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Advances in functional and structural MR image analysis and implementation as FSL
Fast robust automated brain extraction
- Stephen M. Smith
- BiologyHuman brain mapping
- 1 November 2002
An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed and described and examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods.
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima.
Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference
A global optimisation method for robust affine registration of brain images
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
Correspondence of the brain's functional architecture during activation and rest
- Stephen M. Smith, P. Fox, C. Beckmann
- BiologyProceedings of the National Academy of Sciences
- 4 August 2009
Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are…
The WU-Minn Human Connectome Project: An overview
Probabilistic independent component analysis for functional magnetic resonance imaging
An integrated approach to probabilistic independent component analysis for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise is presented and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.