ORCID Profile
0000-0001-6043-0166
Current Organisations
University of Adelaide / South Australian Health and Medical Research Institute (SAHMRI)
,
University of Oxford
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Publisher: Elsevier BV
Date: 04-2011
DOI: 10.1016/J.NEUROIMAGE.2010.12.071
Abstract: With hippoc al atrophy both a clinical biomarker for early Alzheimer's Disease (AD) and implicated in many other neurological and psychiatric diseases, there is much interest in the accurate, reproducible delineation of this region of interest (ROI) in structural MR images. Here we present Fast Marching for Automated Segmentation of the Hippoc us (FMASH): a novel approach using the Sethian Fast Marching (FM) technique to grow a hippoc al ROI from an automatically-defined seed point. Segmentation performance is assessed on two separate clinical datasets, utilising expert manual labels as gold standard to quantify Dice coefficients, false positive rates (FPR) and false negative rates (FNR). The first clinical dataset (denoted CMA) contains normal controls (NC) and atrophied AD patients, whilst the second is a collection of NC and bipolar (BP) patients (denoted BPSA). An optimal and robust stopping criterion is established for the propagating FM front and the final FMASH segmentation estimates compared to two commonly-used methods: FIRST/FSL and Freesurfer (FS). Results show that FMASH outperforms both FIRST and FS on the BPSA data, with significantly higher Dice coefficients (0.80±0.01) and lower FPR. Despite some intrinsic bias for FIRST and FS on the CMA data, due to their training, FMASH performs comparably well on the CMA data, with an average bilateral Dice coefficient of 0.82±0.01. Furthermore, FMASH most accurately captures the hippoc al volume difference between NC and AD, and provides a more accurate estimation of the problematic hippoc us-amygdala border on both clinical datasets. The consistency in performance across the two datasets suggests that FMASH is applicable to a range of clinical data with differing image quality and demographics.
Publisher: Elsevier BV
Date: 07-2022
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: eLife Sciences Publications, Ltd
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 08-01-2014
Publisher: Public Library of Science (PLoS)
Date: 04-01-2012
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: American Geophysical Union (AGU)
Date: 13-12-2014
DOI: 10.1002/2014JD022357
Publisher: Cold Spring Harbor Laboratory
Date: 29-07-2020
DOI: 10.1101/2020.07.28.208579
Abstract: Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise s le differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps and (2) appropriate modelling of s le differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data. We harmonised measures of WMHs across two studies on healthy ageing Specific pre-processing strategies can increase comparability across studies Modelling of biological differences is crucial to provide calibrated measures
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Elsevier BV
Date: 10-2022
DOI: 10.1016/J.MEDIA.2022.102583
Abstract: Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.
Publisher: BMJ
Date: 09-2022
DOI: 10.1136/BMJOPEN-2021-059572
Abstract: Evidence is mounting that poor psychosocial job conditions increase sickness absence, but there is a need for further rigorous prospective research to isolate the influence of psychosocial job quality from other measured and unmeasured confounders. This study used four waves of prospective longitudinal data (spanning 12 years) to investigate the extent to which increases in poor psychosocial job quality are associated with greater relative risk of day of sickness absence. Prospective cohort study. Data were from the Australian PATH Through Life cohort study. The analyses adopted hybrid-regression estimations that isolated the effect of within-person change in psychosocial job quality on sickness absence over time. Participants were from a midlife cohort aged 40–44 at baseline (7644 observations from 2221 participants). Days sickness absence in the past 4 weeks. The results show that after adjusting for a wide range of factors as well as unmeasured between-person differences in job quality, each additional psychosocial job adversity was associated with a 12% increase in the number of days of sickness absence (relative risk ratio: 1.12, 95% CI 1.03 to 1.21). Increases in psychosocial job adversity were also related to greater functional impairment (relative risk ratio: 1.17 (1.05 to 1.30)). The results of this study strengthen existing research highlighting the importance of addressing poor psychosocial job quality as a risk factor for sickness absence.
Publisher: Cold Spring Harbor Laboratory
Date: 08-10-2018
DOI: 10.1101/437608
Abstract: White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the in idual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort. We observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.
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: Springer Science and Business Media LLC
Date: 21-01-2015
DOI: 10.1038/NATURE14101
Publisher: Elsevier BV
Date: 12-2013
Publisher: Wiley
Date: 31-08-2011
DOI: 10.1002/HBM.21344
Publisher: SAGE Publications
Date: 03-02-2022
DOI: 10.1177/02610183211073945
Abstract: People receiving working-age income support payments are often stigmatised as morally and/or behaviourally deficient. We consider the role of the media, as a potential source of structural stigma, in perpetuating negative characterisations of people in receipt of either the Disability Support Pension (DSP) or unemployment benefits (Newstart) during a major period of welfare reform in Australia. Newspaper articles (N = 8290) that appeared in Australia’s five largest newspapers between 2001 and 2016, and referenced either payment were analysed. We found an increased use of fraud language associated with the DSP, which coincides with increased political and policy focus on this payment. We conclude that in a period of increasing political concern with welfare reform, media coverage of welfare recipients is a form of stigma power, acting discursively as symbolic violence.
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: Cold Spring Harbor Laboratory
Date: 23-09-2020
DOI: 10.1101/2020.09.22.20198978
Abstract: Neuromyelitis optica associated with aquaporin-4-antibodies (NMOSD-AQP4) and myelin oligodentrocyte-glycoprotein antibody-associated disorder (MOGAD) have been recently recognised as different from multiple sclerosis. Although conventional MRI may help distinguish multiple sclerosis from antibody-mediated diseases, the use of quantitative and non-conventional imaging may give more pathological information and explain the clinical differences. We compared, using non-conventional imaging, brain MRI findings in 75 subjects in remission with NMOSD-AQP4, MOGAD, multiple sclerosis or healthy controls (HC). Volumetrics, white matter and cortical lesions, and tissue integrity measures using diffusion imaging, were analysed in the four groups along with their association with disability (expanded disability status scale [EDSS] and visual acuity). The volumetric analysis showed that, deep grey matter volumes were significantly lower in multiple sclerosis (p=0.0001) and MOGAD (p=0.02), compared to HC. Relapsing MOGAD had lower white matter, pallidus and hippoc us volumes than in monophasic (p .05). Optic chiasm volume was reduced only in NMOSD-AQP4 who had at least one episode of optic neuritis (ON) (NMOSD-AQP4-ON vs NMOSD-AQP4 p .001, HC p .001, MOGAD-ON p=0.04, multiple sclerosis-ON p=0.02) likely reflecting the recognised posterior location of NMOSD-AQP4-ON and its severity. Lesion volume was greatest in multiple sclerosis followed by MOGAD and in these two diseases, the lesion volume correlated with disease duration (multiple sclerosis R=0.46, p=0.05, MOGAD R=0.81, p .001), cortical thickness (multiple sclerosis R=-0.64, p=0.0042, MOGAD=-0.71, p=0.005) and deep grey matter volumes (multiple sclerosis R=-0.65, p=0.0034, MOGAD R=-0.93, p .001). Lesional-fractional anisotropy (FA) was reduced and mean diffusivity increased in all patients, but overall, FA was only reduced in the non-lesional tissue in multiple sclerosis (p=0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways. Cortical/juxtacortical lesions were seen in a minority of MOGAD, while cortical/juxtacortical and purely cortical lesions were identified in the majority of multiple sclerosis and in none of the NMOSD-AQP4. Non-lesional FA in NMOSD-AQP4, lower white-matter volume and female sex in multiple sclerosis, and lower brainstem volume in MOGAD were the best predictors of EDSS disability (accounting for 46%, 49% and 19% respectively). Worse visual acuity associated with lower optic chiasm volume in NMOSD-AQP4 and lower thalamus volume in MOGAD (accounting for 58% and 35% respectively). Although MOGAD patients had good outcomes, deep grey matter atrophy was present. In contrast, NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, despite greater residual disability as compared with MOGAD patients. NMOSD-AQP4 but not MOGAD patients showed reduced FA in non-lesional tissue.
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: eLife Sciences Publications, Ltd
Date: 27-02-2016
DOI: 10.7554/ELIFE.12047
Abstract: The sensation of breathlessness is the most threatening symptom of respiratory disease. The different sub isions of the midbrain periaqueductal gray (PAG) are intricately (and differentially) involved in integrating behavioural responses to threat in animals, while the PAG has previously only been considered as a single entity in human research. Here we investigate how these in idual PAG columns are differently involved with respiratory threat. Eighteen healthy subjects were conditioned to associate shapes with certain or uncertain impending respiratory load, and scanned the following day during anticipation and application of inspiratory loading using 7 T functional MRI. We showed activity in the ventrolateral PAG (vlPAG) during anticipation of resistive loading, with activity in the lateral PAG (lPAG) during resistive loading, revealing spatially and temporally distinct functions within this structure. We propose that lPAG is involved with sensorimotor responses to breathlessness, while the vlPAG operates within the threat perception network for impending breathlessness.
Publisher: Elsevier BV
Date: 06-2010
Publisher: American Geophysical Union (AGU)
Date: 18-11-2014
DOI: 10.1002/2014JD022358
Abstract: Understanding how precipitation varies as the climate changes is essential to determining the true impact of global warming. This is a difficult task not only due to the large internal variability observed in precipitation but also because of a limited historical record and large biases in simulations of precipitation by general circulation models (GCMs). Here we make use of a technique that spatially and seasonally transforms GCM fields to reduce location biases and investigate the potential of this bias correction to study historical changes. We use two versions of this bias correction—one that conserves intensities and another that conserves integrated precipitation over transformed areas. Focussing on multimodel ensemble means, we find that both versions reduce RMS error in the historical trend by approximately 11% relative to the Global Precipitation Climatology Project (GPCP) data set. By regressing GCMs' historical simulations of precipitation onto radiative forcings, we decompose these simulations into anthropogenic and natural time series. We then perform a simple detection and attribution study to investigate the impact of reducing location biases on detectability. A multiple ordinary least squares regression of GPCP onto the anthropogenic and natural time series, with the assumptions made, finds anthropogenic detectability only when spatial corrections are applied. The result is the same regardless of which form of conservation is used and without reducing the dimensionality of the fields beyond taking zonal means. While “detectability” is dependent both on the exact methodology and the confidence required, this nevertheless demonstrates the potential benefits of correcting location biases in GCMs when studying historical precipitation, especially in cases where a signal was previously undetectable.
Publisher: Elsevier BV
Date: 09-2010
DOI: 10.1016/J.MRI.2010.03.029
Abstract: Transient magnetic fields induce changes in magnetic resonance (MR) images ranging from small, visually undetectable effects (caused, for instance, by neuronal currents) to more significant ones, such as those created by the gradient fields and eddy currents. Accurately simulating these effects may assist in correcting or optimising MR imaging for many applications (e.g., diffusion imaging, current density imaging, use of magnetic contrast agents, neuronal current imaging, etc.). Here we have extended an existing MR simulator (POSSUM) with a model for changing magnetic fields at a very high-resolution time-scale. This simulator captures a realistic range of scanner and physiological artifacts by modeling the scanner environment, pulse sequence details and subject properties (e.g., brain geometry and air-tissue boundaries). The simulations were validated by using previously published experimental data sets. In the first dataset a transient magnetic field was produced by a single conducting wire with varying current litude (between 17 muA and 765 muA). The second was identical except that current litude was fixed (at 7.8 mA) and current timing varied. A very close match between simulated images and experimental data was observed. In addition, these validation results led to the observation that the current-induced effects included ringing in the image, which extended away from the conductor, primarily in the phase-encode direction. This effect had previously not been noticed in the noisy, experimentally-acquired images, demonstrating one way in which simulated images can provide potential insight into imaging experiments.
Publisher: Cold Spring Harbor Laboratory
Date: 26-11-2019
DOI: 10.1101/849570
Abstract: There is a need to understand the histopathological basis of MRI signal characteristics in complex biological matter. Microstructural imaging holds promise for sensitive and specific indicators of the early stages of human neurodegeneration but requires validation against traditional histological markers before it can be reliably applied in the clinical setting. Validation relies on a precise and preferably automatic method to align MRI and histological images of the same tissue, which poses unique challenges compared to more conventional MRI-to-MRI registration. A customisable open-source platform, Tensor Image Registration Library (TIRL) is presented. Based on TIRL, a fully automated pipeline was implemented to align small stained histological images with dissection photographs of corresponding tissue blocks and coronal brain slices, and further with high-resolution (0.5 mm) whole-brain post-mortem MRI data. The pipeline performed three separate deformable registrations to achieve accurate mapping between whole-brain MRI and small-slide histology coordinates. The robustness and accuracy of the in idual registration steps were evaluated using both simulated data and real-life images from 6 different anatomical locations of one post-mortem human brain. The automated registration method demonstrated sub-millimetre accuracy in all steps, robustness against tissue damage, and good reproducibility between experiments. The method also outperformed manual landmark-based slice-to-volume registration, also correcting for curvatures in the slicing plane. Due to the customisability of TIRL, the pipeline can be conveniently adapted for other research needs and is therefore suitable for the large-scale comparison of routinely collected histology and MRI data. TIRL: new framework for prototyping bespoke image registration pipelines Pipeline for automated registration of small-slide histology to whole-brain MRI Slice-to-volume registration accounting for through-plane deformations No need for serial histological s ling
Publisher: IEEE
Date: 2004
Publisher: MDPI AG
Date: 04-01-2022
Abstract: The COVID-19 pandemic has had a significant impact on mental health at the level of the population. The current study adds to the evidence base by examining how the prevalence of psychological distress changed in Australia during the pandemic. The study also assesses the psychometric properties of a new single-item measure of mental distress included in a survey program conducted regularly throughout the pandemic. Data are from 1158 respondents in wave 13 (early July 2020) of the nationally representative Taking the Pulse of the Nation (TTPN) Survey. The questionnaire included the six-item Kessler Psychological Distress Scale (K6) and a new single-item measure of distress. Results show a significant increase in the prevalence of psychological distress in Australia, from 6.3% pre-pandemic to 17.7% in early July 2020 (unadjusted odds ratio = 3.19 95% CI (confidence interval) = 2.51 to 4.05). The new single-item measure of distress is highly correlated with the K6. This study provides a snapshot at one point in time about how mental health worsened in Australia during the COVID-19 pandemic. However, by demonstrating the accuracy of the new single-item measure of distress, this analysis also provides a basis for further research examining the trajectories and correlates of distress in Australia across the pandemic.
Publisher: Wiley
Date: 14-01-2005
DOI: 10.1002/HBM.20080
Publisher: Cold Spring Harbor Laboratory
Date: 03-2023
DOI: 10.1101/2023.02.27.23286245
Abstract: Monoclonal antibodies against tumour necrosis factor (TNF) markedly reduce inflammation and disease activity in rheumatoid arthritis however, the mechanisms through which they affect pain are not fully understood. The aim of this study was to investigate how monoclonal antibodies against TNF alter pain processing and to determine whether neuroimaging can be used as a marker of treatment efficacy and a predictor of treatment response. Functional magnetic resonance imaging was used to study the neural correlates of clinically-relevant pain evoked by pressing the most painful joint of the right hand and experimental pain evoked by a thermal stimulus applied to the right forearm. A flashing checkerboard was used as a control stimulus. Patients with severe rheumatoid arthritis, qualifying for the anti-TNF treatment, were scanned before the beginning of the therapy and then approximately one and six months after the first injection. TNF inhibition was associated with a marked reduction in pain ratings, inflammation, disease activity as well as depression and catastrophising scores. Effective treatment was linked with less pressure-evoked brain activation in the regions involved in the processing of the sensory aspect of pain and in the limbic structures. Baseline pressure-evoked activation in the thalamus predicted future response to treatment. There was no reduction in heat-evoked brain activation on the contrary, there was an increase in the activation in the precuneus, which is involved in interoception. There were no differences in response to the visual stimulus. TNF inhibition strongly reduces brain activation in response to clinically relevant pressure pain but not experimental heat pain and these changes reflect the decrease of nociceptive input from the periphery due to the reduction of inflammation as well as central changes in pain modulation. Neuroimaging methods have the potential to explain and predict treatment effects in inflammatory pain conditions.
Publisher: Hogrefe Publishing Group
Date: 11-2022
DOI: 10.1027/1864-9335/A000502
Abstract: Abstract. The reinforcement sensitivity theory (RST) proposes that neurobiological systems mediate behavior and their functioning can be associated with personality. The functions and associations of RST systems were revised into fight–flight–freeze system (FFFS), behavioral approach/activation system (rBAS), and behavioral inhibition system (rBIS) however, there is limited study of the revised systems due to lack of validated measures. We investigated scale structure, sex invariance, and psychometric properties of the revised RST questionnaire (rRST-Q). The rRST-Q showed good fit as a 5-factor structure with free interfactor correlations and was sex invariant, and associations with personality and mental health measures were consistent with theory and literature. The rRST-Q is a reliable measure, and its use will help understand the link between brain, personality, and behavior.
Publisher: Elsevier BV
Date: 03-2023
Publisher: BMJ
Date: 04-2022
DOI: 10.1136/BMJOPEN-2020-045908
Abstract: Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning. Using plasma protein, lipid and radiological biomarkers, our study will develop predictive algorithms to distinguish TIA from minor stroke (positive control) and TIA-mimics (negative control). Analysis including machine learning employs more sophisticated modelling, allowing non-linear interactions, adapting to datasets and enabling development of multiple specialised test-panels for identification and differentiation. Patients attending the Emergency Department, Stroke Ward or TIA Clinic at the Royal Adelaide Hospital with TIA, minor stroke or TIA-like symptoms will be recruited consecutively by staff-alert for this prospective cohort study. Advanced neuroimaging will be performed for each participant, with images assessed independently by up to three expert neurologists. Venous blood s les will be collected within 48 hours of symptom onset. Plasma proteomic and lipid analysis will use advanced mass spectrometry (MS) techniques. Principal component analysis and hierarchical cluster analysis will be performed using MS software. Output files will be analysed for relative biomarker quantitative differences between the three groups. Differences will be assessed by linear regression, one-way analysis of variance, Kruskal-Wallis H-test, χ 2 test or Fisher’s exact test. Machine learning methods will also be applied including deep learning using neural networks. Patients will provide written informed consent to participate in this grant-funded study. The Central Adelaide Local Health Network Human Research Ethics Committee approved this study (HREC/18/CALHN/384 R20180618). Findings will be disseminated through peer-reviewed publication and conferences data will be managed according to our Data Management Plan (DMP2020-00062).
Publisher: BMJ
Date: 02-2021
DOI: 10.1136/BMJOPEN-2020-039628
Abstract: Cardiovascular disease (CVD) incidence is elevated among people with psychological distress. However, whether the relationship is causal is unclear, partly due to methodological limitations, including limited evidence relating to longer-term rather than single time-point measures of distress. We compared CVD relative risks for psychological distress using single time-point and multi-time-point assessments using data from a large-scale cohort study. We used questionnaire data, with data collection at two time-points (time 1: between 2006 and 2009 time 2: between 2010 and 2015), from CVD-free and cancer-free 45 and Up Study participants, linked to hospitalisation and death records. The follow-up period began at time 2 and ended on 30 November 2017. Psychological distress was measured at both time-points using Kessler 10 (K10), allowing assessment of single time-point (at time 2: high (K10 score: 22–50) vs low (K10 score: )) and multi-time-point (high distress (K10 score: 22–50) at both time-points vs low distress (K10 score: ) at both time-points) measures of distress. Cox regression quantified the association between distress and major CVD, with and without adjustment for sociodemographic and health-related characteristics, including functional limitations. Among 83 906 respondents, 7350 CVD events occurred over 410 719 follow-up person-years (rate: 17.9 per 1000 person-years). Age-adjusted and sex-adjusted rates of major CVD were elevated by 50%–60% among those with high versus low distress for both the multi-time-point (HR=1.63, 95% CI 1.40 to 1.90) and single time-point (HR=1.53, 95% CI 1.39 to 1.69) assessments. HRs for both measures of distress attenuated with adjustment for sociodemographic and health-related characteristics, and there was little evidence of an association when functional limitations were taken into account (multi-time-point HR=1.09, 95% CI 0.93 to 1.27 single time-point HR=1.14, 95% CI 1.02 to 1.26). Irrespective of whether a single time-point or multi-time-point measure is used, the distress–CVD relationship is substantively explained by sociodemographic characteristics and pre-existing physical health-related factors.
Publisher: Elsevier BV
Date: 08-2014
Publisher: Elsevier BV
Date: 07-2009
Publisher: Elsevier BV
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-38868-2_39
Abstract: Deformable medical image registration requires the optimisation of a function with a large number of degrees of freedom. Commonly-used approaches to reduce the computational complexity, such as uniform B-splines and Gaussian image pyramids, introduce translation-invariant homogeneous smoothing, and may lead to less accurate registration in particular for motion fields with discontinuities. This paper introduces the concept of sparse image representation based on supervoxels, which are edge-preserving and therefore enable accurate modelling of sliding organ motions frequently seen in respiratory and cardiac scans. Previous shortcomings of using supervoxels in motion estimation, in particular inconsistent clustering in ambiguous regions, are overcome by employing multiple layers of supervoxels. Furthermore, we propose a new similarity criterion based on a binary shape representation of supervoxels, which improves the accuracy of single-modal registration and enables multimodal registration. We validate our findings based on the registration of two challenging clinical applications of volumetric deformable registration: motion estimation between inhale and exhale phase of CT scans for radiotherapy planning, and deformable multi-modal registration of diagnostic MRI and CT chest scans. The experiments demonstrate state-of-the-art registration accuracy, and require no additional anatomical knowledge with greatly reduced computational complexity.
Publisher: Society for Neuroscience
Date: 02-05-2012
DOI: 10.1523/JNEUROSCI.2543-11.2012
Abstract: Chronic pain is thought to arise because of maladaptive changes occurring within the peripheral nervous system and CNS. The transition from acute to chronic pain is known to involve the spinal cord (Woolf and Salter, 2000). Therefore, to investigate altered human spinal cord function and translate results obtained from other species, a noninvasive neuroimaging technique is desirable. We have investigated the functional response in the cervical spinal cord of 18 healthy human subjects (aged 22–40 years) to noxious thermal and non-noxious tactile stimulation of the left and right forearms. Physiological noise, which is a significant source of signal variability in the spinal cord, was accounted for in the general linear model. Group analysis, performed using a mixed-effects model, revealed distinct regions of activity that were dependent on both the side and the type of stimulation. In particular, thermal stimulation on the medial aspect of the wrist produced activity within the C6/C5 segment ipsilateral to the side of stimulation. Similar to data recorded in animals (Fitzgerald, 1982), painful thermal stimuli produced increased ipsilateral and decreased contralateral blood flow, which may reflect, respectively, excitatory and inhibitory processes. Nonpainful punctate stimulation of the thenar eminence provoked more diffuse activity but was still ipsilateral to the side of stimulation. These results present the first noninvasive evidence for a lateralized response to noxious and non-noxious stimuli in the human spinal cord. The development of these techniques opens the path to understanding, at a subject-specific level, central sensitization processes that contribute to chronic pain states.
Publisher: Elsevier BV
Date: 03-2023
Publisher: IEEE
Date: 05-2012
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11566465_13
Abstract: 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.
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: Cold Spring Harbor Laboratory
Date: 19-06-2023
DOI: 10.1101/2023.06.16.545260
Abstract: Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. Here, we build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling-heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3T scanners from all the 3 major vendors and using 5 neuroimaging modalities, providing more comprehensive coverage than before. We also acquire multiple within-scanner repeats for a subset of subjects, setting baselines for multi-modal scan-rescan variability. Having extracted hundreds of image-derived features, we compare three forms of variability: (i) between-scanner, (ii) within-scanner (within-subject), and (iii) biological (between-subject). We characterise the reliability of features across scanners and use our resource as a testbed to enable new investigations that until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent, while our publicly-released datasets establish references for future studies in this field.
Publisher: Elsevier BV
Date: 03-2013
DOI: 10.1016/J.CORTEX.2012.04.011
Abstract: Patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) may be unaware of their cognitive impairment. The neuroanatomical mechanisms underlying this symptom, termed anosognosia or impaired self-awareness, are still poorly understood. In the present study we aimed to explore the functional correlates of self-awareness in patients with MCI and AD. Fifty-one participants (17 healthy elderly, 17 patients with MCI, and 17 patients with AD), each accompanied by a study partner, took part in a functional magnetic resonance imaging (fMRI) study, in which they were presented with questions regarding themselves (Self condition) or their study partner (Other condition). The study partner was asked to complete a paper questionnaire answering the same questions so the responses of participant and study partner could be compared and "discrepancy" scores calculated for each of the 2 conditions (Self and Other). Behavioural results showed that AD patients had significantly higher "Self discrepancy scores" than controls and MCI patients, whereas there were no significant differences between groups for "Other discrepancy scores". Imaging results showed a significant group-by-condition interaction in brain activation in medial prefrontal and anterior temporal regions, with AD patients showing significantly decreased activation in these regions only for the Self condition. There were no significant differences between Self and Other conditions in either control or MCI groups, suggesting that, in these groups, Self- and Other-appraisal share similar neuroanatomical substrates. Decreased functional activation of medial prefrontal and anterior temporal cortices is associated with impaired self-awareness in AD patients. This dysfunction, which is specific for Self- but not for Other-appraisal, may be a contributing factor to anosognosia in AD.
Publisher: BMJ
Date: 25-10-2013
Abstract: The existence of transsynaptic retrograde degeneration (TRD) in the human visual system has been established, however the dependence of TRD on different factors such as lesion location, size and manner of lesion acquisition has yet to be quantified. We obtained T1-weighted structural and diffusion-weighted images for 26 patients with adult-acquired or congenital hemianopia and 12 age-matched controls. The optic tract (OT) was defined and measured in the structural and diffusion-weighted images, and degeneration assessed by comparing the integrity of tracts in the lesioned and in the undamaged hemisphere. OT degeneration was found in all patients with established lesions, regardless of lesion location. In patients with acquired lesions, the larger the initial lesion, the greater is the resulting TRD. However, this was not the case for congenital patients, who generally showed greater degeneration than would be predicted by lesion size. A better predictor of TRD was the size of the visual field deficit, which was correlated with degeneration across all patients. Interestingly, although diffusion-weighted imaging (DWI) is more frequently used to examine white matter tracts, in this study the T1-weighted scans gave a better indication of the extent of tract degeneration. We conclude that TRD of the OT occurs in acquired and congenital hemianopia, is correlated with visual field loss, and is most severe in congenital cases. Understanding the pattern of TRD may help to predict effects of any visual rehabilitation training.
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: Elsevier BV
Date: 10-2013
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: Elsevier BV
Date: 10-2012
DOI: 10.1016/J.MEDIA.2012.05.008
Abstract: Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this important problem and proposes a modality independent neighbourhood descriptor (MIND) for both linear and deformable multi-modal registration. Based on the similarity of small image patches within one image, it aims to extract the distinctive structure in a local neighbourhood, which is preserved across modalities. The descriptor is based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising. It is able to distinguish between different types of features such as corners, edges and homogeneously textured regions. MIND is robust to the most considerable differences between modalities: non-functional intensity relations, image noise and non-uniform bias fields. The multi-dimensional descriptor can be efficiently computed in a dense fashion across the whole image and provides point-wise local similarity across modalities based on the absolute or squared difference between descriptors, making it applicable for a wide range of transformation models and optimisation algorithms. We use the sum of squared differences of the MIND representations of the images as a similarity metric within a symmetric non-parametric Gauss-Newton registration framework. In principle, MIND would be applicable to the registration of arbitrary modalities. In this work, we apply and validate it for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans. Experimental results show the advantages of MIND over state-of-the-art techniques such as conditional mutual information and entropy images, with respect to clinically annotated landmark locations.
Publisher: Frontiers Media SA
Date: 20-12-2021
DOI: 10.3389/FPSYG.2021.737117
Abstract: The reinforcement sensitivity theory (RST) proposes that neurobiological systems mediate protective and appetitive behaviours and the functioning of these systems is associated to personality traits. In this manner, the RST is a link between neuroscience, behaviour, and personality. The theory evolved to the present revised version describing three systems: fight-flight-freezing, behavioural approach/activation (BAS), and behavioural inhibition (BIS). However, the most widely available measure of the theory, the BIS/BAS scales, only investigates two systems. Using a large longitudinal community survey, we found that the BIS/BAS scales can be re-structured to investigate the three systems of the theory with a BIS scale, three BAS scales, and a separate fight-flight-freezing system (FFFS) scale. The re-structured scales were age, sex, and longitudinally invariant, and associations with personality and mental health measures followed theoretical expectations and previously published associations. The proposed framework can be used to investigate behavioural choices influencing physical and mental health and bridge historical with contemporary research.
Publisher: Cold Spring Harbor Laboratory
Date: 15-07-2017
DOI: 10.1101/163196
Abstract: Clinical pain is difficult to study using standard Blood Oxygenation Level Dependent (BOLD) magnetic resonance imaging because it is often ongoing and, if evoked, it is associated with stimulus-correlated motion. Arterial spin labelling (ASL) offers an attractive alternative. This study used arm repositioning to evoke clinically-relevant musculoskeletal pain in patients with shoulder impingement syndrome. Fifty-five patients were scanned using a multi post-labelling delay pseudo-continuous ASL (pCASL) sequence, first with both arms along the body and then with the affected arm raised into a painful position. Twenty healthy volunteers were scanned as a control group. Arm repositioning resulted in increased perfusion in brain regions involved in sensory processing and movement integration, such as the contralateral primary motor and primary somatosensory cortex, mid- and posterior cingulate cortex, and, bilaterally, in the insular cortex/operculum, putamen, thalamus, midbrain and cerebellum. Perfusion in the thalamus, midbrain and cerebellum was larger in the patient group. Results of a post hoc analysis suggested that the observed perfusion changes were related to pain rather than arm repositioning. This study showed that ASL can be useful in research on clinical ongoing musculoskeletal pain but the technique is not sensitive enough to detect small differences in perfusion.
Publisher: Oxford University Press (OUP)
Date: 10-2012
DOI: 10.1093/BRAIN/AWS242
Publisher: Wiley
Date: 06-05-2022
DOI: 10.1111/ASAP.12313
Abstract: The present study sought to better understand the extent to which negative perceptions of people who receive unemployment benefits is due to their poverty status, their unemployment, and/or their receipt of income support payments. We sought to differentiate these three factors in a vignette‐based experiment drawing on a large Australian general population s le ( N = 778). Participants rated the personality and capability of two fictional characters. The key experimental manipulation of employment status and benefit receipt was embedded in description of other characteristics. Participants rated vignette characters who received unemployment benefits less favorably on personality (conscientiousness, emotional stability, agreeableness), competence, and warmth than characters described as having a job, as being poor, or as not having a job but without mention of receiving benefits. There was a gradient in the strength of negative assessments across these conditions, but only warmth, conscientiousness and employability distinguished between in iduals receiving unemployment benefits and in iduals without a job but no reference to benefit receipt. This study provides new insights showing that receiving benefits due to unemployment contributes to negative perceptions over and above the effects of poverty or being unemployed.
Publisher: Wiley
Date: 05-10-2011
DOI: 10.1002/HBM.21402
Publisher: SAGE Publications
Date: 21-12-2010
Abstract: Background: Lesion dissemination in time and space represents a key feature and diagnostic marker of multiple sclerosis (MS). The correlation between magnetic resonance imaging (MRI) lesion load and disability is only modest, however. Strategic lesion location might at least partially account for this ‘clinico-radiologic paradox’. Objectives: Here we used a non-parametric permutation-based approach to map lesion location probability based on MS lesions identified on T2-weighted MRI. We studied 121 patients with clinically isolated syndrome, relapsing–remitting or secondary progressive MS and correlated these maps to assessments of neurologic and cognitive functions. Results: The Expanded Disability Status Scale correlated with bilateral periventricular lesion location (LL), and sensory and coordination functional system deficits correlated with lesion accumulation in distinct anatomically plausible regions, i.e. thalamus and middle cerebellar peduncule. Regarding cognitive performance, decreased verbal fluency correlated with left parietal LL comprising the putative superior longitudinal fascicle. Delayed spatial recall correlated with _amygdalar, _left frontal and parietal LL. Delayed selective reminding correlated with bilateral frontal and temporal LL. However, only part of the spectrum of cognitive and neurological problems encountered in our cohort could be explained by specific lesion location. Conclusions: Lesion probability mapping supports the association of specific lesion locations with symptom development in MS, but only to limited extent.
Publisher: Wiley
Date: 27-01-2012
DOI: 10.1002/ART.33326
Abstract: To investigate whether structural changes are present in the cortical and subcortical gray matter of the brains of patients with rheumatoid arthritis (RA). We used two surface-based style morphometry analysis programs and a voxel-based style analysis program to compare high-resolution structural magnetic resonance imaging data obtained for 31 RA patients and 25 age- and sex-matched healthy control subjects. We observed an increase in gray matter content in the basal ganglia of RA patients, mainly in the nucleus accumbens and caudate nucleus. There were no differences in the cortical gray matter. Moreover, patients had a smaller intracranial volume. Our results suggest that RA is associated with changes in the subcortical gray matter rather than with cortical gray matter atrophy. Since the basal ganglia play an important role in motor control as well as in pain processing and in modulating behavior in response to aversive stimuli, we suggest that these changes may result from altered motor control or prolonged pain processing. The differences in brain volume may reflect either generalized atrophy or differences in brain development.
Publisher: Springer Science and Business Media LLC
Date: 03-10-2020
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 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: eLife Sciences Publications, Ltd
Date: 23-03-2020
DOI: 10.7554/ELIFE.53232
Abstract: Evolutionary adaptations of temporo-parietal cortex are considered to be a critical specialization of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including local expansion of the cortical sheet or changes in connectivity between cortical areas. We distinguish different types of changes in brain architecture using a computational neuroanatomy approach. We investigate the extent to which between-species alignment, based on cortical myelin, can predict changes in connectivity patterns across macaque, chimpanzee, and human. We show that expansion and relocation of brain areas can predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the temporal lobe connectivity pattern. This approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 04-2015
Publisher: Oxford University Press (OUP)
Date: 02-01-2013
Publisher: Wiley
Date: 03-2021
Publisher: eLife Sciences Publications, Ltd
Date: 28-02-2020
Publisher: Cold Spring Harbor Laboratory
Date: 23-09-2021
DOI: 10.1101/2021.09.21.21263298
Abstract: Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g. blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g. the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline’s generalisability across datasets. Our method provided subject-level detection accuracy 80% on all the datasets (withindataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
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: Association for Research in Vision and Ophthalmology (ARVO)
Date: 11-02-2005
DOI: 10.1167/5.2.1
Publisher: Elsevier BV
Date: 04-2012
DOI: 10.1016/J.NEUROIMAGE.2011.11.077
Abstract: The spinal cord is the main pathway for information between the central and the peripheral nervous systems. Non-invasive functional MRI offers the possibility of studying spinal cord function and central sensitisation processes. However, imaging neural activity in the spinal cord is more difficult than in the brain. A significant challenge when dealing with such data is the influence of physiological noise (primarily cardiac and respiratory), and currently there is no standard approach to account for these effects. We have previously studied the various sources of physiological noise for spinal cord fMRI at 1.5T and proposed a physiological noise model (PNM) (Brooks et al., 2008). An alternative de-noising strategy, selective averaging filter (SAF), was proposed by Deckers et al. (2006). In this study we reviewed and implemented published physiological noise correction methods at higher field (3T) and aimed to find the optimal models for gradient-echo-based BOLD acquisitions. Two general techniques were compared: physiological noise model (PNM) and selective averaging filter (SAF), along with regressors designed to account for specific signal compartments and physiological processes: cerebrospinal fluid (CSF), motion correction (MC) parameters, heart rate (HR), respiration volume per time (RVT), and the associated cardiac and respiratory response functions. Functional responses were recorded from the cervical spinal cord of 18 healthy subjects in response to noxious thermal and non-noxious punctate stimulation. The various combinations of models and regressors were compared in three ways: the model fit residuals, regression model F-tests and the number of activated voxels. The PNM was found to outperform SAF in all three tests. Furthermore, inclusion of the CSF regressor was crucial as it explained a significant amount of signal variance in the cord and increased the number of active cord voxels. Whilst HR, RVT and MC explained additional signal (noise) variance, they were also found (in particular HR and RVT) to have a negative impact on the parameter estimates (of interest)--as they may be correlated with task conditions e.g. noxious thermal stimuli. Convolution with previously published cardiac and respiratory impulse response functions was not found to be beneficial. The other novel aspect of current study is the investigation of the influence of pre-whitening together with PNM regressors on spinal fMRI data. Pre-whitening was found to reduce non-white noise, which was not accounted for by physiological noise correction, and decrease false positive detection rates.
Publisher: Oxford University Press (OUP)
Date: 2020
DOI: 10.1093/BRAINCOMMS/FCAA096
Abstract: Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep-learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this article, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep-learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasize the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. As artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.
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: Springer Science and Business Media LLC
Date: 21-12-2012
Publisher: Wiley
Date: 23-08-2004
DOI: 10.1002/MRM.20194
Abstract: 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 ex le, 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.
Publisher: Wiley
Date: 06-12-2006
DOI: 10.1002/JMRI.20810
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: Wiley
Date: 16-07-2013
DOI: 10.1002/HBM.22282
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: Wiley
Date: 24-11-2008
DOI: 10.1002/JMRI.21623
Abstract: To estimate the importance of respiratory and cardiac effects on signal variability found in functional magnetic resonance imaging data recorded from the brainstem. A modified version of the retrospective image correction (RETROICOR) method (Glover et al, [2000] Magn Reson Med 44:162-167) was implemented on resting brainstem echo-planar imaging (EPI) data in 12 subjects. Fourier series were fitted to image data based on cardiac and respiratory recordings (pulseoximetry and respiratory turbine), including multiplicative terms that accounted for interactions between cardiac and respiratory signals. F-tests were performed on residuals produced by regression analysis. Additionally, we evaluated whether modified RETROICOR improved detection of brainstem activation (in 11 subjects) during a finger opposition task. The optimal model, containing three cardiac (C) and four respiratory (R) harmonics, and one multiplicative (X) term, "3C4R1X," significantly reduced signal variability without overfitting to noise. The application of modified RETROICOR to activation data increased group Z-statistics and reduced putative false-positive activation. In addition to cardiac and respiratory effects, their interaction was also a significant source of physiological noise. The modified RETROICOR model improved detection of brainstem activation and would be usefully applied to any study examining this brain region.
Publisher: Cold Spring Harbor Laboratory
Date: 04-10-2016
DOI: 10.1101/079145
Abstract: The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness richness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a erse set of commonly used neuroimaging algorithms. Magnetic Resonance Imaging (MRI) is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. There are thousands MRI studies performed every year generating a substantial amount of data. At the same time, many new data analysis methods are being developed every year. The potential of using new analysis methods on the variety of existing and newly acquired data is hindered by difficulties in software deployment and lack of support for standardized input data. Here we propose to use container technology to make deployment of a wide range of data analysis techniques easy. In addition, we adapt the existing data analysis tools to interface with data organized in a standardized way. We hope that this approach will enable researchers to access a wider range of methods when analyzing their data which will lead to accelerated progress in human neuroscience.
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.
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: Springer International Publishing
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 21-05-2018
DOI: 10.1101/327205
Abstract: White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model. In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework. We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, ided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 × 10 −5 , which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Springer Science and Business Media LLC
Date: 08-07-2008
DOI: 10.1038/MP.2008.34
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: 2011
DOI: 10.1016/J.NEUROIMAGE.2010.06.044
Abstract: [(11)C]-(+)-PHNO is a D3 preferring PET radioligand which has recently opened the possibility of imaging D3 receptors in the human brain in vivo. This imaging tool allows characterisation of the distribution of D3 receptors in vivo and further investigation of their functional role. The specific [(11)C]-(+)-PHNO signal is a mixture of D3 and D2 components with the relative magnitude of each component determined by the regional receptor densities. An accurate and reproducible delineation of regions of interest (ROI) is therefore important for optimal analysis of human PET data. We present a set of anatomical guidelines for the delineation of D3 relevant ROIs including substantia nigra, hypothalamus, ventral pallidum/substantia innominata, ventral striatum, globus pallidus and thalamus. Delineation of these structures using this approach allowed for high intra- and inter-operator reproducibility. Subsequently we used a selective D3 antagonist to dissect the total [(11)C]-(+)-PHNO signal in each region into its D3 and D2 components and estimated the regional fraction of the D3 signal (f(PHNO)(D3)). In descending order of magnitude the following results for the f(PHNO)(D3) were obtained: hypothalamus=100%, substantia nigra=100%, ventral pallidum/substantia innominata=75%, globus pallidus=65%, thalamus=43%, ventral striatum=26% and precommissural-ventral putamen=6%. An automated approach for the delineation of these anatomical regions of interest was also developed and investigated in terms of its reproducibility and accuracy.
Publisher: Wiley
Date: 05-2000
Publisher: Elsevier BV
Date: 10-2014
Publisher: MDPI AG
Date: 19-03-2021
DOI: 10.3390/DIAGNOSTICS11030551
Abstract: Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large erse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 06-2015
Publisher: Frontiers Media SA
Date: 2013
Publisher: BMJ
Date: 18-11-2021
DOI: 10.1136/OEMED-2020-106840
Abstract: There is a lack of evidence concerning the prospective effect of cumulative exposure to psychosocial job stressors over time on mental ill-health. This study aimed to assess whether cumulative exposure to poor quality jobs places employees at risk of future common mental disorder. Data were from the Personality and Total Health Through Life project (n=1279, age 40–46 at baseline). Data reported on the cumulative exposure to multiple indicators of poor psychosocial job quality over time (ie, a combination of low control, high demands and high insecurity) and future common mental disorder (ie, depressive and/or anxiety symptom scores above a validated threshold) 12 years later. Data were analysed using logistic regression models and controlled for potential confounders across the lifespan. Cumulative exposure to poor-quality work (particularly more secure work) on multiple occasions elevated the risk of subsequent common mental disorder, independent of social, health, verbal intelligence and personality trait confounders (OR=1.30, 95% CI 1.06 to 1.59). Our findings show that cumulative exposure to poor psychosocial job quality over time independently predicts future common mental disorder—supporting the need for workplace interventions to prevent repeated exposure of poor quality work.
Publisher: BMJ
Date: 2021
DOI: 10.1136/BMJOPEN-2020-041698
Abstract: Attention-deficit/hyperactivity disorder (ADHD) is among the most common mental disorders in children and adolescents, and it is a strong risk factor for several adverse psychosocial outcomes over the lifespan. There are large between-country and within-country variations in diagnosis and medication rates. Due to ethical and practical considerations, a few studies have examined the effects of receiving a diagnosis, and there is a lack of research on effects of medication on long-term outcomes. Our project has four aims organised in four work packages: (WP1) To examine the prognosis of ADHD (with and without medication) compared with patients with other psychiatric diagnoses, patients in contact with public sector child and adolescent psychiatric outpatient clinics (without diagnosis) and the general population (WP2) Examine within-country variation in ADHD diagnoses and medication rates by clinics’ catchment area and(WP3) Identify causal effects of being diagnosed with ADHD and (WP4) ADHD medication on long-term outcomes. Our project links several nationwide Norwegian registries. The patient s le is all persons aged 5–18 years that were in contact with public sector child and adolescent psychiatric outpatient clinics in 2009–2011. Our comparative analysis of prognosis will be based on survival analysis and mixed-effects models. Our analysis of variation will apply mixed-effects models and generalised linear models. We have two identification strategies for the effect of being diagnosed with ADHD and of receiving medication on long-term outcomes. Both strategies rely on using preference-based instrumental variables, which in our project are based on provider preferences for ADHD diagnosis and medication. The project is approved by the Regional Ethics Committee, Norway (REC number 2017/2150/REC south-east D). All papers will be published in open-access journals and results will be presented in national and international conferences. ISRCTN11573246 and ISRCTN11891971 .
Publisher: Elsevier BV
Date: 03-2023
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: Ovid Technologies (Wolters Kluwer Health)
Date: 16-01-2013
Publisher: Public Library of Science (PLoS)
Date: 09-03-2017
Publisher: Elsevier BV
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 30-05-2014
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 10-2003
Publisher: Elsevier BV
Date: 08-2022
Publisher: Elsevier BV
Date: 04-2004
Publisher: Royal College of Psychiatrists
Date: 07-2012
DOI: 10.1192/BJP.BP.111.105361
Abstract: Late-life depression is a common and heterogeneous illness, associated with structural abnormalities in both grey and white matter. To examine the relationship between age at onset and magnetic resonance imaging (MRI) measures of grey and white matter to establish whether they support particular hypotheses regarding the anatomy and aetiology of network disruption in late-life depression. We studied 36 participants with late-life depression. Grey matter was examined using T 1 -weighted MRI and analysed using voxel-based morphometry. The hippoc us was automatically segmented and volume and shape analysis performed. White matter was examined using diffusion tensor imaging and analysed using tract-based spatial statistics. Later age at onset was significantly associated with reduced fractional anisotropy of widespread tracts, in particular the anterior thalamic radiation and superior longitudinal fasciculus. Earlier age at onset was associated with reduced hippoc al volume normalised to whole brain size bilaterally. However, no significant correlations were detected using hippoc al shape analysis or voxel-based morphometry. Overall, the results were compatible with the vascular hypothesis, and provided some support for the glucocorticoid cascade hypothesis.
Publisher: Wiley
Date: 25-05-2010
DOI: 10.1002/MRM.22318
Abstract: The inherent distortions in echo-planar imaging that arise due to inhomogeneities in the static magnetic field can lead to difficulties when attempting to obtain structurally accurate diffusion-tensor imaging data. Parallel acceleration techniques can reduce the magnitude of these distortions but do not remove them entirely. Images can be corrected using a measured field map, but this is prone to error. One approach to correcting for these distortions, referred to here as "blip-reversed" echo-planar imaging, involves collecting a second set of images with the phase encoding reversed. Here, a novel approach to collecting blip-reversed echo-planar imaging data for diffusion-tensor imaging is presented: a dual-echo sequence is used in which the phase-encoding direction of the second echo is swapped compared to the first echo. This allows benefits of the blip-reversed approach to be exploited, with only a modest increase in scan time and, due to the extra data acquired, no significant loss of signal-to-noise efficiency. A novel approach to recombining blip-reversed data is also presented, which involves refining the measured field map, using an algorithm to minimize the difference between the corrected images. The field map refinement is also applicable to conventionally acquired blip-reversed sequences.
Publisher: eLife Sciences Publications, Ltd
Date: 20-01-2016
Publisher: Proceedings of the National Academy of Sciences
Date: 24-02-2003
Abstract: Understanding the relationship between the structural and functional organization of the human brain is one of the most important goals of neuroscience. In idual 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 in idual, which can then be correlated with that in idual'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 in iduals, as defined by using fMRI. The anatomical identification of a cortical area (V5/MT) 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.
Publisher: Elsevier BV
Date: 03-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Wiley
Date: 13-07-2006
DOI: 10.1002/MRM.20939
Abstract: 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.
Publisher: Wiley
Date: 11-2002
DOI: 10.1002/MRM.10298
Abstract: These preliminary studies demonstrate that static field inhomogeneity in the human inferior frontal cortex (IFC) is significantly diminished through placement of a small amount of strongly diamagnetic material in the roof of the mouth. As a result, susceptibility-related image artifacts in this region, as observed in blood oxygen level dependent (BOLD) functional MRI (fMRI), are considerably decreased without compromising the spatial or temporal resolution of the study. Simulations of the static field utilizing perturbation theory are shown, which support the experimental results. The limitations and possible future developments of the technique are described. The application of diamagnetic passive shimming on other regions of the brain is also discussed. Routine use of the proposed method within fMRI studies is practicable through subject-specific optimization of the technique utilizing the simulation algorithm.
Publisher: Springer Berlin Heidelberg
Date: 2011
DOI: 10.1007/978-3-642-23623-5_60
Abstract: We present a novel Bayesian framework for non-rigid motion correction and pharmacokinetic parameter estimation in dceMRI sequences which incorporates a physiological image formation model into the similarity measure used for motion correction. The similarity measure is based on the maximization of the joint posterior probability of the transformations which need to be applied to each image in the dataset to bring all images into alignment, and the physiological parameters which best explain the data. The deformation framework used to deform each image is based on the diffeomorphic logDemons algorithm. We then use this method to co-register images from simulated and real dceMRI datasets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.
Publisher: Wiley
Date: 02-08-2023
DOI: 10.1002/HBM.26424
Abstract: In this work we present BIANCA‐MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA‐MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA‐MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA‐MS to other widely used tools. Second, we tested how BIANCA‐MS performs in separate datasets. Finally, we evaluated BIANCA‐MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA‐MS clearly outperformed other available tools in both high‐ and low‐resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA‐MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each in idual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA‐MS is a robust and accurate approach for automated MS lesion segmentation.
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: Ovid Technologies (Wolters Kluwer Health)
Date: 13-03-2013
Publisher: Cold Spring Harbor Laboratory
Date: 24-04-2017
DOI: 10.1101/130385
Abstract: UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40811-3_24
Abstract: Image-guided interventions often rely on deformable multimodal registration to align pre-treatment and intra-operative scans. There are a number of requirements for automated image registration for this task, such as a robust similarity metric for scans of different modalities with different noise distributions and contrast, an efficient optimisation of the cost function to enable fast registration for this time-sensitive application, and an insensitive choice of registration parameters to avoid delays in practical clinical use. In this work, we build upon the concept of structural image representation for multi-modal similarity. Discriminative descriptors are densely extracted for the multi-modal scans based on the "self-similarity context". An efficient quantised representation is derived that enables very fast computation of point-wise distances between descriptors. A symmetric multi-scale discrete optimisation with diffusion reguIarisation is used to find smooth transformations. The method is evaluated for the registration of 3D ultrasound and MRI brain scans for neurosurgery and demonstrates a significantly reduced registration error (on average 2.1 mm) compared to commonly used similarity metrics and computation times of less than 30 seconds per 3D registration.
Publisher: American Physiological Society
Date: 06-2005
Abstract: Color has a profound effect on the perception of odors. For ex le, 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.
Publisher: Cold Spring Harbor Laboratory
Date: 26-07-2020
DOI: 10.1101/2020.07.24.219485
Abstract: White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.
Publisher: Elsevier BV
Date: 06-2011
DOI: 10.1016/J.MRI.2011.02.015
Abstract: Echo-planar diffusion-weighted images can display significant geometric distortions due to eddy current fields. Several preparation schemes have been proposed, which can null eddy currents with a single time constant. The aim of this work was to compare the performance of three such pulse sequences in the presence of multiple components and investigate whether affine registration is capable of correcting for the resulting distortions. A magnetic resonance imaging simulator was used to eliminate potential confounding factors. The doubly refocused sequences showed substantially reduced effects. Applying affine registration to the single spin-echo images leads to reduced residuals, but not to the level observed for the doubly refocused sequences. Modified versions of the standard single spin-echo and doubly refocused sequences performed better than their original counterparts. Affine registration is not sufficient to correct for strong eddy current effects, which should therefore be minimized at source. When the use of a doubly refocused sequence is not possible, a modified single spin-echo sequence should be considered.
Publisher: Elsevier BV
Date: 08-2003
DOI: 10.1016/S1053-8119(03)00225-8
Abstract: 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 h ered 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.
Publisher: Springer Science and Business Media LLC
Date: 25-10-2023
Publisher: IOP Publishing
Date: 23-09-2011
Publisher: Informa UK Limited
Date: 03-10-2022
Publisher: Cold Spring Harbor Laboratory
Date: 23-09-2022
DOI: 10.1101/2022.09.22.509002
Abstract: Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the s le size. Further, we evaluate linear and nonlinear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippoc al volumes in the age range of 45 to 80 years, as an ex le application. Based on these distributions we repeatedly simulated s les for sizes between 50 and 50,000 data points, and for each simulated s le we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1 th , 5 th , 10 th ) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing s le size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large s le sizes. The uncertainty of model performance is substantial for what would often be considered large s les in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across s le sizes, especially for nonlinear ground truth. Surprisingly large s les of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both, bias and variance. Furthermore, extreme caution is needed when attempting to extrapolate beyond the age range included in the source dataset. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.
Publisher: The Eurographics Association
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 10-05-2021
DOI: 10.1186/S12889-021-10876-9
Abstract: In Australia, it is projected that one in four in iduals will be at the nominal retirement age of 65 or over by 2056 this effect is expected to be especially pronounced in rural areas. Previous findings on the effects of retirement on wellbeing have been mixed. The present study explores the effects of employment and retirement on health and wellbeing among a s le of rural Australians. Australian Rural Mental Health Study participants who were aged 45 or over ( N = 2013) were included in a series of analyses to compare the health and wellbeing of in iduals with differing employment and retirement circumstances. Self-reported outcome variables included perceived physical health and everyday functioning, financial wellbeing, mental health, relationships, and satisfaction with life. Across the outcomes, participants who were employed or retired generally reported better health and wellbeing than those not in the workforce. Retired participants rated more highly than employed participants on mental health, relationships, and satisfaction with life. There was also a short-term benefit for perceived financial status for retired participants compared to employed participants, but this effect diminished over time. While retirement is a significant life transition that may affect multiple facets of an in idual’s life, the direction and magnitude of these effects vary depending on the retirement context, namely the pre-retirement and concurrent circumstances within which an in idual is retiring. Personal perceptions of status changes may also contribute to an in idual’s wellbeing more so than objective factors such as income. Policies that promote rural work/retirement opportunities and ersity and address rural disadvantage are needed.
Publisher: Oxford University Press (OUP)
Date: 2013
DOI: 10.1093/BRAIN/AWS325
Abstract: Neurodegeneration is the main cause for permanent disability in multiple sclerosis. The effect of current immunomodulatory treatments on neurodegeneration is insufficient. Therefore, direct neuroprotection and myeloprotection remain an important therapeutic goal. Targeting acid-sensing ion channel 1 (encoded by the ASIC1 gene), which contributes to the excessive intracellular accumulation of injurious Na(+) and Ca(2+) and is over-expressed in acute multiple sclerosis lesions, appears to be a viable strategy to limit cellular injury that is the substrate of neurodegeneration. While blockade of ASIC1 through amiloride, a potassium sparing diuretic that is currently licensed for hypertension and congestive cardiac failure, showed neuroprotective and myeloprotective effects in experimental models of multiple sclerosis, this strategy remains untested in patients with multiple sclerosis. In this translational study, we tested the neuroprotective effects of amiloride in patients with primary progressive multiple sclerosis. First, we assessed ASIC1 expression in chronic brain lesions from post-mortem of patients with progressive multiple sclerosis to identify the target process for neuroprotection. Second, we tested the neuroprotective effect of amiloride in a cohort of 14 patients with primary progressive multiple sclerosis using magnetic resonance imaging markers of neurodegeneration as outcome measures of neuroprotection. Patients with primary progressive multiple sclerosis underwent serial magnetic resonance imaging scans before (pretreatment phase) and during (treatment phase) amiloride treatment for a period of 3 years. Whole-brain volume and tissue integrity were measured with high-resolution T(1)-weighted and diffusion tensor imaging. In chronic brain lesions of patients with progressive multiple sclerosis, we demonstrate an increased expression of ASIC1 in axons and an association with injury markers within chronic inactive lesions. In patients with primary progressive multiple sclerosis, we observed a significant reduction in normalized annual rate of whole-brain volume during the treatment phase, compared with the pretreatment phase (P = 0.018, corrected). Consistent with this reduction, we showed that changes in diffusion indices of tissue damage within major clinically relevant white matter (corpus callosum and corticospinal tract) and deep grey matter (thalamus) structures were significantly reduced during the treatment phase (P = 0.02, corrected). Our results extend evidence of the contribution of ASIC1 to neurodegeneration in multiple sclerosis and suggest that amiloride may exert neuroprotective effects in patients with progressive multiple sclerosis. This pilot study is the first translational study on neuroprotection targeting ASIC1 and supports future randomized controlled trials measuring neuroprotection with amiloride in patients with multiple sclerosis.
Publisher: Elsevier BV
Date: 04-2012
DOI: 10.1016/J.NEUROBIOLAGING.2011.05.018
Abstract: Structural brain changes have been described in both mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, less is known about whether structural changes are detectable earlier, in the asymptomatic phase. Using voxel-based morphometry (VBM) and shape analyses of magnetic resonance imaging (MRI) data, we investigated structural brain differences between groups of healthy subjects, stratified by subsequent diagnoses of MCI or AD during a 10-year follow-up. Images taken at baseline, at least 4 years before any cognitive symptoms, showed that subjects with future cognitive impairment (preclinical AD and MCI) had reduced brain volume in medial temporal lobes, posterior cingulate recuneus, and orbitofrontal cortex, compared with matched subjects who remained cognitively healthy for 10 years (HC). For only those subjects later diagnosed as AD, significantly greater atrophy at baseline was detected in the right medial temporal lobe, which was also confirmed by shape analysis of the right hippoc us in these subjects. Our results demonstrate that structural brain changes occur years before clinical cognitive decline in AD and are localized to regions affected by AD neuropathology.
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: Cold Spring Harbor Laboratory
Date: 12-03-2021
DOI: 10.1101/2021.03.12.435171
Abstract: Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.NEURON.2022.09.012
Abstract: Combining deep learning image analysis methods and large-scale imaging datasets offers many opportunities to neuroscience imaging and epidemiology. However, despite these opportunities and the success of deep learning when applied to a range of neuroimaging tasks and domains, significant barriers continue to limit the impact of large-scale datasets and analysis tools. Here, we examine the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation, and logistical challenges and discuss the problems that still need to be tackled to enable the success of "big data" deep learning approaches beyond research.
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 2010
Publisher: Wiley
Date: 23-11-2022
DOI: 10.1111/JASP.12843
Abstract: The association of societal‐level structural factors with stereotypes and stigma can be examined using the stereotype content model (SCM). The main aim of the current study was to review and synthesize all available research data of SCM dimensions of Warmth and Competence perceptions of welfare recipients, and compare the ratings in different types of social welfare regimes (Nordic, Conservative, and Liberal). To do this, we reviewed all published literature using the SCM methodology to assess stereotypes of welfare recipients and perfomed a cross‐national meta‐regression of 17 datasets (total N = 1797) drawn from six countries representing three types of welfare regimes. In each of the studies, participants were asked how others in their country viewed welfare recipients on the dimensions of warmth and competence. We predicted and found support for the hypothesis that countries with a Nordic welfare regime have a warmer cultural stereotype of welfare recipients than countries with a Liberal or Conservative regime. However, the expected association between Liberal welfare regime and incompetence stereotypes was not found. Supplementary analyses showed that the type of welfare regime better explained country differences in welfare stereotypes than country differences in income inequality. This study demonstrates how stereotypes of warmth and competence vary across welfare regimes, adding to knowledge about how societal‐level factors are related to cultural stereotypes.
Publisher: Elsevier BV
Date: 12-2014
Publisher: Wiley
Date: 18-07-2019
DOI: 10.1111/ADB.12652
Abstract: Cannabis use is highly prevalent and often considered to be relatively harmless. Nonetheless, a subset of regular cannabis users may develop dependence, experiencing poorer quality of life and greater mental health problems relative to non-dependent users. The neuroanatomy characterizing cannabis use versus dependence is poorly understood. We aimed to delineate the contributing role of cannabis use and dependence on morphology of the hippoc us, one of the most consistently altered brain regions in cannabis users, in a large multi-site dataset aggregated across four research sites. We compared hippoc al volume and vertex-level hippoc al shape differences (1) between 121 non-using controls and 140 cannabis users (2) between 106 controls, 50 non-dependent users and 70 dependent users and (3) between a subset of 41 controls, 41 non-dependent users and 41 dependent users, matched on s le characteristics and cannabis use pattern (onset age and dosage). Cannabis users did not differ from controls in hippoc al volume or shape. However, cannabis-dependent users had significantly smaller right and left hippoc i relative to controls and non-dependent users, irrespective of cannabis dosage. Shape analysis indicated localized deflations in the superior-medial body of the hippoc us. Our findings support neuroscientific theories postulating dependence-specific neuroadaptations in cannabis users. Future efforts should uncover the neurobiological risk and liabilities separating dependent and non-dependent use of cannabis.
Publisher: Wiley
Date: 26-01-2021
DOI: 10.1111/DAR.13239
Publisher: Wiley
Date: 31-12-2003
DOI: 10.1002/MRM.10354
Abstract: 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.
Publisher: Springer Berlin Heidelberg
Date: 2011
DOI: 10.1007/978-3-642-23629-7_66
Abstract: Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this problem and proposes a new similarity metric for multi-modal registration, the non-local shape descriptor. It aims to extract the shape of anatomical features in a non-local region. By utilizing the dense evaluation of shape descriptors, this new measure bridges the gap between intensity-based and geometric feature-based similarity criteria. Our new metric allows for accurate and reliable registration of clinical multi-modal datasets and is robust against the most considerable differences between modalities, such as non-functional intensity relations, different amounts of noise and non-uniform bias fields. The measure has been implemented in a non-rigid diffusion-regularized registration framework. It has been applied to synthetic test images and challenging clinical MRI and CT chest scans. Experimental results demonstrate its advantages over the most commonly used similarity metric - mutual information, and show improved alignment of anatomical landmarks.
Publisher: Elsevier BV
Date: 03-2013
DOI: 10.1016/J.NEUROBIOLAGING.2012.07.011
Abstract: Impaired visuospatial associative memory may be one of the earliest changes predicting cognitive impairment and Alzheimer's disease. We explored the relationship between performance on a visuospatial associative memory task (the Placing Test) and brain structure and function in cognitively healthy older adults. First, we performed a voxel-based morphometry correlational analysis on structural magnetic resonance imaging (MRI) data from 144 healthy older adults with their scores on the Placing Test. Second, we carried out a functional MRI study on another group of 28 healthy older adults who performed a similar task during functional MRI. Decreased performance on the Placing Test was associated with increased atrophy in medial-temporal regions. Functional activation of the same regions-controlling for the effect of atrophy-occurred during successful performance of the same task. The colocalization of structural and functional MRI correspondents of visuospatial associative test performance within medial-temporal regions validates multimodal imaging in describing behaviorally relevant variability in the aging brain and suggests that the Placing Test has the potential for detecting early cognitive changes occurring in preclinical phases of Alzheimer's disease.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2012
DOI: 10.1038/NG.2250
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40811-3_40
Abstract: A comprehensive framework for predicting response to therapy on the basis of heterogeneity in dceMRI parameter maps is presented. A motion-correction method for dceMRI sequences is extended to incorporate uncertainties in the pharmacokinetic parameter maps using a variational Bayes framework. Simple measures of heterogeneity (with and without uncertainty) in parameter maps for colorectal cancer tumours imaged before therapy are computed, and tested for their ability to distinguish between responders and non-responders to therapy. The statistical analysis demonstrates the importance of using the spatial distribution of parameters, and their uncertainties, when computing heterogeneity measures and using them to predict response on the basis of the pre-therapy scan. The results also demonstrate the benefits of using the ratio of Ktrans with the bolus arrival time as a biomarker.
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: Elsevier BV
Date: 02-2002
Publisher: Cold Spring Harbor Laboratory
Date: 13-04-2016
DOI: 10.1101/041798
Abstract: Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: pecs/nidm-results.html .
Publisher: Elsevier BV
Date: 07-2012
DOI: 10.1016/J.NEUROIMAGE.2012.03.074
Abstract: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5 T or 3T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. The parameter reprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f=0.5, g=0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f=0.2 with option "B", giving median DOC=0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f=0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f=0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f=0.1 after removal of the neck slices seems to work best for all acquisition protocols.
Location: Australia
Location: Australia
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mark Jenkinson.