Advances in functional and structural MR image analysis and implementation as FSL

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

[1]  Timothy Edward John Behrens,et al.  Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging , 2003, Nature Neuroscience.

[2]  Karl J. Friston,et al.  Modelling Geometric Deformations in Epi Time Series , 2022 .

[3]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[4]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Theory , 2002, NeuroImage.

[5]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[6]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[7]  Mark W. Woolrich,et al.  Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data , 2005, IEEE Transactions on Medical Imaging.

[8]  C. F. Beckmann,et al.  Tensorial extensions of independent component analysis for multisubject FMRI analysis , 2005, NeuroImage.

[9]  Paul M. Matthews,et al.  Spatial consistency of FMRI Resting State Networks across sessions and across subjects , 2004 .

[10]  Karl J. Friston,et al.  Variability in fMRI: An Examination of Intersession Differences , 2000, NeuroImage.

[11]  Mark Jenkinson,et al.  Voxel-Level Cross-Subject Statistical Analysis of Brain Atrophy in early Relapsing Remitting MS patients , 2003 .

[12]  O. Witte,et al.  Functional Mapping of the Human Brain , 2000 .

[13]  Karl J. Friston,et al.  Generalisability, Random Effects & Population Inference , 1998, NeuroImage.

[14]  Alan C. Evans,et al.  A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[15]  S. M. Steve.  SUSAN - a new approach to low level image processing , 1997 .

[16]  Karl J. Friston,et al.  To Smooth or Not to Smooth? Bias and Efficiency in fMRI Time-Series Analysis , 2000, NeuroImage.

[17]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[18]  M. Jenkinson.  A Fast , Automated , N-Dimensional Phase Unwrapping Algorithm FMRIB Technical Report TR 01 MJ 1 , 2003 .

[19]  Stephen M. Smith,et al.  General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.

[20]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[21]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[22]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[23]  Mark W. Woolrich,et al.  Constrained linear basis sets for HRF modelling using Variational Bayes , 2004, NeuroImage.

[24]  Mark W. Woolrich,et al.  Fully Bayesian spatio-temporal modeling of FMRI data , 2004, IEEE Transactions on Medical Imaging.

[25]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[26]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[27]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[29]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[30]  P. Jezzard,et al.  Correction for geometric distortion in echo planar images from B0 field variations , 1995, Magnetic resonance in medicine.

[31]  Mark Jenkinson,et al.  Improved unwarping of EPI images using regularised B0 maps , 2001, NeuroImage.

[32]  Mark Jenkinson,et al.  SIENA — Normalised accurate measurement of longitudinal brain change , 2000, NeuroImage.

[33]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[34]  Timothy Edward John Behrens,et al.  Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. , 2005, Cerebral cortex.

[35]  B. Everitt,et al.  Mixture model mapping of brain activation in functional magnetic resonance images , 1999, Human brain mapping.

[36]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[37]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[38]  Mark Jenkinson,et al.  Perturbation method for magnetic field calculations of nonconductive objects , 2004, Magnetic resonance in medicine.

[39]  Marilena. De Luca,et al.  Low frequency signals in FMRI , 2004 .

[40]  Y. Mukaigawa,et al.  Large Deviations Estimates for Some Non-local Equations I. Fast Decaying Kernels and Explicit Bounds , 2022 .

[41]  Michael Brady,et al.  TIGER - A New Model for Spatio-temporal Realignment of FMRI Data , 2004, ECCV Workshops CVAMIA and MMBIA.

[42]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[43]  Mark W. Woolrich,et al.  Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.

[44]  Steven C.R. Williams,et al.  Real-time fMRI processing and neurofeedback on a conventional Windows PC , 2003 .