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Hugh S. Markus

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9 papers

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YNIMG Journal 2025 Journal Article

LUMEN–A deep learning pipeline for analysis of the 3D morphology of the cerebral lenticulostriate arteries from time-of-flight 7T MRI

Rui Li
Soumick Chatterjee
Yeerfan Jiaerken
Xia Zhou
Chethan Radhakrishna
Philip Benjamin
Stefania Nannoni
Daniel J. Tozer

The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation. We aimed to develop a semi-automated pipeline for quantifying 3D LSA morphology from 7T TOF-MRA in CSVD patients. We used data from a local 7T CSVD study to create a pipeline, LUMEN, comprising two stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a deep learning model, DS6, and compared it against nnU-Net and a Frangi-filter pipeline, MSFDF. For quantification, centrelines of LSAs within basal ganglia were extracted to compute branch counts, length, tortuosity, and maximum curvature. This pipeline was applied to 69 subjects, with results compared to traditional analysis measuring LSA morphology on 2D coronal maximum intensity projection (MIP) images. For vessel segmentation, fine-tuned DS6 achieved the highest test Dice score (0.814±0.029) and sensitivity, whereas nnU-Net achieved the best balanced average Hausdorff distance and precision. Visual inspection confirmed that DS6 was most sensitive in detecting LSAs with weak signals. Across 69 subjects, the pipeline with DS6 identified 23.5 ± 8.5 LSA branches. Branch length inside the basal ganglia was 26.4 ± 3.5 mm, and tortuosity was 1.5 ± 0.1. Extracted LSA metrics from 2D MIP analysis and our 3D analysis showed fair-to-moderate correlations. Outliers highlighted the added value of 3D analysis. This open-source deep-learning-based pipeline offers a validated tool quantifying 3D LSA morphology in CSVD patients from 7T-TOF-MRA for clinical research.

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YNICL Journal 2023 Journal Article

Network structure-function coupling and neurocognition in cerebral small vessel disease

Jonathan Tay
Marco Düring
Esther M.C. van Leijsen
Mayra I. Bergkamp
David G. Norris
Frank-Erik de Leeuw
Hugh S. Markus
Anil M. Tuladhar

BACKGROUND: Cerebral small vessel disease is a leading cause of cognitive decline and vascular dementia. Small vessel disease pathology changes structural brain networks, but its impact on functional networks remains poorly understood. Structural and functional networks are closely coupled in healthy individuals, and decoupling is associated with clinical symptoms in other neurological conditions. We tested the hypothesis that structural-functional network coupling is related to neurocognitive outcomes in 262 small vessel disease patients. METHODS: Participants underwent multimodal magnetic resonance imaging and cognitive assessment in 2011 and 2015. Structural connectivity networks were reconstructed using probabilistic diffusion tractography, while functional connectivity networks were estimated from resting-state functional magnetic resonance imaging. Structural and functional networks were then correlated to calculate a measure of structural-functional network coupling for each participant. RESULTS: Lower whole-brain coupling was associated with reduced processing speed and greater apathy both cross-sectionally and longitudinally. In addition, coupling within the cognitive control network was associated with all cognitive outcomes, suggesting that neurocognitive outcomes in small vessel disease may be related to the functioning of this intrinsic connectivity network. CONCLUSIONS: Our work demonstrates the influence of structural-functional connectivity network decoupling in small vessel disease symptomatology. Cognitive control network function may be investigated in future studies.

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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.

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YNICL Journal 2018 Journal Article

A comparison of functional and tractography based networks in cerebral small vessel disease

Andrew J. Lawrence
Daniel J. Tozer
Emmanuel A. Stamatakis
Hugh S. Markus

Objective: MRI measures of network integrity may be useful disease markers in cerebral small vessel disease (SVD). We compared the sensitivity and reproducibility of MRI derived structural and functional network measures in healthy controls and SVD subjects. Methods: Diffusion tractography and resting state fMRI were used to create connectivity matrices from 26 subjects with symptomatic MRI confirmed lacunar stroke and 19 controls. Matrices were constructed at multiple scales based on a multi-resolution cortical atlas and at multiple thresholds for the matrix density. Network parameters were calculated over the multiple resolutions and thresholds. In addition the reproducibility of structural and functional network parameters was determined in a subset of the subjects (15 SVD, 10 controls) who were scanned twice. Results: Structural networks showed a highly significant loss of network integrity in SVD cases compared to controls, for all network measures. In contrast functional networks showed no difference between SVD and controls. Structural network measures were highly reproducible in both cases and controls, with ICC values consistently over 0.8. In contrast functional network measures showed much poorer reproducibility with ICC values in the range 0.4-0.6 overall, and even lower in SVD cases. Conclusions: Structural networks identify impaired network integrity, and are highly reproducible, in SVD, supporting their use as markers of SVD disease severity. In contrast, functional networks showed low reproducibility, particularly in SVD cases, and were unable to detect differences between SVD cases and controls with this sample size.

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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.

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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).

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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.

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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.

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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.

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