Thomas R. Barrick

YNICL Journal 2022 Journal Article

Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease

• Marco Egle
• Saima Hilal
• Anil M Tuladhar
• Lukas Pirpamer
• Steven Bell
• Edith Hofer
• Marco Duering
• James Wason

BACKGROUND: DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study's objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity. METHODS: Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy. RESULTS: There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer' s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts. CONCLUSION: DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets.

YNIMG Journal 2020 Journal Article

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique

• Thomas R. Barrick
• Catherine A. Spilling
• Carson Ingo
• Jeremy Madigan
• Jeremy D. Isaacs
• Philip Rich
• Timothy L. Jones
• Richard L. Magin

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1–4 ​min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i. e. diffusion coefficient, D in mm2 s−1) and the shape of the power law tail (i. e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, H n, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 ​s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

YNICL Journal 2019 Journal Article

Corrigendum to “Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change” [Neuroimage: Clinical 16 (2017) 330–342]

• Owen A. Williams
• Eva A. Zeestraten
• Philip Benjamin
• Christian Lambert
• Andrew J. Lawrence
• Andrew D. MacKinnon
• Robin G. Morris
• Hugh S. Markus

The authors regret that due to a data coding error, cross-sectional effects from linear mixed effects models were stored, presented and interpreted as longitudinal effects (i.e. interactions with the time variable). As a result, the results presented in Tables 5 and 6 [Table presented] do not represent the associations between change in MRI markers and change in executive function (EF) and information processing speed (IPS) but show the baseline associations. The true longitudinal associations are provided in the corrected Tables 5 and 6. The primary difference in the results is that while change in DSEG θ is significantly associated with change in EF and IPS in univariable analyses, it does not remain significant in the multivariable analyses as previously stated. Furthermore, changes in histogram metrics of FA and MD were not associated to EF and only change in FA NPH was significantly associated with IPS, whereas all four metrics were originally reported to be associated with change in EF and IPS. The changes in the results do effect some of the conclusions in the original article. While DSEG θ is a valid measure of cerebral small vessel disease (SVD) and is associated with change in EF and IPS, we must retract the statement that DSEG θ “provides the strongest predictor of cognitive change” as, in the multivariable models, lacunar infarcts and FA NPH were associated with change in EF and IPS respectively. Nevertheless, DSEG θ provides a marker of SVD severity that is related to changes in cognitive performance. The authors would like to apologize for any inconvenience caused by this error.

YNICL Journal 2019 Journal Article

Tissue-type mapping of gliomas

• Felix Raschke
• Thomas R. Barrick
• Timothy L. Jones
• Guang Yang
• Xujiong Ye
• Franklyn A. Howe

PURPOSE: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. MATERIALS AND METHODS: -weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. RESULTS: H MRS characteristics. CONCLUSIONS: H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.

YNICL Journal 2018 Journal Article

Identifying preclinical vascular dementia in symptomatic small vessel disease using MRI

• Christian Lambert
• Eva Zeestraten
• Owen Williams
• Philip Benjamin
• Andrew J. Lawrence
• Robin G. Morris
• Andrew D. MacKinnon
• Thomas R. Barrick

Sporadic cerebral small vessel disease is an important cause of vascular dementia, a syndrome of cognitive impairment together with vascular brain damage. At post-mortem pure vascular dementia is rare, with evidence of co-existing Alzheimer's disease pathology in 95% of cases. This work used MRI to characterize structural abnormalities during the preclinical phase of vascular dementia in symptomatic small vessel disease. 121 subjects were recruited into the St George's Cognition and Neuroimaging in Stroke study and followed up longitudinally for five years. Over this period 22 individuals converted to dementia. Using voxel-based morphometry, we found structural abnormalities present at baseline in those with preclinical dementia, with reduced grey matter density in the left striatum and hippocampus, and more white matter hyperintensities in the frontal white-matter. The lacunar data revealed that some of these abnormalities may be due to lesions within the striatum and centrum semiovale. Using support vector machines, future dementia could be best predicted using hippocampal and striatal Jacobian determinant data, achieving a balanced classification accuracy of 73%. Using cluster ward linkage we identified four anatomical subtypes. Successful predictions were restricted to groups with lower levels of vascular damage. The subgroup that could not be predicted were younger, further from conversion, had the highest levels of vascular damage, with milder cognitive impairment at baseline but more rapid deterioration in processing speed and executive function, consistent with a primary vascular dementia. In contrast, the remaining groups had decreasing levels of vascular damage and increasing memory impairment consistent with progressively more Alzheimer's-like pathology. Voxel-wise rates of hippocampal atrophy supported these distinctions, with the vascular group closely resembling the non-dementing cohort, whereas the Alzheimer's like group demonstrated global hippocampal atrophy. This work reveals distinct anatomical endophenotypes in preclinical vascular dementia, forming a spectrum between vascular and Alzheimer's like pathology. The latter group can be identified using baseline MRI, with 73% converting within 5 years. It was not possible to predict the vascular dominant dementia subgroup, however 19% of negative predictions with high levels of vascular disease would ultimately develop dementia. It may be that techniques more sensitive to white matter damage, such as diffusion weighted imaging, may prove more useful for this vascular dominant subgroup in the future. This work provides a way to accurately stratify patients using a baseline MRI scan, and has utility in future clinical trials designed to slow or prevent the onset of dementia in these high-risk cohorts.

YNIMG Journal 2017 Journal Article

Defining thalamic nuclei and topographic connectivity gradients in vivo

• Christian Lambert
• Henry Simon
• Jordan Colman
• Thomas R. Barrick

The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections. Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones. This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo.

YNICL Journal 2017 Journal Article

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change

• Owen A. Williams
• Eva A. Zeestraten
• Philip Benjamin
• Christian Lambert
• Andrew J. Lawrence
• Andrew D. MacKinnon
• Robin G. Morris
• Hugh S. Markus

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43–89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p <0. 001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p =0. 002). Change in DSEG θ was also related to change in all other MRI markers (p <0. 017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i. e. DTIs).

YNICL Journal 2015 Journal Article

Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease

• Christian Lambert
• Janakan Sam Narean
• Philip Benjamin
• Eva Zeestraten
• Thomas R. Barrick
• Hugh S. Markus

Cerebral small vessel disease (SVD) is a heterogeneous group of pathological disorders that affect the small vessels of the brain and are an important cause of cognitive impairment. The ischaemic consequences of this disease can be detected using MRI, and include white matter hyperintensities (WMH), lacunar infarcts and microhaemorrhages. The relationship between SVD disease severity, as defined by WMH volume, in sporadic age-related SVD and cortical thickness has not been well defined. However, regional cortical thickness change would be expected due to associated phenomena such as underlying ischaemic white matter damage, and the observation that widespread cortical thinning is observed in the related genetic condition CADASIL (Righart et al., 2013). Using MRI data, we have developed a semi-automated processing pipeline for the anatomical analysis of individuals with cerebral small vessel disease and applied it cross-sectionally to 121 subjects diagnosed with this condition. Using a novel combined automated white matter lesion segmentation algorithm and lesion repair step, highly accurate warping to a group average template was achieved. The volume of white matter affected by WMH was calculated, and used as a covariate of interest in a voxel-based morphometry and voxel-based cortical thickness analysis. Additionally, Gaussian Process Regression (GPR) was used to assess if the severity of SVD, measured by WMH volume, could be predicted from the morphometry and cortical thickness measures. We found significant (Family Wise Error corrected p < 0.05) volumetric decline with increasing lesion load predominately in the parietal lobes, anterior insula and caudate nuclei bilaterally. Widespread significant cortical thinning was found bilaterally in the dorsolateral prefrontal, parietal and posterio-superior temporal cortices. These represent distinctive patterns of cortical thinning and volumetric reduction compared to ageing effects in the same cohort, which exhibited greater changes in the occipital and sensorimotor cortices. Using GPR, the absolute WMH volume could be significantly estimated from the grey matter density and cortical thickness maps (Pearson's coefficients 0.80 and 0.75 respectively). We demonstrate that SVD severity is associated with regional cortical thinning. Furthermore a quantitative measure of SVD severity (WMH volume) can be predicted from grey matter measures, supporting an association between white and grey matter damage. The pattern of cortical thinning and volumetric decline is distinctive for SVD severity compared to ageing. These results, taken together, suggest that there is a phenotypic pattern of atrophy associated with SVD severity.

YNICL Journal 2014 Journal Article

Strategic lacunes and their relationship to cognitive impairment in cerebral small vessel disease

• Philip Benjamin
• Andrew J. Lawrence
• Christian Lambert
• Bhavini Patel
• Ai Wern Chung
• Andrew D. MacKinnon
• Robin G. Morris
• Thomas R. Barrick

OBJECTIVES: Lacunes are an important disease feature of cerebral small vessel disease (SVD) but their relationship to cognitive impairment is not fully understood. To investigate this we determined (1) the relationship between lacune count and total lacune volume with cognition, (2) the spatial distribution of lacunes and the cognitive impact of lacune location, and (3) the whole brain anatomical covariance associated with these strategically located regions of lacune damage. METHODS: One hundred and twenty one patients with symptomatic lacunar stroke and radiological leukoaraiosis were recruited and multimodal MRI and neuropsychological data acquired. Lacunes were mapped semi-automatically and their volume calculated. Lacune location was automatically determined by projection onto atlases, including an atlas which segments the thalamus based on its connectivity to the cortex. Lacune locations were correlated with neuropsychological results. Voxel based morphometry was used to create anatomical covariance maps for these 'strategic' regions. RESULTS: Lacune number and lacune volume were positively associated with worse executive function (number p < 0.001; volume p < 0.001) and processing speed (number p < 0.001; volume p < 0.001). Thalamic lacunes, particularly those in regions with connectivity to the prefrontal cortex, were associated with impaired processing speed (Bonferroni corrected p = 0.016). Regions of associated anatomical covariance included the medial prefrontal, orbitofrontal, anterior insular cortex and the striatum. CONCLUSION: Lacunes are important predictors of cognitive impairment in SVD. We highlight the importance of spatial distribution, particularly of anteromedial thalamic lacunes which are associated with impaired information processing speed and may mediate cognitive impairment via disruption of connectivity to the prefrontal cortex.

YNIMG Journal 2010 Journal Article

White matter structural decline in normal ageing: A prospective longitudinal study using tract-based spatial statistics

• Thomas R. Barrick
• Rebecca A. Charlton
• Chris A. Clark
• Hugh S. Markus

Normal ageing is accompanied by a progressive decline in cognitive function but the mechanisms for this are not fully understood. Nevertheless, the importance of white matter degeneration is supported by diffusion tensor imaging (DTI) studies, although several important questions remain about the pattern and nature of age-related white matter degeneration. Firstly, there is a lack of longitudinal data determining the rate of change in DTI parameters with age, and whether this can be detected over short time periods. Secondly, it is unclear whether observed changes are uniform across the brain or whether accelerated white matter degeneration is localised to particular brain regions, as would support the frontal-ageing hypothesis. This study uses DTI techniques to quantify structural integrity change to determine whether regional differences are apparent in the rate of degeneration during longitudinal follow-up in a sample of healthy middle aged and older adults aged between 50 and 90years. Longitudinal differences in fractional anisotropy, axial and radial diffusivity are investigated using 1D coronal slice profiles, and 2D column maps in standard space, as well as using 3D tract-based spatial statistics (TBSS) to investigate local age-related structural changes on a voxel-by-voxel basis at baseline and two-year follow-up. Results indicate that DTI can detect age-related change in white matter structure over a relatively short follow-up period and that longitudinal analyses reveal significant changes in white matter integrity throughout the brain with no evidence of accelerated decline in the frontal lobe regions during a 2year period. Common changes across different diffusion characteristics are discussed.

YNIMG Journal 2008 Journal Article

Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection

• I. Nigel C. Lawes
• Thomas R. Barrick
• Vengadasalam Murugam
• Natalia Spierings
• David R. Evans
• Marie Song
• Chris A. Clark

The technique of diffusion tensor tractography is gaining increasing prominence as a non-invasive method for studying the architecture of the white matter pathways in the human brain. Numerous studies have been published that attempt to identify or reconstruct particular pathways of interest. An atlas or map of all the pathways in the white matter would be particularly useful for providing detailed anatomical data that is not available in studies based on conventional MRI data. In this paper we present a method for constructing a white matter atlas to define structures from diffusion tensor tractography by making use of the locations of the anatomical terminations of individual streamlines that pass through white matter. We show how a map of unique seed regions can be used to generate tracts of interest. This approach provides anatomical information that can be rapidly applied to MRI datasets for the clear identification of white matter tracts. We show close correspondence of the tracts generated from the atlas with tracts isolated with classical dissection of post-mortem brain tissue.

YNIMG Journal 2005 Journal Article

Automatic analysis of cerebral asymmetry: an exploratory study of the relationship between brain torque and planum temporale asymmetry

• Thomas R. Barrick
• Clare E. Mackay
• Sylvain Prima
• Frederik Maes
• Dirk Vandermeulen
• Timothy J. Crow
• Neil Roberts

Leftward occipital and rightward frontal lobe asymmetry (brain torque) and leftward planum temporale asymmetry have been consistently reported in postmortem and in vivo neuroimaging studies of the human brain. Here automatic image analysis techniques are applied to quantify global and local asymmetries, and investigate the relationship between brain torque and planum temporale asymmetries on T1-weighted magnetic resonance (MR) images of 30 right-handed young healthy subjects (15 male, 15 female). Previously described automatic cerebral hemisphere extraction and 3D interhemispheric reflection-based methods for studying brain asymmetry are applied with a new technique, LowD (Low Dimension), which enables automatic quantification of brain torque. LowD integrates extracted left and right cerebral hemispheres in columns orthogonal to the midsagittal plane (2D column maps), and subsequently integrates slices along the brain's anterior–posterior axis (1D slice profiles). A torque index defined as the magnitude of occipital and frontal lobe asymmetry is computed allowing exploratory investigation of relationships between this global asymmetry and local asymmetries found in the planum temporale. LowD detected significant torque in the 30 subjects with occipital and frontal components found to be highly correlated (P < 0. 02). Significant leftward planum temporale asymmetry was detected (P < 0. 05), and the torque index correlated with planum temporale asymmetry (P < 0. 001). However, torque and total brain volume were not correlated. Therefore, although components of cerebral asymmetry may be related, their magnitude is not influenced by total hemisphere volume. LowD provides increased sensitivity for detection and quantification of brain torque on an individual subject basis, and future studies will apply these techniques to investigate the relationship between cerebral asymmetry and functional laterality.

YNIMG Journal 2002 Journal Article

Voxel-Based Morphometric Comparison of Hippocampal and Extrahippocampal Abnormalities in Patients with Left and Right Hippocampal Atrophy

• Simon S. Keller
• Clare E. Mackay
• Thomas R. Barrick
• Udo C. Wieshmann
• Matthew A. Howard
• Neil Roberts

We used voxel-based morphometry (VBM), an automatic whole-brain MR image analysis technique, to investigate gray matter abnormalities in patients with temporal lobe epilepsy (TLE), in whom hippocampal atrophy (HA) was demonstrated by application of the Cavalieri method of modern design stereology. VBM results (P < 0. 05, corrected) indicated preferential gray matter concentration (GMC) reduction in anterior hippocampus in patients with left HA and posterior hippocampus in patients with right HA. GMC reduction was also found in right dorsal prefrontal cortex in left and right HA patients. Prefrontal atrophy may be due to epileptiform excitotoxic discharges from the reciprocally connected pathological hippocampus, and may be the underlying biological cause for executive dysfunction in patients with TLE. GMC excess in ipsilateral parahippocampal, cerebellar, and pericallosal regions was common to both left and right HA groups relative to controls, and is hypothesized to reflect diminished gray–white matter demarcation, underlying white matter atrophy, or structural displacement due to cerebrospinal fluid expansion. However, bilateral temporal lobe GMC excess was observed in left HA patients, while ipsilateral temporal lobe GMC excess was observed in right HA patients. This work demonstrates methodological consistency between automated VBM and manual stereological analysis of the hippocampus in group comparisons, indicates widespread extrahippocampal gray matter abnormalities in unilateral HA, and suggests that there may be inherent differences in the effect of TLE on temporal lobe structures depending on the side of HA.

YNIMG Journal 2001 Journal Article

Voxel based morphometry of hippocampal and extra-hippocampal effects of unilateral temporal lobe epilepsy

• Simon Keller
• Clare Mackay
• Jacasta Webb
• Christine Denby
• Thomas R. Barrick
• N. Roberts