp, J. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00149 · Cited by 381 · see citing works
Faster permutation inference in brain imaging
Winkler, A. M., Ridgway, G. R., Douaud, G., Nichols, T. E., & Smith, S. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.068 · Cited by 321 · see citing works
Yan, C., Liu, D., Yong He, A., Zou, Q., Zhu, C., Zuo, X., Long, X., & Zang, Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005743 · Cited by 304 · see citing works
Self-citationGender Differences in Brain Functional Connectivity Density
Tomasi, D., & Volkow, N. (2011) · Human Brain Mapping · doi:10.1002/hbm.21252 · Cited by 285 · see citing works
Luo, C., Li, Q., Lai, Y., Yang, X., Qin, Y. F., Liao, W., Li, S., Zhou, D., Yao, D., & Gong, Q. (2010) · Human Brain Mapping · doi:10.1002/hbm.21034 · Cited by 257 · see citing works
Measuring fMRI reliability with the intra-class correlation coefficient
Caceres, A., Hall, D. L., Zelaya, F. O., Williams, S. C. R., & Mehta, M. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.035 · Cited by 255 · see citing works
Estimation of the Probabilities of 3D Clusters in Functional Brain Images
Ledberg, A., Åkerman, S., & Roland, P. E. (1998) · NeuroImage · doi:10.1006/nimg.1998.0336 · Cited by 253 · see citing works
Eyes open and eyes closed as rest conditions: impact on brain activation patterns
Marx, E., Deutschlander, A. B., Stephan, T., Dieterich, M., Wiesmann, M., & Brandt, T. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.026 · Cited by 239 · see citing works
Sex differences in normal age trajectories of functional brain networks
Scheinost, D., Finn, E. S., Tokoglu, F., Shen, X., Papademetris, X., Hampson, M., & Constable, R. T. (2014) · Human Brain Mapping · doi:10.1002/hbm.22720 · Cited by 232 · see citing works
Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M., Esslinger, C., Kirsch, P., Meyer-Lindenberg, A., & Tost, H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.013 · Cited by 227 · see citing works
Default mode network connectivity: effects of age, sex, and analytic approach
Bluhm, R. L., Osuch, E. A., Lanius, R. A., Boksman, K., Neufeld, R. W. J., Théberge, J., & Williamson, P. (2008) · Neuroreport · doi:10.1097/wnr.0b013e328300ebbf · Cited by 222 · see citing works
Test-retest reproducibility of the default-mode network in healthy individuals
Meindl, T., Teipel, S., Elmouden, R., Mueller, S., Koch, W., Dietrich, O., Coates, U., Reiser, M., & Glaser, C. (2010) · Human Brain Mapping · doi:10.1002/hbm.20860 · Cited by 220 · see citing works
Filippi, M., Valsasina, P., Misci, P., Falini, A., Comi, G., & Rocca, M. A. (2013) · Human Brain Mapping · doi:10.1002/hbm.21514 · Cited by 198 · see citing works
Rombouts, S. A. R. B., Barkhof, F., Hoogenraad, F. G. C., Sprenger, M., & Scheltens, P. (1998) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(97)00253-1 · Cited by 169 · see citing works
Zou, Q., Long, X., Zuo, X., Yan, C., Zhu, C., Yang, Y., Liu, D., Yong He, A., & Zang, Y. (2009) · Human Brain Mapping · doi:10.1002/hbm.20728 · Cited by 148 · see citing works
Self-citationHjelmervik, H., Hausmann, M., Osnes, B., Westerhausen, R., & Specht, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103492 · Cited by 138 · see citing works
Consistency and variability in functional localisers
Duncan, K. J. K., Pattamadilok, C., Knierim, I., & Devlin, J. T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.014 · Cited by 131 · see citing works
Liu, D., Dong, Z.-Y., Zuo, X., Wang, J., & Zang, Y. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9187-0 · Cited by 93 · see citing works
Spatial smoothing systematically biases the localization of reward-related brain activity
Sacchet, M. D., & Knutson, B. D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.056 · Cited by 86 · see citing works
Task and task-free FMRI reproducibility comparison for motor network identification
Kristo, G., Rutten, G.-J. M., Raemaekers, M. A. H., de Gelder, B., Rombouts, S. A. R. B., & Ramsey, N. F. (2012) · Human Brain Mapping · doi:10.1002/hbm.22180 · Cited by 72 · see citing works
Xu, C., Li, C., Wu, H., Wu, Y., Hu, S., Zhu, Y., Zhang, W., Wang, L., Zhu, S., Liu, J., et al. (2015) · BioMed Research International · doi:10.1155/2015/183074 · Cited by 49 · see citing works
Beltz, A. M., Berenbaum, S. A., & Wilson, S. J. (2015) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000033 · Cited by 42 · see citing works
Leveraging Statistical Methods to Improve Validity and Reproducibility of Research Findings
Blackford, J. U. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.3730 · Cited by 39 · see citing works
Reproducibility of Resting State Connectivity in Patients with Stable Multiple Sclerosis
Pinter, D., Beckmann, C. F., Koini, M., Pirker, E., Filippini, N., Pichler, A., Fuchs, S., Fazekas, F., & Enzinger, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152158 · Cited by 29 · see citing works
Kilpatrick, L., Istrin, J. J., Gupta, A., Naliboff, B., Tillisch, K., Labus, J., & Mayer, E. (2015) · Biological Psychology · doi:10.1016/j.biopsycho.2015.09.010 · Cited by 22 · see citing works
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Yan, C.-G., Chen, X., Li, L., Castellanos, F. X., Bai, T.-J., Bo, Q.-J., Cao, J., Chen, G.-M., Chen, N.-X., Chen, W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900390116 · Cited by 895 · see citing works
Self-citationLi, J., Duan, X., Cui, Q., Chen, H., & Liao, W. (2019) · Psychological Medicine · doi:10.1017/s0033291718001502 · Cited by 208 · see citing works
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Gonzalez‐Gomez, R., Legaz, A., Moguilner, S., Cruzat, J., Hernández, H., Baez, S., Cocchi, R., Coronel‐Olivero, C., Medel, V., Tagliazuchi, E., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14085 · Cited by 55 · see citing works
Hauptman, M., Blank, I., & Fedorenko, E. (2023) · Cortex · doi:10.1016/j.cortex.2023.01.013 · Cited by 44 · see citing works
Li, G., Hu, J., Zhang, S., Fan, W., Wen, L., Wang, G., & Zhang, D. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.603279 · Cited by 32 · see citing works
Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023) · Network Neuroscience · doi:10.1162/netn_a_00285 · Cited by 10 · see citing works
Zhao, R., Wang, F.-M., Cheng, C., Li, X., Wang, Y., Zhang, F., Li, S.-G., Huang, Y.-H., Zhao, Z.-Y., Wei, W., et al. (2025) · Sleep Medicine · doi:10.1016/j.sleep.2024.11.010 · Cited by 8 · see citing works
- Authors
- Chen, X., Lu, B., & Yan, C.
- Affiliations
- Institute of Psychology, Chinese Academy of Sciences (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Yan, Chao‐Gan
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2017-10-11
- Volume
- 39
- Issue
- 1
- Pages
- 300-318
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6866539
- OpenAlex
- W2762154136
- Cited by
- 340
- References
- 72
- Citations by year
- 2026: 29 · 2025: 36 · 2024: 27 · 2023: 44 · 2022: 53 · 2021: 56 · 2020: 40 · 2019: 33 · 2018: 21 · 2017: 1
- FWCI
- 14.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Funders
- National Natural Science Foundation of China (81671774, 81630031)
- 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
- resting-state fMRI, reproducibility, multiple testing correction, permutation tests, threshold-free cluster enhancement, test-retest reliability, replicability, sample size, amplitude of low-frequency fluctuation, sex differences, positive predictive value, statistical power
- MeSH terms
- Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Rest, Sex Characteristics, Visual Perception, Reproducibility of Results, Sample Size, Young Adult
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