Cited by 875Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

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The effect of scan length on the reliability of resting-state fMRI connectivity estimates

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Birn, Rasmus M., Molloy, Erin K., Patriat, Rémi, Parker, Taurean, Meier, Timothy B., Kirk, Gregory R., Nair, Veena A., Meyerand, M. Elizabeth, Prabhakaran, Vivek·2013·NeuroImage

DOI 10.1016/j.neuroimage.2013.05.099PMC PMC4104183

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Abstract

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

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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 24,532
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 12,187
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
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
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
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429

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Liu, Y., Wang, H., Duan, Y., Huang, J., Ren, Z., Ye, J., Dong, H., Shi, F., Li, K., & Wang, J. (2016) · Radiology · doi:10.1148/radiol.2016152843 · Cited by 69
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Ji, G.-J., Liu, T.-T., Li, Y., Liu, P., Sun, J.-M., Chen, X.-G., Tian, Y.-H., Chen, X.-W., Dahmani, L., Liu, H.-S., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25319 · Cited by 53
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Peng, X., Liu, Q., Hubbard, C. S., Wang, D., Zhu, W., Fox, M., & Liu, H. (2023) · Science Advances · doi:10.1126/sciadv.abq8566 · Cited by 52
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Xu, G., Huo, C., Yin, J., Zhong, Y.-B., Sun, G., Fan, Y., Wang, D., & Li, Z. (2023) · Biomedical Optics Express · doi:10.1364/boe.491610 · Cited by 54
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Wens, V., Bourguignon, M., Goldman, S., Marty, B., Beeck, M. O. D., Clumeck, C., Mary, A., Peigneux, P., Bogaert, P. V., Brookes, M. J., et al. (2014) · Brain Topography · doi:10.1007/s10548-014-0364-8 · Cited by 48
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Haynos, A. F., Camchong, J., Pearson, C. M., Lavender, J. M., Mueller, B. A., Peterson, C. B., Specker, S. M., Raymond, N. C., & Lim, K. O. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa369 · Cited by 49
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Yu, X., Liu, L., Chen, W., Cao, Q., Zepf, F. D., Ji, G., Wu, Z.-M., An, L., Wang, P., Qian, Q., et al. (2016) · Journal of Attention Disorders · doi:10.1177/1087054716661419 · Cited by 42
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Sun, X., Liu, J., Ma, Q., Duan, J., Wang, X., Xu, Y., Xu, Z., Ke Cheng, X. U., Wang, F., Tang, Y., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa155 · Cited by 42
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Wang, S., Zhao, Y., & Li, J. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.01.022 · Cited by 47
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Authors: Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., Nair, V. A., Meyerand, M. E., & Prabhakaran, V.
Affiliations: University of Wisconsin–Madison (US)
University of Rochester (US)
Corresponding author: Birn, Rasmus M.
ORCID: Birn, Rasmus M.
Molloy, Erin K.
Patriat, Rémi
Meier, Timothy B.
Nair, Veena A.
Meyerand, M. Elizabeth
Prabhakaran, Vivek
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2013-12
Volume: 83
Pages: 550-558
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2013.05.099
PMC: PMC4104183
OpenAlex: W2084892943
Cited by: 875
References: 40
Citations by year: 2026: 81 · 2025: 93 · 2024: 72 · 2023: 91 · 2022: 77 · 2021: 101 · 2020: 82 · 2019: 59 · 2018: 62 · 2017: 48 · 2016: 43 · 2015: 40 · 2014: 22 · 2013: 3
FWCI: 19.39
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: NIH (RC1MH090912)
Health Emotions Research Institute
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 functional connectivity, scan length, test-retest reliability, inter-session reliability, within-session reliability, time series length, sampling frequency, longitudinal fMRI, functional brain networks
MeSH terms: Adult, Brain, Brain Mapping, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Nerve Net, Rest, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Reproducibility of Results, Information Storage and Retrieval, Sample Size, Connectome

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The effect of scan length on the reliability of resting-state fMRI connectivity estimates

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