A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses
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TLDR
A novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data is presented and the performance of the proposed model is explored on simulated data, with both block- and event-related design, and on real fMRI data.About:
This article is published in NeuroImage.The article was published on 2014-07-15 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Gibbs sampling & Dirichlet process.read more
Citations
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Journal ArticleDOI
Bayesian statistics and modelling
Rens van de Schoot,Sarah Depaoli,Ruth King,Ruth King,Bianca Kramer,Kaspar Märtens,Mahlet G. Tadesse,Marina Vannucci,Andrew Gelman,Duco Veen,Joukje Willemsen,Christopher Yau,Christopher Yau +12 more
TL;DR: This Primer on Bayesian statistics summarizes the most important aspects of determining prior distributions, likelihood functions and posterior distributions, in addition to discussing different applications of the method across disciplines.
Journal ArticleDOI
Bayesian models for functional magnetic resonance imaging data analysis
TL;DR: A review of the most relevant models developed in recent years for fMRI data, starting from spatiotemporal models for f MRI data that detect task‐related activation patterns and addressing the very important problem of estimating brain connectivity.
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A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.
Ryan Warnick,Michele Guindani,Erik B. Erhardt,Elena A. Allen,Vince D. Calhoun,Marina Vannucci +5 more
TL;DR: This work proposes a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks, and applies it to simulated task -based fMRI data, showing how the approach allows the decoupling of the task-related activations and the functional connectivity states.
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A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
TL;DR: A unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments, distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature.
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Probabilistic model-based functional parcellation reveals a robust, fine-grained subdivision of the striatum
TL;DR: It is suggested that multiple territories within both the affective and motor regions can be distinguished solely using resting state striatal functional MRI from these regions, consistent with existing notions on segregation and integration in parallel cortico-basal ganglia loops.
References
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
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Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
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A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.
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Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
Alexander Strehl,Joydeep Ghosh +1 more
TL;DR: This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings and proposes three effective and efficient techniques for obtaining high-quality combiners (consensus functions).
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