Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segm...

Bibliográfalaš dieđut
Váldodahkkit:	Woolrich, M, Behrens, T, Beckmann, C, Smith, S
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	2005

Geahča maid

Variational Bayes inference of spatial mixture models for segmentation.
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2006)

Multilevel linear modelling for FMRI group analysis using Bayesian inference.
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2004)

Statistical analysis of fMRI data
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2009)

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems.
Dahkki: Makni, S, et al.
Almmustuhtton: (2008)

Bayesian deconvolution of fMRI data using bilinear dynamical systems (vol 42, pg 1381, 2008)
Dahkki: Makni, S, et al.
Almmustuhtton: (2009)

Spatially Adaptive Regularization in Image Segmentation
Dahkki: Laura Antonelli, et al.
Almmustuhtton: (2020-09-01)

Meaningful design and contrast estimability in FMRI.
Dahkki: Smith, S, et al.
Almmustuhtton: (2007)

Network modelling methods for FMRI.
Dahkki: Smith, S, et al.
Almmustuhtton: (2011)

fMRI detection with spatial regularization
Dahkki: Ou, Wanmei
Almmustuhtton: (2006)

Fully Bayesian spatio-temporal modeling of FMRI data.
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2004)

Temporal autocorrelation in univariate linear modeling of FMRI data.
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2001)

Erratum to "Bayesian deconvolution of fMRI data using bilinear dynamical systems" [NeuroImage 42 (2008) 1381-1396] (DOI:10.1016/j.neuroimage.2008.05.052)
Dahkki: Makni, S, et al.
Almmustuhtton: (2009)

Heirarchical fully Bayesian spatio-temporal analysis of FMRI data
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2001)

Functional connectomics from resting-state fMRI
Dahkki: Smith, S, et al.
Almmustuhtton: (2013)

Investigating the intrinsic dimensionality of FMRI data for ICA
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2001)

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination
Dahkki: Sala-Llonch, R, et al.
Almmustuhtton: (2018)

Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models.
Dahkki: Jbabdi, S, et al.
Almmustuhtton: (2009)

Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2006)

Applying FSL to the FIAC data: Model-based and model-free analysis of voice and sentence repetition priming
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2006)

Bayesian inference in FMRI.
Dahkki: Woolrich, M
Almmustuhtton: (2012)

Bayesian inference in FMRI
Dahkki: Woolrich, M
Almmustuhtton: (2012)

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data.
Dahkki: Cole, D, et al.
Almmustuhtton: (2010)

Artefact detection in FMRI data using independent component analysis
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2000)

General multilevel linear modeling for group analysis in FMRI.
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2003)

MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization.
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2011)

Optimising network modelling methods for fMRI
Dahkki: Pervaiz, U, et al.
Almmustuhtton: (2020)

Optimising network modelling methods for fMRI
Dahkki: Usama Pervaiz, et al.
Almmustuhtton: (2020-05-01)

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data
Dahkki: David M Cole, et al.
Almmustuhtton: (2010-04-01)

Variability in fMRI: a re-examination of inter-session differences.
Dahkki: Smith, S, et al.
Almmustuhtton: (2005)

Nonparametric estimation of temporal autocorrelation in FMRI
Dahkki: Woolrich, M, et al.
Almmustuhtton: (2000)

FSL
Dahkki: Jenkinson, M, et al.
Almmustuhtton: (2012)

FSL.
Dahkki: Jenkinson, M, et al.
Almmustuhtton: (2012)

The danger of systematic bias in group-level FMRI-lag-based causality estimation
Dahkki: Smith, S, et al.
Almmustuhtton: (2011)

The danger of systematic bias in group-level FMRI-lag-based causality estimation.
Dahkki: Smith, S, et al.
Almmustuhtton: (2012)

Spatially constrained hierarchical parcellation of the brain with resting-state fMRI.
Dahkki: Blumensath, T, et al.
Almmustuhtton: (2013)

Spatio-temporal accuracy of ICA for FMRI
Dahkki: Beckmann, C, et al.
Almmustuhtton: (2001)

Regularized Mixture Rasch Model
Dahkki: Alexander Robitzsch
Almmustuhtton: (2022-11-01)

Data and model considerations for estimating time-varying functional connectivity in fMRI
Dahkki: Ahrends, C, et al.
Almmustuhtton: (2022)

Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations
Dahkki: Pervaiz, U, et al.
Almmustuhtton: (2022)

The relationship between spatial configuration and functional connectivity of brain regions revisited
Dahkki: Bijsterbosch, J, et al.
Almmustuhtton: (2019)