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Brain Structure and Function

Erschienen in:

07.11.2019 | Review

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

verfasst von: Cota Navin Gupta, Jessica A. Turner, Vince D. Calhoun

Erschienen in: Brain Structure and Function | Ausgabe 9/2019

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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.

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMedPubMedCentral

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

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

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

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 CrossRefPubMedPubMedCentral

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 17:444–451CrossRef

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):140–151CrossRef

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–S172CrossRef

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):133–145CrossRef

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMedPubMedCentral

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 230:37–50CrossRef

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 7:147CrossRef

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

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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

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

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

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 10:388CrossRef

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):1133–1142CrossRef

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 8:179CrossRef

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

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 CrossRefPubMed

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 CrossRefPubMed

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

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 19:813–823CrossRef

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):832–844CrossRef

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):700–712CrossRef

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 50:102–109CrossRef

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

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 CrossRefPubMed

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

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

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):305–312CrossRef

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 6:276CrossRef

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 19:1–7CrossRef

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMed

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 6:10CrossRef

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):992–1000CrossRef

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):678–686CrossRef

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):68–81CrossRef

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):324–335CrossRef

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 48:112–116CrossRef

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):711–724CrossRef

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

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMedPubMedCentral

Titel: Source-based morphometry: a decade of covarying structural brain patterns
verfasst von: Cota Navin Gupta
Jessica A. Turner
Vince D. Calhoun
Publikationsdatum: 07.11.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Brain Structure and Function / Ausgabe 9/2019
Print ISSN: 1863-2653
Elektronische ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-019-01969-8

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

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