Publications by authors named "Mark Jenkinson"

160 Publications

Intracortical diffusion tensor imaging signature of microstructural changes in frontotemporal lobar degeneration.

Alzheimers Res Ther 2021 Oct 22;13(1):180. Epub 2021 Oct 22.

Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.

Background: Frontotemporal lobar degeneration (FTLD) is a neuropathological construct with multiple clinical presentations, including the behavioural variant of frontotemporal dementia (bvFTD), primary progressive aphasia-both non-fluent variant (nfvPPA) and semantic variant (svPPA)-progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), characterised by the deposition of abnormal tau protein in the brain. A major challenge for treating FTLD is early diagnosis and accurate discrimination among different syndromes. The main goal here was to investigate the cortical architecture of FTLD syndromes using cortical diffusion tensor imaging (DTI) analysis and to test its power to discriminate between different clinical presentations.

Methods: A total of 271 individuals were included in the study: 87 healthy subjects (HS), 31 semantic variant primary progressive aphasia (svPPA), 37 behavioural variant (bvFTD), 30 non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), 47 PSP Richardson's syndrome (PSP-RS) and 39 CBS cases. 3T MRI T1-weighted images and DTI scans were analysed to extract three cortical DTI derived measures (AngleR, PerpPD and ParlPD) and mean diffusivity (MD), as well as standard volumetric measurements. Whole brain and regional data were extracted. Linear discriminant analysis was used to assess the group discrimination capability of volumetric and DTI measures to differentiate the FTLD syndromes. In addition, in order to further investigate differential diagnosis in CBS and PSP-RS, a subgroup of subjects with autopsy confirmation in the training cohort was used to select features which were then tested in the test cohort. Three different challenges were explored: a binary classification (controls vs all patients), a multiclass classification (HS vs bvFTD vs svPPA vs nfvPPA vs CBS vs PSP-RS) and an additional binary classification to differentiate CBS and PSP-RS using features selected in an autopsy confirmed subcohort.

Results: Linear discriminant analysis revealed that PerpPD was the best feature to distinguish between controls and all patients (ACC 86%). PerpPD regional values were able to classify correctly the different FTLD syndromes with an accuracy of 85.6%. The PerpPD and volumetric values selected to differentiate CBS and PSP-RS patients showed a classification accuracy of 85.2%.

Conclusions: (I) PerpPD achieved the highest classification power for differentiating healthy controls and FTLD syndromes and FTLD syndromes among themselves. (II) PerpPD regional values could provide an additional marker to differentiate FTD, PSP-RS and CBS.
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http://dx.doi.org/10.1186/s13195-021-00914-4DOI Listing
October 2021

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.

Neurology 2021 Oct 4. Epub 2021 Oct 4.

MS Center Amsterdam, Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, the Netherlands.

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.
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http://dx.doi.org/10.1212/WNL.0000000000012884DOI Listing
October 2021

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images.

Med Image Anal 2021 Aug 17;74:102215. Epub 2021 Aug 17.

Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Australian Institute for Machine Learning (AIML), School of Computer Science, The University of Adelaide, Adelaide, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia.

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 used datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For the source domain, we considered a dataset consisting of data acquired from 3 different scanners, while the target domain consisted of 2 datasets. We evaluated the domain adaptation techniques on the target domain datasets, and additionally evaluated the performance on the source domain test dataset for the adversarial techniques. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for fine-tuning in the target domain. On comparing the performance of different techniques on the target dataset, domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.
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http://dx.doi.org/10.1016/j.media.2021.102215DOI Listing
August 2021

Significant differences in rates of aseptic loosening between two variations of a popular total knee arthroplasty design.

Int Orthop 2021 Aug 15. Epub 2021 Aug 15.

Department of Trauma and Orthopaedic Surgery, Glasgow Royal Infirmary, 82 Castle Street, Gatehouse Building, Glasgow, G4 0RH, UK.

Purpose: The NexGen Legacy Posterior Stabilised (LPS) prosthesis (Zimmer Biomet, Warsaw, IN, USA) has augmentable and non-augmentable tibial baseplate options. We have noted an anecdotal increase in the number of cases requiring early revision for aseptic loosening since adopting the non-augmentable option. The purpose of this study was to ascertain our rates of aseptic tibial loosening for the two implant types within five years of implantation and to investigate the causes for any difference observed.

Methods: A database search was performed for all patients who underwent primary total knee arthroplasty (TKA) using the NexGen LPS between 2009 and 2015. Kaplan-Meier curves were plotted to assess for differences in revision rates between cohorts. We collected and compared data on gender, age, body mass index, component alignment and cement mantle quality as these were factors thought to affect the likelihood of aseptic loosening.

Results: Two thousand one hundred seventy-two TKAs were included with five year follow-up. There were 759 augmentable knees of which 14 were revised and 1413 non-augmentable knees of which 48 were revised. The overall revision rate at five years was 1.84% in the augmentable cohort and 3.4% in the non-augmentable cohort. The revision rate for aseptic loosening was 0.26% in the augmentable group and 1.42% in the non-augmentable group (p = 0.0241).

Conclusions: We have identified increased rates of aseptic loosening in non-augmentable components. This highlights the effect that minor implant changes can have on outcomes. We recommend that clinicians remain alert to implant changes and publish their own results when important trends are observed.
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http://dx.doi.org/10.1007/s00264-021-05151-wDOI Listing
August 2021

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.

Med Image Anal 2021 10 18;73:102184. Epub 2021 Jul 18.

Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK.

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. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve 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, and we also submitted our method to MWSC 2017 to be evaluated on the unseen test datasets. 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 methods of MWSC 2017.
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http://dx.doi.org/10.1016/j.media.2021.102184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505759PMC
October 2021

Cobalt-induced cardiomyopathy - do circulating cobalt levels matter?

Bone Joint Res 2021 Jun;10(6):340-347

Biomedical Engineering, University of Strathclyde, Glasgow, UK.

Elevated levels of circulating cobalt ions have been linked with a wide range of systemic complications including neurological, endocrine, and cardiovascular symptoms. Case reports of patients with elevated blood cobalt ions have described significant cardiovascular complications including cardiomyopathy. However, correlation between the actual level of circulating cobalt and extent of cardiovascular injury has not previously been performed. This review examines evidence from the literature for a link between elevated blood cobalt levels secondary to metal-on-metal (MoM) hip arthroplasties and cardiomyopathy. Correlation between low, moderate, and high blood cobalt with cardiovascular complications has been considered. Elevated blood cobalt at levels over 250 µg/l have been shown to be a risk factor for developing systemic complications and published case reports document cardiomyopathy, cardiac transplantation, and death in patients with severely elevated blood cobalt ions. However, it is not clear that there is a hard cut-off value and cardiac dysfunction may occur at lower levels. Clinical and laboratory research has found conflicting evidence of cobalt-induced cardiomyopathy in patients with MoM hips. Further work needs to be done to clarify the link between severely elevated blood cobalt ions and cardiomyopathy. Cite this article:  2021;10(6):340-347.
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http://dx.doi.org/10.1302/2046-3758.106.BJR-2020-0414.R2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8242681PMC
June 2021

Contrasting the brain imaging features of MOG-antibody disease, with AQP4-antibody NMOSD and multiple sclerosis.

Mult Scler 2021 May 28:13524585211018987. Epub 2021 May 28.

Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK/Oxford University Hospital NHS Foundation Trust, Oxford, UK.

Background: Identifying magnetic resonance imaging (MRI) markers in myelin-oligodendrocytes-glycoprotein antibody-associated disease (MOGAD), neuromyelitis optica spectrum disorder-aquaporin-4 positive (NMOSD-AQP4) and multiple sclerosis (MS) is essential for establishing objective outcome measures.

Objectives: To quantify imaging patterns of central nervous system (CNS) damage in MOGAD during the remission stage, and to compare it with NMOSD-AQP4 and MS.

Methods: 20 MOGAD, 19 NMOSD-AQP4, 18 MS in remission with brain or spinal cord involvement and 18 healthy controls (HC) were recruited. Volumetrics, lesions and cortical lesions, diffusion-imaging measures, were analysed.

Results: Deep grey matter volumes were lower in MOGAD ( = 0.02) and MS ( = 0.0001), compared to HC and were strongly correlated with current lesion volume (MOGAD  = -0.93,  < 0.001, MS  = -0.65,  = 0.0034). Cortical/juxtacortical lesions were seen in a minority of MOGAD, in a majority of MS and in none of NMOSD-AQP4. Non-lesional tissue fractional anisotropy (FA) was only reduced in MS ( = 0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways.

Conclusion: MOGAD patients are left with grey matter damage, and this may be related to persistent white matter lesions. NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, but reduced non-lesional tissue FA. Observations from our study can be used to identify new markers of damage for future multicentre studies.
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http://dx.doi.org/10.1177/13524585211018987DOI Listing
May 2021

Elucidating distinct clinico-radiologic signatures in the borderland between neuromyelitis optica and multiple sclerosis.

J Neurol 2021 May 27. Epub 2021 May 27.

Department of Clinical Neurology, Nuffield Department of Clinical Neuroscienes, University of Oxford, Oxford, UK.

Background: Separating antibody-negative neuromyelitis optica spectrum disorders (NMOSD) from multiple sclerosis (MS) in borderline cases is extremely challenging due to lack of biomarkers. Elucidating different pathologies within the likely heterogenous antibody-negative NMOSD/MS overlap syndrome is, therefore, a major unmet need which would help avoid disability from inappropriate treatment.

Objective: In this study we aimed to identify distinct subgroups within the antibody-negative NMOSD/MS overlap syndrome.

Methods: Twenty-five relapsing antibody-negative patients with NMOSD features underwent a prospective brain and spinal cord MRI. Subgroups were identified by an unsupervised algorithm based on pre-selected NMOSD/MS discriminators.

Results: Four subgroups were identified. Patients from Group 1 termed "MS-like" (n = 6) often had central vein sign and cortical lesions (83% and 67%, respectively). All patients from Group 2 ("spinal MS-like", 8) had short-segment myelitis and no MS-like brain lesions. Group 3 ("classic NMO-like", 6) had high percentage of bilateral optic neuritis and longitudinally extensive transverse myelitis (LETM, 80% and 60%, respectively) and normal brain appearance (100%). Group 4 ("NMO-like with brain involvement", 5) typically had a history of NMOSD-like brain lesions and LETM. When compared with other groups, Group 4 had significantly decreased fractional anisotropy in non-lesioned tracts (0.46 vs. 0.49, p = 0.003) and decreased thalamus volume (0.84 vs. 0.98, p = 0.04).

Conclusions: NMOSD/MS cohort contains distinct subgroups likely corresponding to different pathologies and requiring tailored treatment. We propose that non-conventional MRI might help optimise diagnosis in these challenging patients.
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http://dx.doi.org/10.1007/s00415-021-10619-1DOI Listing
May 2021

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Neuroimage 2021 08 20;237:118189. Epub 2021 May 20.

Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK. Electronic address:

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 study sample differences contributing to differences in WMH variations across studies. 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 sample 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.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285593PMC
August 2021

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.

Diagnostics (Basel) 2021 Mar 19;11(3). Epub 2021 Mar 19.

Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia.

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 diverse 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.
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http://dx.doi.org/10.3390/diagnostics11030551DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003459PMC
March 2021

White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

Neuroimage Clin 2021 7;30:102616. Epub 2021 Mar 7.

Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK.

White matter hyperintensities (WMHs) on T-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T-weighted images (Tw) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and Tw-hypointense/nonTw-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular Tw-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in Tw reveals specific associations with cognitive performance.
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http://dx.doi.org/10.1016/j.nicl.2021.102616DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995650PMC
July 2021

Fronto-parietal involvement in chronic stroke motor performance when corticospinal tract integrity is compromised.

Neuroimage Clin 2021 18;29:102558. Epub 2021 Jan 18.

University of South Australia, IIMPACT in Health, Adelaide, Australia.

Background: Preserved integrity of the corticospinal tract (CST) is a marker of good upper-limb behavior and recovery following stroke. However, there is less understanding of neural mechanisms that might help facilitate upper-limb motor recovery in stroke survivors with extensive CST damage.

Objective: The purpose of this study was to investigate resting state functional connectivity in chronic stroke survivors with different levels of CST damage and to explore neural correlates of greater upper-limb motor performance in stroke survivors with compromised ipsilesional CST integrity.

Methods: Thirty chronic stroke survivors (24 males, aged 64.7 ± 10.8 years) participated in this study. Three experimental sessions were conducted to: 1) obtain anatomical (T1, T2) structural (diffusion) and functional (resting state) MRI sequences, 2) determine CST integrity with transcranial magnetic stimulation (TMS) and conduct assessments of upper-limb behavior, and 3) reconfirm CST integrity status. Participants were divided into groups according to the extent of CST damage. Those in the extensive CST damage group did not show TMS evoked responses and had significantly lower ipsilesional fractional anisotropy.

Results: Of the 30 chronic stroke survivors, 12 were categorized as having extensive CST damage. Stroke survivors with extensive CST damage had weaker functional connectivity in the ipsilesional sensorimotor network and greater functional connectivity in the ipsilesional fronto-parietal network compared to those with preserved CST integrity. For participants with extensive CST damage, improved motor performance was associated with greater functional connectivity of the ipsilesional fronto-parietal network and higher fractional anisotropy of the ipsilesional rostral superior longitudinal fasciculus.

Conclusions: Stroke survivors with extensive CST damage have greater resting state functional connectivity of an ipsilesional fronto-parietal network that appears to be a behaviorally relevant neural mechanism that improves upper-limb motor performance.
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http://dx.doi.org/10.1016/j.nicl.2021.102558DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841401PMC
June 2021

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge.

EClinicalMedicine 2021 Jan 7;31:100683. Epub 2021 Jan 7.

Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom.

Background: The medium-term effects of Coronavirus disease (COVID-19) on organ health, exercise capacity, cognition, quality of life and mental health are poorly understood.

Methods: Fifty-eight COVID-19 patients post-hospital discharge and 30 age, sex, body mass index comorbidity-matched controls were enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments.

Findings: At 2-3 months from disease-onset, 64% of patients experienced breathlessness and 55% reported fatigue. On MRI, abnormalities were seen in lungs (60%), heart (26%), liver (10%) and kidneys (29%). Patients exhibited changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domains. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance were significantly reduced. The extent of extra-pulmonary MRI abnormalities and exercise intolerance correlated with serum markers of inflammation and acute illness severity. Patients had a higher burden of self-reported symptoms of depression and experienced significant impairment in all domains of quality of life compared to controls (<0.0001 to 0.044).

Interpretation: A significant proportion of patients discharged from hospital reported symptoms of breathlessness, fatigue, depression and had limited exercise capacity. Persistent lung and extra-pulmonary organ MRI findings are common in patients and linked to inflammation and severity of acute illness.

Funding: NIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.
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http://dx.doi.org/10.1016/j.eclinm.2020.100683DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808914PMC
January 2021

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Neuroimage 2021 03 30;228:117689. Epub 2020 Dec 30.

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.

Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase in variance due to nonbiological factors such as the differences in acquisition protocols and hardware, which can mask signals of interest. We propose a deep learning based training scheme, inspired by domain adaptation techniques, which uses an iterative update approach to aim to create scanner-invariant features while simultaneously maintaining performance on the main task of interest, thus reducing the influence of scanner on network predictions. We demonstrate the framework for regression, classification and segmentation tasks with two different network architectures. We show that not only can the framework harmonise many-site datasets but it can also adapt to many data scenarios, including biased datasets and limited training labels. Finally, we show that the framework can be extended for the removal of other known confounds in addition to scanner. The overall framework is therefore flexible and should be applicable to a wide range of neuroimaging studies.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903160PMC
March 2021

Detection of Alzheimer's Disease using cortical diffusion tensor imaging.

Hum Brain Mapp 2021 03 11;42(4):967-977. Epub 2020 Nov 11.

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

The aim of this research was to test a novel in-vivo brain MRI analysis method that could be used in clinical cohorts to investigate cortical architecture changes in patients with Alzheimer's Disease (AD). Three cohorts of patients with probable AD and healthy volunteers were used to assess the results of the method. The first group was used as the "Discovery" cohort, the second as the "Test" cohort and the last "ATN" (Amyloid, Tau, Neurodegeneration) cohort was used to test the method in an ADNI 3 cohort, comparing to amyloid and Tau PET. The method can detect altered quality of cortical grey matter in AD patients, providing an additional tool to assess AD, distinguishing between these and healthy controls with an accuracy range between good and excellent. These new measurements could be used within the "ATN" framework as an index of cortical microstructure quality and a marker of Neurodegeneration. Further development may aid diagnosis, patient selection, and quantification of the "Neurodegeneration" component in response to therapies in clinical trials.
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http://dx.doi.org/10.1002/hbm.25271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856641PMC
March 2021

Artificial intelligence for clinical decision support in neurology.

Brain Commun 2020 9;2(2):fcaa096. Epub 2020 Jul 9.

The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Heidelberg, VIC 3084, Australia.

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.
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http://dx.doi.org/10.1093/braincomms/fcaa096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585692PMC
July 2020

Learning patterns of the ageing brain in MRI using deep convolutional networks.

Neuroimage 2021 01 24;224:117401. Epub 2020 Sep 24.

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom.

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors Δ=Age-Age correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between Δ and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the Δ from models such as this network were predictive of any health outcomes.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117401DOI Listing
January 2021

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

Neuroimage 2020 12 29;223:117303. Epub 2020 Aug 29.

Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20-45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762845PMC
December 2020

Cortical diffusivity investigation in posterior cortical atrophy and typical Alzheimer's disease.

J Neurol 2021 Jan 8;268(1):227-239. Epub 2020 Aug 8.

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Objectives: To investigate the global cortical and regional quantitative features of cortical neural architecture in the brains of patients with posterior cortical atrophy (PCA) and typical Alzheimer's disease (tAD) compared with elderly healthy controls (HC).

Methods: A novel diffusion MRI method, that has been shown to correlate with minicolumnar organization changes in the cerebral cortex, was used as a surrogate of neuropathological changes in dementia. A cohort of 15 PCA patients, 23 tAD and 22 healthy elderly controls (HC) were enrolled to investigate the changes in cortical diffusivity among groups. For each subject, 3 T MRI T1-weighted images and diffusion tensor imaging (DTI) scans were analysed to extract novel cortical DTI derived measures (AngleR, PerpPD and ParlPD). Receiver operating characteristics (ROC) curve analysis and the area under the curve (AUC) were used to assess the group discrimination capability of the method.

Results: The results showed that the global cortical DTI derived measures were able to detect differences, in both PCA and tAD patients compared to healthy controls. The AngleR was the best measure to discriminate HC from tAD (AUC = 0.922), while PerpPD was the best measure to discriminate HC from PCA (AUC = 0.961). Finally, the best global measure to differentiate the two patient groups was ParlPD (AUC = 0.771). The comparison between PCA and tAD patients revealed a different pattern of damage within the AD spectrum and the regional comparisons identified significant differences in key regions including parietal and temporal lobe cortical areas. The best AUCs were shown by PerpPD right lingual cortex (AUC = 0.856), PerpPD right superior parietal cortex (AUC = 0.842) and ParlPD right lateral occipital cortex (AUC = 0.826).

Conclusions: Diagnostic group differences were found, suggesting that the new cortical DTI analysis method may be useful to investigate cortical changes in dementia, providing better characterization of neurodegeneration, and potentially aiding differential diagnosis and prognostic accuracy.
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http://dx.doi.org/10.1007/s00415-020-10109-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815619PMC
January 2021

Evidence for a novel subcortical mechanism for posterior cingulate cortex atrophy in HIV peripheral neuropathy.

J Neurovirol 2020 08 10;26(4):530-543. Epub 2020 Jun 10.

Departments of Neurosciences and Psychiatry, University of California San Diego, San Diego, CA, USA.

We previously reported that neuropathic pain was associated with smaller posterior cingulate cortical (PCC) volumes, suggesting that a smaller/dysfunctional PCC may contribute to development of pain via impaired mind wandering. A gap in our previous report was lack of evidence for a mechanism for the genesis of PCC atrophy in HIV peripheral neuropathy. Here we investigate if volumetric differences in the subcortex for those with neuropathic paresthesia may contribute to smaller PCC volumes, potentially through deafferentation of ascending white matter tracts resulting from peripheral nerve damage in HIV neuropathy. Since neuropathic pain and paresthesia are highly correlated, statistical decomposition was used to separate pain and paresthesia symptoms to determine which regions of brain atrophy are associated with both pain and paresthesia and which are associated separately with pain or paresthesia. HIV+ individuals (N = 233) with and without paresthesia in a multisite study underwent structural brain magnetic resonance imaging. Voxel-based morphometry and a segmentation/registration tool were used to investigate regional brain volume changes associated with paresthesia. Analysis of decomposed variables found that smaller midbrain and thalamus volumes were associated with paresthesia rather than pain. However, atrophy in the PCC was related to both pain and paresthesia. Peak thalamic atrophy (p = 0.004; MNI x = - 14, y = - 24, z = - 2) for more severe paresthesia was in a region with reciprocal connections with the PCC. This provides initial evidence that smaller PCC volumes in HIV peripheral neuropathy are related to ascending white matter deafferentation caused by small fiber damage observed in HIV peripheral neuropathy.
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http://dx.doi.org/10.1007/s13365-020-00850-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442648PMC
August 2020

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

Stroke 2020 07 10;51(7):2111-2121. Epub 2020 Jun 10.

Department of Psychiatry (C.F.-N.), University of California, San Diego, La Jolla, CA.

Background And Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.

Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.

Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (), 10q23.1 (), and 10q24.33 ( In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 () and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: (2q32.1), (3q27.1), (5q27.1), and (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.

Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
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http://dx.doi.org/10.1161/STROKEAHA.119.027544DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365038PMC
July 2020

Cross-species cortical alignment identifies different types of anatomical reorganization in the primate temporal lobe.

Elife 2020 03 23;9. Epub 2020 Mar 23.

Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.

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.
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http://dx.doi.org/10.7554/eLife.53232DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180052PMC
March 2020

Quantitative Susceptibility Mapping for Characterization of Intraplaque Hemorrhage and Calcification in Carotid Atherosclerotic Disease.

J Magn Reson Imaging 2020 08 10;52(2):534-541. Epub 2020 Feb 10.

Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.

Background: Carotid artery intraplaque hemorrhage (IPH), an unstable component of atherosclerosis, is associated with an increased risk of stroke.

Purpose: To investigate quantitative susceptibility mapping (QSM) as a tool for the evaluation of IPH and calcification in vivo.

Study Type: Prospective.

Population: Ten healthy volunteers and 15 patients.

Field Strength/sequence: 3.0T Susceptibility-weighted imaging (SWI), magnetization-prepared rapid acquisition with gradient echo (MP-RAGE), T -weighted sampling perfection with application of optimized contrasts using different flip angle evolution (T -SPACE), T -weighted turbo spin-echo (T WI), and time-of-flight (TOF) sequences.

Assessment: The vessel wall area of the carotid artery was measured with QSM and compared with T -SPACE on healthy volunteers. Four radiologists, blinded to clinical history and patient identity, determined the presence and area of IPH on MP-RAGE and QSM, as well as the area of calcification on T -SPACE and QSM.

Statistical Tests: Bland-Altman analysis, Pearson correlation coefficients, linear regression analyses were performed to evaluate the concordance of area measurements. Cohen's kappa (κ) was analyzed to determine the agreement between IPH detections. The paired t-test was used to compare the group differences.

Results: In 423 matched slices, 20.1% (85/423) and 19.6% (83/423) were detected to have IPH on MP-RAGE and QSM, respectively. IPH detection by QSM and MP-RAGE showed good agreement (κ = 0.822, P < 0.001) between the two methods. There was no significant difference in IPH area measurements between QSM and MP-RAGE (7.28 mm  ± 6.41 vs. 7.16 mm  ± 5.99, P = 0.575). There was no significant difference in calcification area measurement between QSM and T -SPACE (3.51 mm  ± 1.78 vs. 3.41 mm  ± 2.02, P = 0.783).

Data Conclusion: QSM is a novel imaging tool for the identification of IPH in patients with carotid atherosclerosis and enables differentiation of IPH and calcification. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:534-541.
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http://dx.doi.org/10.1002/jmri.27064DOI Listing
August 2020

One-year changes in brain microstructure differentiate preclinical Huntington's disease stages.

Neuroimage Clin 2020 3;25:102099. Epub 2019 Dec 3.

Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK. Electronic address:

Objective: To determine whether brain imaging markers of tissue microstructure can detect the effect of disease progression across the preclinical stages of Huntington's disease.

Methods: Longitudinal microstructural changes in diffusion imaging metrics (mean diffusivity and fractional anisotropy) were investigated in participants with presymptomatic Huntington's disease (N = 35) stratified into three preclinical subgroups according to their estimated time until onset of symptoms, compared with age- and gender-matched healthy controls (N = 19) over a 1y period.

Results: Significant differences were found over the four groups in change of mean diffusivity in the posterior basal ganglia and the splenium of the corpus callosum. This overall effect was driven by significant differences between the group far-from-onset (FAR) of symptoms and the groups midway- (MID) and near-the-onset (NEAR) of symptoms. In particular, an initial decrease of mean diffusivity in the FAR group was followed by a subsequent increase in groups closer to onset of symptoms. The seemingly counter-intuitive decrease of mean diffusivity in the group furthest from onset of symptoms might be an early indicator of neuroinflammatory process preceding the neurodegenerative phase. In contrast, the only clinical measure that was able to capture a difference in 1y changes between the preclinical stages was the UHDRS confidence in motor score.

Conclusions: With sensitivity to longitudinal changes in brain microstructure within and between preclinical stages, and potential differential response to distinct pathophysiological mechanisms, diffusion imaging is a promising state marker for monitoring treatment response and identifying the optimal therapeutic window of opportunity in preclinical Huntington's disease.
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http://dx.doi.org/10.1016/j.nicl.2019.102099DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931230PMC
January 2021

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.

Neuroimage 2019 11 31;202:116056. Epub 2019 Jul 31.

Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

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 neurodegenerative diseases affect lesion load and spatial distribution. At the individual 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, a vascular cohort and the cohorts available publicly as a part of a segmentation challenge. We observed that including population-level parametric lesion probabilities with respect to age and using alternative machine learning techniques provided negligible improvement. However, LOCATE provided a substantial improvement in the lesion segmentation performance, when compared to the global thresholding. It allowed to detect more deep lesions and provided better segmentation of periventricular lesion boundaries, despite the differences in the lesion spatial distribution and load across datasets. We further validated LOCATE on a cohort of CADASIL (Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease, and healthy controls, showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996003PMC
November 2019

Relating diffusion tensor imaging measurements to microstructural quantities in the cerebral cortex in multiple sclerosis.

Hum Brain Mapp 2019 10 29;40(15):4417-4431. Epub 2019 Jul 29.

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.

To investigate whether the observed anisotropic diffusion in cerebral cortex may reflect its columnar cytoarchitecture and myeloarchitecture, as a potential biomarker for disease-related changes, we compared postmortem diffusion magnetic resonance imaging scans of nine multiple sclerosis brains with histology measures from the same regions. Histology measurements assessed the cortical minicolumnar structure based on cell bodies and associated axon bundles in dorsolateral prefrontal cortex (Area 9), Heschl's gyrus (Area 41), and primary visual cortex (V1). Diffusivity measures included mean diffusivity, fractional anisotropy of the cortex, and three specific measures that may relate to the radial minicolumn structure: the angle of the principal diffusion direction in the cortex, the component that was perpendicular to the radial direction, and the component that was parallel to the radial direction. The cellular minicolumn microcircuit features were correlated with diffusion angle in Areas 9 and 41, and the axon bundle features were correlated with angle in Area 9 and to the parallel component in V1 cortex. This may reflect the effect of minicolumn microcircuit organisation on diffusion in the cortex, due to the number of coherently arranged membranes and myelinated structures. Several of the cortical diffusion measures showed group differences between MS brains and control brains. Differences between brain regions were also found in histology and diffusivity measurements consistent with established regional variation in cytoarchitecture and myeloarchitecture. Therefore, these novel measures may provide a surrogate of cortical organisation as a potential biomarker, which is particularly relevant for detecting regional changes in neurological disorders.
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http://dx.doi.org/10.1002/hbm.24711DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772025PMC
October 2019

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

Neuroimage Clin 2019 19;23:101904. Epub 2019 Jun 19.

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.
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http://dx.doi.org/10.1016/j.nicl.2019.101904DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603440PMC
August 2020

Assessing Reliability of Myocardial Blood Flow After Motion Correction With Dynamic PET Using a Bayesian Framework.

IEEE Trans Med Imaging 2019 05 19;38(5):1216-1226. Epub 2018 Nov 19.

The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series. Motion correction techniques are challenging to evaluate quantitatively and the impact on MBF reliability is not fully understood. Most metrics, such as signal-to-noise ratio (SNR), are characteristic of static images, and are not specific to motion correction in dynamic data. This study presents a new approach of estimating motion correction quality in dynamic cardiac PET imaging. It relies on calculating a MBF surrogate, K , along with the uncertainty on the parameter. This technique exploits a Bayesian framework, representing the kinetic parameters as a probability distribution, from which the uncertainty measures can be extracted. If the uncertainty extracted is high, the parameter studied is considered to have high variability - or low confidence - and vice versa. The robustness of the framework is evaluated on simulated time activity curves to ensure that the uncertainties are consistently estimated at the multiple levels of noise. Our framework is applied on 40 patient datasets, divided in 4 motion magnitude categories. Experienced observers manually realigned clinical datasets with 3D translations to correct for motion. K uncertainties were compared before and after correction. A reduction of uncertainty after motion correction of up to 60% demonstrates the benefit of motion correction in dynamic PET and as well as provides evidence of the usefulness of the new method presented.
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http://dx.doi.org/10.1109/TMI.2018.2881992DOI Listing
May 2019

Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants.

Neuroimage 2019 02 8;186:286-300. Epub 2018 Nov 8.

Department of Paediatrics, University of Oxford, Oxford, United Kingdom. Electronic address:

The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.
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http://dx.doi.org/10.1016/j.neuroimage.2018.11.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347570PMC
February 2019

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.

Neuroimage 2019 01 22;185:434-445. Epub 2018 Oct 22.

Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

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, divided 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, 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.
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http://dx.doi.org/10.1016/j.neuroimage.2018.10.042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299259PMC
January 2019
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