Literature DB >> 22063092

Bayesian inference in FMRI.

Mark W Woolrich1.   

Abstract

Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain connectivity analysis; and consider how these advancements have spun-off into related areas of neuroscience and some of the challenges that remain. Bayes has brought to the table inference flexibility, incorporation of prior information, adaptive regularisation and model selection. But perhaps more important than these things, is the ability of Bayes to empower the methods researcher with a mathematically principled framework for inferring on any model.
Copyright © 2011 Elsevier Inc. All rights reserved.

Mesh:

Year:  2011        PMID: 22063092     DOI: 10.1016/j.neuroimage.2011.10.047

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  20 in total

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2.  Towards Algorithmic Analytics for Large-scale Datasets.

Authors:  Danilo Bzdok; Thomas E Nichols; Stephen M Smith
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3.  Cluster failure or power failure? Evaluating sensitivity in cluster-level inference.

Authors:  Stephanie Noble; Dustin Scheinost; R Todd Constable
Journal:  Neuroimage       Date:  2019-12-15       Impact factor: 6.556

4.  A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.

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5.  Neural surprise in somatosensory Bayesian learning.

Authors:  Sam Gijsen; Miro Grundei; Robert T Lange; Dirk Ostwald; Felix Blankenburg
Journal:  PLoS Comput Biol       Date:  2021-02-02       Impact factor: 4.475

6.  Analysing brain networks in population neuroscience: a case for the Bayesian philosophy.

Authors:  Danilo Bzdok; Dorothea L Floris; Andre F Marquand
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-02-24       Impact factor: 6.237

Review 7.  Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis.

Authors:  Ming Bo Cai; Michael Shvartsman; Anqi Wu; Hejia Zhang; Xia Zhu
Journal:  Neuropsychologia       Date:  2020-05-17       Impact factor: 3.139

Review 8.  Bayesian statistics: relevant for the brain?

Authors:  Konrad Paul Kording
Journal:  Curr Opin Neurobiol       Date:  2014-01-24       Impact factor: 6.627

9.  Bayesian Models for fMRI Data Analysis.

Authors:  Linlin Zhang; Michele Guindani; Marina Vannucci
Journal:  Wiley Interdiscip Rev Comput Stat       Date:  2015 Jan-Feb

10.  A hierarchical modeling approach to data analysis and study design in a multi-site experimental fMRI study.

Authors:  Bo Zhou; Anna Konstorum; Thao Duong; Kinh H Tieu; William M Wells; Gregory G Brown; Hal S Stern; Babak Shahbaba
Journal:  Psychometrika       Date:  2012-11-28       Impact factor: 2.500

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