Comparing test-retest reliability of dynamic functional connectivity methods

NeuroImage

Volumn 158, Issue , 2017, Pages 155-175

  Choe, Ann S ^a,b   Nebel, Mary Beth ^b,c   Barber, Anita D ^d   Cohen, Jessica R ^e   Xu, Yuting ^c   Pekar, James J ^a,b   Caffo, Brian ^c   Lindquist, Martin A ^c  

^a JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b KENNEDY KRIEGER INSTITUTE   (United States)

^c JOHNS HOPKINS UNIVERSITY   (United States)

^d FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH   (United States)

^e The University of North Carolina at Chapel Hill   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; CONTROLLED STUDY; DYNAMIC CONDITIONAL CORRELATION METHOD; FUNCTIONAL MAGNETIC RESONANCE IMAGING; IMAGE PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; REPRODUCIBILITY; SLIDING WINDOW METHOD; TAPERED SLIDING WINDOW METHOD; TEST RETEST RELIABILITY; BRAIN; COMPARATIVE STUDY; CONNECTOME; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES;

BRAIN; CONNECTOME; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS;

EID: 85022175614     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2017.07.005     Document Type: Article

References (78)

1. Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D., Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 4:3 (2014), 663–676.
2. Allen, E.A., Erhardt, E.B., Damaraju, E., Gruner, W., Segall, J.M., Silva, R.F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., et al. A baseline for the multivariate comparison of resting-state networks. Front. Syst. Neurosci., 5, 2011.
3. Allen, E.A., Erhardt, E.B., Wei, Y., Eichele, T., Calhoun, V.D., Capturing inter-subject variability with group independent component analysis of fmri data: a simulation study. NeuroImage 59:4 (2012), 4141–4159.
4. Anderson, J.S., Ferguson, M.A., Lopez-Larson, M., Yurgelun-Todd, D., Reproducibility of single-subject functional connectivity measurements. Am. J. Neuroradiol. 32:3 (2011), 548–555.
5. Ashburner, J., Friston, K.J., Unified segmentation. NeuroImage 26:3 (2005), 839–851.
6. Bassett, D.S., Wymbs, N.F., Porter, M.A., Mucha, P.J., Carlson, J.M., Grafton, S.T., Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. 108:18 (2011), 7641–7646.
7. Bassett, D.S., Yang, M., Wymbs, N.F., Grafton, S.T., Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18:5 (2015), 744–751.
8. Beckmann, C.F., Smith, S.M., Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23:2 (2004), 137–152.
9. Bell, A.J., Sejnowski, T.J., An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7:6 (1995), 1129–1159.
10. Betzel, R.F., Byrge, L., He, Y., Goñi, J., Zuo, X.-N., Sporns, O., Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage 102 (2014), 345–357.
11. Birn, R.M., Molloy, E.K., Patriat, R., Parker, T., Meier, T.B., Kirk, G.R., Nair, V.A., Meyerand, M.E., Prabhakaran, V., The effect of scan length on the reliability of resting-state fmri connectivity estimates. Neuroimage 83 (2013), 550–558.
12. Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. J. Econ. 31:3 (1986), 307–327.
13. Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna, J.R., Sperling, R.A., Johnson, K.A., Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to alzheimer's disease. J. Neurosci. 29:6 (2009), 1860–1873.
14. Calhoun, V., Adali, T., Pearlson, G., Pekar, J., A method for making group inferences from functional mri data using independent component analysis. Hum. Brain Mapp. 14:3 (2001), 140–151.
15. Calhoun, V.D., Miller, R., Pearlson, G., Adalı, T., The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:2 (2014), 262–274.
16. Carroll, R.J., Ruppert, D., Stefanski, L.A., Crainiceanu, C.M., Measurement Error in Nonlinear Models: a Modern Perspective. 2006, Chapman and Hall/CRC.
17. Chang, C., Glover, G.H., Time–frequency dynamics of resting-state brain connectivity measured with fmri. NeuroImage 50:1 (2010), 81–98.
18. Chen, L., Vu, A., Xu, J., Moeller, S., Ugurbil, K., Yacoub, E., Feinberg, D., Evaluation of highly accelerated simultaneous multi-slice epi for fmri. NeuroImage 104 (2015), 452–459.
19. Cicchetti, D.V., Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol. Assess., 6(4), 1994, 284.
20. Cole, M.W., Pathak, S., Schneider, W., Identifying the brain's most globally connected regions. NeuroImage 49:4 (2010), 3132–3148.
21. Cribben, I., Haraldsdottir, R., Atlas, L.Y., Wager, T.D., Lindquist, M.A., Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage 61:4 (2012), 907–920.
22. Cribben, I., Wager, T.D., Lindquist, M.A., Detecting functional connectivity change points for single-subject fmri data. Front. Comput. Neurosci., 7, 2013.
23. Damaraju, E., Allen, E.A., Belger, A., Ford, J.M., McEwen, S., Mathalon, D.H., Mueller, B.A., Pearlson, G.D., Potkin, S.G., Preda, A., Turner, J.A., Vaidya, J.G., van Erp, T.G., Calhoun, V.D., Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin. 5 (2014), 298–308.
24. De Martino, F., Moerel, M., Ugurbil, K., Formisano, E., Yacoub, E., Less noise, more activation: multiband acquisition schemes for auditory functional mri. Magn. Reson. Med. 74:2 (2015), 462–467.
25. Engle, R., Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J. Bus. Econ. Stat. 20:3 (2002), 339–350.
26. Engle, R.F., Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econ. J. Econ. Soc., 1982, 987–1007.
27. Engle, R.F., Sheppard, K., Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate Garch. Technical report, 2001, National Bureau of Economic Research.
28. Erhardt, E.B., Rachakonda, S., Bedrick, E.J., Allen, E.A., Adali, T., Calhoun, V.D., Comparison of multi-subject ica methods for analysis of fmri data. Hum. Brain Mapp. 32:12 (2011), 2075–2095.
29. Everitt, B.S., Landau, S., Leese, M., Cluster Analysis. 2001 A member of the Hodder Headline Group, London.
30. Filippini, N., MacIntosh, B.J., Hough, M.G., Goodwin, G.M., Frisoni, G.B., Smith, S.M., Matthews, P.M., Beckmann, C.F., Mackay, C.E., Distinct patterns of brain activity in young carriers of the apoe-ε4 allele. Proc. Natl. Acad. Sci. 106:17 (2009), 7209–7214.
31. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J., Frith, C.D., Frackowiak, R.S., et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2:4 (1994), 189–210.
32. Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., et al. The minimal preprocessing pipelines for the human connectome project. NeuroImage 80 (2013), 105–124.
33. Griffanti, L., Salimi-Khorshidi, G., Beckmann, C.F., Auerbach, E.J., Douaud, G., Sexton, C.E., Zsoldos, E., Ebmeier, K.P., Filippini, N., Mackay, C.E., et al. Ica-based artefact removal and accelerated fmri acquisition for improved resting state network imaging. NeuroImage 95 (2014), 232–247.
34. Gu, S., Satterthwaite, T.D., Medaglia, J.D., Yang, M., Gur, R.E., Gur, R.C., Bassett, D.S., Emergence of system roles in normative neurodevelopment. Proc. Natl. Acad. Sci. 112:44 (2015), 13681–13686.
35. Handwerker, D.A., Roopchansingh, V., Gonzalez-Castillo, J., Bandettini, P.A., Periodic changes in fmri connectivity. NeuroImage 63:3 (2012), 1712–1719.
36. Hansen, P.R., Lunde, A., A forecast comparison of volatility models: does anything beat a garch (1, 1)?. J. Appl. Econ. 20:7 (2005), 873–889.
37. Himberg, J., Hyvärinen, A., Esposito, F., Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage 22:3 (2004), 1214–1222.
38. Hlinka, J., Hadrava, M., On the danger of detecting network states in white noise. Front. Comput. Neurosci., 9, 2015.
39. Hudson, A.E., Calderon, D.P., Pfaff, D.W., Proekt, A., Recovery of consciousness is mediated by a network of discrete metastable activity states. Proc. Natl. Acad. Sci. U. S. A. 111:25 (2014), 9283–9288.
40. Hutchison, R.M., Morton, J.B., Tracking the Brain's functional coupling dynamics over development. J. Neurosci. 35:17 (2015), 6849–6859.
41. Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Della Penna, S., Duyn, J.H., Glover, G.H., Gonzalez-Castillo, J., et al. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80 (2013), 360–378.
42. Hutchison, R.M., Womelsdorf, T., Gati, J.S., Everling, S., Menon, R.S., Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34:9 (2013), 2154–2177.
43. Kudela, M., Harezlak, J., Lindquist, M.A., Assessing uncertainty in dynamic functional connectivity. NeuroImage 149 (2017), 165–177.
44. Landman, B.A., Huang, A.J., Gifford, A., Vikram, D.S., Lim, I.A.L., Farrell, J.A., Bogovic, J.A., Hua, J., Chen, M., Jarso, S., et al. Multi-parametric neuroimaging reproducibility: a 3-t resource study. NeuroImage 54:4 (2011), 2854–2866.
45. Laumann, T.O., Gordon, E.M., Adeyemo, B., Snyder, A.Z., Joo, S.J., Chen, M.-Y., Gilmore, A.W., McDermott, K.B., Nelson, S.M., Dosenbach, N.U., et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 87:3 (2015), 657–670.
46. Leonardi, N., Shirer, W.R., Greicius, M.D., Van De Ville, D., Disentangling dynamic networks: separated and joint expressions of functional connectivity patterns in time. Hum. Brain Mapp. 35:12 (2014), 5984–5995.
47. Leonardi, N., Van De Ville, D., On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104 (2015), 430–436.
48. Li, Y.-O., Adalı, T., Calhoun, V.D., Estimating the number of independent components for functional magnetic resonance imaging data. Hum. Brain Mapp. 28:11 (2007), 1251–1266.
49. Liao, X.-H., Xia, M.-R., Xu, T., Dai, Z.-J., Cao, X.-Y., Niu, H.-J., Zuo, X.-N., Zang, Y.-F., He, Y., Functional brain hubs and their test–retest reliability: a multiband resting-state functional mri study. Neuroimage 83 (2013), 969–982.
50. Lindquist, M.A., Xu, Y., Nebel, M.B., Caffo, B.S., Evaluating dynamic bivariate correlations in resting-state fmri: a comparison study and a new approach. NeuroImage 101 (2014), 531–546.
51. Marusak, H.A., Calhoun, V.D., Brown, S., Crespo, L.M., Sala-Hamrick, K., Gotlib, I.H., Thomason, M.E., Dynamic functional connectivity of neurocognitive networks in children. Hum. Brain Mapp. 38:1 (2017), 97–108.
52. Miller, R.L., Yaesoubi, M., Turner, J.A., Mathalon, D., Preda, A., Pearlson, G., Adali, T., Calhoun, V.D., Higher dimensional meta-state analysis reveals reduced resting fmri connectivity dynamism in schizophrenia patients. PLoS One, 11(3), 2016, e0149849.
53. Preti, M.G., Bolton, T.A., Van De Ville, D., The dynamic functional connectome: state-of-the-art and perspectives. NeuroImage, 2016 S1053-8119(16)30788-1.
54. Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P., et al. Sense: sensitivity encoding for fast mri. Magn. Reson. Med. 42:5 (1999), 952–962.
55. Purdon, P.L., Weisskoff, R.M., Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fmri. Hum. Brain Mapp. 6:4 (1998), 239–249.
56. Rashid, B., Damaraju, E., Pearlson, G.D., Calhoun, V.D., Dynamic connectivity states estimated from resting fmri identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Front. Hum. Neurosci., 8, 2014.
57. Sadaghiani, S., Poline, J.-B., Kleinschmidt, A., D'Esposito, M., Ongoing dynamics in large-scale functional connectivity predict perception. Proc. Natl. Acad. Sci. 112:27 (2015), 8463–8468.
58. Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith, S.M., Automatic denoising of functional mri data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90 (2014), 449–468.
59. Shakil, S., Lee, C.-H., Keilholz, S.D., Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. NeuroImage 133 (2016), 111–128.
60. Shou, H., Eloyan, A., Lee, S., Zipunnikov, V., Crainiceanu, A., Nebel, M., Caffo, B., Lindquist, M., Crainiceanu, C., Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (i2c2). Cogn., Affect. Behav. Neurosci. 13:4 (2013), 714–724.
61. Shrout, P., Fleiss, J., Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86:2 (1979), 420–428.
62. Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P., et al. Resting-state fmri in the human connectome project. NeuroImage 80 (2013), 144–168.
63. Stehling, M.K., Turner, R., Mansfield, P., Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science 254:5028 (1991), 43–50.
64. Tagliazucchi, E., Laufs, H., Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82:3 (2014), 695–708.
65. Thompson, W.H., Fransson, P., The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fmri. Front. Hum. Neurosci., 9, 2015.
66. Tibshirani, R., Walther, G., Hastie, T., Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63:2 (2001), 411–423.
67. Uğurbil, K., Xu, J., Auerbach, E.J., Moeller, S., Vu, A.T., Duarte-Carvajalino, J.M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P.F., et al. Pushing spatial and temporal resolution for functional and diffusion mri in the human connectome project. Neuroimage 80 (2013), 80–104.
68. Van Dijk, K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L., Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103:1 (2010), 297–321.
69. Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.-M.H., et al. The wu-minn human connectome project: an overview. NeuroImage 80 (2013), 62–79.
70. Wand, M.P., Jones, M.C., Kernel Smoothing. 1994, Crc Press.
71. Whitlow, C.T., Casanova, R., Maldjian, J.A., Effect of resting-state functional mr imaging duration on stability of graph theory metrics of brain network connectivity. Radiology 259:2 (2011), 516–524.
72. Xu, J., Moeller, S., Auerbach, E.J., Strupp, J., Smith, S.M., Feinberg, D.A., Yacoub, E., Uğurbil, K., Evaluation of slice accelerations using multiband echo planar imaging at 3t. NeuroImage 83 (2013), 991–1001.
73. Xu, Y., Lindquist, M.A., Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fmri data. Front. Neurosci., 9, 2015.
74. Yaesoubi, M., Miller, R.L., Calhoun, V.D., Mutually temporally independent connectivity patterns: a new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender. NeuroImage 107 (2015), 85–94.
75. Yaesoubi, M., Miller, R.L., Calhoun, V.D., Time-varying spectral power of resting-state fmri networks reveal cross-frequency dependence in dynamic connectivity. PLoS One, 12(2), 2017, e0171647.
76. Yang, Z., Craddock, R.C., Margulies, D.S., Yan, C.-G., Milham, M.P., Common intrinsic connectivity states among posteromedial cortex subdivisions: insights from analysis of temporal dynamics. Neuroimage 93 (2014), 124–137.
77. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L.L., Breakspear, M., Time-resolved resting-state brain networks. Proc. Natl. Acad. Sci. 111:28 (2014), 10341–10346.
78. Zuo, X.-N., Xu, T., Jiang, L., Yang, Z., Cao, X.-Y., He, Y., Zang, Y.-F., Castellanos, F.X., Milham, M.P., Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage 65 (2013), 374–386.