Publications by authors named "Hugo Vrenken"

116 Publications

Ultrasensitive immunoassay allows measurement of serum neurofilament heavy in multiple sclerosis.

Mult Scler Relat Disord 2021 Feb 10;50:102840. Epub 2021 Feb 10.

Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurosciences, Neurology Unit, Geneva University Hospital, Geneva, Switzerland.

Background: Neurofilament heavy (NfH) is a promising biomarker for neuro-axonal damage in Multiple Sclerosis (MS). We compared the performance of high-sensitivity serum-NfH immunoassays, with as aim to investigate the value of serum-NfH as biomarker for MS.

Methods: We measured serum-NfH in 76 MS patients with Simoa (one commercial, one in-house) or Luminex assays. Serum-NfH measured by the immunoassay with greatest sensitivity was related to clinical and radiological outcomes with age and sex-adjusted linear regression analysis, and to biological outcomes cerebrospinal fluid (CSF)-NfH, serum neurofilament light (NfL) and CSF-NfL with Spearman's correlation analysis.

Results: With the commercial Simoa assay, we obtained 100% serum-NfH detectability (in-house Simoa: 70%, Luminex: 61%), with lowest coefficient of variation (CV) between duplicates of 11%CV (in-house Simoa: 22%CV, Luminex: 30%CV). Serum-NfH quantified with the commercial Simoa assay was associated with disease duration (standardized beta (sβ) = 0.28, p = 0.034), T2 lesion volume (sβ = 0.23, p = 0.041), and tended to associate with black hole count (sβ = 0.21, p = 0.084) but not with Expanded Disease Disability Score (EDSS) or normalized brain volume (all: p>0.10). Furthermore, serum-NfH showed correlations with CSF-NfH (rho = 0.27, p = 0.018) and serum-NfL (rho=0.44, p < 0.001), but not with CSF-NfL.

Conclusions: Serum-NfH can be quantified with high-sensitivity technology. Cross-sectionally, we observed some weak correlations of serum-NfH with MS disease burden parameters, suggesting there might be some utility for serum-NfH as biomarker for MS disease burden.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.msard.2021.102840DOI Listing
February 2021

Association of Gray Matter Atrophy Patterns with Clinical Phenotype and Progression in Multiple Sclerosis.

Neurology 2021 01 13. Epub 2021 Jan 13.

Massimo Filippi, MD, Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy and Vita-Salute San Raffaele University, Milan, Italy.

Objectives: Grey matter (GM) involvement is clinically relevant in multiple sclerosis (MS). Using source-based morphometry (SBM), we characterized GM atrophy and its 1-year evolution across different MS phenotypes.

Methods: Clinical and MRI data were obtained at 8 European sites from 170 healthy controls (HCs) and 398 MS patients (34 clinically isolated syndromes [CIS], 226 relapsing-remitting [RR], 95 secondary progressive [SP] and 43 primary progressive [PP] MS). Fifty-seven HC and 144 MS underwent 1-year follow-up. Baseline GM loss, atrophy progression and correlations with disability and 1-year clinical worsening were assessed.

Results: SBM identified 26 cerebellar, subcortical, sensory, motor and cognitive GM components. GM atrophy was found in MS HC in almost all components (p=range<0.001-0.04). Compared to HCs, CIS patients showed circumscribed subcortical, cerebellar, temporal and salience GM atrophy, while RRMS patients exhibited widespread GM atrophy. Cerebellar, subcortical, sensorimotor, salience and fronto-parietal GM atrophy was found in PPMS patients HCs, and SPMS RRMS. At 1-year, 21 (15%) patients had clinically worsened. GM atrophy progressed in MS in subcortical, cerebellar, sensorimotor, and fronto-temporo-parietal components. Baseline higher disability was associated (R=0.65) with baseline lower normalized brain volume (beta=-0.13, p=0.001), greater sensorimotor GM atrophy (beta=-0.12, p=0.002) and longer disease duration (beta=0.09, p=0.04). Baseline normalized GM volume (odds ratio=0.98, p=0.008) and cerebellar GM atrophy (odds ratio=0.40, p=0.01) independently predicted clinical worsening (area-under-the-curve=0.83).

Conclusion: GM atrophy differed across disease phenotypes and progressed at 1-year in MS. In addition to global atrophy measures, sensorimotor and cerebellar GM atrophy explained baseline disability and clinical worsening.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1212/WNL.0000000000011494DOI Listing
January 2021

The sequence of structural, functional and cognitive changes in multiple sclerosis.

Neuroimage Clin 2021 24;29:102550. Epub 2020 Dec 24.

Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands. Electronic address:

Background: As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach.

Methods: We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment.

Results: We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions.

Conclusion: Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2020.102550DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804841PMC
December 2020

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study.

Neuroimage Clin 2021 25;29:102549. Epub 2020 Dec 25.

Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA. Electronic address:

Background And Rationale: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining.

Methods: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor.

Results: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings.

Conclusion: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2020.102549DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787946PMC
December 2020

Damage in the Thalamocortical Tracts is Associated With Subsequent Thalamus Atrophy in Early Multiple Sclerosis.

Front Neurol 2020 17;11:575611. Epub 2020 Nov 17.

Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC-Location VUmc, Amsterdam, Netherlands.

In early multiple sclerosis (MS), thalamus atrophy and decreased integrity of the thalamocortical white matter (WM) tracts have been observed. To investigate the temporal association between thalamus volume and WM damage in the thalamocortical tract in subjects with early MS. At two time points, 72 subjects with early MS underwent T1, FLAIR and diffusion tensor imaging. Thalamocortical tracts were identified with probabilistic tractography using left and right thalamus as seed regions. Regression analysis was performed to identify predictors of annual percentage change in both thalamus volumes and integrity of the connected tracts. Significant atrophy was seen in left and right thalamus ( < 0.001) over the follow-up period (13.7 ± 4.8 months), whereas fractional anisotropy (FA) and mean diffusivity (MD) changes of the left and right thalamus tracts were not significant, although large inter-subject variability was seen. Annual percentage change in left thalamus volume was significantly predicted by baseline FA of the left thalamus tracts = 4.284, = 0.042; while no such relation was found for the right thalamus. Annual percentage change in FA or MD of the thalamus tracts was not predicted by thalamus volume or any of the demographic parameters. Over a short follow-up time, thalamus atrophy could be predicted by decreased integrity of the thalamic tracts, but changes in the integrity of the thalamic tracts could not be predicted by thalamus volume. This is the first study showing directionality in the association between thalamus atrophy and connected WM tract damage. These results need to be verified over longer follow-up periods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fneur.2020.575611DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705066PMC
November 2020

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

J Neurol 2020 Dec 3;267(12):3541-3554. Epub 2020 Jul 3.

Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Background: Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied.

Methods: On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed.

Results: ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations.

Conclusions: Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00415-020-10023-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674567PMC
December 2020

Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma.

Front Neurosci 2020 5;14:585. Epub 2020 Jun 5.

Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Amsterdam, Netherlands.

To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnins.2020.00585DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290158PMC
June 2020

Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm.

Ann Neurol 2020 07 6;88(1):93-105. Epub 2020 May 6.

Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

Objective: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes.

Methods: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored.

Results: Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001).

Interpretation: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/ana.25746DOI Listing
July 2020

MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice.

Nat Rev Neurol 2020 Mar 24;16(3):171-182. Epub 2020 Feb 24.

Section of Neuroradiology and Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.

Early evaluation of treatment response and prediction of disease evolution are key issues in the management of people with multiple sclerosis (MS). In the past 20 years, MRI has become the most useful paraclinical tool in both situations and is used clinically to assess the inflammatory component of the disease, particularly the presence and evolution of focal lesions - the pathological hallmark of MS. However, diffuse neurodegenerative processes that are at least partly independent of inflammatory mechanisms can develop early in people with MS and are closely related to disability. The effects of these neurodegenerative processes at a macroscopic level can be quantified by estimation of brain and spinal cord atrophy with MRI. MRI measurements of atrophy in MS have also been proposed as a complementary approach to lesion assessment to facilitate the prediction of clinical outcomes and to assess treatment responses. In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group critically review the application of brain and spinal cord atrophy in clinical practice in the management of MS, considering the role of atrophy measures in prognosis and treatment monitoring and the barriers to clinical use of these measures. On the basis of this review, the group makes consensus statements and recommendations for future research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41582-020-0314-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054210PMC
March 2020

Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset.

Sci Rep 2019 11 14;9(1):16742. Epub 2019 Nov 14.

Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice's similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-52966-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856351PMC
November 2019

Clinically relevant cranio-caudal patterns of cervical cord atrophy evolution in MS.

Neurology 2019 11 14;93(20):e1852-e1866. Epub 2019 Oct 14.

From the Neuroimaging Research Unit (M.A.R., P.V., A.M., P.P., M.F.) and Neurology Unit (M.A.R., P.P., G.C., M.F.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Department of Neurology (C.G., C.Z.), Neurocenter of Southern Switzerland, Regional Hospital Lugano (EOC), Lugano; Faculty of Biomedical Sciences (C.G., C.Z.), Università della Svizzera Italiana, Lugano, Switzerland; Section of Neuroradiology and MRI Unit, Department of Radiology (A.R.), and Department of Neurology/Neuroimmunology (X.M.), Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, Barcelona, Spain; NMR Research Unit (H.K., O.C.), Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London; Nuffield Department of Clinical Neurosciences (L.M., J.P.), University of Oxford, UK; Department of Advanced Medical and Surgical Sciences, and 3T MRI Center, (A.G., A.B.), University of Campania "Luigi Vanvitelli," Naples, Italy; Department of Neurology (A.G., P.E.), Universitätsmedizin Mannheim, University of Heidelberg, Germany; Department of Radiology and Nuclear Medicine (C.L., B.B.) and Institute of Neuroradiology (C.L., B.B.), St. Josef Hospital, Ruhr-University Bochum, Germany; Department of Radiology and Nuclear Medicine (F.B., H.V.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; and Vita-Salute San Raffaele University (P.P., G.C., M.F.), Milan, Italy.

Objective: To characterize the distribution and regional evolution of cervical cord atrophy in patients with multiple sclerosis (MS) in a multicenter dataset.

Methods: MRI and clinical evaluations were acquired from 179 controls and 435 patients (35 clinically isolated syndromes [CIS], 259 relapsing-remitting multiple sclerosis [RRMS], 99 secondary progressive multiple sclerosis [SPMS], and 42 primary progressive multiple sclerosis [PPMS]). Sixty-nine controls and 178 patients underwent a 1-year MRI and clinical follow-up. Patients were classified as clinically stable/worsened according to their disability change. Longitudinal changes of cord atrophy were investigated with linear mixed-effect models. Sample size calculations were performed using age-, sex- and site-adjusted annualized percentage normalized cord cross-sectional area (CSAn) changes.

Results: Baseline CSAn was lower in patients with MS vs controls ( < 0.001), but not different between controls and patients with CIS or between patients with early RRMS (disease duration ≤5 years) and patients with CIS. Patients with late RRMS (disease duration >5 years) showed significant cord atrophy vs patients with early RRMS ( = 0.02). Patients with progressive MS had decreased CSAn ( < 0.001) vs patients with RRMS. Atrophy was located between C1/C2 and C5 in patients with RRMS vs patients with CIS, and widespread along the cord in patients with progressive MS vs patients with RRMS, with an additional C5/C6 involvement in patients with SPMS vs patients with PPMS. At follow-up, CSAn decreased in all phenotypes ( < 0.001), except CIS. Cord atrophy rates were highest in patients with early RRMS and clinically worsened patients, who had a more widespread cord involvement than stable patients. The sample size per arm required to detect a 50% treatment effect was 118 for patients with early RRMS.

Conclusions: Cord atrophy increased in MS during 1 year, except for CIS. Faster atrophy contributed to explain clinical worsening.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1212/WNL.0000000000008466DOI Listing
November 2019

Cortical atrophy accelerates as cognitive decline worsens in multiple sclerosis.

Neurology 2019 10 4;93(14):e1348-e1359. Epub 2019 Sep 4.

From the Departments of Anatomy & Neurosciences (A.J.C.E., M.D.S., K.A.M., H.E.H., M.M.S., J.J.G.G.), Radiology and Nuclear Medicine (I.D., J.W.P., F.B., H.V.), and Neurology (I.D., B.M.J.U.), Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VUmc, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), UCL, London, UK.

Objective: To determine which pathologic process could be responsible for the acceleration of cognitive decline during the course of multiple sclerosis (MS), using longitudinal structural MRI, which was related to cognitive decline in relapsing-remitting MS (RRMS) and progressive MS (PMS).

Methods: A prospective cohort of 230 patients with MS (179 RRMS and 51 PMS) and 59 healthy controls was evaluated twice with 5-year (mean 4.9, SD 0.94) interval during which 22 patients with RRMS converted to PMS. Annual rates of cortical and deep gray matter atrophy as well as lesion volume increase were computed on longitudinal (3T) MRI data and correlated to the annual rate of cognitive decline as measured using an extensive cognitive evaluation at both time points.

Results: The deep gray matter atrophy rate did not differ between PMS and RRMS (-0.82%/year vs -0.71%/year, = 0.11), while faster cortical atrophy was observed in PMS (-0.87%/year vs -0.48%/year, < 0.01). Similarly, faster cognitive decline was observed in PMS compared to RRMS ( < 0.01). Annual cognitive decline was related to the rate of annual lesion volume increase in stable RRMS ( = -0.17, = 0.03) to the rate of annual deep gray matter atrophy in converting RRMS ( = 0.50, = 0.02) and annual cortical atrophy in PMS ( = 0.35, = 0.01).

Conclusions: These results indicate that cortical atrophy and cognitive decline accelerate together during the course of MS. Substrates of cognitive decline shifted from worsening lesional pathology in stable RRMS to deep gray matter atrophy in converting RRMS and to accelerated cortical atrophy in PMS only.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1212/WNL.0000000000008198DOI Listing
October 2019

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral.

Ann Neurol 2019 11 22;86(5):704-713. Epub 2019 Aug 22.

Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.

Objective: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method.

Methods: Baseline and 1-year spinal cord 3-dimensional T1-weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome [CIS], 196 relapsing-remitting MS [RRMS], 34 progressive MS [PMS]), and 82 controls from 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer and multi-field-strength scans. Spinal Cord Toolbox was used for C2-5 segmentation and cross-sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary-shift region of interest. CSA and GBSI percentage annual volume change was calculated.

Results: GBSI provided similar rates of atrophy, but reduced measurement variability compared to CSA in all MS subtypes (CIS: -0.95 ± 2.11% vs -1.19 ± 3.67%; RRMS: -1.74 ± 2.57% vs -1.74 ± 4.02%; PMS: -2.29 ± 2.40% vs -1.29 ± 3.20%) and healthy controls (0.02 ± 2.39% vs -0.56 ± 3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (area under the curve [AUC] = 0.66 vs 0.53; p = 0.03), RRMS (AUC = 0.73 vs 0.59; p < 0.001), PMS (AUC = 0.77 vs 0.53; p < 0.001), and patients with disability progression from patients without progression (AUC = 0.59 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 year was lower for GBSI than CSA (CIS: 106 vs 830; RRMS: 95 vs 335; PMS: 44 vs 215; power = 80%; alpha = 5%).

Interpretation: The registration-based method (GBSI) allowed better separation between MS patients and healthy controls and improved statistical power, when compared with a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019 ANN NEUROL 2019;86:704-713.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/ana.25571DOI Listing
November 2019

Lifespan normative data on rates of brain volume changes.

Neurobiol Aging 2019 09 22;81:30-37. Epub 2019 May 22.

Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy. Electronic address:

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neurobiolaging.2019.05.010DOI Listing
September 2019

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease.

Hum Brain Mapp 2019 09 3;40(13):3900-3909. Epub 2019 Jun 3.

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1-w/T2-w ratio value). We selected structural T1-w and T2-w MR images of 293 AD patients and 172 participants with normal cognition (NC). T1-w/T2-w ratios were computed for the whole brain and within 90 automated anatomical labeling atlas regions using SPM12, compared between groups and correlated with the neuronal injury marker tau in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE). In contrast to our hypothesis, AD patients showed higher whole brain T1-w/T2-w ratios than NC, and regionally in 31 anatomical areas (p < .0005; d = 0.21 to 0.48), predominantly in the inferior parietal lobule, angular gyrus, anterior cingulate, and precuneus. Regional higher T1-w/T2-w values were associated with higher CSF tau concentrations (p < .0005; r = .16 to .22) and worse MMSE scores (p < .0005; r = -.16 to -.21). These higher T1-w/T2-w values in AD seem to contradict previous pathological findings of demyelination and disconnectivity in AD. Future research should further investigate the biological processes reflected by increases in T1-w/T2-w values.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hbm.24638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771703PMC
September 2019

Novel imaging phantom for accurate and robust measurement of brain atrophy rates using clinical MRI.

Neuroimage Clin 2019 4;21:101667. Epub 2019 Jan 4.

Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands.

Brain volume loss, or atrophy, has been proven to be an important characteristic of neurological diseases such as Alzheimer's disease and multiple sclerosis. To use atrophy rate as a reliable clinical biomarker and to increase statistical power in clinical treatment trials, measurement variability needs to be minimized. Among other sources, systematic differences between different MR scanners are suspected to contribute to this variability. In this study we developed and performed initial validation tests of an MR-compatible phantom and analysis software for robust and reliable evaluation of the brain volume loss. The phantom contained three inflatable models of brain structures, i.e. cerebral hemisphere, putamen, and caudate nucleus. Software to reliably quantify volumes form the phantom images was also developed. To validate the method, the phantom was imaged using 3D T1-weighted protocols at three clinical 3T MR scanners from different vendors. Calculated volume change from MRI was compared with the known applied volume change using ICC and mean absolute difference. As assessed by the ICC, the agreement between our developed software and the applied volume change for different structures ranged from 0.999-1 for hemisphere, 0.976-0.998 for putamen, and 0.985-0.999 for caudate nucleus. The mean absolute differences between measured and applied volume change were 109-332 μL for hemisphere, 2.9-11.9 μL for putamen, and 2.2-10.1 μL for caudate nucleus. This method offers a reliable and robust measurement of volume change using MR images and could potentially be used to standardize clinical measurement of atrophy rates.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2019.101667DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350260PMC
January 2020

FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method.

PLoS One 2019 18;14(1):e0210641. Epub 2019 Jan 18.

Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.

Objective: The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).

Materials And Methods: FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).

Results: With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.

Conclusions: FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210641PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338359PMC
October 2019

Contactin-1 and contactin-2 in cerebrospinal fluid as potential biomarkers for axonal domain dysfunction in multiple sclerosis.

Mult Scler J Exp Transl Clin 2018 Oct-Dec;4(4):2055217318819535. Epub 2018 Dec 24.

Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands.

Background: Contactin-1 and contactin-2 are important for the maintenance of axonal integrity.

Objective: To investigate the cerebrospinal fluid levels of contactin-1 and contactin-2 in multiple sclerosis patients and controls, and their potential use as prognostic markers for neurodegeneration.

Methods: Cerebrospinal fluid contactin-1 and contactin-2 were measured in relapsing-remitting multiple sclerosis (41), secondary progressive multiple sclerosis (26) and primary progressive multiple sclerosis patients (13) and controls (18), and in a second cohort with clinically isolated syndrome patients (88, median clinical follow-up period of 2.3 years) and controls (20). Correlations/linear regressions were analysed with other baseline cerebrospinal fluid axonal damage markers and cross-sectional/longitudinal magnetic resonance imaging features.

Results: Contactin-1 and contactin-2 levels were up to 1.4-fold reduced in relapsing-remitting multiple sclerosis (contactin-1:  = 0.01, contactin-2:  = 0.02) and secondary progressive multiple sclerosis (contactin-1:  = 0.05, contactin-2:  = 0.02) compared to controls. In clinically isolated syndrome patients, contactin-1 tended to increase when compared to controls ( = 0.07). Both contactin-1 and contactin-2 correlated with neurofilament light, neurofilament heavy and magnetic resonance imaging metrics differently depending on the disease stage. In clinically isolated syndrome patients, baseline contactin-2 level (β = -0.42,  = 0.04) predicted the longitudinal decline in cortex volume.

Conclusion: Cerebrospinal fluid contactin-1 and contactin-2 reveal axonal dysfunction in various stages of multiple sclerosis and their inclusion to the biomarker panel may provide better insight into the extent of axonal damage/dysfunction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/2055217318819535DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305953PMC
December 2018

Unraveling treatment response in multiple sclerosis: A clinical and MRI challenge.

Neurology 2019 01 26;92(4):180-192. Epub 2018 Dec 26.

From the Department of Neurosciences (C.G., L.P.), San Camillo-Forlanini Hospital, Rome, Italy; Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology (M.T., J.R., J.S.-G., X.M.), and Magnetic Resonance Unit, Department of Radiology (A.R.), Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Spain; Biostatistics Unit (M.P.S.), Department of Health Sciences, University of Genoa; Neuroimaging Research Unit (M.F., M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy; Nuffield Department of Clinical Neurosciences (J.P.), West Wing, John Radcliffe Hospital, Oxford; Institutes of Neurology & Healthcare Engineering (O.C., F.B.), University College London (O.C.), UK; Amsterdam Neuroscience and Department of Radiology and Nuclear Medicine (F.B., H.V.), VU University Medical Center, Amsterdam, the Netherlands; Department of Neurology (J.L.F.), Rigshospitalet Glostrup and University of Copenhagen, Denmark; Neuroradiological Academic Unit (T.A.Y.), Institute of Neurology, London, UK; Department of Neurology (C.E.), Medical University of Graz, Austria; Neurologic Clinic and Policlinic, Department of Medicine (L.K.), Clinical Research, Biomedicine and Biomedical Engineering, University Hospital Basel, University of Basel, Switzerland; Department of Neurology and Psychiatry (C.P.), Sapienza University, Rome; and Neurology and Neurometabolic Unit, Department of Neurological and Behavioral Sciences (N.D.S.), University of Siena, Italy.

Over the last few decades, the improved diagnostic criteria, the wide use of MRI, and the growing availability of effective pharmacologic treatments have led to substantial advances in the management of multiple sclerosis (MS). The importance of early diagnosis and treatment is now well-established, but there is still no consensus on how to define and monitor response to MS treatments. In particular, the clinical relevance of the detection of minimal MRI activity is controversial and recommendations on how to define and monitor treatment response are warranted. An expert panel of the Magnetic Resonance Imaging in MS Study Group analyzed and discussed published studies on treatment response in MS. The evolving concept of no evidence of disease activity and its effect on predicting long-term prognosis was examined, including the option of defining a more realistic target for daily clinical practice: minimal evidence of disease activity. Advantages and disadvantages associated with the use of MRI activity alone and quantitative scoring systems combining on-treatment clinical relapses and MRI active lesions to detect treatment response in the real-world setting were also discussed. While most published studies on this topic involved patients treated with interferon-β, special attention was given to more recent studies providing evidence based on treatment with other and more efficacious oral and injectable drugs. Finally, the panel identified future directions to pursue in this research field.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1212/WNL.0000000000006810DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345120PMC
January 2019

MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.

Brain Imaging Behav 2019 Oct;13(5):1361-1374

Department of Statistics, University of Warwick, Coventry, UK.

Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11682-018-9942-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733701PMC
October 2019

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI.

Neuroimage Clin 2018 26;19:466-475. Epub 2018 Apr 26.

Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2018.04.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030805PMC
January 2019

Progression of regional grey matter atrophy in multiple sclerosis.

Brain 2018 06;141(6):1665-1677

Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article.Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T2 lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T2 lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration (P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/brain/awy088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995197PMC
June 2018

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

J Alzheimers Dis 2018 ;62(4):1827-1839

Institute of Psychology, Leiden University, Leiden, The Netherlands.

Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.

Methods: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).

Results: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).

Conclusion: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/JAD-170893DOI Listing
May 2019

Deep gray matter volume loss drives disability worsening in multiple sclerosis.

Ann Neurol 2018 02 6;83(2):210-222. Epub 2018 Feb 6.

Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.

Objective: Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.

Methods: We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.

Results: SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).

Interpretation: This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/ana.25145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838522PMC
February 2018

Gray matter networks and cognitive impairment in multiple sclerosis.

Mult Scler 2019 03 11;25(3):382-391. Epub 2018 Jan 11.

Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands.

Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS).

Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS.

Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level.

Results: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values.

Conclusion: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1352458517751650DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6393954PMC
March 2019

Thinner cortex in patients with subjective cognitive decline is associated with steeper decline of memory.

Neurobiol Aging 2018 01 20;61:238-244. Epub 2017 Sep 20.

Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands; Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands.

We aimed to investigate associations between regional cortical thickness and rate of decline over time in 4 cognitive domains in patients with subjective cognitive decline (SCD). We included 233 SCD patients with the total number of 654 neuropsychological assessments (median = 3, range = 2-8) and available baseline magnetic resonance imaging from the Amsterdam Dementia Cohort (125 males, age: 63 ± 9, Mini-Mental State Examination score: 28 ± 2). We assessed longitudinal cognitive functioning at baseline and follow-up in 4 cognitive domains (composite Z-scores): memory, attention, executive function, and language. Thickness (millimeter) was estimated using FreeSurfer for frontal, temporal, parietal, cingulate, and occipital cortices. We used linear mixed models to estimate effects of cortical thickness on cognitive performance (dependent variables). There were no associations between cortical thickness and baseline cognition, but a faster subsequent rate of memory loss was associated with thinner cortex of the frontal [β (SE) = 0.20 (0.07)], temporal [β (SE) = 0.18 (0.07)], and occipital [β (SE) = 0.22 (0.09)] cortices (all p < 0.05). These findings illustrate that early cortical changes, particularly in the temporal cortex, herald incipient cognitive decline related to neurodegenerative diseases, most prominently Alzheimer's disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neurobiolaging.2017.09.009DOI Listing
January 2018

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study.

Neuroimage 2017 12 9;163:106-114. Epub 2017 Sep 9.

Department of Radiology and Nuclear Medicine, VUmc, Amsterdam, The Netherlands; Department of Anatomy and Neuroscience, VUmc, Amsterdam, The Netherlands.

Background And Purpose: In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset.

Methods: 70 MS patients (median EDSS of 2.0 [range 0.0-6.5]) were included from a six-center dataset of the MAGNIMS Study Group (www.magnims.eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center.

Results: Compared to the reference mean lesion volume (4.85 ± 7.29 mL), the methods displayed a mean difference of 1.60 ± 4.83 (Cascade), 2.31 ± 7.66 (LGA), 0.44 ± 4.68 (LPA), 1.76 ± 4.17 (Lesion-TOADS) and -1.39 ± 4.10 mL (kNN-TTP). The ICCs were 0.755, 0.713, 0.851, 0.806 and 0.723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0.37 ± 0.23), Lesion-TOADS (SI = 0.35 ± 0.18) and kNN-TTP (SI = 0.44 ± 0.14) than for Cascade (SI = 0.26 ± 0.17) or LGA (SI = 0.31 ± 0.23). All methods showed highly similar results when used on data from a center not used in software parameter optimization.

Conclusion: The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2017.09.011DOI Listing
December 2017

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy.

Neuroimage Clin 2017 30;15:843-853. Epub 2017 Jun 30.

Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, The Netherlands; Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, The Netherlands. Electronic address:

Introduction: Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and MRI data.

Methods: Whole-brain and gray matter (GM) volume were measured with the cross-sectional pipeline of MSmetrix and compared to the outcomes of FreeSurfer (cross-sectional pipeline), SIENAX and SPM. For this comparison we investigated 20 simulated brain images, as well as data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA.

Results: In the simulated data, SIENAX displayed the smallest average deviation compared with the reference whole-brain volume (+ 19.56 ± 10.34 mL), followed by MSmetrix (- 38.15 ± 17.77 mL), SPM (- 42.99 ± 17.12 mL) and FreeSurfer (- 78.51 ± 12.68 mL). A similar pattern was seen . Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study.

Conclusion: MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2017.06.034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540882PMC
June 2018

Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations.

PLoS One 2017 9;12(2):e0166785. Epub 2017 Feb 9.

Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands.

Purpose: Precise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations.

Materials And Methods: This study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions.

Results: For the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible.

Discussion/conclusion: A novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166785PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300281PMC
August 2017