IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v171y2022ics0167947322000342.html
   My bibliography  Save this article

Group linear non-Gaussian component analysis with applications to neuroimaging

Author

Listed:
  • Zhao, Yuxuan
  • Matteson, David S.
  • Mostofsky, Stewart H.
  • Nebel, Mary Beth
  • Risk, Benjamin B.

Abstract

Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementia. However, current methods use a principal component analysis (PCA) step that may remove low-variance features. Linear non-Gaussian component analysis (LNGCA) enables simultaneous dimension reduction and feature estimation including low-variance features in single-subject fMRI. A group LNGCA model is proposed to extract group components shared by more than one subject. Unlike group ICA methods, this novel approach also estimates individual (subject-specific) components orthogonal to the group components. To determine the total number of components in each subject, a parametric resampling test is proposed that samples spatially correlated Gaussian noise to match the spatial dependence observed in data. In simulations, estimated group components achieve higher accuracy compared to group ICA. The method is applied to a resting-state fMRI study on autism spectrum disorder in 342 children (252 typically developing, 90 with autism), where the group signals include resting-state networks. The discovered group components appear to exhibit different levels of temporal engagement in autism versus typically developing children, as revealed using group LNGCA. This novel approach to matrix decomposition is a promising direction for feature detection in neuroimaging.

Suggested Citation

  • Zhao, Yuxuan & Matteson, David S. & Mostofsky, Stewart H. & Nebel, Mary Beth & Risk, Benjamin B., 2022. "Group linear non-Gaussian component analysis with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000342
    DOI: 10.1016/j.csda.2022.107454
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947322000342
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107454?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benjamin B. Risk & David S. Matteson & David Ruppert & Ani Eloyan & Brian S. Caffo, 2014. "An evaluation of independent component analyses with an application to resting-state fMRI," Biometrics, The International Biometric Society, vol. 70(1), pages 224-236, March.
    2. Irina Gaynanova & Gen Li, 2019. "Structural learning and integrative decomposition of multi‐view data," Biometrics, The International Biometric Society, vol. 75(4), pages 1121-1132, December.
    3. Benjamin B. Risk & David S. Matteson & David Ruppert, 2019. "Linear Non-Gaussian Component Analysis Via Maximum Likelihood," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 332-343, January.
    4. Ying Guo & Li Tang, 2013. "A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 970-981, December.
    5. Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
    6. Welvaert, Marijke & Durnez, Joke & Moerkerke, Beatrijs & Berdoolaege, Geert & Rosseel, Yves, 2011. "neuRosim: An R Package for Generating fMRI Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i10).
    7. Ying Guo, 2011. "A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1532-1542, December.
    8. Feng, Qing & Jiang, Meilei & Hannig, Jan & Marron, J.S., 2018. "Angle-based joint and individual variation explained," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 241-265.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liao Zhu & Ningning Sun & Martin T. Wells, 2022. "Clustering Structure of Microstructure Measures," Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 85-95, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ben Wu & Subhadip Pal & Jian Kang & Ying Guo, 2022. "Distributional independent component analysis for diverse neuroimaging modalities," Biometrics, The International Biometric Society, vol. 78(3), pages 1092-1105, September.
    2. Palzer, Elise F. & Wendt, Christine H. & Bowler, Russell P. & Hersh, Craig P. & Safo, Sandra E. & Lock, Eric F., 2022. "sJIVE: Supervised joint and individual variation explained," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    4. Shin Ji-Hyung & Infante-Rivard Claire & Graham Jinko & McNeney Brad, 2012. "Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-23, January.
    5. Chung, Jaewon & Bridgeford, Eric & Arroyo, Jesus & Pedigo, Benjamin D. & Saad-Eldin, Ali & Gopalakrishnan, Vivek & Xiang, Liang & Priebe, Carey E. & Vogelstein, Joshua T., 2020. "Statistical Connectomics," OSF Preprints ek4n3, Center for Open Science.
    6. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    7. Virta, Joni & Lietzén, Niko & Viitasaari, Lauri & Ilmonen, Pauliina, 2024. "Latent model extreme value index estimation," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    8. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    9. Ying Guo & Li Tang, 2013. "A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 970-981, December.
    10. Hutchison, Paul D. & Daigle, Ronald J. & George, Benjamin, 2018. "Application of latent semantic analysis in AIS academic research," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 83-96.
    11. Benjamin B. Risk & David S. Matteson & David Ruppert & Ani Eloyan & Brian S. Caffo, 2014. "An evaluation of independent component analyses with an application to resting-state fMRI," Biometrics, The International Biometric Society, vol. 70(1), pages 224-236, March.
    12. Garikoitz Lerma-Usabiaga & Noah Benson & Jonathan Winawer & Brian A Wandell, 2020. "A validation framework for neuroimaging software: The case of population receptive fields," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-18, June.
    13. Xing Gao & Sungwon Lee & Gen Li & Sungkyu Jung, 2021. "Covariate‐driven factorization by thresholding for multiblock data," Biometrics, The International Biometric Society, vol. 77(3), pages 1011-1023, September.
    14. Ben Wu & Subhadip Pal & Jian Kang & Ying Guo, 2022. "Rejoinder to discussions of “distributional independent component analysis for diverse neuroimaging modalities”," Biometrics, The International Biometric Society, vol. 78(3), pages 1122-1126, September.
    15. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    16. Ani Eloyan & Shanshan Li & John Muschelli & Jim J Pekar & Stewart H Mostofsky & Brian S Caffo, 2014. "Analytic Programming with fMRI Data: A Quick-Start Guide for Statisticians Using R," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    17. Mullally, Conner & Chakravarty, Shourish, 2018. "Are matching funds for smallholder irrigation money well spent?," Food Policy, Elsevier, vol. 76(C), pages 70-80.
    18. Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
    19. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    20. Cardona Jiménez, Johnatan & de B. Pereira, Carlos A., 2021. "Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.