Skip to main content

Advertisement

SpringerLink
Log in

Source-based morphometry: a decade of covarying structural brain patterns

  • Review
  • Published:
Brain Structure and Function Aims and scope Submit manuscript

Abstract

In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14(5):322–336. https://doi.org/10.1038/nrn3465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Andrews TJ, Halpern SD, Purves D (1997) Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J Neurosci 17(8):2859–2868. https://doi.org/10.1523/JNEUROSCI.17-08-02859.1997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arbabshirani MR, Plis S, Sui J, Calhoun VD (2016) Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145:137–165

    Article  Google Scholar 

  • Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11(6):805–821

    Article  CAS  Google Scholar 

  • Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

    Article  Google Scholar 

  • Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A (2008) Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 28(37):9239–48. https://doi.org/10.1523/JNEUROSCI.1929-08.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bergsland N, Horakova D, Dwyer MG, Uher T, Vaneckova M, Tyblova M, Zivadinov R et al (2018) Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. Neuroimage Clin 17:444–451

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Caprihan A, Abbott C, Yamamoto J, Pearlson G, Perrone-Bizzozero N, Sui J, Calhoun VD (2011) Source-based morphometry analysis of group differences in fractional anisotropy in schizophrenia. Brain Connectivity 1(2):133–145

    Article  Google Scholar 

  • Castro E, Gupta CN, Martínez-Ramón M, Calhoun VD, Arbabshirani MR, Turner J (2014) Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. Conf Proc IEEE Eng Med Biol Soc 2014:1513–1516. https://doi.org/10.1109/EMBC.2014.6943889

    Article  PubMed  PubMed Central  Google Scholar 

  • Castro E, Hjelm RD, Plis S, Dihn L, Turner JA, Calhoun VD (2016) Deep independence network analysis of structural brain imaging: application to schizophrenia. IEEE Trans Med Imaging 35(7):1729–1740. https://doi.org/10.1109/TMI.2016.2527717

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen J, Liu J, Calhoun VD, Arias-Vasquez A, Zwiers MP, Gupta CN, Turner JA et al (2014) Exploration of scanning effects in multi-site structural MRI studies. J Neurosci Methods 230:37–50

    Article  Google Scholar 

  • Ciarochi JA, Calhoun VD, Lourens S, Long JD, Johnson HJ, Bockholt HJ, Turner JA et al (2016) Patterns of co-occurring gray matter concentration loss across the Huntington disease prodrome. Front Neurol 7:147

    Article  Google Scholar 

  • Comon P, Jutten C (2010) Handbook of blind source separation: independent component analysis and applications. Academic press, Cambridge

    Google Scholar 

  • Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Changes in grey matter induced by training. Nature 427(6972):311–312. https://doi.org/10.1038/427311a

    Article  CAS  PubMed  Google Scholar 

  • Driemeyer J, Boyke J, Gaser C, Büchel C, May A (2008) Changes in gray matter induced by learning—revisited. PLoS One 3(7):e2669. https://doi.org/10.1371/journal.pone.0002669

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Evans AC (2013) Networks of anatomical covariance. NeuroImage 80:489–504. https://doi.org/10.1016/j.neuroimage.2013.05.054

    Article  CAS  PubMed  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  Google Scholar 

  • Glover GH (2012) Spiral imaging in fMRI. NeuroImage 62(2):706–712. https://doi.org/10.1016/j.neuroimage.2011.10.039

    Article  PubMed  Google Scholar 

  • Grecucci A, Rubicondo D, Siugzdaite R, Surian L, Job R (2016) Uncovering the social deficits in the autistic brain. A source-based morphometric study. Front Neurosci 10:388

    Article  Google Scholar 

  • Gupta CN, Calhoun VD, Rachakonda S, Chen J, Patel V, Liu J, Arias-Vasquez A et al (2015) Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophr Bull 41(5):1133–1142

    Article  Google Scholar 

  • Gupta CN, Castro E, Rachkonda S, van Erp TG, Potkin S, Ford JM, Greve DN et al (2017) Biclustered independent component analysis for complex biomarker and subtype identification from structural magnetic resonance images in schizophrenia. Front Psychiatry 8:179

    Article  CAS  Google Scholar 

  • Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67(337):123–129

    Article  Google Scholar 

  • He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 17(10):2407–2419. https://doi.org/10.1093/cercor/bhl149

    Article  PubMed  Google Scholar 

  • He Y, Chen Z, Gong G, Evans A (2009) Neuronal networks in Alzheimer’s disease. Neuroscientist 15(4):333–350. https://doi.org/10.1177/1073858409334423

    Article  PubMed  Google Scholar 

  • Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4–5):411–430

    Article  Google Scholar 

  • Kiehl KA, Anderson NE, Aharoni E, Maurer JM, Harenski KA, Rao V, Decety J et al (2018) Age of gray matters: neuroprediction of recidivism. Neuroimage Clin 19:813–823

    Article  Google Scholar 

  • Kim T, Lee I, Lee T-W (2006) Independent vector analysis: definition and algorithms. 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, IEEE, pp 1393–1396

  • Kim EY, Magnotta VA, Liu D, Johnson HJ (2014) Stable atlas-based mapped prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data. Magn Reson Imaging 32(7):832–844

    Article  Google Scholar 

  • Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Reiser M et al (2009) Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry 66(7):700–712

    Article  Google Scholar 

  • Kubera KM, Sambataro F, Vasic N, Wolf ND, Frasch K, Hirjak D, Wolf RC et al (2014) Source-based morphometry of gray matter volume in patients with schizophrenia who have persistent auditory verbal hallucinations. Prog Neuropsychopharmacol Biol Psychiatry 50:102–109

    Article  Google Scholar 

  • Lee T-W (1998) Independent component analysis. In independent component analysis. Springer, Berlin, pp 27–66

    Book  Google Scholar 

  • Lerch JP, Worsley K, Shaw WP, Greenstein DK, Lenroot RK, Giedd J, Evans AC (2006) Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. NeuroImage 31(3):993–1003. https://doi.org/10.1016/j.neuroimage.2006.01.042

    Article  PubMed  Google Scholar 

  • Li Y, Adalı T, Calhoun VD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp 28(11):1251–1266

    Article  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  Google Scholar 

  • Palaniyappan L, Mahmood J, Balain V, Mougin O, Gowland PA, Liddle PF (2015) Structural correlates of formal thought disorder in schizophrenia: an ultra-high field multivariate morphometry study. Schizophr Res 168(1):305–312

    Article  Google Scholar 

  • Pearlson GD, Calhoun VD, Liu J (2015) An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders. Front Genet 6:276

    Article  Google Scholar 

  • Premi E, Calhoun V, Garibotto V, Turrone R, Alberici A, Cottini E, Paghera B et al (2017) Source-based morphometry multivariate approach to analyze [123I] FP-CIT SPECT imaging. Mol Imaging Biol 19:1–7

    Article  Google Scholar 

  • Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD (2009) Neurodegenerative diseases target large-scale human brain networks. Neuron 62(1):42–52. https://doi.org/10.1016/j.neuron.2009.03.024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Segall Judith M, Turner JA, van Erp TGM, White T, Bockholt HJ, Gollub RL, Calhoun VD et al (2009) Voxel-based morphometric multisite collaborative study on schizophrenia. Schizophr Bull 35(1):82–95. https://doi.org/10.1093/schbul/sbn150

    Article  PubMed  Google Scholar 

  • Segall Judith Maxine, Allen EA, Jung RE, Erhardt EB, Arja SK, Kiehl KA, Calhoun VD (2012) Correspondence between structure and function in the human brain at rest. Front Neuroinform 6:10

    Article  Google Scholar 

  • Silver M, Montana G, Nichols TE, Alzheimer’s Disease Neuroimaging Initiative (2011) False positives in neuroimaging genetics using voxel-based morphometry data. Neuroimage 54(2):992–1000

    Article  Google Scholar 

  • Sprooten E, Gupta CN, Knowles EE, McKay DR, Mathias SR, Curran JE, Dyer TD et al (2015) Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24. Am J Med Genet Part B Neuropsychiatr Genet 168(8):678–686

    Article  CAS  Google Scholar 

  • Sui J, Adali T, Yu Q, Chen J, Calhoun VD (2012) A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods 204(1):68–81

    Article  Google Scholar 

  • Turner JA, Calhoun VD, Michael A, Van Erp TG, Ehrlich S, Segall JM, Ho B-C et al (2012) Heritability of multivariate gray matter measures in schizophrenia. Twin Res Human Genet 15(03):324–335

    Article  Google Scholar 

  • Wolf RC, Huber M, Lepping P, Sambataro F, Depping MS, Karner M, Freudenmann RW (2014) Source-based morphometry reveals distinct patterns of aberrant brain volume in delusional infestation. Prog Neuropsychopharmacol Biol Psychiatry 48:112–116

    Article  Google Scholar 

  • Xu L, Groth KM, Pearlson G, Schretlen DJ, Calhoun VD (2009a) Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp 30(3):711–724

    Article  Google Scholar 

  • Xu L, Pearlson G, Calhoun VD (2009b) Joint source based morphometry identifies linked gray and white matter group differences. Neuroimage 44(3):777–789

    Article  Google Scholar 

  • Xu L, Adali T, Schretlen D, Pearlson G, Calhoun VD (2011) Structural angle and power images reveal interrelated gray and white matter abnormalities in schizophrenia. Neurol Res Int 2012:735249. https://doi.org/10.1155/2012/735249

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu Q, Du Y, Chen J, He H, Sui J, Pearlson G, Calhoun VD (2017) Comparing brain graphs in which nodes are regions of interest or independent components: a simulation study. J Neurosci Methods 291:61–68. https://doi.org/10.1016/j.jneumeth.2017.08.007

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by NIH 1R01MH094524 to Drs. Turner and Calhoun, as well as P20GM103472, 1R01EB006841, R01EB005846 and NSF grant 1539067 to Dr. Calhoun. The first author acknowledges support from the Indian Institute of Technology Guwahati startup grant during this work.

Author information

Author notes

  1. Jessica A. Turner and Vince D. Calhoun spent equal time on this work.

Authors and Affiliations

  1. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, US

    Cota Navin Gupta, Jessica A. Turner & Vince D. Calhoun

  2. Neural Engineering Lab, Department of Biosciences and Bioengineering (BSBE), Indian Institute of Technology Guwahati, Guwahati, India

    Cota Navin Gupta

  3. Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, USA

    Jessica A. Turner & Vince D. Calhoun

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

    Vince D. Calhoun

Authors
  1. Cota Navin Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica A. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vince D. Calhoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cota Navin Gupta.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical statement

This is a review article and human participants were not involved in this work.

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

Gupta, C.N., Turner, J.A. & Calhoun, V.D. Source-based morphometry: a decade of covarying structural brain patterns. Brain Struct Funct 224, 3031–3044 (2019). https://doi.org/10.1007/s00429-019-01969-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00429-019-01969-8

Keywords

Search

Navigation