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Interpretable brain age prediction using linear latent variable models of functional connectivity

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Listed:
  • Ricardo Pio Monti
  • Alex Gibberd
  • Sandipan Roy
  • Matthew Nunes
  • Romy Lorenz
  • Robert Leech
  • Takeshi Ogawa
  • Motoaki Kawanabe
  • Aapo Hyvärinen

Abstract

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.

Suggested Citation

  • Ricardo Pio Monti & Alex Gibberd & Sandipan Roy & Matthew Nunes & Romy Lorenz & Robert Leech & Takeshi Ogawa & Motoaki Kawanabe & Aapo Hyvärinen, 2020. "Interpretable brain age prediction using linear latent variable models of functional connectivity," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-25, June.
  • Handle: RePEc:plo:pone00:0232296
    DOI: 10.1371/journal.pone.0232296
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    References listed on IDEAS

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    1. Romy Lorenz & Ines R. Violante & Ricardo Pio Monti & Giovanni Montana & Adam Hampshire & Robert Leech, 2018. "Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    2. Henk R Cremers & Tor D Wager & Tal Yarkoni, 2017. "The relation between statistical power and inference in fMRI," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-20, November.
    3. Jun-ichiro Hirayama & Aapo Hyvärinen & Vesa Kiviniemi & Motoaki Kawanabe & Okito Yamashita, 2016. "Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-24, December.
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    Cited by:

    1. Jessica Dafflon & Pedro F. Da Costa & František Váša & Ricardo Pio Monti & Danilo Bzdok & Peter J. Hellyer & Federico Turkheimer & Jonathan Smallwood & Emily Jones & Robert Leech, 2022. "A guided multiverse study of neuroimaging analyses," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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