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A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data

Chen, Shuo ; Bowman, F. DuBois ; Mayberg, Helen S.

Biometrics, 2016-06, Vol.72 (2), p.596-605 [Periódico revisado por pares]

United States: Blackwell Publishing Ltd

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Título:
A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data
Autor: Chen, Shuo ; Bowman, F. DuBois ; Mayberg, Helen S.
Assuntos: Adult ; Algorithms ; Bayes Theorem ; Bayesian analysis ; Bayesian hierarchical model ; BIOMETRIC PRACTICE ; Biometry - methods ; Brain imaging ; Brain Mapping - methods ; Computer Simulation ; Data Interpretation, Statistical ; Depression - diagnostic imaging ; Female ; Functional connectivity ; Humans ; Magnetic Resonance Imaging ; Male ; Markov analysis ; Markov Chains ; MCMC ; Middle Aged ; Models, Neurological ; Models, Statistical ; Monte Carlo Method ; Monte Carlo simulation ; Neuroimaging - statistics & numerical data ; Parameter estimation ; Resting-state fMRI ; Young Adult
É parte de: Biometrics, 2016-06, Vol.72 (2), p.596-605
Notas: istex:A2777C2AED3AC3DB7409AFA9FB165F963A5A6E8C
ArticleID:BIOM12433
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ObjectType-Article-1
SourceType-Scholarly Journals-1
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Descrição: We propose a novel Bayesian hierarchical model for brain imaging data that unifies voxel-level (the most localized unit of measure) and region-level brain connectivity analyses, and yields population-level inferences. Functional connectivity generally refers to associations in brain activity between distinct locations. The first level of our model summarizes brain connectivity for cross-region voxel pairs using a two-component mixture model consisting of connected and nonconnected voxels. We use the proportion of connected voxel pairs to define a new measure of connectivity strength, which reflects the breadth of between-region connectivity. Furthermore, we evaluate the impact of clinical covariates on connectivity between region-pairs at a population level. We perform parameter estimation using Markov chain Monte Carlo (MCMC) techniques, which can be executed quickly relative to the number of model parameters. We apply our method to resting-state functional magnetic resonance imaging (fMRI) data from 32 subjects with major depression and simulated data to demonstrate the properties of our method.
Editor: United States: Blackwell Publishing Ltd
Idioma: Inglês;Francês

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A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data

Chen, Shuo ; Bowman, F. DuBois ; Mayberg, Helen S.

United States: Blackwell Publishing Ltd

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