IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/ek4n3.html
   My bibliography  Save this paper

Statistical Connectomics

Author

Listed:
  • Chung, Jaewon
  • Bridgeford, Eric
  • Arroyo, Jesus
  • Pedigo, Benjamin D.
  • Saad-Eldin, Ali
  • Gopalakrishnan, Vivek
  • Xiang, Liang
  • Priebe, Carey E.
  • Vogelstein, Joshua T.

Abstract

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:osfxxx:ek4n3
    DOI: 10.31219/osf.io/ek4n3
    as

    Download full text from publisher

    File URL: https://osf.io/download/5f344aad8e0d46001abe7014/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/ek4n3?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
    ---><---

    References listed on IDEAS

    as
    1. Crainiceanu, Ciprian M. & Caffo, Brian S. & Luo, Sheng & Zipunnikov, Vadim M. & Punjabi, Naresh M., 2011. "Population Value Decomposition, a Framework for the Analysis of Image Populations," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 775-790.
    2. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    3. Edward R. Scheinerman & Kimberly Tucker, 2010. "Modeling graphs using dot product representations," Computational Statistics, Springer, vol. 25(1), pages 1-16, March.
    4. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    5. Edward Scheinerman & Kimberly Tucker, 2010. "Modeling graphs using dot product representations," Computational Statistics, Springer, vol. 25(1), pages 1-16, March.
    6. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Rejoinder: Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1547-1552, October.
    7. Cape, Joshua & Tang, Minh & Priebe, Carey E., 2019. "On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs," Network Science, Cambridge University Press, vol. 7(3), pages 269-291, September.
    8. 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.
    9. Joshua T Vogelstein & John M Conroy & Vince Lyzinski & Louis J Podrazik & Steven G Kratzer & Eric T Harley & Donniell E Fishkind & R Jacob Vogelstein & Carey E Priebe, 2015. "Fast Approximate Quadratic Programming for Graph Matching," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    10. Daniel L. Sussman & Minh Tang & Donniell E. Fishkind & Carey E. Priebe, 2012. "A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1119-1128, September.
    11. Katharina Eichler & Feng Li & Ashok Litwin-Kumar & Youngser Park & Ingrid Andrade & Casey M. Schneider-Mizell & Timo Saumweber & Annina Huser & Claire Eschbach & Bertram Gerber & Richard D. Fetter & J, 2017. "The complete connectome of a learning and memory centre in an insect brain," Nature, Nature, vol. 548(7666), pages 175-182, August.
    Full references (including those not matched with items on IDEAS)

    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. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    2. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    3. N. Lee & C. Priebe, 2011. "A latent process model for time series of attributed random graphs," Statistical Inference for Stochastic Processes, Springer, vol. 14(3), pages 231-253, October.
    4. Silvia D'Angelo & Marco Alfò & Thomas Brendan Murphy, 2020. "Modeling node heterogeneity in latent space models for multidimensional networks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 324-341, August.
    5. Patrick Rubin‐Delanchy & Joshua Cape & Minh Tang & Carey E. Priebe, 2022. "A statistical interpretation of spectral embedding: The generalised random dot product graph," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1446-1473, September.
    6. Lovato, Ilenia & Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2020. "Model-free two-sample test for network-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    7. Yoder, Jordan & Chen, Li & Pao, Henry & Bridgeford, Eric & Levin, Keith & Fishkind, Donniell E. & Priebe, Carey & Lyzinski, Vince, 2020. "Vertex nomination: The canonical sampling and the extended spectral nomination schemes," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    8. Vainora, J., 2024. "Latent Position-Based Modeling of Parameter Heterogeneity," Cambridge Working Papers in Economics 2455, Faculty of Economics, University of Cambridge.
    9. Ilenia Lovato & Alessia Pini & Aymeric Stamm & Maxime Taquet & Simone Vantini, 2021. "Multiscale null hypothesis testing for network‐valued data: Analysis of brain networks of patients with autism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 372-397, March.
    10. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    11. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    12. 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.
    13. Koen Jochmans, 2024. "Nonparametric identification and estimation of stochastic block models from many small networks," Post-Print hal-04672521, HAL.
    14. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    15. Zhihao Zheng & Christopher S. Own & Adrian A. Wanner & Randal A. Koene & Eric W. Hammerschmith & William M. Silversmith & Nico Kemnitz & Ran Lu & David W. Tank & H. Sebastian Seung, 2024. "Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    16. Falk Bräuning & Siem Jan Koopman, 2016. "The dynamic factor network model with an application to global credit risk," Working Papers 16-13, Federal Reserve Bank of Boston.
    17. Jamie Olson & Kathleen Carley, 2013. "Exact and approximate EM estimation of mutually exciting hawkes processes," Statistical Inference for Stochastic Processes, Springer, vol. 16(1), pages 63-80, April.
    18. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    19. 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.
    20. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:osf:osfxxx:ek4n3. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.