Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 660

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.

Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective

Zuo, Xi-Nian, Xing, Xiu-Xia·2014·Neuroscience & Biobehavioral Reviews
DOI 10.1016/j.neubiorev.2014.05.009
Open this paper in CoChat — free account

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neubiorev.2014.05.009

Cites

71 of 71 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 71 references

Research works printed but not linked: 159

  1. Achard, 2006, A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs, J. Neurosci., 26, 63, 10.1523/JNEUROSCI.3874-05.2006 · DOI · Paper page
  2. Adelstein, 2011, Personality is reflected in the brain's intrinsic functional architecture, PLoS One, 6, e27633, 10.1371/journal.pone.0027633 · DOI · Paper page
  3. Akil, 2011, Challenges and opportunities in mining neuroscience data, Science, 331, 708, 10.1126/science.1199305 · DOI · Paper page
  4. Alivisatos, 2013, Nanotools for neuroscience and brain activity mapping, ACS Nano, 7, 1850, 10.1021/nn4012847 · DOI · Paper page
  5. Alivisatos, 2013, Neuroscience. the brain activity map, Science, 339, 1284, 10.1126/science.1236939 · DOI · Paper page
  6. Alivisatos, 2012, The brain activity map project and the challenge of functional connectomics, Neuron, 74, 970, 10.1016/j.neuron.2012.06.006 · DOI · Paper page
  7. Anderson, 2011, Reproducibility of single-subject functional connectivity measurements, AJNR Am. J. Neuroradiol., 32, 548, 10.3174/ajnr.A2330 · DOI · Paper page
  8. Anderson, 2014, Complexity of low-frequency blood oxygen level-dependent fluctuations covaries with local connectivity, Hum. Brain Map., 35, 1273, 10.1002/hbm.22251 · DOI
  9. Barnhart, 2007, An overview on assessing agreement with continuous measurements, J. Biopharm. Stat., 17, 529, 10.1080/10543400701376480 · DOI · Paper page
  10. Beckmann, 2012, Modelling with independent components, Neuroimage, 62, 891, 10.1016/j.neuroimage.2012.02.020 · DOI · Paper page
  11. Bellec, 2010, Multi-level bootstrap analysis of stable clusters in resting-state fMRI, Neuroimage, 51, 1126, 10.1016/j.neuroimage.2010.02.082 · DOI · Paper page
  12. Bernal-Rusiel, 2012, Alzheimer's Disease Neuroimaging I. Statistical analysis of longitudinal neuroimage data with linear mixed effects models, Neuroimage, 66C, 249
  13. Birn, 2012, The role of physiological noise in resting-state functional connectivity, Neuroimage, 62, 864, 10.1016/j.neuroimage.2012.01.016 · DOI · Paper page
  14. Birn, 2008, The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration, Neuroimage, 40, 644, 10.1016/j.neuroimage.2007.11.059 · DOI · Paper page
  15. Biswal, 2010, Toward discovery science of human brain function, Proc. Natl. Acad. Sci. U. S. A., 107, 4734, 10.1073/pnas.0911855107 · DOI · Paper page
  16. Blumensath, 2013, Spatially constrained hierarchical parcellation of the brain with resting-state fMRI, Neuroimage, 76, 313, 10.1016/j.neuroimage.2013.03.024 · DOI · Paper page
  17. Bright, 2013, Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance, Neuroimage, 83C, 559, 10.1016/j.neuroimage.2013.07.007 · DOI · Paper page
  18. Bright, 2013, Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data, Neuroimage, 64, 526, 10.1016/j.neuroimage.2012.09.043 · DOI
  19. Buckner, 2013, Opportunities and limitations of intrinsic functional connectivity MRI, Nat. Neurosci., 16, 832, 10.1038/nn.3423 · DOI · Paper page
  20. Bullmore, 2011, Brain graphs: graphical models of the human brain connectome, Annu. Rev. Clin. Psychol., 7, 113, 10.1146/annurev-clinpsy-040510-143934 · DOI · Paper page
  21. Castellanos, 2013, Clinical applications of the functional connectome, Neuroimage, 80, 527, 10.1016/j.neuroimage.2013.04.083 · DOI · Paper page
  22. Chang, 2009, Influence of heart rate on the BOLD signal: the cardiac response function, Neuroimage, 44, 857, 10.1016/j.neuroimage.2008.09.029 · DOI · Paper page
  23. Chang, 2009, Effects of model-based physiological noise correction on default mode network anti-correlations and correlations, Neuroimage, 47, 1448, 10.1016/j.neuroimage.2009.05.012 · DOI · Paper page
  24. Chang, 2009, Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI, Neuroimage, 47, 1381, 10.1016/j.neuroimage.2009.04.048 · DOI · Paper page
  25. Chen, 2013, Linear mixed-effects modeling approach to FMRI group analysis, Neuroimage, 73, 176, 10.1016/j.neuroimage.2013.01.047 · DOI · Paper page
  26. Cole, 2010, Advances and pitfalls in the analysis and interpretation of resting-state FMRI data, Front. Syst. Neurosci., 4, 8
  27. Cordes, 2002, Hierarchical clustering to measure connectivity in fMRI resting-state data, Magn. Reson. Imaging, 20, 305, 10.1016/S0730-725X(02)00503-9 · DOI · Paper page
  28. Cox, 2012, AFNI: what a long strange trip it's been, Neuroimage, 62, 743, 10.1016/j.neuroimage.2011.08.056 · DOI · Paper page
  29. Deco, 2011, The dynamical balance of the brain at rest, Neuroscientist, 17, 107, 10.1177/1073858409354384 · DOI · Paper page
  30. Deshpande, 2009, Integrated local correlation: a new measure of local coherence in fMRI data, Hum. Brain Map., 30, 13, 10.1002/hbm.20482 · DOI
  31. Deuker, 2009, Reproducibility of graph metrics of human brain functional networks, Neuroimage, 47, 1460, 10.1016/j.neuroimage.2009.05.035 · DOI · Paper page
  32. Di Martino, 2009, Relationship between cingulo-insular functional connectivity and autistic traits in neurotypical adults, Am. J. Psychiatry, 166, 891, 10.1176/appi.ajp.2009.08121894 · DOI · Paper page
  33. Di Martino, 2014, The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism, Mol. Psychiatry, 19, 659, 10.1038/mp.2013.78 · DOI · Paper page
  34. Dong, 2014, Expertise modulates local regional homogeneity of spontaneous brain activity in the resting brain: An fMRI study using the model of skilled acupuncturists, Hum. Brain Map., 35, 1074, 10.1002/hbm.22235 · DOI
  35. Dosenbach, 2010, Prediction of individual brain maturity using fMRI, Science, 329, 1358, 10.1126/science.1194144 · DOI · Paper page
  36. Erhardt, 2011, Comparison of multi-subject ICA methods for analysis of fMRI data, Hum. Brain Map., 32, 2075, 10.1002/hbm.21170 · DOI · Paper page
  37. Estrada, 2010, Network properties revealed through matrix functions, SIAM Rev., 52, 696, 10.1137/090761070 · DOI · Paper page
  38. Fornito, 2010, What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders?, Curr. Opin. Psychiatry, 23, 239, 10.1097/YCO.0b013e328337d78d · DOI
  39. Fox, 2007, Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior, Neuron, 56, 171, 10.1016/j.neuron.2007.08.023 · DOI · Paper page
  40. Friston, 1994, Functional and effective connectivity in neuroimaging: a synthesis, Hum. Brain Map., 2, 56, 10.1002/hbm.460020107 · DOI · Paper page
  41. Friston, 2005, Models of brain function in neuroimaging, Annu. Rev. Psychol., 56, 57, 10.1146/annurev.psych.56.091103.070311 · DOI · Paper page
  42. Friston, 2011, Functional and effective connectivity: a review, Brain Connect., 1, 13, 10.1089/brain.2011.0008 · DOI · Paper page
  43. Friston, 2011, Network discovery with DCM, Neuroimage, 56, 1202, 10.1016/j.neuroimage.2010.12.039 · DOI · Paper page
  44. Garrett, 2011, The importance of being variable, J. Neurosci., 31, 4496, 10.1523/JNEUROSCI.5641-10.2011 · DOI · Paper page
  45. Garrett, 2013, Moment-to-moment brain signal variability: a next frontier in human brain mapping?, Neurosci. Biobehav. Rev., 37, 610, 10.1016/j.neubiorev.2013.02.015 · DOI · Paper page
  46. Gerstner, 2012, Theory and simulation in neuroscience, Science, 338, 60, 10.1126/science.1227356 · DOI · Paper page
  47. Glasser, 2013, The minimal preprocessing pipelines for the Human Connectome Project, Neuroimage, 80, 105, 10.1016/j.neuroimage.2013.04.127 · DOI · Paper page
  48. Gong, 2009, Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography, Cereb. Cortex, 19, 524, 10.1093/cercor/bhn102 · DOI · Paper page
  49. Greve, 2009, Accurate and robust brain image alignment using boundary-based registration, Neuroimage, 48, 63, 10.1016/j.neuroimage.2009.06.060 · DOI · Paper page
  50. Gur, 2014, Neurocognitive growth charting in psychosis spectrum youths, JAMA Psychiatry, 71, 366, 10.1001/jamapsychiatry.2013.4190 · DOI · Paper page
  51. He, 2007, Small-world anatomical networks in the human brain revealed by cortical thickness from MRI, Cereb. Cortex, 17, 2407, 10.1093/cercor/bhl149 · DOI · Paper page
  52. He, 2009, Uncovering intrinsic modular organization of spontaneous brain activity in humans, PLoS One, 4, e5226, 10.1371/journal.pone.0005226 · DOI · Paper page
  53. Hesselmann, 2008, Spontaneous local variations in ongoing neural activity bias perceptual decisions, Proc. Natl. Acad. Sci. U. S. A., 105, 10984, 10.1073/pnas.0712043105 · DOI · Paper page
  54. van den Heuvel, 2008, Normalized cut group clustering of resting-state FMRI data, PLoS One, 3, e2001, 10.1371/journal.pone.0002001 · DOI · Paper page
  55. Hlinka, 2011, Functional connectivity in resting-state fMRI: is linear correlation sufficient?, Neuroimage, 54, 2218, 10.1016/j.neuroimage.2010.08.042 · DOI · Paper page
  56. Honey, 2007, Network structure of cerebral cortex shapes functional connectivity on multiple time scales, Proc. Natl. Acad. Sci. U. S. A., 104, 10240, 10.1073/pnas.0701519104 · DOI · Paper page
  57. Hu, 2011, Causality analysis of neural connectivity: critical examination of existing methods and advances of new methods, IEEE Trans. Neural. Netw., 22, 829, 10.1109/TNN.2011.2123917 · DOI
  58. Hutchison, 2013, Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, 80, 360, 10.1016/j.neuroimage.2013.05.079 · DOI · Paper page
  59. Jenkinson, 2012, FSL, Neuroimage, 62, 782, 10.1016/j.neuroimage.2011.09.015 · DOI · Paper page
  60. Jiang, 2014, Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization, Brain Struct. Funct.
  61. Jiang, 2013, Brainnetome: a new -ome to understand the brain and its disorders, Neuroimage, 80, 263, 10.1016/j.neuroimage.2013.04.002 · DOI
  62. Jiang, 2013, Brainnetome-wide association studies in schizophrenia: the advances and future, Neurosci. Biobehav. Rev., 37, 2818, 10.1016/j.neubiorev.2013.10.004 · DOI
  63. Jones, 2012, Non-stationarity in the resting brain's modular architecture, PLoS One, 7, e39731, 10.1371/journal.pone.0039731 · DOI · Paper page
  64. Jordan, 2006, Topological keystone species: measures of positional importance in food webs, Oikos, 112, 535, 10.1111/j.0030-1299.2006.13724.x · DOI · Paper page
  65. Kandel, 2013, Neuroscience thinks big (and collaboratively), Nat. Rev. Neurosci., 14, 659, 10.1038/nrn3578 · DOI · Paper page
  66. Kannurpatti, 2008, Detection and scaling of task-induced fMRI-BOLD response using resting state fluctuations, Neuroimage, 40, 1567, 10.1016/j.neuroimage.2007.09.040 · DOI
  67. Kelly, 2012, Characterizing variation in the functional connectome: promise and pitfalls, Trends Cogn. Sci., 16, 181, 10.1016/j.tics.2012.02.001 · DOI · Paper page
  68. Kiviniemi, 2000, Slow vasomotor fluctuation in fMRI of anesthetized child brain, Magn. Reson. Med., 44, 373, 10.1002/1522-2594(200009)44:3<373::AID-MRM5>3.0.CO;2-P · DOI · Paper page
  69. Kiviniemi, 2003, Independent component analysis of nondeterministic fMRI signal sources, Neuroimage, 19, 253, 10.1016/S1053-8119(03)00097-1 · DOI · Paper page
  70. Li, 2002, Alzheimer disease: evaluation of a functional MR imaging index as a marker, Radiology, 225, 253, 10.1148/radiol.2251011301 · DOI · Paper page
  71. Liang, 2013, Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain, Proc. Natl. Acad. Sci. U. S. A., 110, 1929, 10.1073/pnas.1214900110 · DOI · Paper page
  72. Liao, 2013, Functional brain hubs and their test-retest reliability: a multiband resting-state functional MRI study, Neuroimage, 83, 969, 10.1016/j.neuroimage.2013.07.058 · DOI · Paper page
  73. Lichtman, 2011, The big and the small: challenges of imaging the brain's circuits, Science, 334, 618, 10.1126/science.1209168 · DOI · Paper page
  74. Liu, 2013, Complexity and synchronicity of resting state blood oxygenation level-dependent (BOLD) functional MRI in normal aging and cognitive decline, J. Magn. Reson. Imaging, 38, 36, 10.1002/jmri.23961 · DOI
  75. Liu, 2013, Eyes-open/eyes-closed dataset sharing for reproducibility evaluation of resting state fMRI data analysis methods, Neuroinformatics, 11, 469, 10.1007/s12021-013-9187-0 · DOI
  76. Mantzaris, 2013, Dynamic network centrality summarizes learning in the human brain, J. Complex Netw., 1, 83, 10.1093/comnet/cnt001 · DOI
  77. Marcus, 2013, Human connectome project informatics: quality control, database services, and data visualization, Neuroimage, 80, 202, 10.1016/j.neuroimage.2013.05.077 · DOI · Paper page
  78. Marcus, 2011, Informatics and data mining tools and strategies for the human connectome project, Front. Neuroinform., 5, 4, 10.3389/fninf.2011.00004 · DOI · Paper page
  79. Marcus, 2007, The extensible neuroimaging archive toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data, Neuroinformatics, 5, 11, 10.1385/NI:5:1:11 · DOI · Paper page
  80. Margulies, 2010, Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity, MAGMA, 23, 289, 10.1007/s10334-010-0228-5 · DOI · Paper page
  81. Marsaglia, 2003, Evaluating Kolmogorov's distribution, J. Stat. Softw., 8, 1, 10.18637/jss.v008.i18 · DOI · Paper page
  82. Maxim, 2005, Fractional gaussian noise, functional MRI and Alzheimer's disease, Neuroimage, 25, 141, 10.1016/j.neuroimage.2004.10.044 · DOI · Paper page
  83. McAvoy, 2008, Resting states affect spontaneous BOLD oscillations in sensory and paralimbic cortex, J. Neurophysiol., 100, 922, 10.1152/jn.90426.2008 · DOI
  84. Mennes, 2013, Making data sharing work: the FCP/INDI experience, Neuroimage, 82, 683, 10.1016/j.neuroimage.2012.10.064 · DOI · Paper page
  85. Mennes, 2011, Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics, Neuroimage, 54, 2950, 10.1016/j.neuroimage.2010.10.046 · DOI · Paper page
  86. Milham, 2012, Open neuroscience solutions for the connectome-wide association era, Neuron, 73, 214, 10.1016/j.neuron.2011.11.004 · DOI
  87. Molloy, 2014, The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI, Neuroimage, 86, 221, 10.1016/j.neuroimage.2013.09.001 · DOI
  88. Morgan, 2011, Cross hippocampal influence in mesial temporal lobe epilepsy measured with high temporal resolution functional magnetic resonance imaging, Epilepsia, 52, 1741, 10.1111/j.1528-1167.2011.03196.x · DOI
  89. Mowinckel, 2012, Network-specific effects of age and in-scanner subject motion: a resting-state fMRI study of 238 healthy adults, Neuroimage, 63, 1364, 10.1016/j.neuroimage.2012.08.004 · DOI
  90. Muchinsky, 1996, The correction for attenuation, Educ. Psychol. Measure., 56, 63, 10.1177/0013164496056001004 · DOI · Paper page
  91. Mueller, 2013, Individual variability in functional connectivity architecture of the human brain, Neuron, 77, 586, 10.1016/j.neuron.2012.12.028 · DOI · Paper page
  92. Murphy, 2013, Resting-state fmri confounds and cleanup, Neuroimage, 80, 349, 10.1016/j.neuroimage.2013.04.001 · DOI · Paper page
  93. Nooner, 2012, The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry, Front. Neurosci., 6, 152, 10.3389/fnins.2012.00152 · DOI · Paper page
  94. Patriat, 2013, The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated, Neuroimage, 78, 463, 10.1016/j.neuroimage.2013.04.013 · DOI · Paper page
  95. Pendse, 2011, A simple and objective method for reproducible resting state network (RSN) detection in fMRI, PLoS One, 6, e27594, 10.1371/journal.pone.0027594 · DOI
  96. Peng, 2014, Default network connectivity as a vulnerability marker for obsessive compulsive disorder, Psychol. Med., 44, 1475, 10.1017/S0033291713002250 · DOI
  97. Poppe, 2013, Toward a neurometric foundation for probabilistic independent component analysis of fMRI data, Cogn. Affect. Behav. Neurosci., 13, 641, 10.3758/s13415-013-0180-8 · DOI
  98. Power, 2012, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, Neuroimage, 59, 2142, 10.1016/j.neuroimage.2011.10.018 · DOI · Paper page
  99. Power, 2013, Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to carp, Neuroimage, 76, 439, 10.1016/j.neuroimage.2012.03.017 · DOI · Paper page
  100. Qi, 2012, Laplacian centrality: a new centrality measure for weighted networks, Inf. Sci., 194, 240, 10.1016/j.ins.2011.12.027 · DOI
  101. Raichle, 2006, Neuroscience. the brain's dark energy, Science, 314, 1249
  102. Rubinov, 2013, Fledgling pathoconnectomics of psychiatric disorders, Trends Cogn. Sci., 17, 641, 10.1016/j.tics.2013.10.007 · DOI
  103. Rubinov, 2010, Complex network measures of brain connectivity: uses and interpretations, Neuroimage, 52, 1059, 10.1016/j.neuroimage.2009.10.003 · DOI · Paper page
  104. Russell, 2013, If a job is worth doing, it is worth doing twice, Nature, 496, 7447, 10.1038/496007a · DOI
  105. Sadaghiani, 2010, The relation of ongoing brain activity, evoked neural responses, and cognition, Front. Syst. Neurosci., 4, 20
  106. Salimi-Khorshidi, 2014, Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers, Neuroimage, 90, 449, 10.1016/j.neuroimage.2013.11.046 · DOI · Paper page
  107. Salvador, 2005, Neurophysiological architecture of functional magnetic resonance images of human brain, Cereb. Cortex, 15, 1332, 10.1093/cercor/bhi016 · DOI · Paper page
  108. Satterthwaite, 2013, An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data, Neuroimage, 64, 240, 10.1016/j.neuroimage.2012.08.052 · DOI · Paper page
  109. Satterthwaite, 2012, Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth, Neuroimage, 60, 623, 10.1016/j.neuroimage.2011.12.063 · DOI · Paper page
  110. Shehzad, 2009, The resting brain: unconstrained yet reliable, Cereb. Cortex, 19, 2209, 10.1093/cercor/bhn256 · DOI · Paper page
  111. Shou, 2014, Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2), Cogn. Affect. Behav. Neurosci., 13, 714, 10.3758/s13415-013-0196-0 · DOI
  112. Singh, 2009, Biomarkers in psychiatry, Nature, 460, 202, 10.1038/460202a · DOI · Paper page
  113. Smith, 2014, Multiple time scale complexity analysis of resting state FMRI, Brain Imaging Behav., 8, 284, 10.1007/s11682-013-9276-6 · DOI
  114. Song, 2012, Age-related differences in test-retest reliability in resting-state brain functional connectivity, PLoS One, 7, e49847, 10.1371/journal.pone.0049847 · DOI
  115. Song, 2011, REST: a toolkit for resting-state functional magnetic resonance imaging data processing, PLoS One, 6, e25031, 10.1371/journal.pone.0025031 · DOI · Paper page
  116. Sporns, 2013, The human connectome: origins and challenges, Neuroimage, 80, 53, 10.1016/j.neuroimage.2013.03.023 · DOI · Paper page
  117. Sporns, 2000, Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices, Cereb. Cortex, 10, 127, 10.1093/cercor/10.2.127 · DOI · Paper page
  118. Stark, 2008, Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations, J. Neurosci., 28, 13754, 10.1523/JNEUROSCI.4544-08.2008 · DOI · Paper page
  119. Thomason, 2011, Resting-state fMRI can reliably map neural networks in children, Neuroimage, 55, 165, 10.1016/j.neuroimage.2010.11.080 · DOI · Paper page
  120. Tian, 2012, Regional homogeneity of resting state fmri signals predicts stop signal task performance, Neuroimage, 60, 539, 10.1016/j.neuroimage.2011.11.098 · DOI
  121. Tomasi, 2010, Functional connectivity density mapping, Proc. Natl. Acad. Sci. U. S. A., 107, 9885, 10.1073/pnas.1001414107 · DOI · Paper page
  122. Tomasi, 2013, Energetic cost of brain functional connectivity, Proc. Natl. Acad. Sci. U. S. A., 110, 13642, 10.1073/pnas.1303346110 · DOI · Paper page
  123. Turk-Browne, 2013, Functional interactions as big data in the human brain, Science, 342, 580, 10.1126/science.1238409 · DOI · Paper page
  124. Van Dijk, 2012, The influence of head motion on intrinsic functional connectivity MRI, Neuroimage, 59, 431, 10.1016/j.neuroimage.2011.07.044 · DOI · Paper page
  125. Van Essen, 2012, Cortical cartography and Caret software, Neuroimage, 62, 757, 10.1016/j.neuroimage.2011.10.077 · DOI
  126. Van Essen, 2007, Surface-based and probabilistic atlases of primate cerebral cortex, Neuron, 56, 209, 10.1016/j.neuron.2007.10.015 · DOI · Paper page
  127. Van Essen, 2013, The WU-Minn Human Connectome Project: an overview, Neuroimage, 80, 62, 10.1016/j.neuroimage.2013.05.041 · DOI · Paper page
  128. Wang, 2013, Disrupted functional brain connectome in individuals at risk for Alzheimer's disease, Biol. Psychiatry, 73, 472, 10.1016/j.biopsych.2012.03.026 · DOI · Paper page
  129. Wang, 2014, Linking inter-individual differences in the conflict adaptation effect to spontaneous brain activity, Neuroimage, 90C, 146, 10.1016/j.neuroimage.2013.12.055 · DOI
  130. Wang, 2013, A hybrid CPU-GPU accelerated framework for fast mapping of high-resolution human brain connectome, PLoS One, 8, e62789, 10.1371/journal.pone.0062789 · DOI
  131. Wei, 2014, Tai chi chuan optimizes the functional organization of the intrinsic human brain architecture in older adults, Front. Aging Neurosci., 8, 74
  132. Wei, 2013, Can taichi reshape the brain? A brain morphometry study, PLoS One, 8, e61038, 10.1371/journal.pone.0061038 · DOI · Paper page
  133. Wig, 2013, Parcellating an individual subject's cortical and subcortical brain structures using Snowball Sampling of resting-state correlations, Cereb. Cortex
  134. Wink, 2008, Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI, Hum. Brain Map., 29, 791, 10.1002/hbm.20593 · DOI
  135. Wu, 2007, Normal aging decreases regional homogeneity of the motor areas in the resting state, Neurosci. Lett., 423, 189, 10.1016/j.neulet.2007.06.057 · DOI
  136. Xie, 2008, Spatiotemporal nonlinearity in resting-state fMRI of the human brain, Neuroimage, 40, 1672, 10.1016/j.neuroimage.2008.01.007 · DOI
  137. Xu, 2014, Different topological organization of human brain functional networks with eyes open versus eyes closed, Neuroimage, 90, 246, 10.1016/j.neuroimage.2013.12.060 · DOI · Paper page
  138. Yan, 2009, Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load, PLoS One, 4, e5743, 10.1371/journal.pone.0005743 · DOI · Paper page
  139. Yan, 2013, A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics, Neuroimage, 76, 183, 10.1016/j.neuroimage.2013.03.004 · DOI · Paper page
  140. Yan, 2013, Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes, Neuroimage, 80, 246, 10.1016/j.neuroimage.2013.04.081 · DOI · Paper page
  141. Yang, 2007, Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI, Neuroimage, 36, 144, 10.1016/j.neuroimage.2007.01.054 · DOI · Paper page
  142. Yang, 2014, Connectivity trajectory across lifespan differentiates the precuneus from the default network, Neuroimage, 89C, 45, 10.1016/j.neuroimage.2013.10.039 · DOI
  143. Yang, 2008, Ranking and averaging independent component analysis by reproducibility (RAICAR), Hum. Brain Map., 29, 711, 10.1002/hbm.20432 · DOI
  144. Yang, 2012, Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks, Neuroimage, 63, 403, 10.1016/j.neuroimage.2012.06.060 · DOI
  145. Yeo, 2013, Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex, Neuroimage, 88C, 212
  146. Yeo, 2011, The organization of the human cerebral cortex estimated by intrinsic functional connectivity, J. Neurophysiol., 106, 1125, 10.1152/jn.00338.2011 · DOI · Paper page
  147. Zador, 2012, Sequencing the connectome, PLoS Biol., 10, e1001411, 10.1371/journal.pbio.1001411 · DOI
  148. Zhang, 2010, Disease and the brain's dark energy, Nat. Rev. Neurol., 6, 15, 10.1038/nrneurol.2009.198 · DOI · Paper page
  149. Zhao, 2013, Amygdala volume predicts inter-individual differences in fearful face recognition, PLoS One, 8, e74096, 10.1371/journal.pone.0074096 · DOI
  150. Zhong, 2009, Detecting functional connectivity in fMRI using PCA and regression analysis, Brain Topogr., 22, 134, 10.1007/s10548-009-0095-4 · DOI
  151. Zhu, 2014, Temporal reliability and lateralization of the resting-state language network, PLoS One, 9, e85880, 10.1371/journal.pone.0085880 · DOI
  152. Zou, 2009, Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: a resting-state fMRI study, Hum. Brain Map., 30, 3066, 10.1002/hbm.20728 · DOI
  153. Zou, 2013, Intrinsic resting-state activity predicts working memory brain activation and behavioral performance, Hum. Brain Map., 34, 3204, 10.1002/hbm.22136 · DOI · Paper page
  154. Zou, 2008, An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF, J. Neurosci. Methods, 172, 137, 10.1016/j.jneumeth.2008.04.012 · DOI · Paper page
  155. Zuo, 2010, The oscillating brain: complex and reliable, Neuroimage, 49, 1432, 10.1016/j.neuroimage.2009.09.037 · DOI · Paper page
  156. Zuo, 2012, Network centrality in the human functional connectome, Cereb. Cortex, 22, 1862, 10.1093/cercor/bhr269 · DOI · Paper page
  157. Zuo, 2010, Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy, J. Neurosci., 30, 15034, 10.1523/JNEUROSCI.2612-10.2010 · DOI · Paper page
  158. Zuo, 2011, Effects of non-local diffusion on structural mri preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion, PLoS One, 6, e26703, 10.1371/journal.pone.0026703 · DOI
  159. Zuo, 2013, Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space, Neuroimage, 65, 374, 10.1016/j.neuroimage.2012.10.017 · DOI · Paper page

Cited by

See all 660

Metadata

Authors
Zuo, X.-N., & Xing, X.-X.
Venue
Neuroscience & Biobehavioral Reviews
Publisher
Elsevier BV
Published
2014-09
Volume
45
Pages
100-118
ISSN
0149-7634 (ISSN-L)
DOI
10.1016/j.neubiorev.2014.05.009
OpenAlex
W1984111197
Cited by
660
References
71
FWCI
28.94
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • National Institute of Mental Health (R01MH094639-01)
  • National Institutes of Health (1U54MH091657)
  • Beijing University of Technology
  • McDonnell Center for Systems Neuroscience
  • National Natural Science Foundation of China (81201153, 81220108014)
  • Chinese Academy of Sciences
  • University of Washington

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. https://app.cochat.ai/papers/10.1016/j.neubiorev.2014.05.009
The paper
Zuo, X.-N., & Xing, X.-X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neuroscience & Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2014.05.009

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.