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A Fully Bayesian Inference Framework for Population Studies of the Brain Microstructure

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Abstract

Models of the diffusion-weighted signal are of strong interest for population studies of the brain microstructure. These studies are typically conducted by extracting a scalar property from the model and subjecting it to null hypothesis significance testing. This process has two major limitations: the reported p-value is a weak predictor of the reproducibility of findings and evidence for the absence of microstructural alterations cannot be gained. To overcome these limitations, this paper proposes a Bayesian framework for population studies of the brain microstructure represented by multi-fascicle models. A hierarchical model is built over the biophysical parameters of the microstructure. Bayesian inference is performed by Hamiltonian Monte Carlo sampling and results in a joint posterior distribution over the latent microstructure parameters for each group. Inference from this posterior enables richer analyses of the brain microstructure beyond the dichotomy of significance testing. Using synthetic and in-vivo data, we show that our Bayesian approach increases reproducibility of findings from population studies and opens new opportunities in the analysis of the brain microstructure.

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Author information

Authors and Affiliations

  1. Computational Radiology Laboratory, Harvard Medical School, Boston, USA

    Maxime Taquet, Benoît Scherrer, Jurriaan M. Peters, Sanjay P. Prabhu & Simon K. Warfield

Authors
  1. Maxime Taquet

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  2. Benoît Scherrer

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  3. Jurriaan M. Peters

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  4. Sanjay P. Prabhu

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  5. Simon K. Warfield

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Editor information

Editors and Affiliations

  1. MIT CSAIL, 32 Vassar Street, 02139, Cambridge, MA, USA

    Polina Golland

  2. Department of Radiology, Brigham and Women’s Hospital, 75 Francis St., 02115, Boston, MA, USA

    Nobuhiko Hata

  3. CNRS/Inria Research Unit Visages, IRISA, Campus Universitaire de Beaulieu, 35042, Rennes Cedex, France

    Christian Barillot

  4. Pattern Recognition Lab, University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Germany

    Joachim Hornegger

  5. Harvard School of Engineering and Applied Sciences, 323 Pierce Hall, 29 Oxford Street, 02138, Cambridge, MA, USA

    Robert Howe

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© 2014 Springer International Publishing Switzerland

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Taquet, M., Scherrer, B., Peters, J.M., Prabhu, S.P., Warfield, S.K. (2014). A Fully Bayesian Inference Framework for Population Studies of the Brain Microstructure. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_4

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