Biomedical subjects

Mark Jenkinson

Publications and source records attributed to Mark Jenkinson.

At least 19 recordsLinked to original sources

PubMed2007

Two-dimensional population map of cortical connections in the human internal capsule.

PURPOSE: To exploit diffusion imaging tractography to produce a two-dimensional (xy) probabilistic population map of the cortical connections within the human internal capsule (IC). MATERIALS AND METHODS: Diffusion tensor imaging (DTI) was carried out on 11 healthy volunteers. We parceled an axial section of the IC according to its connections to the prefrontal, premotor, primary motor, primary somatosensory, posterior parietal, and occipital cortices using our locally developed probabilistic algorithm. RESULTS: A consistent topographical organization was generated that was consistent with our anatomical knowledge of the IC. In addition, our study shows that in humans the anterior half of the IC is occupied by the prefrontal cortex. CONCLUSION: This map may be of clinical use for correlating neurological deficit with hemispheric lesions, particularly those that affect sensorimotor function.

Adult↗

PubMed2007

Manifestations of early brain recovery associated with abstinence from alcoholism.

Chronic alcohol abuse results in morphological, metabolic, and functional brain damage which may, to some extent, be reversible with early effects upon abstinence. Although morphometric, spectroscopic, and neuropsychological indicators of cerebral regeneration have been described previously, the overall amount and spatial preference of early brain recovery attained by abstinence and its associations with other indicators of regeneration are not well established. We investigated global and local brain volume changes in a longitudinal two-timepoint study with T1-weighted MRI at admission and after short-term (6-7 weeks) sobriety follow-up in 15 uncomplicated, recently detoxified alcoholics. Volumetric brain gain was related to metabolic and neuropsychological recovery. On admission and after short-term abstinence, structural image evaluation using normalization of atrophy (SIENA), its voxelwise statistical extension to multiple subjects, proton MR spectroscopy (1H-MRS), and neuropsychological tests were applied. Upon short-term sobriety, 1H-MRS levels of cerebellar choline and frontomesial N-acetylaspartate (NAA) were significantly augmented. Automatically detected global brain volume gain amounted to nearly two per cent on average and was spatially significant around the superior vermis, perimesencephalic, periventricular and frontal brain edges. It correlated positively with the percentages of cerebellar and frontomesial choline increase, as detected by 1H-MRS. Moreover, frontomesial NAA gains were associated with improved performance on the d2-test of attention. In 10 age- and gender-matched healthy control subjects, no significant brain volume or metabolite changes were observed. Although cerebral osmotic regulations may occur initially upon sobriety, significant increases of cerebellar choline and frontomesial NAA levels detected at stable brain water integrals and creatine concentrations, serum electrolytes and red blood cell indices in our patient sample suggest that early brain recovery through abstinence does not simply reflect rehydration. Instead, even the adult human brain and particularly its white matter seems to possess genuine capabilities for regrowth. Our findings emphasize metabolic as well as regionally distinct morphological capacities for partial brain recovery from toxic insults of chronic alcoholism and substantiate early measurable benefits of therapeutic sobriety. Further understanding of the precise mechanisms of this recovery may become a valuable model of brain regeneration with relevance for other disorders.

Adult↗

PubMed2006

Meaningful design and contrast estimability in FMRI.

Optimising the efficiency of an experimental design is known to be of great importance. However, existing methods for calculating design rank deficiency and contrast estimability (an important aspect of experimental design) relate to computational precision rather than image noise and are therefore not very meaningful. For example, a contrast between two experimental conditions may be mathematically "estimable" while requiring a huge differential BOLD response for statistical significance to be reached. In this paper we formulate standard efficiency equations in terms of required BOLD effect, and use this to generate measures of rank/estimability which are meaningful. This takes into account the strength and smoothness of the timeseries noise and is applicable to complex contrasts; we show how to re-express several regressors and an associated contrast vector as a single equivalent regressor, so that we can calculate the contrast's effective peak-peak height unambiguously. We also present some example results on typical designs, and characterise noise results from a range of typical FMRI acquisitions, in order to allow experimenters to apply efficiency estimation in advance of acquiring data.

Artifacts↗

PubMed2006

Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.

Amyotrophic Lateral Sclerosis↗

PubMed2006

Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.

Brain Mapping↗

PubMed2006

Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts.

Functional magnetic resonance imaging (FMRI) is a noninvasive method of imaging brain function in vivo. However, images produced in FMRI experiments are imperfect and contain several artifacts that contaminate the data. These artifacts include rigid-body motion effects, B0-field inhomogeneities, chemical shift, and eddy currents. To investigate these artifacts, with the eventual aim of minimizing or removing them completely, a computational model of the FMR image acquisition process was built that can simulate all of the above-mentioned artifacts. This paper gives an overview of the development of the FMRI simulator. The simulator uses the Bloch equations together with a geometric definition of the object (brain) and a varying T2* model for the BOLD activations. Furthermore, it simulates rigid-body motion of the object by solving Bloch equations for given motion parameters that are defined for an object moving continuously in time, including during the read-out period, which is a novel approach in the area of MRI computer simulations. With this approach it is possible, in a controlled and precise way, to simulate the full effects of various rigid-body motion artifacts in FMRI data (e.g. spin-history effects, B0-motion interaction, and within-scan motion blurring) and therefore formulate and test algorithms for their reduction.

Algorithms↗

PubMed2005

Independent anatomical and functional measures of the V1/V2 boundary in human visual cortex.

The cerebral cortex has both anatomical and functional specialization, but the level of correspondence between the two in the human brain has remained largely elusive. Recent successes in high-resolution magnetic resonance imaging of myeloarchitecture patterns in the cortex suggest that it may now be possible to compare directly human anatomy and function in vivo. We independently investigated the anatomical and functional borders between primary and secondary human visual areas (V1 and V2) in vivo. Functional borders were mapped with functional magnetic resonance imaging (fMRI) using a narrow, vertical black and white contrast-reversing wedge. In three separate scanning sessions, anatomical images were collected at three different slice orientations (300 microm x 300 microm, slice thickness, 1.5 mm). The anatomical signature of V1 was determined by the presence of a hypointense band in the middle of the cortical gray matter. The band was identified in between 81% and 33% (mean 57%) of V1 defined using fMRI, and less than 5% of the identified band was in cortex outside V1. Intensity profiles taken through the gray matter on the V1 and V2 sides of the functional border indicate a measurable difference in the size of the hypointense band for all subjects. This is the first demonstration that the definition of V1 by fMRI closely matches the anatomically defined striate cortex in the human brain. The development of very high-resolution structural MRI may permit the definition of cortical areas based on myeloarchitecture when functional definition is not possible.

Adult↗

PubMed2005

Color of scents: chromatic stimuli modulate odor responses in the human brain.

Color has a profound effect on the perception of odors. For example, strawberry-flavored drinks smell more pleasant when colored red than green and descriptions of the "nose" of a wine are dramatically influenced by its color. Using functional magnetic resonance imaging, we demonstrate a neurophysiological correlate of these cross-modal visual influences on olfactory perception. Subjects were scanned while exposed either to odors or colors in isolation or to color-odor combinations that were rated on the basis of how well they were perceived to match. Activity in caudal regions of the orbitofrontal cortex and in the insular cortex increased progressively with the perceived congruency of the odor-color pairs. These findings demonstrate the neuronal correlates of olfactory response modulation by color cues in brain areas previously identified as encoding the hedonic value of smells.

Adult↗

PubMed2005

Variability in fMRI: a re-examination of inter-session differences.

We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results.

Adult↗

PubMed2005

Generalised overlap measures for assessment of pairwise and groupwise image registration and segmentation.

Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.

Algorithms↗

PubMed2004

Perturbation method for magnetic field calculations of nonconductive objects.

Inhomogeneous magnetic fields produce artifacts in MR images including signal dropout and spatial distortion. A novel perturbative method for calculating the magnetic field to first order (error is second order) within and around nonconducting objects is presented. The perturbation parameter is the susceptibility difference between the object and its surroundings (for example, approximately 10 ppm in the case of brain tissue and air). This method is advantageous as it is sufficiently accurate for most purposes, can be implemented as a simple convolution with a voxel-based object model, and is linear. Furthermore, the method is simple to use and can quickly calculate the field for any orientation of an object using a set of precalculated basis images.

Head↗

PubMed2004

Multilevel linear modelling for FMRI group analysis using Bayesian inference.

Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects variance components. Inferring on these models is nontrivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and top-level regression parameters. Due to the typically small numbers of sessions or subjects in neuroimaging, the choice of prior is critical. To alleviate this problem, we introduce to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an information-theoretic sense. We propose two inference techniques at the top level for multilevel hierarchies (a fast approach and a slower more accurate approach). We also demonstrate that we can infer on the top level of multilevel hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a noncentral multivariate t distribution between them.

Bayes Theorem↗

PubMed2004

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).

Bayes Theorem↗

PubMed2004

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).

Bayes Theorem↗

PubMed2003

In vivo identification of human cortical areas using high-resolution MRI: an approach to cerebral structure-function correlation.

Understanding the relationship between the structural and functional organization of the human brain is one of the most important goals of neuroscience. Individual variability in brain structure means that it is essential to obtain this information from the same subject. To date, this has been almost impossible. Even though noninvasive functional imaging techniques such as functional MRI (fMRI) are now commonplace, there is no complementary noninvasive structural technique. We present an in vivo method of examining the detailed neuroanatomy of any individual, which can then be correlated with that individual's own functional results. This method utilizes high-resolution structural MRI to identify distinct cortical regions based on cortical lamination structure. We demonstrate that the observed MR lamination patterns relate to myeloarchitecture through a correlation of histology with MRI. In vivo high-resolution MRI studies identify striate cortex, as well as visual area V5, in four individuals, as defined by using fMRI. The anatomical identification of a cortical area (V5MT) outside of striate cortex is a significant advance, proving it possible to identify extra-striate cortical areas and demonstrating that in vivo structural mapping of the human cerebral cortex is possible.

Brain↗

PubMed2003

Fast, automated, N-dimensional phase-unwrapping algorithm.

This work investigates the general problem of phase unwrapping for arbitrary N-dimensional phase maps. A cost function-based approach is outlined that leads to an integer programming problem. To solve this problem, a best-pair-first region merging approach is adopted as the optimization method. The algorithm was implemented and tested with 3D MRI medical data for venogram studies, as well as for fMRI applications in EPI unwarping and rapid, automated shimming.

Algorithms↗

PubMed2003

Protocol to determine the optimal intraoral passive shim for minimisation of susceptibility artifact in human inferior frontal cortex.

At higher static magnetic field (B(0)) strengths (>/=3 T), the study of human inferior frontal cortex (IFC) when utilising a variety of MRI techniques is severely hampered by the presence of susceptibility artifacts. This is particularly the case for blood oxygenation level-dependent functional MRI, where large signal voids are generally encountered in the frontal lobes. A previous study described an approach to artifact correction involving a mouth insert consisting of a prototype diamagnetic passive shim [Magn. Reson. Med. 48 (2002), 906]. Here we extend that method by investigating the effect of five different intraoral passive shims on B(0) homogeneity and echoplanar imaging susceptibility artifacts within the brain, and particularly the IFC, of six subjects. The optimal passive shim is shown to be subject- and study-specific, providing an average reduction in mean absolute B(0) offset within the IFC of 57%, along with a concomitant reduction in echoplanar susceptibility artifact. All subjects were at ease while wearing the intraoral shims. A 4-min in vivo protocol to determine the optimal passive shim from the available set, utilising intrinsic structural and B(0) subject data, is described and shown to be accurate and reliable.

Artifacts↗

PubMed2003

General multilevel linear modeling for group analysis in FMRI.

This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established "summary statistics" approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.

Algorithms↗