Skip to main content
SpringerLink
Log in

Identifying Shared Neuroanatomic Architecture Between Cognitive Traits Through Multiscale Morphometric Correlation Analysis

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14394))

  • 179 Accesses

Abstract

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals’ cognitive status.

Z. Wen and J. Bao—These authors contributed equally to this work.

This work was supported in part by the NIH grants U01 AG068057, RF1 AG063481, U01 AG066833, R01 LM013463, P30 AG073105, and R01 AG071470, and the NSF grant IIS 1837964. Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu), which was funded by NIH U01 AG024904.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Bao, J., et al.: Identifying imaging genetic associations via regional morphometricity estimation. In: Pacific Symposium on Biocomputing, vol. 27, pp. 97–108 (2022)

    Google Scholar 

  3. Couvy-Duchesne, B., et al.: A unified framework for association and prediction from vertex-wise grey-matter structure. Hum. Brain Mapp. 41(14), 4062–4076 (2020)

    Article  Google Scholar 

  4. Gilmour, A.R., Thompson, R., Cullis, B.R.: Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 1440–1450 (1995)

    Google Scholar 

  5. Harville, D.A.: Maximum likelihood approaches to variance component estimation and to related problems. J. Am. Stat. Assoc. 72(358), 320–338 (1977)

    Article  MathSciNet  Google Scholar 

  6. Jennrich, R.I., Sampson, P.: Newton-raphson and related algorithms for maximum likelihood variance component estimation. Technometrics 18(1), 11–17 (1976)

    Article  MathSciNet  Google Scholar 

  7. Laird, N.M., Ware, J.H.: Random-effects models for longitudinal data. Biometrics 963–974 (1982)

    Google Scholar 

  8. Lee, S.H., Van Der Werf, J.H.: An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree. Genet. Sel. Evol. 38(1), 1–19 (2006)

    Article  Google Scholar 

  9. Liu, D., Lin, X., Ghosh, D.: Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 63(4), 1079–1088 (2007)

    Article  MathSciNet  Google Scholar 

  10. Meyer, K., Smith, S.: Restricted maximum likelihood estimation for animal models using derivatives of the likelihood. Genet. Sel. Evol. 28(1), 23–49 (1996)

    Article  Google Scholar 

  11. Sabuncu, M.R., Ge, T., Holmes, A.J., et al.: Morphometricity as a measure of the neuroanatomical signature of a trait. Proc. Natl. Acad. Sci. 113(39), E5749–E5756 (2016)

    Article  Google Scholar 

  12. Shen, L., et al.: ADNI: identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. Multimodal Brain Image Anal. 7012, 27–34 (2011)

    Article  Google Scholar 

  13. Shen, L., Thompson, P.M.: Brain imaging genomics: integrated analysis and machine learning. Proc. IEEE 108(1), 125–162 (2020)

    Article  Google Scholar 

  14. Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)

    Article  Google Scholar 

  15. Wan, J., et al.: Identifying the neuroanatomical basis of cognitive impairment in Alzheimer’s disease by correlation- and nonlinearity-aware sparse Bayesian learning. IEEE Trans. Med. Imaging 33(7), 1475–1487 (2014)

    Article  Google Scholar 

  16. Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A.J., Shen, L.: Identifying ad-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. Med. Image Comput. Comput. Assist. Interv. 14(Pt 3), 115–23 (2011)

    Google Scholar 

  17. Wang, X., et al.: Exploring automated machine learning for cognitive outcome prediction from multimodal brain imaging using streamline. AMIA Jt. Summits Transl. Sci. Proc. 2023, 544–553 (2023)

    Google Scholar 

  18. Weiner, M.W., Veitch, D.P., Aisen, P.S., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 9(5), e111-94 (2013)

    Article  Google Scholar 

  19. Weiner, M.W., Veitch, D.P., Aisen, P.S., et al.: Recent publications from the Alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimer’s Dementia 13(4), e1–e85 (2017)

    Article  Google Scholar 

  20. Wright, I., et al.: A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 2(4), 244–252 (1995)

    Article  Google Scholar 

  21. Yan, J., et al.: Alzheimer’s disease neuroimaging initiative: cortical surface biomarkers for predicting cognitive outcomes using group L2,1 norm. Neurobiol. Aging 36(Suppl. 1), S185–S193 (2015)

    Article  Google Scholar 

  22. Yang, J., Lee, S.H., Goddard, M.E., Visscher, P.M.: GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88(1), 76–82 (2011)

    Article  Google Scholar 

  23. Zhu, S., Wathen, A.J.: Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models. arXiv preprint arXiv:1805.05188 (2018)

Download references

Author information

Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Zixuan Wen, Jingxuan Bao, Shu Yang, Christos Davatzikos & Li Shen

  2. Indiana University, Indianapolis, IN, USA

    Shannon L. Risacher & Andrew J. Saykin

  3. University of Southern California, Los Angels, CA, USA

    Paul M. Thompson

  4. University of Maryland, College Park, MD, USA

    Heng Huang

  5. Yale University, New Haven, CT, USA

    Yize Zhao

Authors
  1. Zixuan Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingxuan Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shannon L. Risacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew J. Saykin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paul M. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christos Davatzikos
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Heng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yize Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Li Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Shen .

Editor information

Editors and Affiliations

  1. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Jonghye Woo

  2. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Alessa Hering

  3. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Wilson Silva

  4. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Xiang Li

  5. A*STAR, Institute of High Performance Computing, Singapore, Singapore

    Huazhu Fu

  6. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Xiaofeng Liu

  7. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Fangxu Xing

  8. University of Maryland Baltimore County, Baltimore, MD, USA

    Sanjay Purushotham

  9. National Institutes of Health, Bethesda, MD, USA

    Tejas S. Mathai

  10. National Institutes of Health, Bethesda, MD, USA

    Pritam Mukherjee

  11. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Max De Grauw

  12. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Regina Beets Tan

  13. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Valentina Corbetta

  14. University Hospital Freiburg, Freiburg, Germany

    Elmar Kotter

  15. University of Bern, Bern, Switzerland

    Mauricio Reyes

  16. University of Tübingen, Tübingen, Germany

    Christian F. Baumgartner

  17. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Quanzheng Li

  18. University of Southern California, Los Angeles, CA, USA

    Richard Leahy

  19. Peking University, Beijing, China

    Bin Dong

  20. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Hao Chen

  21. Vanderbilt University, Nashville, TN, USA

    Yuankai Huo

  22. University of Sydney, Camperdown, NSW, Australia

    Jinglei Lv

  23. Institute of High Performance Computing, Singapore, Singapore

    Xinxing Xu

  24. Hong Kong University of Science and Technology, Hong Kong, China

    Xiaomeng Li

  25. Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Dwarikanath Mahapatra

  26. University of Alberta, Edmonton, AB, Canada

    Li Cheng

  27. LITIS, University of Rouen, Rouen, France

    Caroline Petitjean

  28. ImViA Laboratory, University of Burgundy, Dijon, France

    Benoît Presles

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wen, Z. et al. (2023). Identifying Shared Neuroanatomic Architecture Between Cognitive Traits Through Multiscale Morphometric Correlation Analysis. In: Woo, J., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. MICCAI 2023. Lecture Notes in Computer Science, vol 14394. Springer, Cham. https://doi.org/10.1007/978-3-031-47425-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47425-5_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47424-8

  • Online ISBN: 978-3-031-47425-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation