Skip to main content
SpringerLink
Log in

Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state

  • Original Article
  • Published:
Brain Structure and Function Aims and scope Submit manuscript

Abstract

A functional magnetic resonance imaging (fMRI) experiment produces complex-valued images consisting of pairwise magnitude and phase images. As different perspective on the same magnetic source, fMRI magnitude and phase data are complementary for brain function analysis. We collected 600-subject fMRI data during rest, decomposed via group-level independent component analysis (ICA) (mICA and pICA for magnitude and phase respectively), and calculated brain functional network connectivity matrices (mFC and pFC). The pFC matrix shows a fewer of significant connections balanced across positive and negative relationships. In comparison, the mFC matrix contains a positively-biased pattern with more significant connections. Our experiment data analyses also show that human brain maintains a whole-brain connection balance in resting state across an age span from 10 to 76 years, however, phase and magnitude data analyses reveal different connection-specific age effects on significant positive and negative subnetwork couplings.

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
Fig. 6

Similar content being viewed by others

Abbreviations

BOLD:

Blood oxygenation level dependent

fMRI:

Functional magnetic resonance imaging

FDR:

False discovery rate

mICA:

Magnitude-data independent component analysis (ICA)

pICA:

Phase-data ICA

mFC:

Magnitude-depicted function network connectivity (FC)

pFC:

Phase-depicted FC

References

  • Abou Elseoud A, Littow H, Remes J, Starck T, Nikkinen J, Nissila J, Timonen M, Tervonen O, Kiviniemi V (2011) Group-ICA model order highlights patterns of functional brain connectivity. Front Syst Neurosci 5:37

    Article  PubMed  PubMed Central  Google Scholar 

  • Adhikari MH, Hacker CD, Siegel JS, Griffa A, Hagmann P, Deco G, Corbetta M (2017) Decreased integration and information capacity in stroke measured by whole brain models of resting state activity. Brain 140(4):1068–1085

    Article  PubMed  PubMed Central  Google Scholar 

  • Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, Havlicek M, Rachakonda S, Fries J, Kalyanam R, Michael AM, Caprihan A, Turner JA, Eichele T, Adelsheim S, Bryan AD, Bustillo J, Clark VP, Feldstein Ewing SW, Filbey F, Ford CC, Hutchison K, Jung RE, Kiehl KA, Kodituwakku P, Komesu YM, Mayer AR, Pearlson GD, Phillips JP, Sadek JR, Stevens M, Teuscher U, Thoma RJ, Calhoun VD (2011) A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 5:2

    PubMed  PubMed Central  Google Scholar 

  • Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24(3):663–676

    Article  PubMed  Google Scholar 

  • Aragri A, Scarabino T, Seifritz E, Comani S, Cirillo S, Tedeschi G, Esposito F, Di Salle F (2006) How does spatial extent of fMRI datasets affect independent component analysis decomposition? Hum Brain Mapp 27(9):736–746

    Article  PubMed  PubMed Central  Google Scholar 

  • Arbabshirani MR, Calhoun VD (2011) Functional network connectivity during rest and task: comparison of healthy controls and schizophrenic patients. Conf Proc IEEE Eng Med Biol Soc 2011:4418–4421

    Google Scholar 

  • Arja SK, Feng Z, Chen Z, Caprihan A, Kiehl KA, Adali T, Calhoun VD (2009) Changes in fMRI magnitude data and phase data observed in block-design and event-related tasks. Neuroimage 59(4):3748–3761

    Google Scholar 

  • Balla DZ, Sanchez-Panchuelo RM, Wharton SJ, Hagberg GE, Scheffler K, Francis ST, Bowtell R (2014) Functional quantitative susceptibility mapping (fQSM). Neuroimage 100:112–124

    Article  PubMed  Google Scholar 

  • Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360(1457):1001–1013

    Article  PubMed  PubMed Central  Google Scholar 

  • Bianciardi M, van Gelderen P, Duyn JH (2014) Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T. Hum Brain Mapp 35(5):2191–2205

    Article  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198

    Article  CAS  PubMed  Google Scholar 

  • Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14(3):140–151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Calhoun VD, Liu J, Adali T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(1 Suppl):S163–S172

    Article  PubMed  Google Scholar 

  • Caliandro P, Reale G, Vecchio F, Iacovelli C, Miraglia F, Masi G, Rossini PM (2017) Defining a functional network homeostasis after stroke: EEG-based approach is complementary to functional MRI. Brain 140(12):e71

    Article  PubMed  Google Scholar 

  • Chen Z, Calhoun VD (2011) Two pitfalls of BOLD fMRI magnitude-based neuroimage analysis: non-negativity and edge effect. J Neurosci Methods 199(2):363–369

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen Z, Calhoun V (2013) Understanding the morphological mismatch between magnetic susceptibility source and T2* image. Magn Reson Insights 6:65–81

    PubMed  PubMed Central  Google Scholar 

  • Chen Z, Calhoun V (2015) Nonlinear magnitude and linear phase behaviors of T2* imaging: theoretical approximation and Monte Carlo simulation. Magn Reson Imaging 33(4):390–400

    Article  PubMed  Google Scholar 

  • Chen Z, Calhoun V (2016) T2* phase imaging and processing for magnetic susceptibility mapping. Biomed Phys Eng Express 2:025015

    Article  Google Scholar 

  • Chen Z, Caprihan A, Damaraju E, Rachakonda S, Calhoun V (2018a) Functional brain connectivity in resting-state fMRI using phase and magnitude data. J Neurosci Methods 293:299–309

    Article  PubMed  Google Scholar 

  • Chen Z, Robinson J, Calhoun V (2018b) Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping. PLoS One 13(1):e0191266

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dagher J, Nael K (2017) MR phase imaging with bipolar acquisition. NMR Biomed 34(4):e3523

    Article  Google Scholar 

  • Damoiseaux JS (2017) Effects of aging on functional and structural brain connectivity. Neuroimage 160:32–40

    Article  PubMed  Google Scholar 

  • Dodel S, Herrmann JM, Geisel T (2002) Functional connectivity by cross-correlation clustering. Neurocomputing 44–46:1065–1070

    Article  Google Scholar 

  • Du Y, Fan Y (2013) Group information guided ICA for fMRI data analysis. Neuroimage 69:157–197

    Article  PubMed  Google Scholar 

  • Du Y, Pearlson GD, Liu J, Sui J, Yu Q, He H, Castro E, Calhoun VD (2015) A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. Neuroimage 122:272–280

    Article  PubMed  Google Scholar 

  • Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD (2011) Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32(12):2075–2095

    Article  PubMed  Google Scholar 

  • Feng Z, Caprihan A, Blagoev KB, Calhoun VD (2009) Biophysical modeling of phase changes in BOLD fMRI. Neuroimage 47(2):540–548

    Article  PubMed  Google Scholar 

  • Ferreira LK, Regina AC, Kovacevic N, Martin Mda G, Santos PP, Carneiro Cde G, Kerr DS, Amaro E Jr, McIntosh AR, Busatto GF (2016) Aging effects on whole-brain functional connectivity in adults free of cognitive and psychiatric disorders. Cereb Cortex 26(9):3851–3865

    Article  PubMed  Google Scholar 

  • Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102(27):9673–9678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101(6):3270–3283

    Article  PubMed  PubMed Central  Google Scholar 

  • Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15(4):870–878

    Article  PubMed  Google Scholar 

  • Guo Y, Pagnoni G (2008) A unified framework for group independent component analysis for multi-subject fMRI data. Neuroimage 42(3):1078–1093

    Article  PubMed  Google Scholar 

  • Jafri MJ, Pearlson GD, Stevens M, Calhoun VD (2008) A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage 39(4):1666–1681

    Article  PubMed  Google Scholar 

  • Kee Y, Liu Z, Zhou L, Dimov A, Cho J, de Rochefort L, Seo JK, Wang Y (2017) Quantitative susceptibility mapping (QSM) algorithms: mathematical rationale and computational implementations. IEEE Trans Biomed Eng 64(11):2531–2545

    Article  PubMed  Google Scholar 

  • Kringelbach ML, Green AL, Aziz TZ (2011) Balancing the brain: resting state networks and deep brain stimulation. Front Integr Neurosci 5:8

    Article  PubMed  PubMed Central  Google Scholar 

  • Lewis LD, Setsompop K, Rosen BR, Polimeni JR (2016) Fast fMRI can detect oscillatory neural activity in humans. Proc Natl Acad Sci USA 113(43):E6679–E6685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li YO, Adali T, Calhoun VD (2007) A feature-selective independent component analysis method for functional MRI. Int J Biomed Imaging 2007:15635

    PubMed  PubMed Central  Google Scholar 

  • Litwin-Kumar A, Doiron B (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci 15(11):1498–1505

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu Y, Huang L, Li M, Zhou Z, Hu D (2015) Anticorrelated networks in resting-state fMRI-BOLD data. Bio Med Mater Eng 26(Suppl 1):S1201–S1211

    Article  Google Scholar 

  • Marino J, Schummers J, Lyon DC, Schwabe L, Beck O, Wiesing P, Obermayer K, Sur M (2005) Invariant computations in local cortical networks with balanced excitation and inhibition. Nat Neurosci 8(2):194–201

    Article  CAS  PubMed  Google Scholar 

  • McKeown MJ, Sejnowski TJ (1998) Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp 6(5–6):368–372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McKeown MJ, Hansen LK, Sejnowsk TJ (2003) Independent component analysis of functional MRI: what is signal and what is noise? Curr Opin Neurobiol 13(5):620–629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Meadows R (2011) Finding balance in cortical networks. PLoS Biol 9(3):e1001035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Menon RS (2002) Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magn Reson Med 47(1):1–9

    Article  PubMed  Google Scholar 

  • Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44(3):893–905

    Article  PubMed  Google Scholar 

  • Ozbay PS, Warnock G, Rossi C, Kuhn F, Akin B, Pruessmann KP, Nanz D (2016) Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: a group level comparison with BOLD fMRI and PET. Neuroimage 137:52–60

    Article  PubMed  Google Scholar 

  • Robinson SD, Dymerska B, Bogner W, Barth M, Zaric O, Goluch S, Grabner G, Deligianni X, Bieri O, Trattnig S (2015) Combining phase images from array coils using a short echo time reference scan (COMPOSER). Magn Reson Med 77:318–327

    Article  PubMed  PubMed Central  Google Scholar 

  • Robinson SD, Bredies K, Khabipova D, Dymerska B, Marques JP, Schweser F (2017a) An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 30(4):e3601

    Article  Google Scholar 

  • Robinson SD, Dymerska B, Bogner W, Barth M, Zaric O, Goluch S, Grabner G, Deligianni X, Bieri O, Trattnig S (2017b) Combining phase images from array coils using a short echo time reference scan (COMPOSER). Magn Reson Med 77(1):318–327

    Article  PubMed  Google Scholar 

  • Rowe DB (2005) Modeling both the magnitude and phase of complex-valued fMRI data. Neuroimage 25(4):1310–1324

    Article  PubMed  Google Scholar 

  • Rowe DB (2009) Magnitude and phase signal detection in complex-valued fMRI data. Magn Reson Med 62(5):1356–1357 (author reply 1358-60)

    Article  PubMed  PubMed Central  Google Scholar 

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  • Schweser F, Deistung A, Reichenbach J (2016) Foundations of MRI phase imaging and processing for quantitative susceptibility mapping (QSM). Z Med Phys 26(1):6–34

    Article  PubMed  Google Scholar 

  • Wu JT, Wu HZ, Yan CG, Chen WX, Zhang HY, He Y, Yang HS (2011) Aging-related changes in the default mode network and its anti-correlated networks: a resting-state fMRI study. Neurosci Lett 504(1):62–67

    Article  CAS  PubMed  Google Scholar 

  • Xu J, Potenza MN, Calhoun VD (2013) Spatial ICA reveals functional activity hidden from traditional fMRI GLM-based analyses. Front Neurosci 7:154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the funding support of NIH Grant P20GM103472. The authors declare no competing financial interest.

Author information

Authors and Affiliations

  1. The Mind Research Network and LBERI, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA

    Zikuan Chen & Vince Calhoun

  2. School of Physics and Astronomy, Yunnan University, Kunming, 650091, China

    Qing Zhou

  3. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA

    Vince Calhoun

Authors
  1. Zikuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vince Calhoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zikuan Chen.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Informed consent

All the human subjects provided written consent for MRI scanning under the approval of IRB at the University of New Mexico.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Zhou, Q. & Calhoun, V. Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state. Brain Struct Funct 224, 1489–1503 (2019). https://doi.org/10.1007/s00429-019-01850-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00429-019-01850-8

Keywords

Search

Navigation