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.
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References
Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805–821 (2000)
Bao, J., et al.: Identifying imaging genetic associations via regional morphometricity estimation. In: Pacific Symposium on Biocomputing, vol. 27, pp. 97–108 (2022)
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)
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)
Harville, D.A.: Maximum likelihood approaches to variance component estimation and to related problems. J. Am. Stat. Assoc. 72(358), 320–338 (1977)
Jennrich, R.I., Sampson, P.: Newton-raphson and related algorithms for maximum likelihood variance component estimation. Technometrics 18(1), 11–17 (1976)
Laird, N.M., Ware, J.H.: Random-effects models for longitudinal data. Biometrics 963–974 (1982)
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)
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)
Meyer, K., Smith, S.: Restricted maximum likelihood estimation for animal models using derivatives of the likelihood. Genet. Sel. Evol. 28(1), 23–49 (1996)
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)
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)
Shen, L., Thompson, P.M.: Brain imaging genomics: integrated analysis and machine learning. Proc. IEEE 108(1), 125–162 (2020)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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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
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