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Predicting phenotypes from brain connection structure

Publication ,  Journal Article
Guha, S; Jung, R; Dunson, D
Published in: Journal of the Royal Statistical Society. Series C: Applied Statistics
June 1, 2022

This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high-dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. These methods are sensitive to feature choice and inevitably discard important information. Instead, we propose a nonparametric Bayes class of models that utilize the entire adjacency matrix defining brain region connections to adaptively detect predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson–Dirichlet processes to find a lower dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a ‘small n, small q’ problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are found to outperform existing approaches and applied to a creative reasoning dataset.

Duke Scholars

Published In

Journal of the Royal Statistical Society. Series C: Applied Statistics

DOI

EISSN

1467-9876

ISSN

0035-9254

Publication Date

June 1, 2022

Volume

71

Issue

3

Start / End Page

639 / 668

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics
 

Citation

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Guha, S., Jung, R., & Dunson, D. (2022). Predicting phenotypes from brain connection structure. Journal of the Royal Statistical Society. Series C: Applied Statistics, 71(3), 639–668. https://doi.org/10.1111/rssc.12549
Guha, S., R. Jung, and D. Dunson. “Predicting phenotypes from brain connection structure.” Journal of the Royal Statistical Society. Series C: Applied Statistics 71, no. 3 (June 1, 2022): 639–68. https://doi.org/10.1111/rssc.12549.
Guha S, Jung R, Dunson D. Predicting phenotypes from brain connection structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 2022 Jun 1;71(3):639–68.
Guha, S., et al. “Predicting phenotypes from brain connection structure.” Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 71, no. 3, June 2022, pp. 639–68. Scopus, doi:10.1111/rssc.12549.
Guha S, Jung R, Dunson D. Predicting phenotypes from brain connection structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 2022 Jun 1;71(3):639–668.
Journal cover image

Published In

Journal of the Royal Statistical Society. Series C: Applied Statistics

DOI

EISSN

1467-9876

ISSN

0035-9254

Publication Date

June 1, 2022

Volume

71

Issue

3

Start / End Page

639 / 668

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics