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Bose G, Healy BC, Saxena S, Saleh F, Glanz BI, Bakshi R, Weiner HL, Chitnis T. Increasing Neurofilament and Glial Fibrillary Acidic Protein After Treatment Discontinuation Predicts Multiple Sclerosis Disease Activity. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:e200167. [PMID: 37813595 PMCID: PMC10574823 DOI: 10.1212/nxi.0000000000200167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/17/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Stable patients with multiple sclerosis (MS) may discontinue treatment, but the risk of disease activity is unknown. Serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) are biomarkers of subclinical disease activity and may help risk stratification. In this study, sNfL and sGFAP levels in stable patients were evaluated before and after treatment discontinuation to determine association with disease activity. METHODS This observational study included patients enrolled in the Comprehensive Longitudinal Investigation in MS at the Brigham and Women's Hospital who discontinued treatment after >2 years disease activity-free. Two serum samples within 2 years, before and after treatment stop, were sent for sNfL and sGFAP measurements by single-molecule array. Biannual neurologic examinations and yearly MRI scans determined disease activity by 3 time-to-event outcomes: 6-month confirmed disability worsening (CDW), clinical attacks, and MRI activity (new T2 or contrast-enhancing lesions). Associations between each outcome and log-transformed sNfL and sGFAP levels pretreatment stop and posttreatment stop and the percent change were estimated using multivariable Cox regression analysis adjusting for age, disability, disease duration, and duration from attack before treatment stop. RESULTS Seventy-eight patients (92% female) discontinued treatment at a median (interquartile range) age of 48.5 years (39.0-55.7) and disease duration of 12.3 years (7.5-18.8) and were followed up for 6.3 years (4.2-8.5). CDW occurred in 27 patients (35%), new attacks in 19 (24%), and new MRI activity in 26 (33%). Higher posttreatment stop sNfL level was associated with CDW (adjusted hazard ratio (aHR) 2.80, 95% CI 1.36-5.76, p = 0.005) and new MRI activity (aHR 3.09, 95% CI 1.42-6.70, p = 0.004). Patients who had >100% increase in sNfL level from pretreatment stop to posttreatment stop had greater risk of CDW (HR 3.87, 95% CI 1.4-10.7, p = 0.009) and developing new MRI activity (HR 4.02, 95% CI 1.51-10.7, p = 0.005). Patients who had >50% increase in sGFAP level also had greater risk of CDW (HR 5.34, 95% CI 1.4-19.9, p = 0.012) and developing new MRI activity (HR 5.16, 95% CI 1.71-15.6, p = 0.004). DISCUSSION Stable patients who discontinue treatment may be risk stratified by sNfL and sGFAP levels measured before and after discontinuing treatment. Further studies are needed to validate findings and determine whether resuming treatment in patients with increasing biomarker levels reduces risk of subsequent disease activity.
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Affiliation(s)
- Gauruv Bose
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Brian C Healy
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Shrishti Saxena
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Fermisk Saleh
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Bonnie I Glanz
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Rohit Bakshi
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Howard L Weiner
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada
| | - Tanuja Chitnis
- From the Department of Neurology (G.B., B.C.H., S.S., F.S., B.I.G., R.B., H.L.W., T.C.), Brigham and Women's Hospital, Boston, MA; Harvard Medical School (G.B., B.C.H., B.I.G., R.B., H.L.W., T.C.), Boston, MA; The University of Ottawa and Ottawa Hospital Research Institute (G.B.), Ottawa, Canada.
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Lacharie M, Villa A, Milidonis X, Hasaneen H, Chiribiri A, Benedetti G. Role of pulmonary perfusion magnetic resonance imaging for the diagnosis of pulmonary hypertension: A review. World J Radiol 2023; 15:256-273. [PMID: 37823020 PMCID: PMC10563854 DOI: 10.4329/wjr.v15.i9.256] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/16/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023] Open
Abstract
Among five types of pulmonary hypertension, chronic thromboembolic pulmonary hypertension (CTEPH) is the only curable form, but prompt and accurate diagnosis can be challenging. Computed tomography and nuclear medicine-based techniques are standard imaging modalities to non-invasively diagnose CTEPH, however these are limited by radiation exposure, subjective qualitative bias, and lack of cardiac functional assessment. This review aims to assess the methodology, diagnostic accuracy of pulmonary perfusion imaging in the current literature and discuss its advantages, limitations and future research scope.
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Affiliation(s)
- Miriam Lacharie
- Oxford Centre of Magnetic Resonance Imaging, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Adriana Villa
- Department of Diagnostic and Interventional Radiology, German Oncology Centre, Limassol 4108, Cyprus
| | - Xenios Milidonis
- Deep Camera MRG, CYENS Centre of Excellence, Nicosia, Cyprus, Nicosia 1016, Cyprus
| | - Hadeer Hasaneen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Kings Coll London, Div Imaging Sci, St Thomas Hospital, London WC2R 2LS, United Kingdom
| | - Giulia Benedetti
- Department of Cardiovascular Imaging and Biomedical Engineering, King’s College London, London WC2R 2LS, United Kingdom
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The Retrospective Study of Magnetic Resonance Imaging Signal Intensity Ratio in the Quantitative Diagnosis of Temporomandibular Condylar Resorption in Young Female Patients. J Pers Med 2023; 13:jpm13030378. [PMID: 36983560 PMCID: PMC10057084 DOI: 10.3390/jpm13030378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/24/2023] Open
Abstract
According to the literature, there is no reliable and quantitative method available for the diagnosis and prognosis of early or potential temporomandibular joint (TMJ) condylar resorption (CR) thus far. The purpose of this study was to raise a new noninvasive method to quantitatively evaluate condylar quality using the signal intensity ratio (SIR) on magnetic resonance imaging (MRI) in order to assist in the diagnosis of TMJ CR. A retrospective exploratory study was performed to compare the condyle-to-cerebral cortex signal intensity ratios (SIR) on MRI among young female patients. We included 60 patients, and they were divided into three groups: the bilateral normal TMJ group (group 1), the bilateral TMJ anterior disc displacement (ADD) but without CR group (group 2), and the bilateral TMJ anterior disc displacement (ADD) with CR group (group 3). The SIR difference between the three groups was analyzed by the Kruskal–Wallis test (K-W test). The sensitivity, specificity, accuracy, and area under curve (AUC) were calculated by the receiver operating characteristic (ROC) curves. There was high consistency between the surgeon and the radiologist in the evaluation of the magnetic signal intensity with intraclass correlation coefficients of 0.939–0.999. The average SIR was 1.07 in the bilateral normal TMJ group (group 1), 1.03 in the ADD without CR group (group 2), and 0.78 in the ADD with CR group (group 3). It could be found by the K-W test that group 3 was significantly different from group 1 and group 2 (p < 0.05), while there was no significant difference between group 1 and group 2. The optimal critical SIR value was 0.96 for the diagnosis of CR according to the ROC curves and Youden index (p < 0.001, AUC = 0.9). The condyle-to-cerebral cortex SIR can be used as a noninvasive diagnostic tool for the quantitative evaluation of condylar quality and diagnosis and prognosis of CR. SIR ≥ 0.96 indicates a healthy condyle, while SIR < 0.96 is considered the optimal critical value for the diagnosis of CR. These findings are important for personalized and accurate treatment and prognosis prediction.
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AbdelRazek MA, Tummala S, Khalid F, Tauhid S, Jalkh Y, Khalil S, Hurwitz S, Zurawski J, Bakshi R. Exploring the effect of glatiramer acetate on cerebral gray matter atrophy in multiple sclerosis. J Neurol Sci 2023; 444:120501. [PMID: 36481574 DOI: 10.1016/j.jns.2022.120501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND PURPOSE Cerebral gray matter (GM) atrophy is a proposed measure of neuroprotection in multiple sclerosis (MS). Glatiramer acetate (GA) limits clinical relapses, MRI lesions, and whole brain atrophy in relapsing-remitting MS (RRMS). The effect of GA on GM atrophy remains unclear. We assessed GM atrophy in patients with RRMS starting GA therapy in comparison to a cohort of patients with clinically benign RRMS (BMS). DESIGN/METHODS We studied 14 patients at GA start [age (mean ± SD) 44.2 ± 7.0 years, disease duration (DD) 7.2 ± 6.4 years, Expanded Disability Status Scale score (EDSS) (median,IQR) 1.0,2.0] and 6 patients with BMS [age 43.0 ± 6.1 years, DD 18.1 ± 8.4 years, EDSS 0.5,1.0]. Brain MRI was obtained at baseline and one year later (both groups) and two years later in all patients in the GA group except one who was lost to follow-up. Semi-automated algorithms assessed cerebral T2 hyperintense lesion volume (T2LV), white matter fraction (WMF), GM fraction (GMF), and brain parenchymal fraction (BPF). The exact Wilcoxon-Mann-Whitney test compared the groups. The Wilcoxon signed rank test assessed longitudinal changes within groups. RESULTS During the first year, MRI changes did not differ significantly between groups (p > 0.15). Within the BMS group, WMF and BPF decreased during the first year (p = 0.03). Within the GA group, there was no significant change in MRI measures during each annual period (p > 0.05). Over two years, the GA group had a significant increase in T2LV and decrease in WMF (p < 0.05), while GMF and BPF remained stable (p > 0.05). MRI changes in brain volumes (GMF or WMF) in the first year in the GA group were not significantly different from those in the BMS group (p > 0.5). CONCLUSIONS In this pilot study with a small sample size, patients with RRMS started on GA did not show significant GM or whole brain atrophy over 2 years, resembling MS patients with a clinically benign disease course.
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Affiliation(s)
| | - Subhash Tummala
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fariha Khalid
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shahamat Tauhid
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Youmna Jalkh
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Samar Khalil
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shelley Hurwitz
- Departments of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan Zurawski
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rohit Bakshi
- Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Departments of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Bose G, Healy BC, Lokhande HA, Sotiropoulos MG, Polgar‐Turcsanyi M, Anderson M, Glanz BI, Guttman CRG, Bakshi R, Weiner HL, Chitnis T. Early predictors of clinical and MRI outcomes using LASSO in multiple sclerosis. Ann Neurol 2022; 92:87-96. [DOI: 10.1002/ana.26370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/28/2022] [Accepted: 04/10/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Gauruv Bose
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Brian C. Healy
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Hrishikesh A. Lokhande
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Marinos G. Sotiropoulos
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Mariann Polgar‐Turcsanyi
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Mark Anderson
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Bonnie I. Glanz
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Charles R. G. Guttman
- Harvard Medical School Boston MA US
- Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital Boston MA US
| | - Rohit Bakshi
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Howard L. Weiner
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Tanuja Chitnis
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
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BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Med Image Anal 2021; 74:102233. [PMID: 34655865 PMCID: PMC9916535 DOI: 10.1016/j.media.2021.102233] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 09/04/2021] [Accepted: 09/10/2021] [Indexed: 01/11/2023]
Abstract
Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.
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Healy BC, Glanz BI, Swallow E, Signorovitch J, Hagan K, Silva D, Pelletier C, Chitnis T, Weiner H. Confirmed disability progression provides limited predictive information regarding future disease progression in multiple sclerosis. Mult Scler J Exp Transl Clin 2021; 7:2055217321999070. [PMID: 33953937 PMCID: PMC8042549 DOI: 10.1177/2055217321999070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 02/09/2021] [Indexed: 11/16/2022] Open
Abstract
Background Although confirmed disability progression (CDP) is a common outcome in multiple sclerosis (MS) clinical trials, its predictive value for long-term outcomes is uncertain. Objective To investigate whether CDP at month 24 predicts subsequent disability accumulation in MS. Methods The Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women's Hospital includes participants with relapsing-remitting MS or clinically isolated syndrome with Expanded Disability Status Scale (EDSS) scores ≤5 (N = 1214). CDP was assessed as a predictor of time to EDSS score 6 (EDSS 6) and to secondary progressive MS (SPMS) using a Cox proportional hazards model; adjusted models included additional clinical/participant characteristics. Models were compared using Akaike's An Information Criterion. Results CDP was directionally associated with faster time to EDSS 6 in univariate analysis (HR = 1.61 [95% CI: 0.83, 3.13]). After adjusting for month 24 EDSS, CDP was directionally associated with slower time to EDSS 6 (adjusted HR = 0.65 [0.32, 1.28]). Models including CDP had worse fit statistics than those using EDSS scores without CDP. When models included clinical and magnetic resonance imaging measures, T2 lesion volume improved fit statistics. Results were similar for time to SPMS. Conclusions CDP was less predictive of time to subsequent events than other MS clinical features.
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Melazzini L, Vitali P, Olivieri E, Bolchini M, Zanardo M, Savoldi F, Di Leo G, Griffanti L, Baselli G, Sardanelli F, Codari M. White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2020; 53:1732-1743. [PMID: 33345393 DOI: 10.1002/jmri.27479] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. PURPOSE To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. STUDY TYPE Systematic review and meta-analysis. POPULATION In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. FIELD STRENGTH/SEQUENCE 1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T2 -weighted fast spin echo sequences or gradient echo T1 -weighted sequences. ASSESSMENT After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. STATISTICAL TESTS The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed. RESULTS Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88-5.53 cm3 ). At meta-regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27). DATA CONCLUSION The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Luca Melazzini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Paolo Vitali
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Emanuele Olivieri
- Medicine and Surgery Medical School, Università degli Studi di Milano, Milano, Italy
| | - Marco Bolchini
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Moreno Zanardo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Filippo Savoldi
- Postgraduate School in Radiology, Università degli Studi di Milano, Milano, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Ludovica Griffanti
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Oxford, UK
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
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Denis de Senneville B, Manjón JV, Coupé P. RegQCNET: Deep quality control for image-to-template brain MRI affine registration. Phys Med Biol 2020; 65:225022. [PMID: 32906089 DOI: 10.1088/1361-6560/abb6be] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert's visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information. The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in the processing pipeline.
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10
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Bakshi R, Healy BC, Dupuy SL, Kirkish G, Khalid F, Gundel T, Asteggiano C, Yousuf F, Alexander A, Hauser SL, Weiner HL, Henry RG. Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study. J Neuroimaging 2020; 30:212-218. [PMID: 31994814 DOI: 10.1111/jon.12688] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND AND PURPOSE Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations. METHODS Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV). RESULTS The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data. CONCLUSIONS MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.
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Affiliation(s)
- Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA.,Department of Radiology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Brian C Healy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Sheena L Dupuy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Gina Kirkish
- Department of Neurology, University of California, San Francisco, CA
| | - Fariha Khalid
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Tristan Gundel
- Department of Neurology, University of California, San Francisco, CA
| | - Carlo Asteggiano
- Department of Neurology, University of California, San Francisco, CA
| | - Fawad Yousuf
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Amber Alexander
- Department of Neurology, University of California, San Francisco, CA
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, CA
| | - Howard L Weiner
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, CA
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- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
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11
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Guo C, Niu K, Luo Y, Shi L, Wang Z, Zhao M, Wang D, Zhu W, Zhang H, Sun L. Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification. Front Neurosci 2019; 13:679. [PMID: 31354406 PMCID: PMC6635556 DOI: 10.3389/fnins.2019.00679] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 06/13/2019] [Indexed: 11/13/2022] Open
Abstract
Objectives: To evaluate white matter hyperintensities (WMH) quantification reproducibility from multiple aspects of view and examine the effects of scan-rescan procedure, types of scanner, imaging protocols, scanner software upgrade, and automatic segmentation tools on WMH quantification results using magnetic resonance imaging (MRI). Methods: Six post-stroke subjects (4 males; mean age = 62.8, range = 58-72 years) were scanned and rescanned with both 3D T1-weighted, 2D and 3D T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI across four different MRI scanners within 12 h. Two automated WMH segmentation and quantification tools were used to measure WMH volume based on each MR scan. Robustness was assessed using the coefficient of variation (CV), Dice similarity coefficient (DSC), and intra-class correlation (ICC). Results: Experimental results show that the best reproducibility was achieved by using 3D T2-FLAIR MRI under intra-scanner setting with CV ranging from 2.69 to 2.97%, while the largest variability resulted from comparing WMH volumes measured based on 2D T2-FLAIR MRI with those of 3D T2-FLAIR MRI, with CV values in the range of 15.62%-29.33%. The WMH quantification variability based on 2D MRIs is larger than 3D MRIs due to their large slice thickness. The DSC of WMH segmentation labels between intra-scanner MRIs ranges from 0.63 to 0.77, while that for inter-scanner MRIs is in the range of 0.63-0.65. In addition to image acquisition, the choice of automatic WMH segmentation tool also has a large impact on WMH quantification. Conclusion: WMH reproducibility is one of the primary issues to be considered in multicenter and longitudinal studies. The study provides solid guidance in assisting multicenter and longitudinal study design to achieve meaningful results with enough power.
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Affiliation(s)
- Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Kai Niu
- Department of Otorhinolaryngology Head and Neck Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yishan Luo
- BrainNow Medical Technology Limited, Sha Tin, Hong Kong
| | - Lin Shi
- BrainNow Medical Technology Limited, Sha Tin, Hong Kong.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong
| | - Zhuo Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Meng Zhao
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
| | - Defeng Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong
| | - Wan'an Zhu
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Li Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
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12
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Hemond CC, Healy BC, Tauhid S, Mazzola MA, Quintana FJ, Gandhi R, Weiner HL, Bakshi R. MRI phenotypes in MS: Longitudinal changes and miRNA signatures. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2019; 6:e530. [PMID: 30800720 PMCID: PMC6384020 DOI: 10.1212/nxi.0000000000000530] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/09/2018] [Indexed: 12/20/2022]
Abstract
Objective To classify and immunologically characterize persons with MS based on brain lesions and atrophy and their associated microRNA profiles. Methods Cerebral T2-hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF) were quantified and used to define MRI phenotypes as follows: type I: low T2LV, low atrophy; type II: high T2LV, low atrophy; type III: low T2LV, high atrophy; type IV: high T2LV, high atrophy, in a large cross-sectional cohort (n = 1,088) and a subset with 5-year lngitudinal follow-up (n = 153). Serum miRNAs were assessed on a third MS cohort with 2-year MRI phenotype stability (n = 98). Results One-third of the patients had lesion-atrophy dissociation (types II or III) in both the cross-sectional and longitudinal cohorts. At 5 years, all phenotypes had progressive atrophy (p < 0.001), disproportionally in type II (BPF -2.28%). Only type IV worsened in physical disability. Types I and II showed a 5-year MRI phenotype conversion rate of 33% and 46%, whereas III and IV had >90% stability. Type II switched primarily to IV (91%); type I switched primarily to II (47%) or III (37%). Baseline higher age (p = 0.006) and lower BPF (p < 0.001) predicted 5-year phenotype conversion. Each MRI phenotype demonstrated an miRNA signature whose underlying biology implicates blood-brain barrier pathology: hsa.miR.22.3p, hsa.miR.361.5p, and hsa.miR.345.5p were the most valid differentiators of MRI phenotypes. Conclusions MRI-defined MS phenotypes show high conversion rates characterized by the continuation of either predominant neurodegeneration or inflammation and support the partial independence of these 2 measures. MicroRNA signatures of these phenotypes suggest a role for blood-brain barrier integrity.
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Affiliation(s)
- Christopher C Hemond
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Brian C Healy
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Shahamat Tauhid
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Maria A Mazzola
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Francisco J Quintana
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Roopali Gandhi
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Howard L Weiner
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Rohit Bakshi
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
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13
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Hemond CC, Glanz BI, Bakshi R, Chitnis T, Healy BC. The neutrophil-to-lymphocyte and monocyte-to-lymphocyte ratios are independently associated with neurological disability and brain atrophy in multiple sclerosis. BMC Neurol 2019; 19:23. [PMID: 30755165 PMCID: PMC6371437 DOI: 10.1186/s12883-019-1245-2] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 01/30/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Serum hematological indices such as the neutrophil-lymphocyte ratio (NLR) or monocyte-lymphocyte ratio (MLR) have been used as biomarkers of pathogenic inflammation and prognostication in multiple areas of medicine; recent evidence shows correlation with psychological parameters as well. OBJECTIVES/AIMS To characterize clinical, neuroimaging, and psycho-neuro-immunological associations with NLR and MLR in persons with multiple sclerosis (MS). METHODS We identified a large cohort of clinically well-defined patients from our longitudinal database that included MS-related outcomes, disease-modifying therapy, patient-reported outcome (PRO) measures, and quantified cerebral MRI at 1.5 T. We queried hospital records for complete blood counts within 2 months of each clinic visit and excluded those obtained during clinical relapses. Four hundred eighty-three patients, with a mean of 3 longitudinal observations each, were identified who met these criteria. Initial analyses assessed the association between NLR and MLR as the outcomes, and psychological and demographic predictors in univariable and multivariable models controlling for age, gender and treatment. The second set of analyses assessed the association between clinical and MRI outcomes including whole brain atrophy and T2-hyperintense lesion volume, with NLR and MLR as predictors in univariable and multivariable models. All analyses used a mixed effects linear or logistic regression model with repeated measures. RESULTS Unadjusted analyses demonstrated significant associations between higher (log-transformed) NLR (but not MLR) and PRO measures including increasing depression (p = 0.01), fatigue (p < 0.01), and decreased physical quality of life (p < 0.01). Higher NLR and MLR strongly predicted increased MS-related disability as assessed by the Expanded Disability Status Scale, independent of all demographic, clinical, treatment-related, and psychosocial variables (p < 0.001). Lastly, higher NLR and MLR significantly discriminated progressive from relapsing status (p ≤ 0.01 for both), and higher MLR correlated with increased whole-brain atrophy (p < 0.05) but not T2 hyperintense lesion volume (p > 0.05) even after controlling for all clinical and demographic covariates. Sensitivity analyses using a subset of untreated patients (N = 146) corroborated these results. CONCLUSIONS Elevated NLR and MLR may represent hematopoetic bias toward increased production and pro-inflammatory priming of the myeloid innate immune system (numerator) in conjunction with dysregulated adaptive immune processes (denominator), and consequently reflect a complementary and independent marker for severity of MS-related neurological disability and MRI outcomes.
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Affiliation(s)
- Christopher C. Hemond
- Department of Neurology, University of Massachusetts Medical Center, 55 Lake Ave North, Worcester, MA 01655 USA
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Brigham & Women’s Hospital, Boston, MA USA
| | - Bonnie I. Glanz
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Brigham & Women’s Hospital, Boston, MA USA
| | - Rohit Bakshi
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Brigham & Women’s Hospital, Boston, MA USA
- Department of Radiology, Harvard Medical School, Boston, MA USA
| | - Tanuja Chitnis
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Brigham & Women’s Hospital, Boston, MA USA
| | - Brian C. Healy
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Brigham & Women’s Hospital, Boston, MA USA
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14
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Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Vallee E, Vidaurre D, Webster M, McCarthy P, Rorden C, Daducci A, Alexander DC, Zhang H, Dragonu I, Matthews PM, Miller KL, Smith SM. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 2018; 166:400-424. [PMID: 29079522 PMCID: PMC5770339 DOI: 10.1016/j.neuroimage.2017.10.034] [Citation(s) in RCA: 875] [Impact Index Per Article: 125.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 12/22/2022] Open
Abstract
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
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Affiliation(s)
- Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Neal K Bangerter
- Electrical and Computer Engineering, Brigham Young University, UT, USA
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Moises Hernandez-Fernandez
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Emmanuel Vallee
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK
| | - Matthew Webster
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Christopher Rorden
- Department of Psychology and McCausland Center for Brain Imaging, University of South Carolina, SC, USA
| | - Alessandro Daducci
- Computer Science Department, University of Verona, Italy; Radiology Department, University Hospital Center, Switzerland
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | | | - Paul M Matthews
- Division of Brain Sciences, Imperial College, London, UK; UK Dementia Research Institute, London, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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15
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Meier DS, Guttmann CRG, Tummala S, Moscufo N, Cavallari M, Tauhid S, Bakshi R, Weiner HL. Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI. J Neuroimaging 2017; 28:36-47. [PMID: 29235194 PMCID: PMC5814929 DOI: 10.1111/jon.12491] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 11/12/2017] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE A pipeline for fully automated segmentation of 3T brain MRI scans in multiple sclerosis (MS) is presented. This 3T morphometry (3TM) pipeline provides indicators of MS disease progression from multichannel datasets with high‐resolution 3‐dimensional T1‐weighted, T2‐weighted, and fluid‐attenuated inversion‐recovery (FLAIR) contrast. 3TM segments white (WM) and gray matter (GM) and cerebrospinal fluid (CSF) to assess atrophy and provides WM lesion (WML) volume. METHODS To address nonuniform distribution of noise/contrast (eg, posterior fossa in 3D‐FLAIR) of 3T magnetic resonance imaging, the method employs dual sensitivity (different sensitivities for lesion detection in predefined regions). We tested this approach by assigning different sensitivities to supratentorial and infratentorial regions, and validated the segmentation for accuracy against manual delineation, and for precision in scan‐rescans. RESULTS Intraclass correlation coefficients of .95, .91, and .86 were observed for WML and CSF segmentation accuracy and brain parenchymal fraction (BPF). Dual sensitivity significantly reduced infratentorial false‐positive WMLs, affording increases in global sensitivity without decreasing specificity. Scan‐rescan yielded coefficients of variation (COVs) of 8% and .4% for WMLs and BPF and COVs of .8%, 1%, and 2% for GM, WM, and CSF volumes. WML volume difference/precision was .49 ± .72 mL over a range of 0–24 mL. Correlation between BPF and age was r = .62 (P = .0004), and effect size for detecting brain atrophy was Cohen's d = 1.26 (standardized mean difference vs. healthy controls). CONCLUSIONS This pipeline produces probability maps for brain lesions and tissue classes, facilitating expert review/correction and may provide high throughput, efficient characterization of MS in large datasets.
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Affiliation(s)
- Dominik S Meier
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Medical Image Analysis Center, University Hospital Basel, Switzerland
| | - Charles R G Guttmann
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Subhash Tummala
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicola Moscufo
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michele Cavallari
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shahamat Tauhid
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rohit Bakshi
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Howard L Weiner
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Departments of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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16
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Cavallari M, Egorova S, Healy BC, Palotai M, Prieto JC, Polgar‐Turcsanyi M, Tauhid S, Anderson M, Glanz B, Chitnis T, Guttmann CR. Evaluating the Association between Enlarged Perivascular Spaces and Disease Worsening in Multiple Sclerosis. J Neuroimaging 2017; 28:273-277. [DOI: 10.1111/jon.12490] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 10/23/2017] [Accepted: 11/12/2017] [Indexed: 11/30/2022] Open
Affiliation(s)
- Michele Cavallari
- Center for Neurological ImagingDepartment of RadiologyBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Svetlana Egorova
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Brian C. Healy
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Miklos Palotai
- Center for Neurological ImagingDepartment of RadiologyBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Juan Carlos Prieto
- Center for Neurological ImagingDepartment of RadiologyBrigham and Women's HospitalHarvard Medical School Boston MA
| | | | - Shahamat Tauhid
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Mark Anderson
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Bonnie Glanz
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Tanuja Chitnis
- Partners Multiple Sclerosis CenterBrigham and Women's HospitalHarvard Medical School Boston MA
| | - Charles R.G. Guttmann
- Center for Neurological ImagingDepartment of RadiologyBrigham and Women's HospitalHarvard Medical School Boston MA
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17
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Diaz-Cruz C, Chua AS, Malik MT, Kaplan T, Glanz BI, Egorova S, Guttmann CRG, Bakshi R, Weiner HL, Healy BC, Chitnis T. The effect of alcohol and red wine consumption on clinical and MRI outcomes in multiple sclerosis. Mult Scler Relat Disord 2017; 17:47-53. [PMID: 29055473 DOI: 10.1016/j.msard.2017.06.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 05/25/2017] [Accepted: 06/21/2017] [Indexed: 01/10/2023]
Abstract
BACKGROUND Alcohol and in particular red wine have both immunomodulatory and neuroprotective properties, and may exert an effect on the disease course of multiple sclerosis (MS). OBJECTIVE To assess the association between alcohol and red wine consumption and MS course. METHODS MS patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) who completed a self-administered questionnaire about their past year drinking habits at a single time point were included in the study. Alcohol and red wine consumption were measured as servings/week. The primary outcome was the Expanded Disability Status Scale (EDSS) at the time of the questionnaire. Secondary clinical outcomes were the Multiple Sclerosis Severity Score (MSSS) and number of relapses in the year before the questionnaire. Secondary MRI outcomes included brain parenchymal fraction and T2 hyperintense lesion volume (T2LV). Appropriate regression models were used to test the association of alcohol and red wine intake on clinical and MRI outcomes. All analyses were controlled for sex, age, body mass index, disease phenotype (relapsing vs. progressive), the proportion of time on disease modifying therapy during the previous year, smoking exposure, and disease duration. In the models for the MRI outcomes, analyses were also adjusted for acquisition protocol. RESULTS 923 patients (74% females, mean age 47 ± 11 years, mean disease duration 14 ± 9 years) were included in the analysis. Compared to abstainers, patients drinking more than 4 drinks per week had a higher likelihood of a lower EDSS score (OR, 0.41; p = 0.0001) and lower MSSS (mean difference, - 1.753; p = 0.002) at the time of the questionnaire. Similarly, patients drinking more than 3 glasses of red wine per week had greater odds of a lower EDSS (OR, 0.49; p = 0.0005) and lower MSSS (mean difference, - 0.705; p = 0.0007) compared to nondrinkers. However, a faster increase in T2LV was observed in patients consuming 1-3 glasses of red wine per week compared to nondrinkers. CONCLUSIONS Higher total alcohol and red wine intake were associated with a lower cross-sectional level of neurologic disability in MS patients but increased T2LV accumulation. Further studies should explore a potential cause-effect neuroprotective relationship, as well as the underlying biological mechanisms.
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Affiliation(s)
- Camilo Diaz-Cruz
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | - Alicia S Chua
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Tamara Kaplan
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | - Bonnie I Glanz
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Svetlana Egorova
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Charles R G Guttmann
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Rohit Bakshi
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Howard L Weiner
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Brian C Healy
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
| | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Partners Pediatric Multiple Sclerosis Center, Massachusetts General Hospital, Boston, MA, USA.
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18
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Zhao Y, Healy BC, Rotstein D, Guttmann CRG, Bakshi R, Weiner HL, Brodley CE, Chitnis T. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS One 2017; 12:e0174866. [PMID: 28379999 PMCID: PMC5381810 DOI: 10.1371/journal.pone.0174866] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 03/16/2017] [Indexed: 12/04/2022] Open
Abstract
Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group. Interpretation SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.
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Affiliation(s)
- Yijun Zhao
- Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America
| | - Brian C. Healy
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Dalia Rotstein
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
| | - Charles R. G. Guttmann
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
| | - Rohit Bakshi
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
| | - Howard L. Weiner
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
| | - Carla E. Brodley
- College of Computer and Information Science, Northeastern, Boston, Massachusetts, United States of America
| | - Tanuja Chitnis
- Partners MS Center, Brigham and Women’s Hospital, Brookline, Massachusetts, United States of America
- * E-mail:
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19
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Healy BC, Buckle GJ, Ali EN, Egorova S, Khalid F, Tauhid S, Glanz BI, Chitnis T, Guttmann CRG, Weiner HL, Bakshi R. Characterizing Clinical and MRI Dissociation in Patients with Multiple Sclerosis. J Neuroimaging 2017; 27:481-485. [PMID: 28261936 PMCID: PMC5600109 DOI: 10.1111/jon.12433] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 01/25/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Two common approaches for measuring disease severity in multiple sclerosis (MS) are the clinical exam and brain magnetic resonance imaging (MRI) scan. Although most patients show similar disease severity on both measures, some patients have clinical/MRI dissociation. METHODS Subjects from a comprehensive care MS center who had a concurrent brain MRI, spinal cord MRI, clinical examination, and patient reported outcomes were classified into three groups based on the Expanded Disability Status Scale (EDSS) and cerebral T2 hyperintense lesion volume (T2LV). The first group was the low lesion load/high disability group (LL/HD) with T2LV < 2 ml and EDSS ≥ 3. The second group was the high lesion load/low disability group (HL/LD) with T2LV > 6 ml and EDSS ≤ 1.5. All remaining subjects were classified as not dissociated. The three groups were compared using regression techniques for unadjusted analyses and to adjust for age, disease duration, and gender. RESULTS Twenty‐two subjects were classified as LL/HD (4.1%; 95% CI: 2.6%, 6.2%), and 50 subjects were classified as HL/LD (9.4%; 95% CI: 7.0%, 12.2%). Subjects in the LL/HD group were more likely to have a progressive form of MS and had significantly lower physical quality of life in adjusted and unadjusted analysis. Subjects in HL/LD had significantly more gadolinium‐enhancing lesions, and subjects in the LL/HD group had significantly more cervical spinal cord lesions. CONCLUSIONS Our results indicate that dissociation may occur between physical disability and cerebral lesion volume in either direction in patients with MS. Type of MS, brain atrophy, and spinal cord lesions may help to bridge this dissociation.
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Affiliation(s)
- Brian C Healy
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Guy J Buckle
- Neuroimaging Research, MS Institute at Shepard Center, Atlanta, GA
| | - Eman N Ali
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Svetlana Egorova
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Fariha Khalid
- Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Boston, MA
| | - Shahamat Tauhid
- Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Boston, MA
| | - Bonnie I Glanz
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Howard L Weiner
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Rohit Bakshi
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Laboratory for Neuroimaging Research, Brigham and Women's Hospital, Boston, MA
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20
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Lindemer ER, Greve DN, Fischl BR, Augustinack JC, Salat DH. Regional staging of white matter signal abnormalities in aging and Alzheimer's disease. Neuroimage Clin 2017; 14:156-165. [PMID: 28180074 PMCID: PMC5279704 DOI: 10.1016/j.nicl.2017.01.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/02/2017] [Accepted: 01/20/2017] [Indexed: 11/07/2022]
Abstract
White matter lesions, quantified as 'white matter signal abnormalities' (WMSA) on neuroimaging, are common incidental findings on brain images of older adults. This tissue damage is linked to cerebrovascular dysfunction and is associated with cognitive decline. The regional distribution of WMSA throughout the cerebral white matter has been described at a gross scale; however, to date no prior study has described regional patterns relative to cortical gyral landmarks which may be important for understanding functional impact. Additionally, no prior study has described how regional WMSA volume scales with total global WMSA. Such information could be used in the creation of a pathologic 'staging' of WMSA through a detailed regional characterization at the individual level. Magnetic resonance imaging data from 97 cognitively-healthy older individuals (OC) aged 52-90 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were processed using a novel WMSA labeling procedure described in our prior work. WMSA were quantified regionally using a procedure that segments the cerebral white matter into 35 bilateral units based on proximity to landmarks in the cerebral cortex. An initial staging was performed by quantifying the regional WMSA volume in four groups based on quartiles of total WMSA volume (quartiles I-IV). A consistent spatial pattern of WMSA accumulation was observed with increasing quartile. A clustering procedure was then used to distinguish regions based on patterns of scaling of regional WMSA to global WMSA. Three patterns were extracted that showed high, medium, and non-scaling with global WMSA. Regions in the high-scaling cluster included periventricular, caudal and rostral middle frontal, inferior and superior parietal, supramarginal, and precuneus white matter. A data-driven staging procedure was then created based on patterns of WMSA scaling and specific regional cut-off values from the quartile analyses. Individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were then additionally staged, and significant differences in the percent of each diagnostic group in Stages I and IV were observed, with more AD individuals residing in Stage IV and more OC and MCI individuals residing in Stage I. These data demonstrate a consistent regional scaling relationship between global and regional WMSA that can be used to classify individuals into one of four stages of white matter disease. White matter staging could play an important role in a better understanding and the treatment of cerebrovascular contributions to brain aging and dementia.
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Affiliation(s)
- Emily R. Lindemer
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R. Fischl
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Jean C. Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA, USA
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21
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De Guio F, Jouvent E, Biessels GJ, Black SE, Brayne C, Chen C, Cordonnier C, De Leeuw FE, Dichgans M, Doubal F, Duering M, Dufouil C, Duzel E, Fazekas F, Hachinski V, Ikram MA, Linn J, Matthews PM, Mazoyer B, Mok V, Norrving B, O'Brien JT, Pantoni L, Ropele S, Sachdev P, Schmidt R, Seshadri S, Smith EE, Sposato LA, Stephan B, Swartz RH, Tzourio C, van Buchem M, van der Lugt A, van Oostenbrugge R, Vernooij MW, Viswanathan A, Werring D, Wollenweber F, Wardlaw JM, Chabriat H. Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. J Cereb Blood Flow Metab 2016; 36:1319-37. [PMID: 27170700 PMCID: PMC4976752 DOI: 10.1177/0271678x16647396] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 03/20/2016] [Indexed: 12/11/2022]
Abstract
Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.
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Affiliation(s)
- François De Guio
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France
| | - Eric Jouvent
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France Department of Neurology, AP-HP, Lariboisière Hospital, Paris, France
| | - Geert Jan Biessels
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra E Black
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Carol Brayne
- Department of Public Health and Primary Care, Cambridge University, Cambridge, UK
| | - Christopher Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Frank-Eric De Leeuw
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Department of Neurology, Nijmegen, The Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Fergus Doubal
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany
| | | | - Emrah Duzel
- Department of Cognitive Neurology and Dementia Research, University of Magdeburg, Magdeburg, Germany
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Vladimir Hachinski
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada
| | - M Arfan Ikram
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Neurology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jennifer Linn
- Department of Neuroradiology, University Hospital Munich, Munich, Germany
| | - Paul M Matthews
- Department of Medicine, Division of Brain Sciences, Imperial College London, London, UK
| | | | - Vincent Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Bo Norrving
- Department of Clinical Sciences, Neurology, Lund University, Lund, Sweden
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Luciano A Sposato
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada
| | - Blossom Stephan
- Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle, UK
| | - Richard H Swartz
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | | | - Mark van Buchem
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Meike W Vernooij
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Anand Viswanathan
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - David Werring
- Department of Brain Repair and Rehabilitation, Stroke Research Group, UCL, London, UK
| | - Frank Wollenweber
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany
| | - Joanna M Wardlaw
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France Department of Neurology, AP-HP, Lariboisière Hospital, Paris, France
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22
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Fatigue predicts disease worsening in relapsing-remitting multiple sclerosis patients. Mult Scler 2016; 22:1841-1849. [DOI: 10.1177/1352458516635874] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 01/03/2016] [Accepted: 01/19/2016] [Indexed: 01/22/2023]
Abstract
Background: It is unclear whether fatigue is a consequence or a predictive trait of disease worsening. Objective: To investigate the predictive value of fatigue toward conversion to confirmed moderate–severe disability in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: We retrospectively selected from the Comprehensive Longitudinal Investigations in MS at the Brigham and Women’s Hospital (CLIMB) study cohort RRMS patients who converted to confirmed (⩾2 years) Expanded Disability Status Scale (EDSS) score ⩾3 within a follow-up period ⩾3 years. We contrasted the Modified Fatigue Impact Scale (MFIS) score of 33 converters, obtained at least 1 year before conversion to EDSS ⩾3, with that of 33 non-converter RRMS patients matched for baseline characteristics. Results: Total MFIS score was higher in converter versus non-converter MS patients (median 37 vs 13; p < 0.0001). EDSS and Center for Epidemiological Studies Depression scale (CES-D) scores were also higher in the converters (median EDSS 1.5 vs 0, p < 0.0001; median CES-D 30 vs 24, p < 0.0001) and were both associated with MFIS score (EDSS: rho = 0.42, p = 0.0005; CES-D: rho = 0.72, p < 0.0001). After adjusting for EDSS and CES-D in multivariate analysis, MFIS remained a significant predictor of subsequent conversion to confirmed EDSS ⩾3. Conclusion: Fatigue is a promising indicator of risk for conversion to confirmed moderate–severe disability in RRMS patients.
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23
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Khalid F, Tauhid S, Chua AS, Healy BC, Stankiewicz JM, Weiner HL, Bakshi R. A longitudinal uncontrolled study of cerebral gray matter volume in patients receiving natalizumab for multiple sclerosis. Int J Neurosci 2016; 127:396-403. [PMID: 27143245 DOI: 10.1080/00207454.2016.1185421] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Brain atrophy in multiple sclerosis (MS) selectively affects gray matter (GM), which is highly relevant to disability and cognitive impairment. We assessed cerebral GM volume (GMV) during one year of natalizumab therapy. DESIGN/METHODS Patients with relapsing-remitting (n = 18) or progressive (n = 2) MS had MRI ∼1 year apart during natalizumab treatment. At baseline, patients were on natalizumab for (mean ± SD) 16.6 ± 10.9 months with age 38.5 ± 7.4 and disease duration 9.7 ± 4.3 years. RESULTS At baseline, GMV was 664.0 ± 56.4 ml, Expanded Disability Status Scale (EDSS) score was 2.3 ± 2.0, timed 25-foot walk (T25FW) was 6.1±3.4 s; two patients (10%) had gadolinium (Gd)-enhancing lesions. At follow-up, GMV was 663.9 ± 60.2 mL; EDSS was 2.6 ± 2.1 and T25FW was 5.9 ± 2.9 s. One patient had a mild clinical relapse during the observation period (0.052 annualized relapse rate for the entire cohort). No patients had Gd-enhancing lesions at follow-up. Linear mixed-effect models showed no significant change in annualized GMV [estimated mean change per year 0.338 mL, 95% confidence interval -9.66, 10.34, p = 0.94)], GM fraction (p = 0.92), whole brain parenchymal fraction (p = 0.64), T2 lesion load (p = 0.64), EDSS (p = 0.26) or T25FW (p = 0.79). CONCLUSIONS This pilot study shows no GM atrophy during one year of natalizumab MS therapy. We also did not detect any loss of whole brain volume or progression of cerebral T2 hyperintense lesion volume during the observation period. These MRI results paralleled the lack of clinical worsening.
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Affiliation(s)
- Fariha Khalid
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
| | - Shahamat Tauhid
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
| | - Alicia S Chua
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
| | - Brian C Healy
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA.,c c Biostatistics Center, Massachusetts General Hospital, Harvard Medical School , Boston, MA , USA
| | - James M Stankiewicz
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
| | - Howard L Weiner
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
| | - Rohit Bakshi
- a a Laboratory for Neuroimaging Research, Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA.,b b Laboratory for Neuroimaging Research, Department of Radiology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School , Boston, MA , USA
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24
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Kim G, Tauhid S, Dupuy SL, Tummala S, Khalid F, Healy BC, Bakshi R. An MRI-defined measure of cerebral lesion severity to assess therapeutic effects in multiple sclerosis. J Neurol 2016; 263:531-8. [PMID: 26754005 PMCID: PMC4785194 DOI: 10.1007/s00415-015-8009-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 12/22/2015] [Accepted: 12/23/2015] [Indexed: 12/18/2022]
Abstract
Assess the sensitivity of the Magnetic Resonance Disease Severity Scale (MRDSS), based on cerebral lesions and atrophy, for treatment monitoring of glatiramer acetate (GA) in relapsing-remitting multiple sclerosis (MS). This retrospective non-randomized pilot study included patients who started daily GA [n = 23, age (median, range) 41 (26.2, 53.1) years, Expanded Disability Status Scale (EDSS) score 1.0 (0, 3.5)], or received no disease-modifying therapy (noDMT) [n = 21, age 44.8 (28.2, 55.4), EDSS 0 (0, 2.5)] for 2 years. MRDSS was the sum of z-scores (normalized to a reference sample) of T2 hyperintense lesion volume (T2LV), the ratio of T1 hypointense LV to T2LV (T1/T2), and brain parenchymal fraction (BPF) multiplied by negative 1. The two groups were compared by Wilcoxon rank sum tests; within group change was assessed by Wilcoxon signed rank tests. Glatiramer acetate subjects had less progression than noDMT on T1/T2 [(median z-score change (range), 0 (−1.07, 1.20) vs. 0.41 (−0.30, 2.51), p = 0.003)] and MRDSS [0.01 (−1.33, 1.28) vs. 0.46 (−1.57, 2.46), p = 0.01]; however, not on BPF [0.12 (−0.18, 0.58) vs. 0.10 (−1.47,0.50), p = 0.59] and T2LV [−0.03 (−0.90, 0.57) vs. 0.01 (−1.69, 0.34), p = 0.40]. While GA subjects worsened only on BPF [0.12 (−0.18, 0.58), p = 0.001], noDMT worsened on BPF [0.10 (−1.47, 0.50), p = 0.002], T1/T2 [0.41 (−0.30, 2.51), p = 0.0002], and MRDSS [0.46 (−1.57, 2.46), p = 0.0006]. These preliminary findings show the potential of two new cerebral MRI metrics to track MS therapeutic response. The T1/T2, an index of the destructive potential of lesions, may provide particular sensitivity to treatment effects.
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Affiliation(s)
- Gloria Kim
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Shahamat Tauhid
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Sheena L Dupuy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Subhash Tummala
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Fariha Khalid
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Brian C Healy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA.
- Laboratory for Neuroimaging Research, One Brookline Place, Brookline, MA, 02445, USA.
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Chua AS, Egorova S, Anderson MC, Polgar-Turcsanyi M, Chitnis T, Weiner HL, Guttmann CR, Bakshi R, Healy BC. Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models. Neuroimage Clin 2015; 8:606-10. [PMID: 26199872 PMCID: PMC4506959 DOI: 10.1016/j.nicl.2015.06.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 06/24/2015] [Accepted: 06/28/2015] [Indexed: 11/24/2022]
Abstract
Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.
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Affiliation(s)
- Alicia S. Chua
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | - Svetlana Egorova
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mark C. Anderson
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Howard L. Weiner
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Charles R.G. Guttmann
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Rohit Bakshi
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian C. Healy
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
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Chua AS, Egorova S, Anderson MC, Polgar-Turcsanyi M, Chitnis T, Weiner HL, Guttmann CRG, Bakshi R, Healy BC. Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis. Neuroimage 2015; 119:81-8. [PMID: 26093330 DOI: 10.1016/j.neuroimage.2015.06.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 05/22/2015] [Accepted: 06/11/2015] [Indexed: 11/24/2022] Open
Abstract
Automated segmentation of brain MRI scans into tissue classes is commonly used for the assessment of multiple sclerosis (MS). However, manual correction of the resulting brain tissue label maps by an expert reader remains necessary in many cases. Since automated segmentation data awaiting manual correction are "missing", we proposed to use multiple imputation (MI) to fill-in the missing manually-corrected MRI data for measures of normalized whole brain volume (brain parenchymal fraction-BPF) and T2 hyperintense lesion volume (T2LV). Automated and manually corrected MRI measures from 1300 patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) were identified. Simulation studies were conducted to assess the performance of MI with missing data both missing completely at random and missing at random. An imputation model including the concurrent automated data as well as clinical and demographic variables explained a high proportion of the variance in the manually corrected BPF (R(2)=0.97) and T2LV (R(2)=0.89), demonstrating the potential to accurately impute the missing data. Further, our results demonstrate that MI allows for the accurate estimation of group differences with little to no bias and with similar precision compared to an analysis with no missing data. We believe that our findings provide important insights for efficient correction of automated MRI measures to obviate the need to perform manual correction on all cases.
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Affiliation(s)
- Alicia S Chua
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | - Svetlana Egorova
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mark C Anderson
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Howard L Weiner
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Charles R G Guttmann
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Rohit Bakshi
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian C Healy
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA.
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Tomas-Fernandez X, Warfield SK. A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1349-61. [PMID: 25616008 PMCID: PMC4506921 DOI: 10.1109/tmi.2015.2393853] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
White matter (WM) lesions are thought to play an important role in multiple sclerosis (MS) disease burden. Recent work in the automated segmentation of white matter lesions from magnetic resonance imaging has utilized a model in which lesions are outliers in the distribution of tissue signal intensities across the entire brain of each patient. However, the sensitivity and specificity of lesion detection and segmentation with these approaches have been inadequate. In our analysis, we determined this is due to the substantial overlap between the whole brain signal intensity distribution of lesions and normal tissue. Inspired by the ability of experts to detect lesions based on their local signal intensity characteristics, we propose a new algorithm that achieves lesion and brain tissue segmentation through simultaneous estimation of a spatially global within-the-subject intensity distribution and a spatially local intensity distribution derived from a healthy reference population. We demonstrate that MS lesions can be segmented as outliers from this intensity model of population and subject. We carried out extensive experiments with both synthetic and clinical data, and compared the performance of our new algorithm to those of state-of-the art techniques. We found this new approach leads to a substantial improvement in the sensitivity and specificity of lesion detection and segmentation.
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Tohka J. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review. World J Radiol 2014; 6:855-864. [PMID: 25431640 PMCID: PMC4241492 DOI: 10.4329/wjr.v6.i11.855] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/03/2014] [Accepted: 09/24/2014] [Indexed: 02/06/2023] Open
Abstract
Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.
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Tauhid S, Neema M, Healy BC, Weiner HL, Bakshi R. MRI phenotypes based on cerebral lesions and atrophy in patients with multiple sclerosis. J Neurol Sci 2014; 346:250-4. [PMID: 25220114 DOI: 10.1016/j.jns.2014.08.047] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 07/29/2014] [Accepted: 08/29/2014] [Indexed: 12/20/2022]
Abstract
BACKGROUND While disease categories (i.e. clinical phenotypes) of multiple sclerosis (MS) are established, there remains MRI heterogeneity among patients within those definitions. MRI-defined lesions and atrophy show only moderate inter-correlations, suggesting that they represent partly different processes in MS. We assessed the ability of MRI-based categorization of cerebral lesions and atrophy in individual patients to identify distinct phenotypes. METHODS We studied 175 patients with MS [age (mean ± SD) 42.7 ± 9.1 years, 124 (71%) women, Expanded Disability Status (EDSS) score 2.5 ± 2.3, n = 18 (10%) clinically isolated demyelinating syndrome (CIS), n=115 (66%) relapsing-remitting (RR), and n = 42 (24%) secondary progressive (SP)]. Brain MRI measures included T2 hyperintense lesion volume (T2LV) and brain parenchymal fraction (to assess whole brain atrophy). Medians were used to create bins for each parameter, with patients assigned a low or high severity score. RESULTS Four MRI phenotype categories emerged: Type I = low T2LV/mild atrophy [n = 67 (38%); CIS = 14, RR = 47, SP = 6]; Type II = high T2LV/mild atrophy [n = 21 (12%); RR = 19, SP = 2]; Type III = low T2LV/high atrophy [n = 21 (12%); CIS = 4, RR = 16, SP = 1]; and Type IV = high T2LV/high atrophy [n = 66 (38%); RR = 33, S P = 33]. Type IV was the most disabled and was the only group showing a correlation between T2LV vs. BPF and MRI vs. EDSS score (all p < 0.05). CONCLUSIONS We described MRI-categorization based on the relationship between lesions and atrophy in individual patients to identify four phenotypes in MS. Most patients have congruent extremes related to the degree of lesions and atrophy. However, many have a dissociation. Longitudinal studies will help define the stability of these patterns and their role in risk stratification.
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Affiliation(s)
- Shahamat Tauhid
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Mohit Neema
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Brian C Healy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Howard L Weiner
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA.
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30
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Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 2014; 98:324-35. [DOI: 10.1016/j.neuroimage.2014.04.056] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 03/26/2014] [Accepted: 04/21/2014] [Indexed: 11/17/2022] Open
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Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images. Neuroradiology 2014; 56:265-81. [DOI: 10.1007/s00234-014-1322-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 01/08/2014] [Indexed: 10/25/2022]
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32
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Xia Z, Secor E, Chibnik LB, Bove RM, Cheng S, Chitnis T, Cagan A, Gainer VS, Chen PJ, Liao KP, Shaw SY, Ananthakrishnan AN, Szolovits P, Weiner HL, Karlson EW, Murphy SN, Savova GK, Cai T, Churchill SE, Plenge RM, Kohane IS, De Jager PL. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One 2013; 8:e78927. [PMID: 24244385 PMCID: PMC3823928 DOI: 10.1371/journal.pone.0078927] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 09/17/2013] [Indexed: 12/28/2022] Open
Abstract
Objective To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
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Affiliation(s)
- Zongqi Xia
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Elizabeth Secor
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Lori B. Chibnik
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Riley M. Bove
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suchun Cheng
- Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Tanuja Chitnis
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Andrew Cagan
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
| | - Vivian S. Gainer
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
| | - Pei J. Chen
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Katherine P. Liao
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Stanley Y. Shaw
- Harvard Medical School, Boston, Massachusetts, United States of America
- Center for System Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Ashwin N. Ananthakrishnan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Peter Szolovits
- Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Howard L. Weiner
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Elizabeth W. Karlson
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Shawn N. Murphy
- Harvard Medical School, Boston, Massachusetts, United States of America
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
- Laboratory of Computer Science, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
| | - Guergana K. Savova
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Tianxi Cai
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Susanne E. Churchill
- i2b2/National Center for Biomedical Computing, Partners HealthCare, Boston, Massachusetts, United States of America
| | - Robert M. Plenge
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Isaac S. Kohane
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Philip L. De Jager
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- * E-mail:
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Elliott C, Arnold DL, Collins DL, Arbel T. Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1490-503. [PMID: 23613032 DOI: 10.1109/tmi.2013.2258403] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.
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Affiliation(s)
- Colm Elliott
- Centre for Intelligent Machines, McGill University, Montreal, QC, H3A 0E9 Canada.
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Bove R, Musallam A, Healy BC, Houtchens M, Glanz BI, Khoury S, Guttmann CR, De Jager PL, Chitnis T. No sex-specific difference in disease trajectory in multiple sclerosis patients before and after age 50. BMC Neurol 2013; 13:73. [PMID: 23822612 PMCID: PMC3707791 DOI: 10.1186/1471-2377-13-73] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Accepted: 06/20/2013] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND The disease course in multiple sclerosis (MS) is influenced by many factors, including age, sex, and sex hormones. Little is known about sex-specific changes in disease course around age 50, which may represent a key biological transition period for reproductive aging. METHODS Male and female subjects with no prior chemotherapy exposure were selected from a prospective MS cohort to form groups representing the years before (38-46 years, N=351) and after (54-62 years, N=200)age 50. Primary analysis assessed for interaction between effects of sex and age on clinical (Expanded Disability Status Scale, EDSS; relapse rate) and radiologic (T2 lesion volume, T2LV; brain parenchymal fraction, BPF) outcomes. Secondarily, we explored patient-reported outcomes (PROs). RESULTS As expected, there were age- and sex- related changes with male and older cohorts showing worse disease severity (EDSS), brain atrophy (BPF), and more progressive course.There was no interaction between age and sex on cross-sectional adjusted clinical (EDSS, relapse rate) or radiologic (BPF, T2LV) measures, or on 2-year trajectories of decline.There was a significant interaction between age and sex for a physical functioning PRO (SF-36): the older female cohort reported lower physical functioning than men (p=0.002). There were no differences in depression (Center for Epidemiological Study - Depression, CES-D) or fatigue (Modified Fatigue Impact Scale, MFIS) scores. CONCLUSIONS There was no interaction between age and sex suggestive of an effect of reproductive aging on clinical or radiologic progression. Prospective analyses across the menopausal transition are needed.
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Affiliation(s)
- Riley Bove
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
| | - Alexander Musallam
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
| | - Brian C Healy
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
- Massachusetts General Hospital Biostatistics Center, Boston 02114, MA, USA
| | - Maria Houtchens
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
| | - Bonnie I Glanz
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
| | - Samia Khoury
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
| | - Charles R Guttmann
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
| | - Philip L De Jager
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
- Center for Neurologic Disease, Harvard Medical School, 77 Avenue Louis Pasteur, NRB168, Boston 02115, MA, USA
| | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Brookline 02445, MA, USA
- Harvard Medical School, Boston 02115, MA, USA
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García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal 2013; 17:1-18. [DOI: 10.1016/j.media.2012.09.004] [Citation(s) in RCA: 153] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2011] [Revised: 09/06/2012] [Accepted: 09/17/2012] [Indexed: 01/21/2023]
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Karimaghaloo Z, Shah M, Francis SJ, Arnold DL, Collins DL, Arbel T. Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1181-1194. [PMID: 22318484 DOI: 10.1109/tmi.2012.2186639] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.
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Affiliation(s)
- Zahra Karimaghaloo
- Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada.
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Moodie J, Healy BC, Buckle GJ, Gauthier SA, Glanz BI, Arora A, Ceccarelli A, Tauhid S, Han XM, Venkataraman A, Chitnis T, Khoury SJ, Guttmann CRG, Weiner HL, Neema M, Bakshi R. Magnetic resonance disease severity scale (MRDSS) for patients with multiple sclerosis: a longitudinal study. J Neurol Sci 2011; 315:49-54. [PMID: 22209496 DOI: 10.1016/j.jns.2011.11.040] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Accepted: 11/30/2011] [Indexed: 11/25/2022]
Abstract
BACKGROUND We previously described a composite MRI scale combining T1-lesions, T2-lesions and whole brain atrophy in multiple sclerosis (MS): the magnetic resonance disease severity scale (MRDSS). OBJECTIVE Test strength of the MRDSS vs. individual MRI measures for sensitivity to longitudinal change. METHODS We studied 84 MS patients over a 3.2±0.3 year follow-up. Baseline and follow-up T2-lesion volume (T2LV), T1-hypointense lesion volume (T1LV), and brain parenchymal fraction (BPF) were measured. MRDSS was the combination of standardized T2LV, T1/T2 ratio and BPF. RESULTS Patients had higher MRDSS at follow-up vs. baseline (p<0.001). BPF decreased (p<0.001), T1/T2 increased (p<0.001), and T2LV was unchanged (p>0.5). Change in MRDSS was larger than the change in MRI subcomponents. While MRDSS showed significant change in relapsing-remitting (RR) (p<0.001) and secondary progressive (SP) phenotypes (p<0.05), BPF and T1/T2 ratio changed only in RRMS (p<0.001). Longitudinal change in MRDSS was significantly different between RRMS and SPMS (p=0.0027); however, change in the individual MRI components did not differ. Evaluation with respect to predicting on-study clinical worsening as measured by EDSS revealed a significant association only for T2LV (p=0.038). CONCLUSION Results suggest improved sensitivity of MRDSS to longitudinal change vs. individual MRI measures. MRDSS has particularly high sensitivity in RRMS.
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Affiliation(s)
- Jennifer Moodie
- Department of Neurology, University of Massachusetts, Boston, MA, USA
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Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 2011; 54:787-807. [DOI: 10.1007/s00234-011-0992-6] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 11/29/2011] [Indexed: 01/29/2023]
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Sorond FA, Kiely DK, Galica A, Moscufo N, Serrador JM, Iloputaife I, Egorova S, Dell'Oglio E, Meier DS, Newton E, Milberg WP, Guttmann CRG, Lipsitz LA. Neurovascular coupling is impaired in slow walkers: the MOBILIZE Boston Study. Ann Neurol 2011; 70:213-20. [PMID: 21674588 DOI: 10.1002/ana.22433] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2010] [Revised: 03/03/2011] [Accepted: 03/18/2011] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Neurovascular coupling may be involved in compensatory mechanisms responsible for preservation of gait speed in elderly people with cerebrovascular disease. Our study examines the association between neurovascular coupling in the middle cerebral artery and gait speed in elderly individuals with impaired cerebral vasoreactivity. METHODS Twenty-two fast and 20 slow walkers in the lowest quartile of cerebral vasoreactivity were recruited from the MOBILIZE Boston Study. Neurovascular coupling was assessed in bilateral middle cerebral arteries by measuring cerebral blood flow during the N-Back task. Cerebral white matter hyperintensities were measured for each group using magnetic resonance imaging. RESULTS Neurovascular coupling was attenuated in slow compared to fast walkers (2.8%; 95% confidence interval [CI], -0.9 to 6.6 vs 8.2%; 95% CI, 4.7-11.8; p = 0.02). The odds ratio of being a slow walker was 6.4 (95% CI, 1.7-24.9; p = 0.007) if there was a high burden of white matter hyperintensity; however, this risk increased to 14.5 (95% CI, 2.3-91.1; p = 0.004) if neurovascular coupling was also attenuated. INTERPRETATION Our results suggest that intact neurovascular coupling may help preserve mobility in elderly people with cerebral microvascular disease.
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Affiliation(s)
- Farzaneh A Sorond
- Department of Neurology, Stroke Division, Brigham and Women's Hospital, Women's Hospital, Boston, MA 02115, USA.
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Alfano B, Comerci M, Larobina M, Prinster A, Hornak JP, Selvan SE, Amato U, Quarantelli M, Tedeschi G, Brunetti A, Salvatore M. An MRI digital brain phantom for validation of segmentation methods. Med Image Anal 2011; 15:329-39. [DOI: 10.1016/j.media.2011.01.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2010] [Revised: 12/29/2010] [Accepted: 01/18/2011] [Indexed: 10/18/2022]
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Abstract
INTRODUCTION Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. METHODS For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. RESULTS This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. CONCLUSIONS Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.
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Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology 2011; 54:299-320. [PMID: 21584674 DOI: 10.1007/s00234-011-0886-7] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2011] [Accepted: 04/29/2011] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. METHODS For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. RESULTS This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. CONCLUSIONS Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.
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Affiliation(s)
- Daryoush Mortazavi
- School of Engineering, Deakin University, Geelong, Victoria 3216, Australia.
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Holland CM, Charil A, Csapo I, Liptak Z, Ichise M, Khoury SJ, Bakshi R, Weiner HL, Guttmann CR. The Relationship between Normal Cerebral Perfusion Patterns and White Matter Lesion Distribution in 1,249 Patients with Multiple Sclerosis. J Neuroimaging 2011; 22:129-36. [DOI: 10.1111/j.1552-6569.2011.00585.x] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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HLA (A-B-C and -DRB1) alleles and brain MRI changes in multiple sclerosis: a longitudinal study. Genes Immun 2010; 12:183-90. [PMID: 21179117 DOI: 10.1038/gene.2010.58] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Several major histocompatibility complex (MHC) alleles have been postulated to influence the susceptibility to multiple sclerosis (MS), as well as its clinical/radiological course. In this longitudinal observation, we further explored the impact of human leukocyte antigen (HLA) class I/II alleles on MS outcomes, and we tested the hypothesis that HLA DRB1*1501 might uncover different strata of MS subjects harboring distinct MHC allele associations with magnetic resonance imaging (MRI) measures. Five hundred eighteen MS patients with two-digit HLA typing and at least one brain MRI were recruited for the study. T2-weighted hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF) were acquired at each time point. The association between allele count and MRI values was determined using linear regression modeling controlling for age, disease duration and gender. Analyses were also stratified by the presence/absence of HLA DRB1*1501. HLA DRB1*04 was associated with higher T2LV (P=0.006); after stratification, its significance remained only in the presence of HLA DRB1*1501 (P=0.012). The negative effect of HLA DRB1*14 on T2LV was exerted in DRB1*1501-negative group (P=0.012). Longitudinal analysis showed that HLA DRB1*10 was significantly protective on T2LV accrual in the presence of HLA DRB1*1501 (P=0.002). Although the majority of our results did not withstand multiple comparison correction, the differential impact of several HLA alleles in the presence/absence of HLA DRB1*1501 suggests that they may interact in determining the different phenotypic expressions of MS.
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Xia Z, Chibnik LB, Glanz BI, Liguori M, Shulman JM, Tran D, Khoury SJ, Chitnis T, Holyoak T, Weiner HL, Guttmann CRG, De Jager PL. A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis. PLoS One 2010; 5:e14169. [PMID: 21152065 PMCID: PMC2994939 DOI: 10.1371/journal.pone.0014169] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2010] [Accepted: 11/10/2010] [Indexed: 11/30/2022] Open
Abstract
Background Brain atrophy and cognitive dysfunction are neurodegenerative features of Multiple Sclerosis (MS). We used a candidate gene approach to address whether genetic variants implicated in susceptibility to late onset Alzheimer's Disease (AD) influence brain volume and cognition in MS patients. Methods/Principal Findings MS subjects were genotyped for five single nucleotide polymorphisms (SNPs) associated with susceptibility to AD: PICALM, CR1, CLU, PCK1, and ZNF224. We assessed brain volume using Brain Parenchymal Fraction (BPF) measurements obtained from Magnetic Resonance Imaging (MRI) data and cognitive function using the Symbol Digit Modalities Test (SDMT). Genotypes were correlated with cross-sectional BPF and SDMT scores using linear regression after adjusting for sex, age at symptom onset, and disease duration. 722 MS patients with a mean (±SD) age at enrollment of 41 (±10) years were followed for 44 (±28) months. The AD risk-associated allele of a non-synonymous SNP in the PCK1 locus (rs8192708G) is associated with a smaller average brain volume (P = 0.0047) at the baseline MRI, but it does not impact our baseline estimate of cognition. PCK1 is additionally associated with higher baseline T2-hyperintense lesion volume (P = 0.0088). Finally, we provide technical validation of our observation in a subset of 641 subjects that have more than one MRI study, demonstrating the same association between PCK1 and smaller average brain volume (P = 0.0089) at the last MRI visit. Conclusion/Significance Our study provides suggestive evidence for greater brain atrophy in MS patients bearing the PCK1 allele associated with AD-susceptibility, yielding new insights into potentially shared neurodegenerative process between MS and late onset AD.
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Affiliation(s)
- Zongqi Xia
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Lori B. Chibnik
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Bonnie I. Glanz
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Maria Liguori
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Institute of Neurological Sciences, National Research Council, Mangone, Italy
| | - Joshua M. Shulman
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Dong Tran
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Samia J. Khoury
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Tanuja Chitnis
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Todd Holyoak
- Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Howard L. Weiner
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Charles R. G. Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Philip L. De Jager
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- * E-mail:
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Khoury SJ, Healy BC, Kivisäkk P, Viglietta V, Egorova S, Guttmann CRG, Wedgwood JF, Hafler DA, Weiner HL, Buckle G, Cook S, Reddy S. A randomized controlled double-masked trial of albuterol add-on therapy in patients with multiple sclerosis. ACTA ACUST UNITED AC 2010; 67:1055-61. [PMID: 20837847 DOI: 10.1001/archneurol.2010.222] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND Interleukin 12 (IL-12), a cytokine that promotes generation of helper T cells subtype 1, is increased in multiple sclerosis. Albuterol sulfate, a β2-adrenergic agonist, reduces IL-12 expression, so we tested the effect of albuterol as an add-on treatment to glatiramer acetate therapy. OBJECTIVES To investigate the clinical and immunologic effects of albuterol treatment as an add-on therapy in patients starting glatiramer acetate treatment. DESIGN Single-center double-masked clinical trial. SETTING Academic research. Patients Subjects with relapsing-remitting multiple sclerosis. MAIN OUTCOME MEASURES In this single-center double-masked clinical trial, subjects with relapsing-remitting multiple sclerosis were randomized to receive a subcutaneous injection of glatiramer acetate (20 mg) plus an oral dose of placebo daily for 2 years or a subcutaneous injection of glatiramer acetate (20 mg) plus an oral dose of albuterol daily for 2 years. The primary clinical efficacy measurement was the change in Multiple Sclerosis Functional Composite at 2 years, and the primary immunologic end point was the change in expression of IL-13 and interferon γ at each study time point. The classification level of evidence from this trial is C for each question, as this is the first class II clinical trial addressing the efficacy of glatiramer acetate plus albuterol. RESULTS Forty-four subjects were randomized to receive glatiramer acetate plus albuterol or glatiramer acetate plus placebo, and 39 subjects contributed to the analysis. Improvement in the Multiple Sclerosis Functional Composite was observed in the glatiramer acetate plus albuterol group at the 6-month (P = .005) and 12-month (P = .04) time points but not at the 24-month time point. A delay in the time to first relapse was also observed in the glatiramer acetate plus albuterol group (P = .03). Immunologically, IL-13 and interferon-γ production decreased in both treatment groups, and a treatment effect on IL-13 production was observed at the 12-month time point (P < .05). Adverse events were generally mild, and only 3 moderate or severe events were considered related to the treatment. CONCLUSION Treatment with glatiramer acetate plus albuterol is well tolerated and improves clinical outcomes in patients with multiple sclerosis. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00039988.
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Affiliation(s)
- Samia J Khoury
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Healy BC, Liguori M, Tran D, Chitnis T, Glanz B, Wolfish C, Gauthier S, Buckle G, Houtchens M, Stazzone L, Khoury S, Hartzmann R, Fernandez-Vina M, Hafler DA, Weiner HL, Guttmann CRG, De Jager PL. HLA B*44: protective effects in MS susceptibility and MRI outcome measures. Neurology 2010; 75:634-40. [PMID: 20713950 DOI: 10.1212/wnl.0b013e3181ed9c9c] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE In addition to the main multiple sclerosis (MS) major histocompatibility complex (MHC) risk allele (HLA DRB1*1501), investigations of the MHC have implicated several class I MHC loci (HLA A, HLA B, and HLA C) as potential independent MS susceptibility loci. Here, we evaluate the role of 3 putative protective alleles in MS: HLA A*02, HLA B*44, and HLA C*05. METHODS Subjects include a clinic-based patient sample with a diagnosis of either MS or a clinically isolated syndrome (n = 532), compared to subjects in a bone marrow donor registry (n = 776). All subjects have 2-digit HLA data. Logistic regression was used to determine the independence of each allele's effect. We used linear regression and an additive model to test for correlation between an allele and MRI and clinical measures of disease course. RESULTS After accounting for the effect of HLA DRB1*1501, both HLA A*02 and HLA B*44 are validated as susceptibility alleles (p(A*02) 0.00039 and p(B*44) 0.00092) and remain significantly associated with MS susceptibility in the presence of the other allele. Although A*02 is not associated with MS outcome measures, HLA B*44 demonstrates association with a better radiologic outcome both in terms of brain parenchymal fraction and T2 hyperintense lesion volume (p = 0.03 for each outcome). CONCLUSION The MHC class I alleles HLA A*02 and HLA B*44 independently reduce susceptibility to MS, but only HLA B*44 appears to influence disease course, preserving brain volume and reducing the burden of T2 hyperintense lesions in subjects with MS.
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Affiliation(s)
- B C Healy
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, 77 Avenue Louis Pasteur, NRB 168c, Boston, MA 02115, USA
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Sampat MP, Healy BC, Meier DS, Dell'Oglio E, Liguori M, Guttmann CRG. Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements. Neuroimage 2010; 52:1367-73. [PMID: 20362675 DOI: 10.1016/j.neuroimage.2010.03.075] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2009] [Revised: 02/20/2010] [Accepted: 03/26/2010] [Indexed: 12/01/2022] Open
Abstract
The measurement of brain atrophy from magnetic resonance imaging (MRI) has become an established method of estimating disease severity and progression in multiple sclerosis (MS). Most commonly reported in the form of brain parenchymal fraction (BPF), it is more sensitive to the degenerative component of the disease and shows progression more reliably than lesion burden. Typically, the reliability of BPF and other morphometric measurements is assessed by evaluating scan-rescan experiments. While these experiments provide good estimates of real-life error related to imperfect patient repositioning in the MRI scanner, measurement variance due to physiological and reversible pathological fluctuations in brain volume are not taken into account. In this work, we propose a new model for estimating variability in serial morphometry, particularly the BPF measurement. Specifically, we attempt to detect and explicitly model the remaining sources of error to more accurately describe the overall variability in BPF measurements. Our results show that sources of variability beyond subject repositioning error are important and cannot be ignored. We demonstrate that scan-rescan experiments only provide a lower bound on the true error in repeated measurements of patients' BPF. We have estimated the variance due to patient repositioning during scan-rescan (sigma(sr)(2) = 3.0e-06), variance assigned to physiological fluctuations (sigma(p)(2) = 5.74e-06) and the variance associated with lesion activity (sigma(les)(2) = 1.09e-05). These variance components can be used to determine the relative impact of their sources on sample size estimates for studies investigating change over time in MS patients. Our results demonstrate that sample size calculations based exclusively on scan-rescan variability (sigma(sr)) are likely to underestimate the number of patients required. If the physiological variability (sigma(p)) is incorporated in sample size calculations, the required sample size would increase by a factor of 5.69 based on standard t-test sample size calculation.
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Affiliation(s)
- Mehul P Sampat
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
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Akselrod-Ballin A, Galun M, Gomori JM, Filippi M, Valsasina P, Basri R, Brandt A. Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI. IEEE Trans Biomed Eng 2009; 56:2461-9. [PMID: 19758850 DOI: 10.1109/tbme.2008.926671] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ayelet Akselrod-Ballin
- Computational Radiology Laboratory, Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Multiple sclerosis lesion detection using constrained GMM and curve evolution. Int J Biomed Imaging 2009; 2009:715124. [PMID: 19756161 PMCID: PMC2742654 DOI: 10.1155/2009/715124] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2008] [Revised: 06/03/2009] [Accepted: 07/15/2009] [Indexed: 11/22/2022] Open
Abstract
This paper focuses on
the detection and segmentation of Multiple
Sclerosis (MS) lesions in magnetic resonance
(MRI) brain images. To capture the complex
tissue spatial layout, a probabilistic model
termed Constrained Gaussian Mixture Model (CGMM)
is proposed based on a mixture of multiple
spatially oriented Gaussians per tissue. The
intensity of a tissue is considered a global
parameter and is constrained, by a
parameter-tying scheme, to be the same value for
the entire set of Gaussians that are related to
the same tissue. MS lesions are identified as
outlier Gaussian components and are grouped to
form a new class in addition to the healthy
tissue classes. A probability-based curve
evolution technique is used to refine the
delineation of lesion boundaries. The proposed
CGMM-CE algorithm is used to segment 3D MRI
brain images with an arbitrary number of
channels. The CGMM-CE algorithm is automated
and does not require an atlas for initialization
or parameter learning. Experimental results on
both standard brain MRI simulation data and real
data indicate that the proposed method
outperforms previously suggested approaches,
especially for highly noisy data.
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