ORCID Profile
0000-0001-8166-069X
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 08-2012
DOI: 10.1016/J.NEUROIMAGE.2011.09.015
Abstract: FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.
Publisher: Wiley
Date: 06-07-2020
DOI: 10.1002/NBM.4348
Publisher: Elsevier BV
Date: 06-2011
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 07-2020
DOI: 10.1161/STROKEAHA.119.027544
Abstract: Periventricular white matter hyperintensities (WMH PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
Publisher: Oxford University Press (OUP)
Date: 21-11-2007
DOI: 10.1093/BRAIN/AWL303
Abstract: 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 s le 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.
Publisher: Elsevier BV
Date: 09-2014
DOI: 10.1016/J.NEUROIMAGE.2014.04.030
Abstract: When defining an MRI protocol, brain researchers need to set multiple interdependent parameters that define repetition time (TR), voxel size, field-of-view (FOV), etc. Typically, researchers aim to image the full brain, making the expected FOV an important parameter to consider. Especially in 2D-EPI sequences, non-wasteful FOV settings are important to achieve the best temporal and spatial resolution. In practice, however, imperfect FOV size estimation often results in partial brain coverage for a significant number of participants per study, or, alternatively, an unnecessarily large voxel-size or number of slices to guarantee full brain coverage. To provide normative FOV guidelines we estimated population distributions of brain size in the x-, y-, and z-direction using data from 14,781 in iduals. Our results indicated that 11mm in the z-direction differentiate between obtaining full brain coverage for 90% vs. 99.9% of participants. Importantly, we observed that rotating the FOV to optimally cover the brain, and thus minimize the number of slices needed, effectively reduces the required inferior-superior FOV size by ~5%. For a typical adult imaging study, 99.9% of the population can be imaged with full brain coverage when using an inferior-superior FOV of 142mm, assuming optimal slice orientation and minimal within-scan head motion. By providing population distributions for brain size in the x-, y-, and z-direction we improve the potential for obtaining full brain coverage, especially in 2D-EPI sequences used in most functional and diffusion MRI studies. We further enable optimization of related imaging parameters including the number of slices, TR and total acquisition time.
Publisher: Elsevier BV
Date: 07-2007
DOI: 10.1016/J.NEUROIMAGE.2007.04.035
Abstract: Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimer's disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.
Publisher: Wiley
Date: 05-2000
Publisher: Elsevier BV
Date: 03-2009
DOI: 10.1016/J.NEUROIMAGE.2008.10.055
Abstract: Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.
Publisher: Elsevier BV
Date: 07-2006
DOI: 10.1016/J.NEUROIMAGE.2006.02.024
Abstract: 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 ex le TBSS results from several diffusion imaging studies.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
Publisher: Elsevier BV
Date: 06-2001
DOI: 10.1016/S1361-8415(01)00036-6
Abstract: Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework - one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.
Publisher: Cold Spring Harbor Laboratory
Date: 19-05-2021
DOI: 10.1101/2021.05.19.21257316
Abstract: SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T 1 , T 2 -FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 minutes. The automated imaging pipeline derives 1575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, these quantitative measures were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed that recovered COVID-19 patients had a decrease in frontal grey matter volumes, an increased burden of white matter hyperintensities, and reduced mean diffusivity in the total and normal appearing white matter in the posterior thalamic radiation and sagittal stratum, relative to controls. These differences were generally more prominent in patients who received organ support. Increased T 2 * in the thalamus was also observed in recovered COVID-19 patients, with a more prominent increase for non-critical patients. This initial evidence of brain changes in COVID-19 survivors prompts the need for further investigations. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in large-scale studies. The pipeline is widely applicable and will contribute to new analyses to hopefully clarify the medium to long-term effects of COVID-19. UK Biobank brain MRI protocol and pipeline was adapted for multiorgan MRI of COVID-19 High-quality brain MRI data from 5 modalities are acquired in 17 minutes Analysis pipeline derives 1575 IDPs of brain anatomy, perfusion, and microstructure Evidence of brain changes in COVID-19 survivors was found in the C-MORE study This MRI protocol is now being used in multiple large-scale studies on COVID-19
Publisher: Proceedings of the National Academy of Sciences
Date: 07-02-2012
Abstract: Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brain's different functional networks spontaneously fluctuate in their activity level each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks one ideally wants a network model that explicitly allows overlap, for ex le, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging s ling rate. We identify multiple “temporal functional modes,” including several that sub ide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
Publisher: Springer Science and Business Media LLC
Date: 03-2007
Abstract: There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. Among the measures, the most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies local tract directionality and integrity. Many multi-subject imaging studies are using FA images to localize brain changes related to development, degeneration and disease. In a recent paper, we presented a new approach, tract-based spatial statistics (TBSS), which aims to solve crucial issues of cross-subject data alignment, allowing localized cross-subject statistical analysis. This works by transforming the data from the centers of the tracts that are consistent across a study's subjects into a common space. In this protocol, we describe the MRI data acquisition and analysis protocols required for TBSS studies of localized change in brain connectivity across multiple subjects.
Publisher: Elsevier BV
Date: 03-2006
DOI: 10.1016/J.NEUROIMAGE.2005.09.036
Abstract: Functionally significant landmarks in the brain do not necessarily align with local sulcal and gyral architecture in a manner that is consistent across in iduals. However, the functional specialisation underlying these landmarks is strongly constrained by the connectional architecture of the region. Here, we explore this relationship in the supplementary motor area (SMA) and pre-SMA in the medial frontal cortex of the human brain. Using diffusion tensor, conventional and functional MR imaging, we find that the location of the functional boundary between SMA and preSMA is more consistent with respect to specific features of the local white matter as it approaches neocortex than with respect to the local gyral and sulcal anatomy in the region.
Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 2007
DOI: 10.1016/J.NEUROIMAGE.2006.09.019
Abstract: 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 ex le, 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 ex le 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.
Publisher: Springer Science and Business Media LLC
Date: 30-05-2014
Publisher: Springer Berlin Heidelberg
Date: 2008
DOI: 10.1007/978-3-540-85988-8_49
Abstract: The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a erse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.
Publisher: Oxford University Press (OUP)
Date: 09-2007
DOI: 10.1093/BRAIN/AWM184
Abstract: Adolescent-onset schizophrenia provides an exceptional opportunity to explore the neuropathology of schizophrenia free from the potential confounds of prolonged periods of medication and disease interactions with age-related neurodegeneration. Our aim was to investigate structural grey and white matter abnormalities in adolescent-onset schizophrenia. Whole-brain voxel-wise investigation of both grey matter topography and white matter integrity (Fractional Anisotropy) were carried out on 25 adolescent-onset schizophrenic patients and 25 healthy adolescents. We employed a refined voxel-based morphometry-like approach for grey matter analysis and the recently introduced method of tract-based spatial statistics (TBSS) for white matter analysis. Both kinds of studies revealed widespread abnormalities characterized by a lower fractional anisotropy neuroanatomically associated with localized reduced grey matter in the schizophrenic group. The grey matter changes can either be interpreted as the result of a locally reduced cortical thickness or as a manifestation of different patterns of gyrification. There was a widespread reduction of anisotropy in the white matter, especially in the corpus callosum. We speculate that the anisotropy changes relate to the functional changes in brain connectivity that are thought to play a central role in the clinical expression of the disease. The distribution of grey matter changes was consistent with clinical features of the disease. For ex le, grey and white matter abnormalities found in the Heschl's gyrus, the parietal operculum, left Broca's area and the left arcuate fasciculus (similar to previous findings in adult-onset schizophrenia) are likely to relate to functional impairments of language and auditory perception. In addition, in contrast to earlier studies, we found striking abnormalities in the primary sensorimotor and premotor cortices and in white matter tracts susbserving motor control (mainly the pyramidal tract). This novel finding suggests a new potential marker of altered white matter maturation specific to adolescent-onset schizophrenia. Together, our observations suggest that the neuropathology of adolescent-onset schizophrenia involves larger and widespread changes than in the adult form, consistent with the greater clinical severity.
Publisher: Springer Science and Business Media LLC
Date: 07-09-2020
DOI: 10.1038/S41467-020-18201-5
Abstract: Healthy cognitive ageing is a societal and public health priority. Cerebrovascular risk factors increase the likelihood of dementia in older people but their impact on cognitive ageing in younger, healthy brains is less clear. The UK Biobank provides cognition and brain imaging measures in the largest population cohort studied to date. Here we show that cognitive abilities of healthy in iduals (N = 22,059) in this s le are detrimentally affected by cerebrovascular risk factors. Structural equation modelling revealed that cerebrovascular risk is associated with reduced cerebral grey matter and white matter integrity within a fronto-parietal brain network underlying executive function. Notably, higher systolic blood pressure was associated with worse executive cognitive function in mid-life (44–69 years), but not in late-life ( years). During mid-life this association did not occur in the systolic range of 110–140 mmHg. These findings suggest cerebrovascular risk factors impact on brain structure and cognitive function in healthy people.
Publisher: Elsevier BV
Date: 2010
Publisher: Elsevier BV
Date: 10-2003
Publisher: Elsevier BV
Date: 09-2002
Abstract: Quantitative measurement of brain size, shape, and temporal change (for ex le, in order to estimate atrophy) is increasingly important in biomedical image analysis applications. New methods of structural analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method of longitudinal (temporal change) analysis, SIENA, was presented previously. In this paper, improvements to this method are described, and also an extension of SIENA to a new method for cross-sectional (single time point) analysis. The methods are fully automated, robust, and accurate: 0.15% brain volume change error (longitudinal): 0.5-1% brain volume accuracy for single-time point (cross-sectional). A particular advantage is the relative insensitivity to differences in scanning parameters. The methods provide easy manual review of their output by the automatic production of summary images which show the results of the brain extraction, registration, tissue segmentation, and final atrophy estimation.
Publisher: Elsevier BV
Date: 04-2004
Publisher: Springer Science and Business Media LLC
Date: 09-2012
DOI: 10.1038/NATURE11405
Publisher: Elsevier BV
Date: 09-2013
DOI: 10.1016/J.BIOPSYCH.2013.04.015
Abstract: Functional magnetic resonance imaging (fMRI) has great potential for measuring mechanisms of functional changes in Alzheimer's disease (AD) and mild cognitive impairment, but task fMRI studies have produced conflicting results, partly due to failure to account for underlying morphological changes and to variations in patients' ability to perform the tasks. Resting fMRI has potential for assessing brain function independently from a task, but greater understanding of how networks of resting functional connectivity relate to the functioning of the brain is needed. We combined resting fMRI and task fMRI to examine the correspondence between these methods in in iduals with cognitive impairment. Eighty elderly (25 control subjects, 25 mild cognitive impairment, 30 AD) underwent a combined multimodal magnetic resonance imaging protocol including task fMRI and resting fMRI. Task fMRI data were acquired during the execution of a memory paradigm designed to account for differences in task performance. Structural and physiological confounds were modeled for both fMRI modalities. Successful recognition was associated with increased task fMRI activation in lateral prefrontal regions in AD relative to control subjects this overlapped with increased resting fMRI functional connectivity in the same regions. Our results show that task fMRI and resting fMRI are sensitive markers of residual ability over the known changes in brain morphology and cognition occurring in AD and suggest that resting fMRI has a potential to measure the effect of new treatments.
Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 10-2018
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2019
DOI: 10.1101/661348
Abstract: Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b -value shells. Such schemes are characterised by the number of shells acquired, and the specific b -value and number of directions s led for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm 2 with 20, 64, 88 & 128 DW directions per shell respectively. A data driven method is presented to design multi-shell diffusion MRI acquisition schemes ( b -values and no. directions). This method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal. When applied in neonates, the data suggest that a b =0 + 3 shell strategy is appropriate
Publisher: Elsevier BV
Date: 06-2010
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11784012_2
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-38868-2_40
Abstract: Group neuroimaging studies of the cerebral cortex benefit from accurate, surface-based, cross-subject alignment for investigating brain architecture, function and connectivity. There is an increasing amount of high quality data available. However, establishing how different modalities correlate across groups remains an open research question. One reason for this is that the current methods for registration, based on cortical folding, provide sub-optimal alignment of some functional subregions of the brain. A more flexible framework is needed that will allow robust alignment of multiple modalities. We adapt the Fast Primal-Dual (Fast-PD) approach for discrete Markov Random Field (MRF) optimisation to spherical registration by reframing the deformation labels as a discrete set of rotations and propose a novel regularisation term, derived from the geodesic distance between rotation matrices. This formulation allows significant flexibility in the choice of similarity metric. To this end we propose a new multivariate cost function based on the discretisation of a graph-based mutual information measure. Results are presented for alignment driven by scalar metrics of curvature and myelination, and multivariate features derived from functional task performance. These experiments demonstrate the potential of this approach for improving the integration of complementary brain data sets in the future.
Publisher: Oxford University Press (OUP)
Date: 28-05-2009
DOI: 10.1093/BRAIN/AWP126
Abstract: Early-onset schizophrenia appears to be clinically more severe than the adult-onset form of the disease. In a previous study, we showed that anatomically related grey and white matter abnormalities found in adolescents patients were larger and more widespread than what had been reported in the literature on adult schizophrenia. Particularly, we found novel structural abnormalities in the primary sensorimotor and premotor systems. Here, we tested alternative hypotheses: either this striking sensorimotor-related pattern is an artefact due to a better sensitivity of the methods, or apparent greater structural abnormalities in the early-onset population are specifically associated with earlier disease onset. Then, if we were to find such characteristic structural pattern, we would test whether these anatomical abnormalities would remain static or, conversely, show dynamic changes in the still developing brain. To address these questions, we combined a cross-sectional study of brain structure for adolescent-onset patients (n = 25) and adult-onset patients (n = 35) and respective matched healthy subjects with a longitudinal study of adolescent-onset patients (n = 12, representative subset of the cross-sectional group) and matched healthy controls for >2 years. Looking at differences between adolescent and adult patients' grey matter volume and white matter microstructure abnormalities, we first confirmed the specificity (especially in motor-related areas) and the greater severity of structural abnormalities in the adolescent patients. Closer examination revealed, however, that such greater anomalies seemed to arise because adolescent patients fail to follow the same developmental time course as the healthy control group. Longitudinal analysis of a representative subset of the adolescent patient and matched healthy populations corroborated the delayed and altered maturation in both grey and white matters. Structural abnormalities specific to adolescent-onset schizophrenia in the sensori-motor cortices and corticospinal tract were less marked or even disappeared within the longitudinal period of observation, grey matter abnormalities in adolescent patients evolving towards the adult-onset pattern as defined by recent meta-analyses of adult schizophrenia. Combining cross-sectional adolescent and adult datasets with longitudinal adolescent dataset allowed us to find a unique, abnormal trajectory of grey matter maturation regardless of the age at onset of symptoms and of disease duration, with a lower and later peak than for healthy subjects. Taken together, these results suggest common aetiological mechanisms for adolescent- and adult-onset schizophrenia with an altered neurodevelopmental time course in the schizophrenic patients that is particularly salient in adolescence.
Publisher: Elsevier BV
Date: 2010
DOI: 10.1016/J.NEUROIMAGE.2009.09.001
Abstract: Alzheimer's disease (AD) is associated with neuronal loss not only in the hippoc us and amygdala but also in the thalamus. Anterodorsal, centromedial, and pulvinar nuclei are the main sites of degeneration in AD. Here we combined shape analysis and diffusion tensor imaging (DTI) tractography to study degeneration in AD in the thalamus and its connections. Structural and diffusion tensor MRI scans were obtained from 16 AD patients and 22 demographically similar healthy volunteers. The thalamus, hippoc us, and amygdala were automatically segmented using our locally developed algorithm, and group comparisons were carried out for each surface vertex. We also employed probabilistic diffusion tractography to obtain connectivity measures between in idual thalamic voxels and hippoc us/amygdala voxels and to segment the internal medullary lamina (IML). Shape analysis showed significant bilateral regional atrophy in the dorsal-medial part of the thalamus in AD patients compared to controls. Probabilistic tractography demonstrated that these regions are mainly connected with the hippoc us, temporal, and prefrontal cortex. Intrathalamic FA comparisons showed reductions in the anterodorsal region of thalamus. Intrathalamic tractography from this region revealed that the IML was significantly smaller in AD patients than in controls. We suggest that these changes can be attributed to the degeneration of the anterodorsal and intralaminar nuclei, respectively. In addition, based on previous neuropathological reports, ventral and dorsal-medial shape change in the thalamus in AD patients is likely to be driven by IML atrophy. This combined shape and connectivity analysis provides MRI evidence of regional thalamic degeneration in AD.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Wiley
Date: 14-01-2005
DOI: 10.1002/HBM.20080
Publisher: Elsevier BV
Date: 2004
DOI: 10.1016/J.NEUROIMAGE.2004.07.051
Abstract: 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).
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 10-2009
DOI: 10.1016/J.NEUROIMAGE.2009.05.029
Abstract: The automation of segmentation of subcortical structures in the brain is an active research area. We have comprehensively evaluated four novel methods of fully automated segmentation of subcortical structures using volumetric, spatial overlap and distance-based measures. Two methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a brain atlas (EMS), and two incorporate statistical models of shape and appearance - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a erse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed better than the others according to all three classes of metrics. In summary over all structures, the ranking by the Dice coefficient was CFL, BAM, joint EMS and PAM. The Hausdorff distance ranked the methods as CFL, joint PAM and BAM, EMS, whilst percentage absolute volumetric difference ranked them as joint CFL and PAM, joint BAM and EMS. Furthermore, as we had four methods of performing segmentation, we investigated whether the results obtained by each method were more similar to each other than to the manual segmentations using Williams' Index. Reassuringly, the Williams' Index was close to 1 for most subjects (mean=1.02, sd=0.05), indicating better agreement of each method with the gold standard than with the other methods. However, 2% of cases (mainly amygdala and nucleus accumbens) had values outside 3 standard deviations of the mean.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 05-2001
DOI: 10.1097/00004728-200105000-00022
Abstract: Quantitative measurement of change in brain size and shape (e.g., to estimate atrophy) is an important current area of research. New methods of change analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method has been developed that achieves high estimation accuracy. A fully automated method of longitudinal change analysis is presented here, which automatically segments brain from nonbrain in each image, registers the two brain images while using estimated skull images to constrain scaling and skew, and finally estimates brain surface motion by tracking surface points to subvoxel accuracy. The method described has been shown to be accurate ( approximately 0.2% brain volume change error) and to achieve high robustness (no failures in several hundred analyses over a range of different data sets).
Publisher: Springer Science and Business Media LLC
Date: 09-2002
DOI: 10.1007/S00415-002-0837-7
Abstract: Previous studies have established the clinical relevance of hypointense lesions ("black holes") on T1-weighted MRI as a surrogate marker for pathological change [36]. In contrast to measuring the volume of "black holes", the direct measurement of T1 values allows an objective assessment of the changes contributing to hypointensity both in the focal lesions and in the normal appearing white matter (NAWM). The aims of this study were first, to determine the relationship between T1 values in the NAWM and in discrete lesions, second, to test the relationship between white matter T1 changes and measures of disability and third, to determine whether pathology leading to T1 change occurred in thalamic grey matter of patients with multiple sclerosis. 24 patients with clinically definite multiple sclerosis (13 with relapsing-remitting multiple sclerosis and 11 with secondary progressive multiple sclerosis) and 11 controls participated. White matter T1 histograms and mean T1 values for the thalamus were generated from whole brain T1 relaxation time maps measured using a novel echo-planar imaging based MRI sequence at 3Tesla. Tissue segmentation based on T2- and T1-weighted images allowed independent study of changes in lesions and NAWM. White matter T1 histograms from the patient group showed a reduced peak height and a shift towards higher T1 values (p = 0.028) relative to controls. The mean thalamic T1 was greater for secondary progressive patients than for healthy controls (p = 0.03). Mean white matter T1 values correlated significantly with disability (r = 0.48, p = 0.02). The mean T1 value in the T1-hypointense lesions correlated strongly with the mean T1 value in the NAWM (r = 0.80, p < 0.001). No significant relationship was found between mean white matter T1 value and cerebral volume (r = -0.23, p = 0.31). The T1 measurements extend previous observations suggesting that changes in the NAWM occur in parallel with pathology in lesions of MS. T1 measurements of either the total or NAWM therefore may provide a potentially observer- and scanner- independent marker of pathology relevant to disability in MS.
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Berlin Heidelberg
Date: 2009
DOI: 10.1007/978-3-642-04268-3_87
Abstract: Registration of brain structures should bring anatomically equivalent areas into correspondence which is usually done using information from structural MRI modalities. Correspondence can be improved by using other image modalities that provide complementary data. In this paper we propose and evaluate two novel surface registration algorithms which improve within-surface correspondence in brain structures. Both approaches use a white-matter tract similarity function (derived from probabilistic tractography) to match areas of similar connectivity patterns. The two methods differ in the way the deformation field is calculated and in how the multi-scale registration framework is implemented. We validated both algorithms using artificial and real image ex les, in both cases showing high registration consistency and the ability to find differences in thalamic sub-structures between Alzheimer's disease and control subjects. The results suggest differences in thalamic connectivity predominantly in the medial dorsal parts of the left thalamus.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Stephen Smith.