J.M. Brady

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 FMRIBs Software Library (FSL).

Characterization and propagation of uncertainty in diffusion-weighted MR imaging.

A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate. Magn Reson Med 50:1077–1088, 2003.

Removal of FMRI environment artifacts from EEG data using optimal basis sets.

The combination of functional magnetic resonance imaging (FMRI) and electroencephalography (EEG) has received much recent attention, since it potentially offers a new tool for neuroscientists that makes simultaneous use of the strengths of the two modalities. However, EEG data collected in such experiments suffer from two kinds of artifact. First, gradient artifacts are caused by the switching of magnetic gradients during FMRI. Second, ballistocardiographic (BCG) artifacts related to cardiac activities further contaminate the EEG data. Here we present new methods to remove both kinds of artifact. The methods are based primarily on the idea that temporal variations in the artifacts can be captured by performing temporal principal component analysis (PCA), which leads to the identification of a set of basis functions which describe the temporal variations in the artifacts. These basis functions are then fitted to, and subtracted from, EEG data to produce artifact-free results. In addition, we also describe a robust algorithm for the accurate detection of heart beat peaks from poor quality electrocardiographic (ECG) data that are collected for the purpose of BCG artifact removal. The methods are tested and are shown to give superior results to existing methods. The methods also demonstrate the feasibility of simultaneous EEG/FMRI experiments using the relatively low EEG sampling frequency of 2048 Hz.

ASSET-2: real-time motion segmentation and shape tracking

This paper describes a system for detecting and tracking moving objects in a moving world. The feature-based optic flow field is segmented into clusters with affine internal motion which are tracked over time. The system runs in real-time, and is accurate and reliable. >

Fully Bayesian spatio-temporal modeling of FMRI data

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

A scene segmenter; visual tracking of moving vehicles

Abstract In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described. ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion. The moving objects are tracked, and their outlines are accurately estimated. The ASSET system provides a useful source of world information, for example, in the area of autonomous vehicle guidance.

Non-Rigid Motion Estimation and Spatio-Temporal Realignment in FMRI

Existing approaches to the problem of subject motion artefacts in FMRI data have applied rigid-body registration techniques to what is a non-rigid problem. We propose a model which can account for the non-linear characteristics of movement effects, known to result from the acquisition methods used to form these images. The model also facilitates the proper application of temporal corrections which are needed to compensate for acquisition delays. Results of an implementation based on this model reveal that it is possible to correct for these effects, leading to accurate re-alignment and timing correction.

A Scene Segmenter; Visual Tracking of Moving Vehicles

Abstract In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described. ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion. The moving objects are tracked, and their outlines are accurately estimated. The ASSET system will provide a useful source of world information, for example, in the area of autonomous vehicle guidance.