Skip to main content
SpringerLink
Log in

Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression

  • Research Article
  • Published:
Radiological Physics and Technology Aims and scope Submit manuscript

Abstract

In multisite studies, differences in imaging acquisition systems could affect the reproducibility of the results when examining changes in brain function using resting-state functional magnetic resonance imaging (rs-fMRI). This is also important for longitudinal studies, in which changes in equipment settings can occur. This study examined the reproducibility of functional connectivity (FC) metrics estimated from rs-fMRI data acquired using scanner receiver coils with different numbers of channels. This study involved 80 rs-fMRI datasets from 20 healthy volunteers scanned in two independent imaging sessions using both 12- and 32-channel coils for each session. We used independent component analysis (ICA) to evaluate the FC of canonical resting-state networks (RSNs) and graph theory to calculate several whole-brain network metrics. The effect of global signal regression (GSR) as a preprocessing step was also considered. Comparisons within and between receiver coils were performed. Irrespective of the GSR, RSNs derived from rs-fMRI data acquired using the same receiver coil were reproducible, but not from different receiver coils. However, both the GSR and the channel count of the receiver coil have discernible effects on the reproducibility of network metrics estimated using whole-brain network analysis. The data acquired using the 32-channel coil tended to have better reproducibility than those acquired using the 12-channel coil. Our findings suggest that the reproducibility of FC metrics estimated from rs-fMRI data acquired using different receiver coils showed some level of dependence on the preprocessing method and the type of analysis performed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–41. https://doi.org/10.1002/mrm.1910340409.

    Article  CAS  PubMed  Google Scholar 

  2. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp. 1994;2:56–78. https://doi.org/10.1002/hbm.460020107.

    Article  Google Scholar 

  3. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA. 2003;100:253–8. https://doi.org/10.1073/pnas.0135058100.

    Article  CAS  PubMed  Google Scholar 

  4. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc B Biol Sci. 2005;360:1001–13. https://doi.org/10.1098/rstb.2005.1634.

    Article  Google Scholar 

  5. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56. https://doi.org/10.1523/JNEUROSCI.5587-06.2007.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA. 2004;101:4637–42. https://doi.org/10.1073/pnas.0308627101.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cereb Cortex. 2015;25:3723–42. https://doi.org/10.1093/cercor/bhu246.

    Article  PubMed  Google Scholar 

  8. Menon V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci. 2011;15:483–506. https://doi.org/10.1016/j.tics.2011.08.003.

    Article  PubMed  Google Scholar 

  9. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98. https://doi.org/10.1038/nrn2575.

    Article  CAS  PubMed  Google Scholar 

  10. Chen S, Ross TJ, Zhan W, Myers CS, Chuang KS, Heishman SJ, et al. Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Res. 2008;1239:141–51. https://doi.org/10.1016/j.brainres.2008.08.028.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA. 2006;103:13848–53. https://doi.org/10.1073/pnas.0601417103.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Choe AS, Jones CK, Joel SE, Muschelli J, Belegu V, Caffo BS, et al. Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. PLoS ONE. 2015;10:e0140134. https://doi.org/10.1371/journal.pone.0140134.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Bagarinao E, Tsuzuki E, Yoshida Y, Ozawa Y, Kuzuya M, Otani T, et al. Effects of gradient coil noise and gradient coil replacement on the reproducibility of resting state networks. Front Hum Neurosci. 2018;12:148. https://doi.org/10.3389/fnhum.2018.00148.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Huang L, Wang X, Baliki MN, Wang L, Apkarian AV, Parrish TB. Reproducibility of structural, resting-state BOLD and DTI data between identical scanners. PLoS ONE. 2012;7:e47684. https://doi.org/10.1371/journal.pone.0047684.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Paolini M, Keeser D, Ingrisch M, Werner N, Kindermann N, Reiser M, et al. Resting-state networks in healthy adult subjects: a comparison between a 32-element and an 8-element phased array head coil at 3.0 Tesla. Acta radiol. 2015;56:605–13. https://doi.org/10.1177/0284185114567703.

    Article  PubMed  Google Scholar 

  16. Anteraper SA, Whitfield-Gabrieli S, Keil B, Shannon S, Gabrieli JD, Triantafyllou C. Exploring functional connectivity networks with multichannel brain array coils. Brain Connect. 2013;3:302–15. https://doi.org/10.1089/brain.2012.0113.

    Article  PubMed  Google Scholar 

  17. Power JD, Plitt M, Laumann TO, Martin A. Sources and implications of whole-brain fMRI signals in humans. Neuroimage. 2017;146:609–25. https://doi.org/10.1016/j.neuroimage.2016.09.038.

    Article  PubMed  Google Scholar 

  18. Murphy K, Fox MD. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage. 2017;154:169–73. https://doi.org/10.1016/j.neuroimage.2016.11.052.

    Article  PubMed  Google Scholar 

  19. Li J, Bolt T, Bzdok D, Nomi JS, Yeo BTT, Spreng RN, et al. Topography and behavioral relevance of the global signal in the human brain. Sci Rep. 2019;9:14286. https://doi.org/10.1038/s41598-019-50750-8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Yang GJ, Murray JD, Glasser M, Pearlson GD, Krystal JH, Schleifer C, et al. Altered global signal topography in schizophrenia. Cereb Cortex. 2017;27:5156–69. https://doi.org/10.1093/cercor/bhw297.

    Article  PubMed  Google Scholar 

  21. Mugler JP, Brookeman JR. Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magn Reson Med. 1990;15:152–7. https://doi.org/10.1002/mrm.1910150117.

    Article  PubMed  Google Scholar 

  22. Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–51. https://doi.org/10.1016/j.neuroimage.2005.02.018.

    Article  PubMed  Google Scholar 

  23. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–54. https://doi.org/10.1016/j.neuroimage.2011.10.018.

    Article  PubMed  Google Scholar 

  24. Triantafyllou C, Hoge RD, Krueger G, Wiggins CJ, Potthast A, Wiggins GC, et al. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage. 2005;26:243–50. https://doi.org/10.1016/j.neuroimage.2005.01.007.

    Article  CAS  PubMed  Google Scholar 

  25. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012;22:158–65. https://doi.org/10.1093/cercor/bhr099.

    Article  CAS  PubMed  Google Scholar 

  26. Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, et al. Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc Natl Acad Sci. 2009;106:7209–14. https://doi.org/10.1073/pnas.0811879106.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1–25. https://doi.org/10.1002/hbm.1058.

    Article  PubMed  Google Scholar 

  28. Smith SM, Nichols TE. Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:83–98. https://doi.org/10.1016/j.neuroimage.2008.03.061.

    Article  PubMed  Google Scholar 

  29. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89. https://doi.org/10.1006/nimg.2001.0978.

    Article  CAS  PubMed  Google Scholar 

  30. Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci. 2015;9:386. https://doi.org/10.3389/fnhum.2015.00386.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. 2011;70:334–42. https://doi.org/10.1016/j.biopsych.2011.05.018.

    Article  PubMed  Google Scholar 

  32. He Y, Dagher A, Chen Z, Charil A, Zijdenbos A, Worsley K, et al. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain. 2009;132:3366–79. https://doi.org/10.1093/brain/awp089.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Li L, Zeng L, Lin Z-J, Cazzell M, Liu H. Tutorial on use of intraclass correlation coefficients for assessing interest reliability and its application in functional near-infrared spectroscopy–based brain imaging. J Biomed Opt. 2015;20: 050801. https://doi.org/10.1117/1.JBO.20.5.050801.

    Article  Google Scholar 

  34. Wiggins GC, Triantafyllou C, Potthast A, Reykowski A, Nittka M, Wald LL. 32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometry. Magn Reson Med. 2006;56:216–23. https://doi.org/10.1002/mrm.20925.

    Article  CAS  PubMed  Google Scholar 

  35. Albrecht J, Burke M, Haegler K, Schöpf V, Kleemann AM, Paolini M, et al. Potential impact of a 32-channel receiving head coil technology on the results of a functional MRI paradigm. Clin Neuroradiol. 2010;20:223–9. https://doi.org/10.1007/s00062-010-0029-2.

    Article  CAS  PubMed  Google Scholar 

  36. Kaza E, Klose U, Lotze M. Comparison of a 32-channel with a 12-channel head coil: are there relevant improvements for functional imaging? J Magn Reson Imaging. 2011;34:173–83. https://doi.org/10.1002/jmri.22614.

    Article  PubMed  Google Scholar 

  37. Arnold S, Whitfield-Gabrieli S, Shannon S, Gabrieli J, Triantafyllou C. Improved detection of functional connectivity MRI with 32-channel phased array head coil. Proc Intl Soc Mag Reson Med. 2011;19:1637.

    Google Scholar 

  38. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage. 2014;90:449–68. https://doi.org/10.1016/j.neuroimage.2013.11.046.

    Article  PubMed  Google Scholar 

  39. Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage. 2014;95:232–47. https://doi.org/10.1016/j.neuroimage.2014.03.034.

    Article  PubMed  Google Scholar 

  40. Erdoğan SB, Tong Y, Hocke LM, Lindsey KP, de Frederick B. Correcting for blood arrival time in global mean regression enhances functional connectivity analysis of resting state fMRI-BOLD signals. Front Hum Neurosci. 2016;10:311. https://doi.org/10.3389/fnhum.2016.00311.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Brain Protein Ageing and Dementia Control; 26117002) from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan.

Funding

Funding

Ministry of Education, Culture, Sports, Science and Technology (MEXT), 26117002, Gen Sobue.

Author information

Authors and Affiliations

  1. Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Sanae Kato, Haruo Isoda & Shuji Koyama

  2. Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan

    Epifanio Bagarinao, Haruo Isoda & Shuji Koyama

  3. Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan

    Epifanio Bagarinao, Haruo Isoda, Shuji Koyama, Hirohisa Watanabe, Satoshi Maesawa, Masahisa Katsuno, Shinji Naganawa, Norio Ozaki & Gen Sobue

  4. Department of Neurology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan

    Hirohisa Watanabe

  5. Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Hirohisa Watanabe, Kazuhiro Hara & Masahisa Katsuno

  6. Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Satoshi Maesawa

  7. Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Shinji Naganawa

  8. Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Norio Ozaki

  9. Department of Neurology, Aichi Medical University, Nagakute, Aichi, Japan

    Gen Sobue

Authors
  1. Sanae Kato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Epifanio Bagarinao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haruo Isoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuji Koyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hirohisa Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Satoshi Maesawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kazuhiro Hara
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Masahisa Katsuno
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shinji Naganawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Norio Ozaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gen Sobue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Epifanio Bagarinao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board (IRB) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Human/Animal rights

This article does not contain any studies with animals performed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kato, S., Bagarinao, E., Isoda, H. et al. Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression. Radiol Phys Technol 15, 298–310 (2022). https://doi.org/10.1007/s12194-022-00670-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12194-022-00670-6

Keywords

Search

Navigation