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Kampaite A, Gustafsson R, York EN, Foley P, MacDougall NJJ, Bastin ME, Chandran S, Waldman AD, Meijboom R. Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review. PLoS One 2024; 19:e0299634. [PMID: 38551913 PMCID: PMC10980255 DOI: 10.1371/journal.pone.0299634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 02/13/2024] [Indexed: 04/01/2024] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterised by neuroinflammation and neurodegeneration. Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting MS (pwRRMS). An increased understanding of brain changes and mechanisms underlying fatigue and depression in RRMS could lead to more effective interventions and enhancement of quality of life. To elucidate the relationship between depression and fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: studied participants with RRMS (n ≥ 20; ≥ 18 years old) and differentiated between MS subtypes; published between 2001-01-01 and 2023-01-18; used fatigue and depression assessments validated for MS; included brain structural, functional magnetic resonance imaging (fMRI) or diffusion MRI (dMRI). Sixty studies met the criteria: 18 dMRI (15 fatigue, 5 depression) and 22 fMRI (20 fatigue, 5 depression) studies. The literature was heterogeneous; half of studies reported no correlation between brain connectivity measures and fatigue or depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression. Fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, ventral tegmental area, striatum, default mode and attention networks, and supramarginal, precentral, and postcentral gyri. Qualitative analysis suggests structural and functional connectivity changes, possibly due to axonal and/or myelin loss, in the cortico-thalamic-basal-ganglial and cortico-limbic network may underlie fatigue and depression in pwRRMS, respectively, but the overall results were inconclusive, possibly explained by heterogeneity and limited number of studies. This highlights the need for further studies including advanced MRI to detect more subtle brain changes in association with depression and fatigue. Future studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrates underlying these symptoms in pwRRMS.
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Affiliation(s)
- Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Rebecka Gustafsson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter Foley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Niall J. J. MacDougall
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, Edinburgh Imaging Facility, University of Edinburgh, Edinburgh, United Kingdom
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Harper JG, York EN, Meijboom R, Kampaite A, Thrippleton MJ, Kearns PKA, Valdés Hernández MDC, Chandran S, Waldman AD. Quantitative T 1 brain mapping in early relapsing-remitting multiple sclerosis: longitudinal changes, lesion heterogeneity and disability. Eur Radiol 2023:10.1007/s00330-023-10351-6. [PMID: 37943312 DOI: 10.1007/s00330-023-10351-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/20/2023] [Accepted: 08/29/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES To quantify brain microstructural changes in recently diagnosed relapsing-remitting multiple sclerosis (RRMS) using longitudinal T1 measures, and determine their associations with clinical disability. METHODS Seventy-nine people with recently diagnosed (< 6 months) RRMS were recruited from a single-centre cohort sub-study, and underwent baseline and 1-year brain MRI, including variable flip angle T1 mapping. Median T1 was measured in white matter lesions (WML), normal-appearing white matter (NAWM), cortical/deep grey matter (GM), thalami, basal ganglia and medial temporal regions. Prolonged T1 (≥ 2.00 s) and supramedian T1 (relative to cohort WML values) WML voxel counts were also measured. Longitudinal change was assessed with paired t-tests and compared with Bland-Altman limits of agreement from healthy control test-retest data. Regression analyses determined relationships with Expanded Disability Status Scale (EDSS) score and dichotomised EDSS outcomes (worsening or stable/improving). RESULTS Sixty-two people with RRMS (mean age 37.2 ± 10.9 [standard deviation], 48 female) and 11 healthy controls (age 44 ± 11, 7 female) contributed data. Prolonged and supramedian T1 WML components increased longitudinally (176 and 463 voxels, respectively; p < .001), and were associated with EDSS score at baseline (p < .05) and follow-up (supramedian: p < .01; prolonged: p < .05). No cohort-wide median T1 changes were found; however, increasing T1 in WML, NAWM, cortical/deep GM, basal ganglia and thalami was positively associated with EDSS worsening (p < .05). CONCLUSION T1 is sensitive to brain microstructure changes in early RRMS. Prolonged WML T1 components and subtle changes in NAWM and GM structures are associated with disability. CLINICAL RELEVANCE STATEMENT MRI T1 brain mapping quantifies disability-associated white matter lesion heterogeneity and subtle microstructural damage in normal-appearing brain parenchyma in recently diagnosed RRMS, and shows promise for early objective disease characterisation and stratification. KEY POINTS • Quantitative T1 mapping detects brain microstructural damage and lesion heterogeneity in recently diagnosed relapsing-remitting multiple sclerosis. • T1 increases in lesions and normal-appearing parenchyma, indicating microstructural damage, are associated with worsening disability. • Brain T1 measures are objective markers of disability-relevant pathology in early multiple sclerosis.
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Affiliation(s)
- James G Harper
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
| | - Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK.
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Patrick K A Kearns
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK.
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
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Meijboom R, York EN, Kampaite A, Harris MA, White N, Valdés Hernández MDC, Thrippleton MJ, MacDougall NJJ, Connick P, Hunt DPJ, Chandran S, Waldman AD. Patterns of brain atrophy in recently-diagnosed relapsing-remitting multiple sclerosis. PLoS One 2023; 18:e0288967. [PMID: 37506096 PMCID: PMC10381059 DOI: 10.1371/journal.pone.0288967] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in recently-diagnosed RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N = 354) underwent 3T structural MRI <6 months after diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish regional patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the brain. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, cerebellar GM, brainstem, amygdala, basal ganglia, hippocampus, accumbens, thalamus and ventral diencephalon. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 26 regions and 16/26; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread GM and NAWM atrophy was observed in this large recently-diagnosed RRMS cohort, particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions; indicative of neurodegeneration across tissue types, and in accord with limited previous studies in early disease. Volumetric and VBM results emphasise different features of longitudinal lobar and loco-regional change, however identify consistent atrophy patterns across individuals. Atrophy measures targeted to specific brain regions may provide improved markers of neurodegeneration, and potential future imaging stratifiers and endpoints for clinical decision making and therapeutic trials.
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Affiliation(s)
- Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mathew A Harris
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - N J J MacDougall
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - David P J Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
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Chang YT, Kearns PKA, Carson A, Gillespie DC, Meijboom R, Kampaite A, Valdés Hernández MDC, Weaver C, Stenson A, MacDougall N, O'Riordan J, Macleod MA, Carod-Artal FJ, Connick P, Waldman AD, Chandran S, Foley P. Network analysis characterizes key associations between subjective fatigue and specific depressive symptoms in early relapsing-remitting multiple sclerosis. Mult Scler Relat Disord 2023; 69:104429. [PMID: 36493562 DOI: 10.1016/j.msard.2022.104429] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/26/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Fatigue is common and disabling in multiple sclerosis (MS), yet its mechanisms are poorly understood. In particular, overlap in measures of fatigue and depression complicates interpretation. We applied a multivariate network approach to quantify relationships between fatigue and other variables in early MS. METHODS Data were collected from patients with newly diagnosed immunotherapy-naïve relapsing-remitting MS at baseline and month 12 follow-up in FutureMS, a Scottish nationally representative cohort. Subjective fatigue was assessed by Fatigue Severity Scale. Detailed phenotyping included measures assessing each of physical disability, affective disorders, cognitive performance, sleep quality, and structural brain imaging. Network analysis was conducted to estimate partial correlations between variables. Baseline networks were compared between those with persistent and remitted fatigue at one-year follow up. RESULTS Data from 322 participants at baseline, and 323 at month 12, were included. At baseline, 154 patients (47.8%) reported clinically significant fatigue. In the network analysis, fatigue severity showed strongest connections with depression, followed by Expanded Disability Status Scale. Conversely, fatigue severity was not linked to objective cognitive performance or brain imaging variables. Even after controlling for measurement of "tiredness" in our measure of depression, four specific depressive symptoms remained linked to fatigue. Results were consistent at baseline and month 12. Overall network strength was not significantly different between groups with persistent and remitted fatigue (4.89 vs 2.90, p = 0.11). CONCLUSIONS Our findings support robust links between subjective fatigue and depression in early relapsing-remitting MS. Shared mechanisms between specific depressive symptoms and fatigue could be key targets of treatment and research in MS-related fatigue.
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Affiliation(s)
- Yuan-Ting Chang
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Patrick K A Kearns
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - David C Gillespie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Christine Weaver
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Amy Stenson
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | | | | | - Peter Connick
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Peter Foley
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
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York EN, Meijboom R, Thrippleton MJ, Bastin ME, Kampaite A, White N, Chandran S, Waldman AD. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. Neuroimage Clin 2022; 36:103228. [PMID: 36265199 PMCID: PMC9668599 DOI: 10.1016/j.nicl.2022.103228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Quantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. METHODS Seventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained. 3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated. Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests (α = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. RESULTS In NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and -0.057 respectively, all FDR-corrected p < 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p < 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. DISCUSSION G-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. CONCLUSION MTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom; UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom.
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Barnes A, Ballerini L, Valdés Hernández MDC, Chappell FM, Muñoz Maniega S, Meijboom R, Backhouse EV, Stringer MS, Duarte Coello R, Brown R, Bastin ME, Cox SR, Deary IJ, Wardlaw JM. Topological relationships between perivascular spaces and progression of white matter hyperintensities: A pilot study in a sample of the Lothian Birth Cohort 1936. Front Neurol 2022; 13:889884. [PMID: 36090857 PMCID: PMC9449650 DOI: 10.3389/fneur.2022.889884] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves-1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves-1-2: 73%; 2-3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.
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Affiliation(s)
- Abbie Barnes
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Lucia Ballerini
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria del C. Valdés Hernández
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Francesca M. Chappell
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Susana Muñoz Maniega
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Ellen V. Backhouse
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael S. Stringer
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Roberto Duarte Coello
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rosalind Brown
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R. Cox
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Ian J. Deary
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M. Wardlaw
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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Kearns PKA, Martin SJ, Chang J, Meijboom R, York EN, Chen Y, Weaver C, Stenson A, Hafezi K, Thomson S, Freyer E, Murphy L, Harroud A, Foley P, Hunt D, McLeod M, O'Riordan J, Carod-Artal FJ, MacDougall NJJ, Baranzini SE, Waldman AD, Connick P, Chandran S. FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis. BMJ Open 2022; 12:e058506. [PMID: 35768080 PMCID: PMC9244691 DOI: 10.1136/bmjopen-2021-058506] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
PURPOSE Multiple sclerosis (MS) is an immune-mediated, neuroinflammatory disease of the central nervous system and in industrialised countries is the most common cause of progressive neurological disability in working age persons. While treatable, there is substantial interindividual heterogeneity in disease activity and response to treatment. Currently, the ability to predict at diagnosis who will have a benign, intermediate or aggressive disease course is very limited. There is, therefore, a need for integrated predictive tools to inform individualised treatment decision making. PARTICIPANTS Established with the aim of addressing this need for individualised predictive tools, FutureMS is a nationally representative, prospective observational cohort study of 440 adults with a new diagnosis of relapsing-remitting MS living in Scotland at the time of diagnosis between May 2016 and March 2019. FINDINGS TO DATE The study aims to explore the pathobiology and determinants of disease heterogeneity in MS and combines detailed clinical phenotyping with imaging, genetic and biomarker metrics of disease activity and progression. Recruitment, baseline assessment and follow-up at year 1 is complete. Here, we describe the cohort design and present a profile of the participants at baseline and 1 year of follow-up. FUTURE PLANS A third follow-up wave for the cohort has recently begun at 5 years after first visit and a further wave of follow-up is funded for year 10. Longer-term follow-up is anticipated thereafter.
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Affiliation(s)
- Patrick K A Kearns
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Chromatin Lab, Genome Regulation Section, The University of Edinburgh MRC Human Genetics Unit, Edinburgh, UK
- Department of Clinical Neurosciences, Royal Infirmary of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Department of Neurology, Institute of Clinical Neurosciences, NHS Greater Glasgow and Clyde, Glasgow, UK
- Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Sarah J Martin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Department of Neurology, Institute of Clinical Neurosciences, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Jessie Chang
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Elizabeth N York
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Yingdi Chen
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
| | - Christine Weaver
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Amy Stenson
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | | | - Stacey Thomson
- Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Elizabeth Freyer
- Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Lee Murphy
- Wellcome Trust Clinical Research Facility, Edinburgh, UK
| | - Adil Harroud
- Department of Neurology, Weill Institute of Clinical Neuroscience, San Francisco, California, USA
| | - Peter Foley
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - David Hunt
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Margaret McLeod
- Department of Neurology, Aberdeen Royal Infirmary, Aberdeen, UK
| | - Jonathon O'Riordan
- Tayside Centre for Clinical Neurosciences, University of Dundee Division of Neuroscience, Dundee, UK
| | | | - Niall J J MacDougall
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Department of Neurology, Wishaw General Hospital, Wishaw, UK
| | - Sergio E Baranzini
- Department of Neurology, Weill Institute of Clinical Neuroscience, San Francisco, California, USA
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
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8
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Dieckhaus H, Meijboom R, Okar S, Wu T, Parvathaneni P, Mina Y, Chandran S, Waldman AD, Reich DS, Nair G. Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation. Top Magn Reson Imaging 2022; 31:31-39. [PMID: 35767314 PMCID: PMC9258518 DOI: 10.1097/rmr.0000000000000296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data. MATERIALS AND METHODS C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison. RESULTS C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class. CONCLUSIONS These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.
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Affiliation(s)
- Henry Dieckhaus
- qMRI Core Facility, NINDS, National Institutes of Health, Bethesda, MD, USA
| | | | - Serhat Okar
- Translational Neuroradiology Section, NINDS, National Institutes of Health, Bethesda, MD, USA
| | - Tianxia Wu
- Clinical Trials Unit, NINDS, National Institutes of Health, Bethesda, MD, USA
| | - Prasanna Parvathaneni
- Translational Neuroradiology Section, NINDS, National Institutes of Health, Bethesda, MD, USA
| | - Yair Mina
- Viral Immunology Section, NINDS, National Institutes of Health, Bethesda, MD, USA
- Sackler Faculty of Medicine, Tel Aviv University, Israel
| | | | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Daniel S. Reich
- Translational Neuroradiology Section, NINDS, National Institutes of Health, Bethesda, MD, USA
| | - Govind Nair
- qMRI Core Facility, NINDS, National Institutes of Health, Bethesda, MD, USA
- Corresponding Author: Govind Nair, Room 5C440, 10 Center Drive, Bethesda MD 20892, ; 301-402-6391
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9
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du Preez A, Meijboom R, Smit E. Low-Cost 3D-Printed Reactionware for the Determination of Fatty Acid Content in Edible Oils using a Base-Catalyzed Transesterification Method in Continuous Flow. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02233-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractA low-cost flow system was designed, manufactured, and tested to perform automated base-catalyzed transesterification of triacylglycerols to determine the fatty acid content in edible oils. In combination with traditional gas chromatographic analysis (GC-FID), this approach provides a semi-automated process that requires minimal manual intervention. The main flow system components, namely syringe pumps, connectors (i.e., flangeless fittings), and reactors, were manufactured using 3D-printing technology, specifically fused deposition modeling (FDM). By fine-tuning 3D-printer settings, high-quality leak-tight fittings with standard threading were manufactured in polypropylene (PP), which reduced the overall cost of the flow system significantly. Due to the enhanced reactivity in flow, lower catalyst concentrations (≤ 1.5 wt.%) were needed compared to traditional batch reactions (5 wt.%). The suitability of the automated flow method was determined by comparing results with the certified fatty acid content in sunflower seed oil from Helianthus annuus. Acceptable levels of accuracy (relative errors < 5%) and precision (RSD values ≤ 0.02%) were achieved. The mostly 3D-printed flow system was successfully used to determine the fatty acid content of sunflower and other commercial edible oils, namely avocado oil, canola oil, extra virgin olive oil, and a canola and olive oil blend. Linoleic acid (C18:2) was the major component in sunflower oil, whereas all other oils consisted mainly of oleic acid (C18:1). The fatty acid content of the edible oils was comparable to certified and literature values.
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10
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York EN, Thrippleton MJ, Meijboom R, Hunt DPJ, Waldman AD. Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. Brain Commun 2022; 4:fcac088. [PMID: 35652121 PMCID: PMC9149789 DOI: 10.1093/braincomms/fcac088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/17/2021] [Accepted: 03/31/2022] [Indexed: 11/28/2022] Open
Abstract
Myelin-sensitive MRI such as magnetization transfer imaging has been widely used in multiple sclerosis. The influence of methodology and differences in disease subtype on imaging findings is, however, not well established. Here, we systematically review magnetization transfer brain imaging findings in relapsing-remitting multiple sclerosis. We examine how methodological differences, disease effects and their interaction influence magnetization transfer imaging measures. Articles published before 06/01/2021 were retrieved from online databases (PubMed, EMBASE and Web of Science) with search terms including 'magnetization transfer' and 'brain' for systematic review, according to a pre-defined protocol. Only studies that used human in vivo quantitative magnetization transfer imaging in adults with relapsing-remitting multiple sclerosis (with or without healthy controls) were included. Additional data from relapsing-remitting multiple sclerosis subjects acquired in other studies comprising mixed disease subtypes were included in meta-analyses. Data including sample size, MRI acquisition protocol parameters, treatments and clinical findings were extracted and qualitatively synthesized. Where possible, effect sizes were calculated for meta-analyses to determine magnetization transfer (i) differences between patients and healthy controls; (ii) longitudinal change and (iii) relationships with clinical disability in relapsing-remitting multiple sclerosis. Eighty-six studies met inclusion criteria. MRI acquisition parameters varied widely, and were also underreported. The majority of studies examined the magnetization transfer ratio in white matter, but magnetization transfer metrics, brain regions examined and results were heterogeneous. The analysis demonstrated a risk of bias due to selective reporting and small sample sizes. The pooled random-effects meta-analysis across all brain compartments revealed magnetization transfer ratio was 1.17 per cent units (95% CI -1.42 to -0.91) lower in relapsing-remitting multiple sclerosis than healthy controls (z-value: -8.99, P < 0.001, 46 studies). Linear mixed-model analysis did not show a significant longitudinal change in magnetization transfer ratio across all brain regions [β = 0.12 (-0.56 to 0.80), t-value = 0.35, P = 0.724, 14 studies] or normal-appearing white matter alone [β = 0.037 (-0.14 to 0.22), t-value = 0.41, P = 0.68, eight studies]. There was a significant negative association between the magnetization transfer ratio and clinical disability, as assessed by the Expanded Disability Status Scale [r = -0.32 (95% CI -0.46 to -0.17); z-value = -4.33, P < 0.001, 13 studies]. Evidence suggests that magnetization transfer imaging metrics are sensitive to pathological brain changes in relapsing-remitting multiple sclerosis, although effect sizes were small in comparison to inter-study variability. Recommendations include: better harmonized magnetization transfer acquisition protocols with detailed methodological reporting standards; larger, well-phenotyped cohorts, including healthy controls; and, further exploration of techniques such as magnetization transfer saturation or inhomogeneous magnetization transfer ratio.
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Affiliation(s)
- Elizabeth N. York
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
- Correspondence to: Elizabeth N. York Centre for Clinical
Brain Sciences University of Edinburgh, Edinburgh BioQuarter Chancellors
Building, Edinburgh EH16 4SB, UK E-mail:
| | | | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
| | - David P. J. Hunt
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of
Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic,
University of Edinburgh, Edinburgh, UK
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of
Edinburgh, Edinburgh, UK
- Correspondence may also be addressed to: Adam D. Waldman
E-mail:
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11
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Jochems ACC, Muñoz Maniega S, Del C Valdés Hernández M, Barclay G, Anblagan D, Ballerini L, Meijboom R, Wiseman S, Taylor AM, Corley J, Chappell FM, Backhouse EV, Stringer MS, Dickie DA, Bastin ME, Deary IJ, Cox SR, Wardlaw JM. Contribution of white matter hyperintensities to ventricular enlargement in older adults. Neuroimage Clin 2022; 34:103019. [PMID: 35490587 PMCID: PMC9062739 DOI: 10.1016/j.nicl.2022.103019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/24/2022] [Accepted: 04/23/2022] [Indexed: 11/16/2022]
Abstract
Lateral ventricles might increase due to generalized tissue loss related to brain atrophy. Alternatively, they may expand into areas of tissue loss related to white matter hyperintensities (WMH). We assessed longitudinal associations between lateral ventricle and WMH volumes, accounting for total brain volume, blood pressure, history of stroke, cardiovascular disease, diabetes and smoking at ages 73, 76 and 79, in participants from the Lothian Birth Cohort 1936, including MRI data from all available time points. Lateral ventricle volume increased steadily with age, WMH volume change was more variable. WMH volume decreased in 20% and increased in remaining subjects. Over 6 years, lateral ventricle volume increased by 3% per year of age, 0.1% per mm Hg increase in blood pressure, 3.2% per 1% decrease of total brain volume, and 4.5% per 1% increase of WMH volume. Over time, lateral ventricle volumes were 19% smaller in women than men. Ventricular and WMH volume changes are modestly associated and independent of general brain atrophy, suggesting that their underlying processes do not fully overlap.
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Affiliation(s)
- Angela C C Jochems
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Gayle Barclay
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Devasuda Anblagan
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Lucia Ballerini
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Rozanna Meijboom
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Stewart Wiseman
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Adele M Taylor
- Lothian Birth Cohorts Group, The University of Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohorts Group, The University of Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Francesca M Chappell
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ellen V Backhouse
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Michael S Stringer
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - David Alexander Dickie
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary & Life Sciences, Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
| | - Mark E Bastin
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts Group, The University of Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts Group, The University of Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts Group, The University of Edinburgh, UK.
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12
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Meijboom R, Wiseman SJ, York EN, Bastin ME, Valdés Hernández MDC, Thrippleton MJ, Mollison D, White N, Kampaite A, Ng Kee Kwong K, Rodriguez Gonzalez D, Job D, Weaver C, Kearns PKA, Connick P, Chandran S, Waldman AD. Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland. Wellcome Open Res 2022; 7:94. [PMID: 36865371 PMCID: PMC9971644 DOI: 10.12688/wellcomeopenres.17731.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 12/22/2022] Open
Abstract
Introduction: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear. Biomarkers predictive of disease course are urgently needed to allow improved stratification for current disease modifying therapies and future targeted treatments aimed at neuroprotection and remyelination. Magnetic resonance imaging (MRI) can detect disease activity and underlying damage non-invasively in vivo at the micro and macrostructural level. FutureMS is a prospective Scottish longitudinal multi-centre cohort study, which focuses on deeply phenotyping patients with recently diagnosed relapsing-remitting MS (RRMS). Neuroimaging is a central component of the study and provides two main primary endpoints for disease activity and neurodegeneration. This paper provides an overview of MRI data acquisition, management and processing in FutureMS. FutureMS is registered with the Integrated Research Application System (IRAS, UK) under reference number 169955. Methods and analysis: MRI is performed at baseline (N=431) and 1-year follow-up, in Dundee, Glasgow and Edinburgh (3T Siemens) and in Aberdeen (3T Philips), and managed and processed in Edinburgh. The core structural MRI protocol comprises T1-weighted, T2-weighted, FLAIR and proton density images. Primary imaging outcome measures are new/enlarging white matter lesions (WML) and reduction in brain volume over one year. Secondary imaging outcome measures comprise WML volume as an additional quantitative structural MRI measure, rim lesions on susceptibility-weighted imaging, and microstructural MRI measures, including diffusion tensor imaging and neurite orientation dispersion and density imaging metrics, relaxometry, magnetisation transfer (MT) ratio, MT saturation and derived g-ratio measures. Conclusions: FutureMS aims to reduce uncertainty around disease course and allow for targeted treatment in RRMS by exploring the role of conventional and advanced MRI measures as biomarkers of disease severity and progression in a large population of RRMS patients in Scotland.
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Affiliation(s)
- Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Stewart J. Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Maria del C. Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Daisy Mollison
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Koy Ng Kee Kwong
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - David Rodriguez Gonzalez
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Dominic Job
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Christine Weaver
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Patrick K. A. Kearns
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
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13
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Ng Kee Kwong KC, Mollison D, Meijboom R, York EN, Kampaite A, Martin SJ, Hunt DPJ, Thrippleton MJ, Chandran S, Waldman AD. Rim lesions are demonstrated in early relapsing-remitting multiple sclerosis using 3 T-based susceptibility-weighted imaging in a multi-institutional setting. Neuroradiology 2022; 64:109-117. [PMID: 34664112 PMCID: PMC8724059 DOI: 10.1007/s00234-021-02768-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/06/2021] [Indexed: 12/01/2022]
Abstract
PURPOSE Rim lesions, characterised by a paramagnetic rim on susceptibility-based MRI, have been suggested to reflect chronic inflammatory demyelination in multiple sclerosis (MS) patients. Here, we assess, through susceptibility-weighted imaging (SWI), the prevalence, longitudinal volume evolution and clinical associations of rim lesions in subjects with early relapsing-remitting MS (RRMS). METHODS Subjects (n = 44) with recently diagnosed RRMS underwent 3 T MRI at baseline (M0) and 1 year (M12) as part of a multi-centre study. SWI was acquired at M12 using a 3D segmented gradient-echo echo-planar imaging sequence. Rim lesions identified on SWI were manually segmented on FLAIR images at both time points for volumetric analysis. RESULTS Twelve subjects (27%) had at least one rim lesion at M12. A linear mixed-effects model, with 'subject' as a random factor, revealed mixed evidence for the difference in longitudinal volume change between rim lesions and non-rim lesions (p = 0.0350 and p = 0.0556 for subjects with and without rim lesions, respectively). All 25 rim lesions identified showed T1-weighted hypointense signal. Subjects with and without rim lesions did not differ significantly with respect to age, disease duration or clinical measures of disability (p > 0.05). CONCLUSION We demonstrate that rim lesions are detectable in early-stage RRMS on 3 T MRI across multiple centres, although their relationship to lesion enlargement is equivocal in this small cohort. Identification of SWI rims was subjective. Agreed criteria for defining rim lesions and their further validation as a biomarker of chronic inflammation are required for translation of SWI into routine MS clinical practice.
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Affiliation(s)
- Koy Chong Ng Kee Kwong
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Daisy Mollison
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | | | - David P. J. Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor’s Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK
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14
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Ng Kee Kwong KC, Mollison D, Meijboom R, York EN, Kampaite A, Martin SJ, Hunt DPJ, Thrippleton MJ, Chandran S, Waldman AD. Correction to: Rim lesions are demonstrated in early relapsing-remitting multiple sclerosis using 3 T‑based susceptibility‑weighted imaging in a multi‑institutional setting. Neuroradiology 2021; 64:211. [PMID: 34738181 DOI: 10.1007/s00234-021-02844-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Koy Chong Ng Kee Kwong
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Daisy Mollison
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | | | - David P J Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh bioQuarter, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
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15
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York EN, Martin SJ, Meijboom R, Thrippleton MJ, Bastin ME, Carter E, Overell J, Connick P, Chandran S, Waldman AD, Hunt DPJ. MRI-derived g-ratio and lesion severity in newly diagnosed multiple sclerosis. Brain Commun 2021; 3:fcab249. [PMID: 34877533 PMCID: PMC8643503 DOI: 10.1093/braincomms/fcab249] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 08/24/2021] [Accepted: 08/30/2021] [Indexed: 01/19/2023] Open
Abstract
Myelin loss is associated with axonal damage in established multiple sclerosis. This relationship is challenging to study in vivo in early disease. Here, we ask whether myelin loss is associated with axonal damage at diagnosis by combining non-invasive neuroimaging and blood biomarkers. We performed quantitative microstructural MRI and single-molecule ELISA plasma neurofilament measurement in 73 patients with newly diagnosed, immunotherapy naïve relapsing-remitting multiple sclerosis. Myelin integrity was evaluated using aggregate g-ratios, derived from magnetization transfer saturation and neurite orientation dispersion and density imaging diffusion data. We found significantly higher g-ratios within cerebral white matter lesions (suggesting myelin loss) compared with normal-appearing white matter (0.61 versus 0.57, difference 0.036, 95% CI: 0.029-0.043, P < 0.001). Lesion volume (Spearman's rho rs= 0.38, P < 0.001) and g-ratio (rs= 0.24, P < 0.05) correlated independently with plasma neurofilament. In patients with substantial lesion load (n = 38), those with higher g-ratio (defined as greater than median) were more likely to have abnormally elevated plasma neurofilament than those with normal g-ratio (defined as less than median) [11/23 (48%) versus 2/15 (13%), P < 0.05]. These data suggest that, even at multiple sclerosis diagnosis, reduced myelin integrity is associated with axonal damage. MRI-derived g-ratio may provide useful additional information regarding lesion severity and help to identify individuals with a high degree of axonal damage at disease onset.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Sarah-Jane Martin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | | | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Edwin Carter
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - James Overell
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - David P J Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
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16
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Ng Kee Kwong KC, Mollison D, Meijboom R, York EN, Kampaite A, Thrippleton MJ, Chandran S, Waldman AD. The prevalence of paramagnetic rim lesions in multiple sclerosis: A systematic review and meta-analysis. PLoS One 2021; 16:e0256845. [PMID: 34495999 PMCID: PMC8425533 DOI: 10.1371/journal.pone.0256845] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/17/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Recent findings from several studies have shown that paramagnetic rim lesions identified using susceptibility-based MRI could represent potential diagnostic and prognostic biomarkers in multiple sclerosis (MS). Here, we perform a systematic review and meta-analysis of the existing literature to assess their pooled prevalence at lesion-level and patient-level. METHODS Both database searching (PubMed and Embase) and handsearching were conducted to identify studies allowing the lesion-level and/or patient-level prevalence of rim lesions or chronic active lesions to be calculated. Pooled prevalence was estimated using the DerSimonian-Laird random-effects model. Subgroup analysis and meta-regression were performed to explore possible sources of heterogeneity. PROSPERO registration: CRD42020192282. RESULTS 29 studies comprising 1230 patients were eligible for analysis. Meta-analysis estimated pooled prevalences of 9.8% (95% CI: 6.6-14.2) and 40.6% (95% CI: 26.2-56.8) for rim lesions at lesion-level and patient-level, respectively. Pooled lesion-level and patient-level prevalences for chronic active lesions were 12.0% (95% CI: 9.0-15.8) and 64.8% (95% CI: 54.3-74.0), respectively. Considerable heterogeneity was observed across studies (I2>75%). Subgroup analysis revealed a significant difference in patient-level prevalence between studies conducted at 3T and 7T (p = 0.0312). Meta-regression analyses also showed significant differences in lesion-level prevalence with respect to age (p = 0.0018, R2 = 0.20) and disease duration (p = 0.0018, R2 = 0.48). Other moderator analyses demonstrated no significant differences according to MRI sequence, gender and expanded disability status scale (EDSS). CONCLUSION In this study, we show that paramagnetic rim lesions may be present in an important proportion of MS patients, notwithstanding significant variation in their assessment across studies. In view of their possible clinical relevance, we believe that clear guidelines should be introduced to standardise their assessment across research centres to in turn facilitate future analyses.
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Affiliation(s)
| | - Daisy Mollison
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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Meijboom R, Gardarsdottir H, Becker M, Ten Wolde S, Egberts T, Giezen T. POS0637 INCIDENCE AND DETERMINANTS ASSOCIATED WITH RETRANSITIONING FROM BIOSIMILAR SB4 TO ORIGINATOR ETANERCEPT. Ann Rheum Dis 2021. [DOI: 10.1136/annrheumdis-2021-eular.3636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:The market entry of the etanercept biosimilar SB4 (BS-ETA) reduced prices; and, therefore, many patients in clinical practice are transitioned from originator etanercept (OR-ETA) to BS-ETA. However, previous studies demonstrated that 2.7-17.1% of patients who transitioned from OR-ETA to BS-SB4, retransitioned to OR-ETA (i.e. restarted originator), which might reduce the (financial) benefits of biosimilars. Insight in the incidence of retransitioning and characteristics of patients who are most likely to retransition, can provide lessons to clinicians for successful introduction of biosimilars.Objectives:To assess the incidence of retransitioning from BS-ETA to OR-ETA in patients with a rheumatic disease (RD) and to identify determinants thereof.Methods:All patients diagnosed with RD who transitioned in 2016 from OR-ETA to BS-ETA in a Dutch general teaching hospital (the Spaarne Gasthuis, Haarlem/Hoofddorp) were included in this cohort study. All patients were followed until retransitioning, switching to another biological, discontinuing of biological treatment death, loss to follow up or until censor date. The incidence of retransitioning and duration of BS-ETA use was assessed using the Kaplan-Meier method. Potential determinants for retransitioning, including age, gender, BS-SB4 dosing interval, use of other biologicals prior to OR-ETA, initiation or intensification of corticosteroids or immunomodulators, hospitalisations and the number of outpatient visits to the rheumatology department, were assessed in a nested case control study, using (multivariate) conditional logistic regression.Results:In total, 342 patients (median age 57.8 years, 53.5% females, median follow-up 2.7 years) were included. 9.4% of patients had retransitioned to OR-ETA one year after transitioning. Additionally, one year after transitioning 69.7% of patients were still treated with BS-ETA, 3.8% switched to other treatment and 17.1% discontinued all biological treatment. At the end of follow-up (median 2,7 years), 46 patients (13.5%) retransitioned to OR-ETA; median time until retransitioning was 0.50 (IQR 0.98) years.Univariate determinants for retransitioning included female gender (OR 2.37, 95% CI 1.18-7.74), initiating or intensifying corticosteroids or immunomodulators (OR 3.24, 95% CI 1.38-7.63) and number of visits to the rheumatology department (OR 2.32, 95% CI 1.70-3.17). Based on the multivariate analysis, only the number of visits to the rheumatology department was associated with retransitioning (OR 2.19 95% CI 1.60-3.00), as demonstrated in Table 1.Conclusion:When introducing BS-SB4 in clinical care, clinicians should anticipate on about one in seven patients retransitioning to OR-ETA. These patients might be identified prior to retransitioning based on their contacts to the rheumatology department. Information specifically aiming for their concerns might prevent them from retransitioning. However, more qualitative studies are needed to explore patients’ underlying reasons for retransitioning, in order to improve the introduction of biosimilars in clinical care.Table 1.Casesn = 46Controlsn = 184OR(univariate)95% CIOR(multivariate)95% CIAge, years (median, IQR)59.0 (16.5)56.5 (22.3)1.01 (0.98–1.03)-GenderMale15 (32.6%)97 (52.7%)RefRefFemale31 (67.4%)87 (47.3%)2.37 (1.18–7.74)1.36 (0.60–3.07)BS-SB4 dosing interval (index date)7 days35 (76.1%)129 (70.1%)Ref->7 days11 (23.9%)55 (29.9%)0.72 (0.33–1.56)Previous use other biologicalNo43 (93.5%)172 (93.5%)Ref-Yes3 (6.5%)12 (6.5%)1.00 (0.27–3.74)Initiation/ intensification corticosteroid/ immunomodulatorNo35 (76.1%)168 (91.3%)RefRefYes11 (23.9%)16 (8.7%)3.24 (1.38–7.63)2.31 (0.84–6.38)HospitalisationNo41 (89.1%)166 (90.2%)Ref-Yes5 (10.9%)18 (9.8%)1.12 (0.40–3.18)No. visits rheumatology departmentRef(median, IQR)2 (2)0 (1)2.32(1.70–3.17)2.19 (1.60–3.00)Disclosure of Interests:None declared
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Corley J, Cox SR, Taylor AM, Hernandez MV, Maniega SM, Ballerini L, Wiseman S, Meijboom R, Backhouse EV, Bastin ME, Wardlaw JM, Deary IJ. Dietary patterns, cognitive function, and structural neuroimaging measures of brain aging. Exp Gerontol 2020; 142:111117. [DOI: 10.1016/j.exger.2020.111117] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 09/10/2020] [Accepted: 10/09/2020] [Indexed: 12/17/2022]
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19
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Rachmadi MF, Valdés-Hernández MDC, Li H, Guerrero R, Meijboom R, Wiseman S, Waldman A, Zhang J, Rueckert D, Wardlaw J, Komura T. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images. Comput Med Imaging Graph 2019; 79:101685. [PMID: 31846826 DOI: 10.1016/j.compmedimag.2019.101685] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/02/2019] [Accepted: 11/13/2019] [Indexed: 01/29/2023]
Abstract
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- School of Informatics, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | | | - Hongwei Li
- Computing, School of Science and Engineering, University of Dundee, Dundee, UK; Department of Informatics, Technical University of Munich, Germany
| | | | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Stewart Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Adam Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jianguo Zhang
- Computing, School of Science and Engineering, University of Dundee, Dundee, UK; Department of Computer Science and Engineering, Southern University of Science and Technology, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Taku Komura
- School of Informatics, University of Edinburgh, Edinburgh, UK
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20
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Meijboom R, Steketee RME, Jiskoot LC, Bron EE, van der Lugt A, van Swieten JC, Smits M. Qualitative Assessment of Longitudinal Changes in Phenocopy Frontotemporal Dementia. Front Neurol 2019; 10:1207. [PMID: 31798526 PMCID: PMC6874122 DOI: 10.3389/fneur.2019.01207] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 10/29/2019] [Indexed: 01/22/2023] Open
Abstract
Phenocopy frontotemporal dementia (phFTD) shares core characteristics with behavioral variant frontotemporal dementia (bvFTD), yet without associated cognitive deficits and brain abnormalities on conventional magnetic resonance imaging (MRI), and without progression. Using advanced MRI techniques, we previously observed subtle structural and functional brain changes in phFTD similar to bvFTD. The aim of the current study was to follow these as well as cognition in phFTD over time, by means of a descriptive case series. Cognition, gray matter (GM) volume and white matter (WM) microstructure, and perfusion of 6 phFTD patients were qualitatively compared longitudinally (3-years follow-up), and cross-sectionally with baseline data from 9 bvFTD patients and 17 controls. For functional brain changes, arterial spin labeling (ASL) was performed to assess GM perfusion. For structural brain changes, diffusion tensor imaging was performed to assess WM microstructure and T1w imaging to assess GM volume. MRI acquisition was performed at 3T (General Electric, US). Clinical profiles of phFTD cases at follow-up are described. At follow-up phFTD patients showed clinical symptomatology similar to bvFTD, but had a relatively stable clinical profile. Longitudinal qualitative comparisons in phFTD showed some deterioration of language and memory function, a stable pattern of structural brain abnormalities and increased perfusion over time. Additionally, both baseline and follow-up cognitive scores and structural values in phFTD were generally in between those of controls and bvFTD. Although a descriptive case series does not allow for strong conclusions, these observations in a unique longitudinal phFTD patient cohort are suggestive of the notion that phFTD and bvFTD may belong to the same disease spectrum. They may also provide a basis for further longitudinal studies in phFTD, specifically exploring the structural vs. functional brain changes. Such studies are essential for improved insight, accurate diagnosis, and appropriate treatment of phFTD.
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Affiliation(s)
- Rozanna Meijboom
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands.,Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Netherlands
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21
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Muñoz Maniega S, Meijboom R, Chappell FM, Valdés Hernández MDC, Starr JM, Bastin ME, Deary IJ, Wardlaw JM. Spatial Gradient of Microstructural Changes in Normal-Appearing White Matter in Tracts Affected by White Matter Hyperintensities in Older Age. Front Neurol 2019; 10:784. [PMID: 31404147 PMCID: PMC6673707 DOI: 10.3389/fneur.2019.00784] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/08/2019] [Indexed: 01/08/2023] Open
Abstract
Background and Purpose: White matter hyperintensities (WMH) are commonly seen on structural MRI of older adults and are a manifestation of underlying and adjacent tissue damage. WMH may contribute to cortical disconnection and cognitive dysfunction, but it is unclear how WMH affect intersecting or nearby white matter tract integrity. This study investigated the effects of WMH on tract microstructure by determining the spatial distribution of water diffusion characteristics in white matter tract areas adjacent to both intersecting and nearby WMH. Methods: We used diffusion and structural MRI data from 52 representative participants from the Lothian Birth Cohort 1936 (72.2 ± 0.7 years) including a range of WMH burden. We segmented WMH, reconstructed 18 main white mater tracts using automated quantitative tractography and identified intersections between tracts and WMH. We measured mean diffusivity (MD) and fractional anisotropy (FA) in tract tissue at 2 mm incremental distances from tract-intersecting and non-intersecting (nearby) WMH. Results: We observed a spatial gradient of FA and MD abnormalities for most white matter tracts which diminished with a similar distance pattern for tract-intersecting and nearby WMH. Overall, FA was higher, while MD was lower around nearby WMH compared with tract-intersecting WMH. However, for some tracts, FA was lower in areas immediately surrounding nearby WMH, although with faster normalization than in FA values surrounding tract-intersecting WMH. Conclusion: WMH have similar effects on tract infrastructure, whether they be intersecting or nearby. However, the observed differences in tract water diffusion properties around WMH suggest that degenerative processes in small vessel disease may propagate further along the tract for intersecting WMH, while in some areas of the brain there is a larger and more localized accumulation of axonal damage in tract tissue nearby a non-connected WMH. Longitudinal studies should address differential effects of intersecting vs. nearby WMH progression and how they contribute to cognitive aging.
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Affiliation(s)
- Susana Muñoz Maniega
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Rozanna Meijboom
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
- Department of Radiology and Nuclear Medicine, Erasmus MC–University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Francesca M. Chappell
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria del C. Valdés Hernández
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - John M. Starr
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M. Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
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22
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Meijboom R, Steketee RME, Ham LS, Mantini D, Bron EE, van der Lugt A, van Swieten JC, Smits M. Exploring quantitative group-wise differentiation of Alzheimer's disease and behavioural variant frontotemporal dementia using tract-specific microstructural white matter and functional connectivity measures at multiple time points. Eur Radiol 2019; 29:5148-5159. [PMID: 30859283 PMCID: PMC6719324 DOI: 10.1007/s00330-019-06061-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 01/07/2019] [Accepted: 02/01/2019] [Indexed: 12/13/2022]
Abstract
Objectives This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD). Methods Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared. Results Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD. Conclusions This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD. Key Points • The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach. • Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer’s disease. • Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer’s disease. Electronic supplementary material The online version of this article (10.1007/s00330-019-06061-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R Meijboom
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - R M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - L S Ham
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - D Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, Lido, Italy
| | - E E Bron
- Biomedical Imaging Group Rotterdam - Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - A van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - J C van Swieten
- Department of Neurology, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
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23
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Wiseman SJ, Meijboom R, Valdés Hernández MDC, Pernet C, Sakka E, Job D, Waldman AD, Wardlaw JM. Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing. Trials 2019; 20:21. [PMID: 30616680 PMCID: PMC6323670 DOI: 10.1186/s13063-018-3113-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 12/06/2018] [Indexed: 11/10/2022] Open
Abstract
Background Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. Methods We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. Results The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. Conclusions Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.
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Affiliation(s)
- Stewart J Wiseman
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. .,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK. .,CCBS, Chancellor's Building, Royal Infirmary of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
| | - Rozanna Meijboom
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Cyril Pernet
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Eleni Sakka
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Dominic Job
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Adam D Waldman
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
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Meijboom R, Steketee RME, Ham LS, van der Lugt A, van Swieten JC, Smits M. Differential Hemispheric Predilection of Microstructural White Matter and Functional Connectivity Abnormalities between Respectively Semantic and Behavioral Variant Frontotemporal Dementia. J Alzheimers Dis 2018; 56:789-804. [PMID: 28059782 DOI: 10.3233/jad-160564] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Semantic dementia (SD) and behavioral variant frontotemporal dementia (bvFTD), subtypes of frontotemporal dementia, are characterized by distinct clinical symptoms and neuroimaging features, with predominant left temporal grey matter (GM) atrophy in SD and bilateral or right frontal GM atrophy in bvFTD. Such differential hemispheric predilection may also be reflected by other neuroimaging features, such as brain connectivity. This study investigated white matter (WM) microstructure and functional connectivity differences between SD and bvFTD, focusing on the hemispheric predilection of these differences. Eight SD and 12 bvFTD patients, and 17 controls underwent diffusion tensor imaging and resting state functional MRI at 3T. Whole-brain WM microstructure was assessed to determine distinct WM tracts affected in SD and bvFTD. For these tracts, diffusivity measures and lateralization indices were calculated. Functional connectivity was established for GM regions affected in early stage SD or bvFTD. Results of a direct comparison between SD and bvFTD are reported. Whole-brain WM microstructure abnormalities were more pronounced in the left hemisphere in SD and bilaterally- with a slight predilection for the right- in bvFTD. Lateralization of tract-specific abnormalities was seen in SD only, toward the left hemisphere. Functional connectivity of disease-specific regions was mainly decreased bilaterally in SD and in the right hemisphere in bvFTD. SD and bvFTD show WM microstructure and functional connectivity abnormalities in different regions, that are respectively more pronounced in the left hemisphere in SD and in the right hemisphere in bvFTD. This indicates differential hemispheric predilection of brain connectivity abnormalities between SD and bvFTD.
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Affiliation(s)
- Rozanna Meijboom
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
| | - Leontine S Ham
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, The Netherlands
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25
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Desobgo ZSC, Stafford RA, Ndinteh DT, Metcalfe DJA, Meijboom R. Impact of Gaseous Carbon Dioxide and Boiling Power on Dimethyl Sulfide Stripping Behavior during Wort Boiling. Journal of the American Society of Brewing Chemists 2018. [DOI: 10.1094/asbcj-2017-4458-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Z. S. C. Desobgo
- Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg 2028, South Africa
| | - R. A. Stafford
- Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg 2028, South Africa
| | - D. T. Ndinteh
- Department of Applied Chemistry, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg 2028, South Africa
| | - D. J. A. Metcalfe
- Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg 2028, South Africa
| | - R. Meijboom
- Department of Chemistry, Faculty of Science, University of Johannesburg, P.O. Box 17011, Auckland Park Kingsway, Johannesburg 2028, South Africa
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26
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Gotink RA, Meijboom R, Vernooij MW, Smits M, Hunink MM. 8-week Mindfulness Based Stress Reduction induces brain changes similar to traditional long-term meditation practice – A systematic review. Brain Cogn 2016; 108:32-41. [DOI: 10.1016/j.bandc.2016.07.001] [Citation(s) in RCA: 152] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 06/24/2016] [Accepted: 07/05/2016] [Indexed: 01/01/2023]
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27
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Meijboom R, Steketee RME, de Koning I, Osse RJ, Jiskoot LC, de Jong FJ, van der Lugt A, van Swieten JC, Smits M. Functional connectivity and microstructural white matter changes in phenocopy frontotemporal dementia. Eur Radiol 2016; 27:1352-1360. [PMID: 27436017 PMCID: PMC5334426 DOI: 10.1007/s00330-016-4490-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 06/12/2016] [Accepted: 06/22/2016] [Indexed: 01/06/2023]
Abstract
Objectives Phenocopy frontotemporal dementia (phFTD) is a rare and poorly understood clinical syndrome. PhFTD shows core behavioural variant FTD (bvFTD) symptoms without associated cognitive deficits and brain abnormalities on conventional MRI and without progression. In contrast to phFTD, functional connectivity and white matter (WM) microstructural abnormalities have been observed in bvFTD. We hypothesise that phFTD belongs to the same disease spectrum as bvFTD and investigated whether functional connectivity and microstructural WM changes similar to bvFTD are present in phFTD. Methods Seven phFTD patients without progression or alternative psychiatric diagnosis, 12 bvFTD patients and 17 controls underwent resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI). Default mode network (DMN) connectivity and WM measures were compared between groups. Results PhFTD showed subtly increased DMN connectivity and subtle microstructural changes in frontal WM tracts. BvFTD showed abnormalities in similar regions as phFTD, but had lower increased DMN connectivity and more extensive microstructural WM changes. Conclusions Our findings can be interpreted as neuropathological changes in phFTD and are in support of the hypothesis that phFTD and bvFTD may belong to the same disease spectrum. Advanced MRI techniques, objectively identifying brain abnormalities, would therefore be potentially suited to improve the diagnosis of phFTD. Key points • PhFTD shows brain abnormalities that are similar to bvFTD. • PhFTD shows increased functional connectivity in the parietal default mode network. • PhFTD shows microstructural white matter abnormalities in the frontal lobe. • We hypothesise phFTD and bvFTD may belong to the same disease spectrum. Electronic supplementary material The online version of this article (doi:10.1007/s00330-016-4490-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R Meijboom
- Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - R M E Steketee
- Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - I de Koning
- Neuropsychology, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - R J Osse
- Psychiatry, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - L C Jiskoot
- Neuropsychology, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
- Neurology, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - F J de Jong
- Neurology, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - A van der Lugt
- Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - J C van Swieten
- Neurology, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands
| | - M Smits
- Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre, Rotterdam, The Netherlands.
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28
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Steketee RME, Meijboom R, de Groot M, Bron EE, Niessen WJ, van der Lugt A, van Swieten JC, Smits M. Concurrent white and gray matter degeneration of disease-specific networks in early-stage Alzheimer's disease and behavioral variant frontotemporal dementia. Neurobiol Aging 2016; 43:119-28. [PMID: 27255821 DOI: 10.1016/j.neurobiolaging.2016.03.031] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Revised: 03/22/2016] [Accepted: 03/30/2016] [Indexed: 01/08/2023]
Abstract
This study investigates regional coherence between white matter (WM) microstructure and gray matter (GM) volume and perfusion measures in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) using a correlational approach. WM-GM coherence, compared with controls, was stronger between cingulum WM and frontotemporal GM in AD, and temporoparietal GM in bvFTD. In addition, in AD compared with controls, coherence was stronger between inferior fronto-occipital fasciculus WM microstructure and occipital GM perfusion. In this first study assessing regional WM-GM coherence in AD and bvFTD, we show that WM microstructure and GM volume and perfusion measures are coherent, particularly in regions implicated in AD and bvFTD pathology. This indicates concurrent degeneration in disease-specific networks. Our methodology allows for the detection of incipient abnormalities that go undetected in conventional between-group analyses.
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Affiliation(s)
- Rebecca M E Steketee
- Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Rozanna Meijboom
- Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Marius de Groot
- Department of Epidemiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands; Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands; Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands
| | - Aad van der Lugt
- Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Marion Smits
- Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, the Netherlands.
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Bron EE, Smits M, van der Flier WM, Vrenken H, Barkhof F, Scheltens P, Papma JM, Steketee RME, Méndez Orellana C, Meijboom R, Pinto M, Meireles JR, Garrett C, Bastos-Leite AJ, Abdulkadir A, Ronneberger O, Amoroso N, Bellotti R, Cárdenas-Peña D, Álvarez-Meza AM, Dolph CV, Iftekharuddin KM, Eskildsen SF, Coupé P, Fonov VS, Franke K, Gaser C, Ledig C, Guerrero R, Tong T, Gray KR, Moradi E, Tohka J, Routier A, Durrleman S, Sarica A, Di Fatta G, Sensi F, Chincarini A, Smith GM, Stoyanov ZV, Sørensen L, Nielsen M, Tangaro S, Inglese P, Wachinger C, Reuter M, van Swieten JC, Niessen WJ, Klein S. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 2015; 111:562-79. [PMID: 25652394 DOI: 10.1016/j.neuroimage.2015.01.048] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 01/21/2015] [Accepted: 01/24/2015] [Indexed: 12/31/2022] Open
Abstract
Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.
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Affiliation(s)
- Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
| | - Marion Smits
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands; Department of Epidemiology & Biostatistics, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Carolina Méndez Orellana
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Madalena Pinto
- Department of Neurology, Hospital de São João, Porto, Portugal
| | | | - Carolina Garrett
- Department of Neurology, Hospital de São João, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - António J Bastos-Leite
- Department of Medical Imaging, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Ahmed Abdulkadir
- Department of Psychiatry & Psychotherapy, University Medical Centre Freiburg, Germany; Department of Neurology, University Medical Centre Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Olaf Ronneberger
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Nicola Amoroso
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - Roberto Bellotti
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - David Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Colombia
| | | | | | | | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unit Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Katja Franke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Ledig
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Ricardo Guerrero
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Tong Tong
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Katherine R Gray
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Elaheh Moradi
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Jussi Tohka
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Alexandre Routier
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Stanley Durrleman
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Alessia Sarica
- Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Di Fatta
- School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Francesco Sensi
- National Institute of Nuclear Physics, Branch of Genoa, Italy
| | | | - Garry M Smith
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Zhivko V Stoyanov
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Sabina Tangaro
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Paolo Inglese
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Christian Wachinger
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | - Martin Reuter
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | | | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
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Owalude S, Odebunmi E, Eke U, Meijboom R, Nesterov V, Singleton E, Coville N. Synthesis and X-ray studies of ruthenium(II) complexes containing hydrazine and benzyl isocyanide ligands. B CHEM SOC ETHIOPIA 2013. [DOI: 10.4314/bcse.v27i3.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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31
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Roodt A, Muller A, Otto S, Meijboom R. Packing behaviour in pseudo Vaska-type complexes. Acta Crystallogr A 2006. [DOI: 10.1107/s0108767306094633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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