Clinical Neurology and Neurosurgery 115S (2013) S49–S54
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Retinal nerve fiber thickness and MRI white matter abnormalities in healthy relatives of multiple sclerosis patients Tereza Gabelic a,b , Bianca Weinstock-Guttman c , Rebecca Melia a , Norah Lincoff c , Muhammad W. Masud c , Cheryl Kennedy a , Vesna Brinar b , Deepa P. Ramasamy a , Ellen Carl a , Niels Bergsland a , Murali Ramanathan c,d , Robert Zivadinov a,c,∗ a
Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York, Buffalo, USA Department of Neurology, Referral Centre for Demyelinating Disease of the Central Nervous System, University Hospital Centre Zagreb, Zagreb, Croatia c The Jacobs Neurological Institute, Department of Neurology, University at Buffalo, State University of New York, Buffalo, USA d Department of Pharmaceutical Sciences, State University of New York, Buffalo, USA b
a r t i c l e
i n f o
Keywords: Healthy relatives of MS patients OCT MRI RIS DTI
a b s t r a c t Objectives: To compare retinal nerve fiber (RNFL) thickness and conventional and non-conventional MRI characteristics of healthy controls (HCs) from the general population (non-fHC) to healthy relatives of multiple sclerosis (MS) patients (fHC). Methods: Sixty-eight (68) HCs underwent optical coherence tomography (OCT) and 3T MRI examination. Subjects were classified based on whether or not there was a family history of MS. The study enrolled 40 non-fHC who had no relatives with MS and 28 fHC with at least one relative affected with MS. The associations between OCT parameters and conventional and non-conventional MRI measures were investigated. Results: There were no significant OCT or conventional and non-conventional MRI measureable differences between the non-fHC and fHC groups. Periventricular localization and total volume of white matter (WM) signal abnormalities (SA) were more common in the fHC group but the differences did not reach a level of significance. A significant association between decreased RNFL thickness with increased volume (p = 0.001), number (p = 0.003) and frequency of ≥9 T2 (p = 0.003) WM SAs on MRI was found in the fHC group. No association between OCT and MRI parameters was detected in the non-fHC group. Conclusion: There is an association between decreased RNFL thickness on OCT and increased WM injury in healthy relatives of MS patients. Further studies should explore the pathophysiology of these findings. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system characterized by demyelination, axonal dysfunction and neuronal loss [1]. The severity of axonal injury has a critical role in the development of permanent disability [2]; and its investigation of using multiple diagnostic techniques is of importance for better understanding the underlying pathobiology of MS. Magnetic resonance imaging (MRI) is a key diagnostic and monitoring tool in MS. Conventional MRI techniques are limited in their ability to assess axonal loss [3–5] but non-conventional MRI
∗ Corresponding author at: Buffalo Neuroimaging Anslysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. Tel.: +1 716 859 7031; fax: +1 716 859 4005. E-mail address: [email protected] (R. Zivadinov). 0303-8467/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.clineuro.2013.09.021
techniques such as diffusion tensor imaging (DTI) can provide additional information about axonal and myelin integrity [5]. DTI reveals that T1 hypointense lesions exhibit increased mean diffusivity (MD) and decreased fractional anisotropy (FA), which are indicative of a more severe underlying destructive pathology, compared to T2 hyperintense lesions [6]. DTI can identify abnormalities in normal appearing white matter (NAWM) and gray matter (GM) in MS patients, even before lesions are visible on conventional MRI [7]. Clinical correlation was observed between DTI changes and progressive stages of the disease [8], increased disability [9] and severity of cognitive impairment. [10,11]. In the last 5–10 years, optical coherence tomography (OCT) has been gaining increased attention in addition to MRI as a promising non-invasive method for the quantification of neurodegeneration in MS [12,13]. The retina is the only place where a tissue layer made up of axons can be imaged directly so quantification of the retinal nerve fiber layer (RNFL) thickness and total macular volume (TMV) have been proposed as potential surrogate markers for the
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assessment of neuroaxonal degeneration in MS [13]. OCT studies have shown thinning of the RNFL both in MS patients with or without a history of optic neuritis [14]. Several studies confirmed a relationship between decreased RNFL thickness and increased disability in MS patients [15–17]. Moreover, decreased RNFL thickness was found to relate to more advanced brain atrophy [18,19], disease burden [20,21] and DTI measures [21,22]. Although MS is predominantly a sporadic disease, there is a genetic predisposition with a familial MS history. O’Gorman et al. estimated the overall contribution of the known MS loci to the genetic rick of MS to be 18–24% [23]. MRI studies in healthy relatives of MS patients showed an increased incidence of white matter (WM) signal abnormalities (SA) consistent with MS [24–26]. Against this background, the aim of this study was to investigate the association between OCT and MRI measures (including DTI) in a cohort of healthy controls (HCs) with and without a familial history of MS.
Inversion-Recovery (FLAIR). Pulse sequence characteristics were as follows: All scans were acquired with a 256 × 256 matrix and a 25.6 cm field of view (FOV) for an in-plane resolution of 1 × 1 mm2 with a phase FOV (pFOV) of 75% and one average. Sequence-specific parameters were as follows: for the PD/T2: 3-mm-thick slices with no gap, echo time (TE)1/TE2/repetition time (TR) = 12/95/3000 ms, echo train length (ETL) = 14, and for the FLAIR scans, 3-mm-thick slices with no gap, TE/inversion time (TI)/TR = 120/2100/8500 ms. DTI sequence was also acquired as part of the MRI protocol with 3-mm thick slices, no gap, a 96 × 96 matrix, a 32 cm FOV and a 75% pFOV, resulting in a voxel size of 3.33 mm × 2.50 mm × 3.00 mm. The sequence used a TE/TR of 81.8/8200 ms, 1 average and an ASSET (parallel imaging) factor of 2, resulting in a total acquisition time of 2:28. DTI-specific parameters were 15 directions with a b-value of 800 s/mm2 .
2. Materials and methods
The MRI analyses were blinded to the subjects’ demographic and group characteristics. The WM SA number and volume (WM-SAV) were outlined using a semi-automated edge detection contouring/thresholding technique as previously described [29]. The regional localization of WM SAs was determined based on their presence in the juxtracortical, periventricular, infratentorial and deep WM regions. In addition, we outlined areas of dirty appearing WM (DAWM). The DAWM was defined as a uniform, non-focal area of signal increase on the FLAIR/T2/PD-WI, with a subtly increased signal intensity compared with the signal intensity of NAWM, as previously proposed [30]. The DAWM showed a relatively diffusely defined border of DAWM areas compared with focal WM lesions and was tapered off toward the NAWM. A fully automated processing pipeline was used to calculate MD, radial diffusivity (RD) and axial diffusivity (AD) in the WM SAs. First, the DTI image was corrected for eddy currents and deskulled. Next, a diffusion tensor model was fit at each voxel, using DTIfit, part of the FSL package [31]. The resulting MD, RD and AD maps were then masked to calculate mean values. In order to focus our study on axonal and myelin integrity and decrease number of comparisons, only these DTI measures were included into study.
2.1. Subjects This was a prospective study in which HC without known CNS and ophthalmologic pathology or neurological complaints were recruited from the following volunteer sources: (1) hospital personnel, (2) respondents to a local newspaper advertisement and (3) relatives of MS patients that are followed in our center. The inclusion criteria required: (1) fulfilling the health screen questionnaire requirements containing information regarding medical history (illnesses, surgeries, medications, family history of MS, etc.), (2) normal physical examination, (3) having at least one MS relative followed in our center (for healthy relatives of MS patients) as well as (4) being able to undergo MRI scanning and OCT examination. Subjects with: (1) pre-existing medical conditions known to be associated with CNS pathology (e.g., neurodegenerative disorder, cerebrovascular disease, cognitive impairment, history of psychiatric disorders, seizures, trauma, etc.), (2) patients with comorbid ocular conditions not related to MS, including advanced glaucoma, significant refractive errors (±8 diopters), and (3) previous known history of retinal pathology (i.e., diabetic retinopathy) as ascertained by a detailed history and examination were excluded from the study [20]. All subjects underwent physical, neurological, OCT and MRI examinations and were assessed with a structured questionnaire administered in-person by a trained interviewer unaware of the subjects’ HC status. The questionnaire contained information related to demographic characteristics, presence of autoimmune and other concomitant diseases, vascular risk factors and environmental factors, as well as a family history of MS, as previously described [27]. Race/ethnicity was determined using the classifications of the US Census Bureau. Subjects were divided into two groups depending on the presence of relatives with MS: (1) nonfamilial HC (non-fHC) – subjects without known relatives affected with MS, or (2) healthy relatives of MS patients (fHC) – subjects with at least one relative (first, second, third degree) affected with MS. Classification of first, second and third degree relatives was used as described previously [28]. 2.2. MRI acquisition All subjects were examined on a 3T GE Signa Excite HD 12.0 TwinSpeed 8-channel scanner (General Electric, Milwaukee, WI), using an 8-channel head and neck (HDNV) coil. MRI sequences included multi-planar dual fast spin-echo (FSE) proton density (PD) and T2-weighted image (WI) as well as Fluid-Attenuated
2.3. MRI analysis
2.4. Optical coherence tomography (OCT) Subjects underwent OCT examination (Spectralis, Heidelberg, Germany) to measure RNFL thickness and TMV of both eyes. All scans were obtained by an experienced technician trained in OCT scan capture and in the recognition of common artifacts and errors in OCT imaging. The fast RNFL thickness scan protocol was used for OCT (computes the average of 3 circumferential scans for 360◦ around the optic disk; 256 axial scans; diameter, 3.4 mm). The optic disk was centered in all scans by the scanning technician and scanning was performed without the use of pharmacologic dilation. The technician ensured that the retinal sections were centered within the scanning window and that the target signal strength was ≥7 (7–10), ensuring high quality of OCT scans. Analysis of the retinal images was performed using the Heidelberg Spectralis software. All scans were reviewed for sufficient signal strength, correct centering and beam placement as well as segmentation. 2.5. Statistical analysis Statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS), version 16.0 (SPSS, Inc., Chicago IL). Demographic, OCT and MRI differences between groups were
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Table 1 Demographic and optical coherence tomography characteristics of non-familial controls and healthy relatives of multiple sclerosis patients.
Age, years, mean (SD) median Sex, female (n%) Vascular risk factors, n (%) Hypertension Heart disease Smoking Obesity (BMI > 30) Diabetes mellitus type I OCT parameters, mean (SD) median RNFL thickness right, mean (SD) median RNFL thickness left, mean (SD) median RNFL thickness total, mean (SD) median TMV right, mean (SD) median TMV left, mean (SD) median TMV total, mean (SD) median
Non-fHC (N = 40)
fHC (N = 28)
p value
39.6 (14.7) 45
38.0 (18.5) 35
0.678
34 (85.0)
19 (67.8)
0.084
6 (15.0) 1 (2.5) 15 (37.5) 8 (20.0) 1 (2.5)
5 (17.8) 3 (10.7) 10 (35.7) 6 (21.4) 0
0.505 0.157 0.599 0.544 0.569
92.9 (11.3) 92.0 93.1 (11.4) 92.0 92.8 (10.7) 92.5 8.5 (0.4) 8.4 8.5 (0.4) 8.5 8.5 (0.4) 8.5
96.3 (9.3) 96.5 96.1 (9.7) 95.5 96.2 (9.7) 96.0 8.6 (0.6) 8.6 8.6 (0.4) 8.6 8.6 (0.5) 0.7
0.189 0.271 0.187 0.441 0.303 0.552
non-fHC – non-familial healthy controls; fHC – healthy relatives of multiple sclerosis patients; OCT – optical coherence tomography; SD – standard deviation; RNFL – retinal nerve fiber layer; TMV – total macular volume. Comparison between groups was performed using t-test for continuous variables and chi-square test for categorical variables.
tested using the t-test, chi-square test and Mann–Whitney rank sum U-test, as appropriate. To explore whether there was a relationship between the OCT/MRI measures and vascular risk factors (hypertension, heart disease, smoking and obesity), we employed a multivariate logistic regression model in which sex was used as a covariate and the MRI measures and vascular risk factors were used as independent variables. Association of OCT, MRI and vascular risk factor analysis were separately investigated for the non-fHC and the fHC group. The Benjamini–Hochberg correction was used to minimize the false discovery rate at p < 0.05 level [32]. All p-values were calculated using a two-tailed test. 3. Results 3.1. Subject characteristics A total of 68 subjects (non-fHC: n = 40, fHC: n = 28) were enrolled in this study. The demographic and clinical characteristics of the subjects are listed in Table 1. There were no significant age or sex
differences between the study groups. Also, there were no significant differences between the groups regarding the presence of vascular risk factors such as hypertension, smoking, obesity, diabetes and heart disease. OCT measures of the groups are listed in Table 1. With respect to RNFL thickness and TMV, there were no significant differences between the non-fHC and the fHC groups.
3.2. WM SA characteristics of the groups MRI characteristics of WM SAs and their anatomical localization are presented in Table 2. In total, 35.7% of the fHC and 32.5% of the non-fHC group presented with WM SA. No significant differences were observed between non-fHC and fHC groups for presence, number and anatomical localization of WM SAs. Periventricular localization (25% vs 10%) and WM-SAV (222.8 vs. 81.7 mm3 ) were higher in the fHC compared to the non-fHCs but the differences did not reach a level of significance.
Table 2 Conventional white matter signal abnormalities and diffusion tensor imaging characteristics on MRI in non-familial controls and healthy relatives of multiple sclerosis patients. Non-fHC (N = 40) Subjects with WM SAs, n (%) Subjects with WM JC SAs, n (%) Subjects with WM PVL SAs, n (%) Subjects with WM IT SAs, n (%) Subjects with DWM SA, n (%) Subjects with ≥9 WM SAs, n (%) WM-SAN mean (SD) median WM JC SAN mean (SD) median WM PVL SAN mean (SD) median WM IT SAN mean (SD) median WM DWM SAN mean (SD) median WM- SAV, mean (SD) median DAWM-SAV, mean (SD) median WM-SAV + DAWM-SAV mean (SD) median WM SA, MD mean (SD) median (×10−4 ) WM SA, RD mean (SD) median (×10−4 ) WM SA, AD mean (SD) median (×10−4 )
13 (32.5) 2 (5.0) 4 (10.0) 0 13 (32.5) 3.0 (7.5) 1.9 (4.5) 0 0.1 (0.3) 0 0.1 (0.5) 0 0.0 (0) 0 1.7 (4.1) 0 81.7 (201.6) 0 656.3 (594.7) 438.0 738.0 (710.2) 507.0 10.3 (1.7) 9.6 8.8 (1.8) 8.2 13.4 (1.5) 12.7
fHC (N = 28) 10 (35.7) 4 (14.3) 7 (25) 1 (3.5) 9 (32.1) 4.0 (14.3) 3.0 (5.1) 0 0.2 (0.568) 0 0.5 (0.922) 0 0.1 (0.189) 0 2.3 (4.2) 0 222.8 (414.3) 0 592.6 (382.6) 579.1 815.5 (674.3) 712.4 10.3 (1.7) 10.0 8.9 (1.9) 8.5 13.2 (1.5) 12.7
p value 0.492 0.185 0.094 0.412 0.594 0.305 0.544 0.188 0.091 0.232 0.736 0.460 0.636 0.350 0.763 0.880 0.564
non-fHC non – familial healthy controls; fHC – healthy relatives of multiple sclerosis patient; SD – standard deviation; SAs – signal abnormalities; SAN – signal abnormality number; JC – juxtacortical; PVL – periventricular; IT – infratentorial, DWM – deep white matter, SAV – signal abnormality volume; DAWM – dirty appearing white matter; DTI – diffusion tensor imaging; MD – mean diffusivity; RD – radial diffusivity; AD – axial diffusivity. The differences between the groups were compared using the chi-square test or Mann–Whitney U-test. The SAV is expressed in millimeter cubes (mm3 ).
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Table 3 Regression analysis of MRI variables and vascular risk factors with gender as covariate in group of healthy relatives with multiple sclerosis patients. RNFL thickness ˇ ≥9 WM SA WM SAN WM SAV WM SA DTI MD WM SA DTI AD WM SA DTI RD DAWM−SAV Hypertension Smoking Obesity (BMI>30) Heart disease
−0.572 −0.615 −0.633 −0.086 −0.144 −0.063 −0.348 −0.387 0.291 −0.308 0.022
TMV p value *
0.003 0.002* 0.001* 0.753 0.600 0.818 0.077 0.058 0.316 0.159 0.860
ˇ
p value
−0.373 −0.332 −0.360 −0.494 −0.487 −0.491 −0.066 −0.419 0.513 −0.263 −0.041
0.067 0.124 0.089 0.053 0.060 0.055 0.746 0.038 0.061 0.231 0.753
SAN – signal abnormalities number; SAV – signal abnormality volume; WM – white matter; DTI – diffusion tensor imaging; MD – mean diffusivity; AD – axial diffusivity; RD – radial diffusivity; DAWM – dirty appearing white matter. Multiple linear regression was done with gender as covariate. p Values were corrected using Benjamini–Hochberg analysis. * Represents values that remain significant after Benjamini–Hochberg analysis (p < 0.05).
3.3. MRI DTI characteristics of the groups MRI DTI WM SA measures including MD, RD and AD were included in the analysis and are listed in Table 2. In all three DTI subgroup analyses, there were no significant differences between the two HC groups. 3.4. Relationship between MRI parameters, OCT and vascular risk factors In multivariate regression analysis, the OCT variables (RNFL thickness and TMV) were used as dependent variables, MRI outcomes and vascular risk factors as independent variables and gender as covariate. Multivariate regression analysis in the fHC group is shown in Table 3. In the non-fHC group, no significant associations were observed in regression analysis with any of the used variables. When RNFL thickness was used as the dependent variable, a significant association was observed with the following MRI variables: ≥9 T2 WM SA, WM SAN and WM SAV. When TMV was used as the dependent variable, no significant associations were observed in the explored MRI variables. A similar regression analysis model using vascular risk factors as independent variables did not show an association with OCT measures. 4. Discussion The present study is the first to evaluate the relationship between OCT and 3T MRI outcomes in healthy relatives of MS patients. No significant differences were found between the non-fHC and the fHC groups in RNFL thickness or TMV. While periventricular localization and increased WM SA disease burden were more frequent in fHC, this difference did not reach a level of significance compared to non-fHC. No WM SA DTI differences between the two groups were found. Interestingly, in regression analysis, there was a significant association between conventional MRI variables (>9 T2 WM SA, WM SAV and WM SAN) and decreased RNFL thickness only in the fHC group. After being introduced in research studies almost a decade ago, OCT has been widely adopted and was recently proposed
as an outcome measure in remyelinating as well as neuroprotective clinical trials in MS [13,33]. OCT showed a relationship with the clinical stage of the disease, lesion burden and brain atrophy [9,12,18–21,34]. It is an inexpensive, well-tolerated and highly reproducible method, which potentially allows for monitoring disease progression. OCT also revealed RNFL thinning suggestive of clinically silent axonal loss ongoing in MS patients, independently of the optic neuritis history [35,36]. A recent meta-analysis of several OCT studies by Petzold et al. [14] suggested that the silent loss of RNFL thickness supports the growing evidence of parallel neurodegenerative and inflammatory process in MS [37,38]. In the present study, no differences were found in explored OCT parameters between the two HC groups indicating that there was not a greater loss of RNFL thickness or TMV ongoing in fHC compared to nonfHC. MRI monitoring of disease progression and response to therapy is one of the most important goals in MS clinical trials [4,12,13], but it is known that conventional MRI measures are not sensitive enough to differentiate between demyelination and neurodegeneration [3]. Conventional MRI analysis in our study showed increased WM SAV in the fHC group yet this difference did not reach a level of significance. Previous studies using conventional brain MRI showed an increase in WM SAs consistent with MS in the healthy relatives of MS patients [24,25,39]. Although advanced MRI techniques, showed better correlations with clinical [5,40] and visual disability [21,22,41], the present study did not reveal any differences in DTI parameters between the study groups. These findings should be interpreted with caution though due to a small number of participants. To the best of our knowledge, DTI has not yet been studied in healthy relatives of MS patients. Previous non-conventional MRI studies of asymptomatic relatives of MS patients showed significant magnetization transfer ratio (MTR) changes in WM SAs, indicative of demyelinating pathology [42,43], although some other studies showed no differences in MTR of WM in the siblings of MS patients [44]. In multivariate regression analysis, a significant correlation was observed between decreased RNFL thickness and the following conventional MRI variables: ≥9 T2 lesions, WM SAN and WM SAV, only in the fHC group. This is, indeed, an interesting finding. Similar results using conventional MRI were described previously, showing a strong correlation between increased T2 lesion burden and decreased RNFL thickness in MS patients [20,21]. It was also described that RNFL thickness loss indicates more severe WM than GM damage in early MS [45], which is also in line with our results of increased WM SAV in asymptomatic relatives of MS patients, and its association with OCT in regression analysis. Healthy relatives of MS patients are under increased risk of developing the disease [23]. The risk of developing MS in a firstdegree relative of the affected patient is 30–50 fold higher than in the general population [46], while second and third degree relatives also show an increased risk for developing MS [47]. It could be useful to evaluate healthy subjects who are at higher risk for developing the disease using sensitive methods proven efficient in monitoring MS patients such as OCT. An important limitation of our study is the small number of subjects, especially in the fHC group, that could have limited our ability to show differences between the two groups. Therefore, further studies with a larger sample of healthy relative of MS patients are necessary. In conclusion, we found an association between decreased RNFL thickness on OCT and increased WM injury in the HCs of familial MS relatives. Further studies should explore the pathophysiology of these findings.
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Conflict of interest statement Study funding: The authors have nothing to disclose. Disclosures: Tereza Gabelic, Rebecca Melia, Cheryl Kennedy, Deepa Ramasamy, Ellen Carl, Niels Bergsland, Muhammad W. Masud, Norah Lincoff and Vesna Brinar have nothing to disclose. Dr. Bianca Weinstock-Guttman has participated in speaker’s bureaus and served as a consultant for Biogen Idec, Teva Neurosciences, EMD Serono, Pfizer, Novartis, Genzyme, and Acorda. She also has received grant/research support from the agencies listed above as well as ITN, Questcor and Shire. No other industry financial relationships exist. Murali Ramanathan received research funding or consulting fees from EMD Serono, Biogen Idec, Allergan, Netezza, Pfizer, Novartis, the National Multiple Sclerosis Society, the Department of Defense, Jog for the Jake Foundation, the National Institute of Health and National Science Foundation. He received compensation for serving as an Editor from the American Association of Pharmaceutical Scientists. These are unrelated to the research presented in this report. Robert Zivadinov received personal compensation from Teva Pharmaceuticals, Biogen Idec, EMD Serono, Novartis and SanofiGenzyme for speaking and consultant fees. Dr. Zivadinov received financial support for research activities from Biogen Idec, Teva Pharmaceuticals, EMD Serono, Novartis and Sanofi-Genzyme. Acknowledgements Support from the National Multiple Sclerosis Society (RG3743 and RG4836-A-5) and the Department of Defense Multiple Sclerosis Program (MS090122) to the Ramanathan laboratory is gratefully acknowledged. References [1] Silber E, Sharief MK. Axonal degeneration in the pathogenesis of multiple sclerosis. J Neurol Sci 1999;170:11–8. [2] Lassmann H. Axonal and neuronal pathology in multiple sclerosis: what have we learnt from animal models. Exp Neurol 2010;225:2–8. [3] Poloni G, Minagar A, Haacke EM, Zivadinov R. Recent developments in imaging of multiple sclerosis. Neurologist 2011;17:185–204. [4] Zivadinov R. Can imaging techniques measure neuroprotection and remyelination in multiple sclerosis? Neurology 2007;68:S72–82 (discussion S91–76). [5] Rovaris M, Gass A, Bammer R, Hickman SJ, Ciccarelli O, Miller DH, et al. Diffusion MRI in multiple sclerosis. Neurology 2005;65:1526–32. [6] Filippi M, Iannucci G, Cercignani M, Assunta Rocca M, Pratesi A, Comi G. A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging. Arch Neurol 2000;57:1017–21. [7] Rocca MA, Cercignani M, Iannucci G, Comi G, Filippi M. Weekly diffusionweighted imaging of normal-appearing white matter in MS. Neurology 2000;55:882–4. [8] Tavazzi E, Dwyer MG, Weinstock-Guttman B, Lema J, Bastianello S, Bergamaschi R, et al. Quantitative diffusion weighted imaging measures in patients with multiple sclerosis. Neuroimage 2007;36:746–54. [9] Harrison DM, Shiee N, Bazin PL, Newsome SD, Ratchford JN, Pham D, et al. Tractspecific quantitative MRI better correlates with disability than conventional MRI in multiple sclerosis. J Neurol 2013;260:397–406. [10] Rovaris M, Iannucci G, Falautano M, Possa F, Martinelli V, Comi G, et al. Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis: an exploratory study with diffusion tensor MR imaging. J Neurol Sci 2002;195:103–9. [11] Benedict RH, Bruce J, Dwyer MG, Weinstock-Guttman B, Tjoa C, Tavazzi E, et al. Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Mult Scler 2007;13:722–30. [12] Frohman E, Costello F, Zivadinov R, Stuve O, Conger A, Winslow H, et al. Optical coherence tomography in multiple sclerosis. Lancet Neurol 2006;5: 853–63. [13] Barkhof F, Calabresi PA, Miller DH, Reingold SC. Imaging outcomes for neuroprotection and repair in multiple sclerosis trials. Nat Rev Neurol 2009;5:256–66. [14] Petzold A, de Boer JF, Schippling S, Vermersch P, Kardon R, Green A, et al. Optical coherence tomography in multiple sclerosis: a systematic review and metaanalysis. Lancet Neurol 2010;9:921–32.
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Clinical Neurology and Neurosurgery journal homepage: www.elsevier.com/locate/clineuro
Retinal nerve fiber thickness and MRI white matter abnormalities in healthy relatives of multiple sclerosis patients Tereza Gabelic a,b , Bianca Weinstock-Guttman c , Rebecca Melia a , Norah Lincoff c , Muhammad W. Masud c , Cheryl Kennedy a , Vesna Brinar b , Deepa P. Ramasamy a , Ellen Carl a , Niels Bergsland a , Murali Ramanathan c,d , Robert Zivadinov a,c,∗ a
Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York, Buffalo, USA Department of Neurology, Referral Centre for Demyelinating Disease of the Central Nervous System, University Hospital Centre Zagreb, Zagreb, Croatia c The Jacobs Neurological Institute, Department of Neurology, University at Buffalo, State University of New York, Buffalo, USA d Department of Pharmaceutical Sciences, State University of New York, Buffalo, USA b
a r t i c l e
i n f o
Keywords: Healthy relatives of MS patients OCT MRI RIS DTI
a b s t r a c t Objectives: To compare retinal nerve fiber (RNFL) thickness and conventional and non-conventional MRI characteristics of healthy controls (HCs) from the general population (non-fHC) to healthy relatives of multiple sclerosis (MS) patients (fHC). Methods: Sixty-eight (68) HCs underwent optical coherence tomography (OCT) and 3T MRI examination. Subjects were classified based on whether or not there was a family history of MS. The study enrolled 40 non-fHC who had no relatives with MS and 28 fHC with at least one relative affected with MS. The associations between OCT parameters and conventional and non-conventional MRI measures were investigated. Results: There were no significant OCT or conventional and non-conventional MRI measureable differences between the non-fHC and fHC groups. Periventricular localization and total volume of white matter (WM) signal abnormalities (SA) were more common in the fHC group but the differences did not reach a level of significance. A significant association between decreased RNFL thickness with increased volume (p = 0.001), number (p = 0.003) and frequency of ≥9 T2 (p = 0.003) WM SAs on MRI was found in the fHC group. No association between OCT and MRI parameters was detected in the non-fHC group. Conclusion: There is an association between decreased RNFL thickness on OCT and increased WM injury in healthy relatives of MS patients. Further studies should explore the pathophysiology of these findings. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system characterized by demyelination, axonal dysfunction and neuronal loss [1]. The severity of axonal injury has a critical role in the development of permanent disability [2]; and its investigation of using multiple diagnostic techniques is of importance for better understanding the underlying pathobiology of MS. Magnetic resonance imaging (MRI) is a key diagnostic and monitoring tool in MS. Conventional MRI techniques are limited in their ability to assess axonal loss [3–5] but non-conventional MRI
∗ Corresponding author at: Buffalo Neuroimaging Anslysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. Tel.: +1 716 859 7031; fax: +1 716 859 4005. E-mail address: [email protected] (R. Zivadinov). 0303-8467/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.clineuro.2013.09.021
techniques such as diffusion tensor imaging (DTI) can provide additional information about axonal and myelin integrity [5]. DTI reveals that T1 hypointense lesions exhibit increased mean diffusivity (MD) and decreased fractional anisotropy (FA), which are indicative of a more severe underlying destructive pathology, compared to T2 hyperintense lesions [6]. DTI can identify abnormalities in normal appearing white matter (NAWM) and gray matter (GM) in MS patients, even before lesions are visible on conventional MRI [7]. Clinical correlation was observed between DTI changes and progressive stages of the disease [8], increased disability [9] and severity of cognitive impairment. [10,11]. In the last 5–10 years, optical coherence tomography (OCT) has been gaining increased attention in addition to MRI as a promising non-invasive method for the quantification of neurodegeneration in MS [12,13]. The retina is the only place where a tissue layer made up of axons can be imaged directly so quantification of the retinal nerve fiber layer (RNFL) thickness and total macular volume (TMV) have been proposed as potential surrogate markers for the
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assessment of neuroaxonal degeneration in MS [13]. OCT studies have shown thinning of the RNFL both in MS patients with or without a history of optic neuritis [14]. Several studies confirmed a relationship between decreased RNFL thickness and increased disability in MS patients [15–17]. Moreover, decreased RNFL thickness was found to relate to more advanced brain atrophy [18,19], disease burden [20,21] and DTI measures [21,22]. Although MS is predominantly a sporadic disease, there is a genetic predisposition with a familial MS history. O’Gorman et al. estimated the overall contribution of the known MS loci to the genetic rick of MS to be 18–24% [23]. MRI studies in healthy relatives of MS patients showed an increased incidence of white matter (WM) signal abnormalities (SA) consistent with MS [24–26]. Against this background, the aim of this study was to investigate the association between OCT and MRI measures (including DTI) in a cohort of healthy controls (HCs) with and without a familial history of MS.
Inversion-Recovery (FLAIR). Pulse sequence characteristics were as follows: All scans were acquired with a 256 × 256 matrix and a 25.6 cm field of view (FOV) for an in-plane resolution of 1 × 1 mm2 with a phase FOV (pFOV) of 75% and one average. Sequence-specific parameters were as follows: for the PD/T2: 3-mm-thick slices with no gap, echo time (TE)1/TE2/repetition time (TR) = 12/95/3000 ms, echo train length (ETL) = 14, and for the FLAIR scans, 3-mm-thick slices with no gap, TE/inversion time (TI)/TR = 120/2100/8500 ms. DTI sequence was also acquired as part of the MRI protocol with 3-mm thick slices, no gap, a 96 × 96 matrix, a 32 cm FOV and a 75% pFOV, resulting in a voxel size of 3.33 mm × 2.50 mm × 3.00 mm. The sequence used a TE/TR of 81.8/8200 ms, 1 average and an ASSET (parallel imaging) factor of 2, resulting in a total acquisition time of 2:28. DTI-specific parameters were 15 directions with a b-value of 800 s/mm2 .
2. Materials and methods
The MRI analyses were blinded to the subjects’ demographic and group characteristics. The WM SA number and volume (WM-SAV) were outlined using a semi-automated edge detection contouring/thresholding technique as previously described [29]. The regional localization of WM SAs was determined based on their presence in the juxtracortical, periventricular, infratentorial and deep WM regions. In addition, we outlined areas of dirty appearing WM (DAWM). The DAWM was defined as a uniform, non-focal area of signal increase on the FLAIR/T2/PD-WI, with a subtly increased signal intensity compared with the signal intensity of NAWM, as previously proposed [30]. The DAWM showed a relatively diffusely defined border of DAWM areas compared with focal WM lesions and was tapered off toward the NAWM. A fully automated processing pipeline was used to calculate MD, radial diffusivity (RD) and axial diffusivity (AD) in the WM SAs. First, the DTI image was corrected for eddy currents and deskulled. Next, a diffusion tensor model was fit at each voxel, using DTIfit, part of the FSL package [31]. The resulting MD, RD and AD maps were then masked to calculate mean values. In order to focus our study on axonal and myelin integrity and decrease number of comparisons, only these DTI measures were included into study.
2.1. Subjects This was a prospective study in which HC without known CNS and ophthalmologic pathology or neurological complaints were recruited from the following volunteer sources: (1) hospital personnel, (2) respondents to a local newspaper advertisement and (3) relatives of MS patients that are followed in our center. The inclusion criteria required: (1) fulfilling the health screen questionnaire requirements containing information regarding medical history (illnesses, surgeries, medications, family history of MS, etc.), (2) normal physical examination, (3) having at least one MS relative followed in our center (for healthy relatives of MS patients) as well as (4) being able to undergo MRI scanning and OCT examination. Subjects with: (1) pre-existing medical conditions known to be associated with CNS pathology (e.g., neurodegenerative disorder, cerebrovascular disease, cognitive impairment, history of psychiatric disorders, seizures, trauma, etc.), (2) patients with comorbid ocular conditions not related to MS, including advanced glaucoma, significant refractive errors (±8 diopters), and (3) previous known history of retinal pathology (i.e., diabetic retinopathy) as ascertained by a detailed history and examination were excluded from the study [20]. All subjects underwent physical, neurological, OCT and MRI examinations and were assessed with a structured questionnaire administered in-person by a trained interviewer unaware of the subjects’ HC status. The questionnaire contained information related to demographic characteristics, presence of autoimmune and other concomitant diseases, vascular risk factors and environmental factors, as well as a family history of MS, as previously described [27]. Race/ethnicity was determined using the classifications of the US Census Bureau. Subjects were divided into two groups depending on the presence of relatives with MS: (1) nonfamilial HC (non-fHC) – subjects without known relatives affected with MS, or (2) healthy relatives of MS patients (fHC) – subjects with at least one relative (first, second, third degree) affected with MS. Classification of first, second and third degree relatives was used as described previously [28]. 2.2. MRI acquisition All subjects were examined on a 3T GE Signa Excite HD 12.0 TwinSpeed 8-channel scanner (General Electric, Milwaukee, WI), using an 8-channel head and neck (HDNV) coil. MRI sequences included multi-planar dual fast spin-echo (FSE) proton density (PD) and T2-weighted image (WI) as well as Fluid-Attenuated
2.3. MRI analysis
2.4. Optical coherence tomography (OCT) Subjects underwent OCT examination (Spectralis, Heidelberg, Germany) to measure RNFL thickness and TMV of both eyes. All scans were obtained by an experienced technician trained in OCT scan capture and in the recognition of common artifacts and errors in OCT imaging. The fast RNFL thickness scan protocol was used for OCT (computes the average of 3 circumferential scans for 360◦ around the optic disk; 256 axial scans; diameter, 3.4 mm). The optic disk was centered in all scans by the scanning technician and scanning was performed without the use of pharmacologic dilation. The technician ensured that the retinal sections were centered within the scanning window and that the target signal strength was ≥7 (7–10), ensuring high quality of OCT scans. Analysis of the retinal images was performed using the Heidelberg Spectralis software. All scans were reviewed for sufficient signal strength, correct centering and beam placement as well as segmentation. 2.5. Statistical analysis Statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS), version 16.0 (SPSS, Inc., Chicago IL). Demographic, OCT and MRI differences between groups were
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Table 1 Demographic and optical coherence tomography characteristics of non-familial controls and healthy relatives of multiple sclerosis patients.
Age, years, mean (SD) median Sex, female (n%) Vascular risk factors, n (%) Hypertension Heart disease Smoking Obesity (BMI > 30) Diabetes mellitus type I OCT parameters, mean (SD) median RNFL thickness right, mean (SD) median RNFL thickness left, mean (SD) median RNFL thickness total, mean (SD) median TMV right, mean (SD) median TMV left, mean (SD) median TMV total, mean (SD) median
Non-fHC (N = 40)
fHC (N = 28)
p value
39.6 (14.7) 45
38.0 (18.5) 35
0.678
34 (85.0)
19 (67.8)
0.084
6 (15.0) 1 (2.5) 15 (37.5) 8 (20.0) 1 (2.5)
5 (17.8) 3 (10.7) 10 (35.7) 6 (21.4) 0
0.505 0.157 0.599 0.544 0.569
92.9 (11.3) 92.0 93.1 (11.4) 92.0 92.8 (10.7) 92.5 8.5 (0.4) 8.4 8.5 (0.4) 8.5 8.5 (0.4) 8.5
96.3 (9.3) 96.5 96.1 (9.7) 95.5 96.2 (9.7) 96.0 8.6 (0.6) 8.6 8.6 (0.4) 8.6 8.6 (0.5) 0.7
0.189 0.271 0.187 0.441 0.303 0.552
non-fHC – non-familial healthy controls; fHC – healthy relatives of multiple sclerosis patients; OCT – optical coherence tomography; SD – standard deviation; RNFL – retinal nerve fiber layer; TMV – total macular volume. Comparison between groups was performed using t-test for continuous variables and chi-square test for categorical variables.
tested using the t-test, chi-square test and Mann–Whitney rank sum U-test, as appropriate. To explore whether there was a relationship between the OCT/MRI measures and vascular risk factors (hypertension, heart disease, smoking and obesity), we employed a multivariate logistic regression model in which sex was used as a covariate and the MRI measures and vascular risk factors were used as independent variables. Association of OCT, MRI and vascular risk factor analysis were separately investigated for the non-fHC and the fHC group. The Benjamini–Hochberg correction was used to minimize the false discovery rate at p < 0.05 level [32]. All p-values were calculated using a two-tailed test. 3. Results 3.1. Subject characteristics A total of 68 subjects (non-fHC: n = 40, fHC: n = 28) were enrolled in this study. The demographic and clinical characteristics of the subjects are listed in Table 1. There were no significant age or sex
differences between the study groups. Also, there were no significant differences between the groups regarding the presence of vascular risk factors such as hypertension, smoking, obesity, diabetes and heart disease. OCT measures of the groups are listed in Table 1. With respect to RNFL thickness and TMV, there were no significant differences between the non-fHC and the fHC groups.
3.2. WM SA characteristics of the groups MRI characteristics of WM SAs and their anatomical localization are presented in Table 2. In total, 35.7% of the fHC and 32.5% of the non-fHC group presented with WM SA. No significant differences were observed between non-fHC and fHC groups for presence, number and anatomical localization of WM SAs. Periventricular localization (25% vs 10%) and WM-SAV (222.8 vs. 81.7 mm3 ) were higher in the fHC compared to the non-fHCs but the differences did not reach a level of significance.
Table 2 Conventional white matter signal abnormalities and diffusion tensor imaging characteristics on MRI in non-familial controls and healthy relatives of multiple sclerosis patients. Non-fHC (N = 40) Subjects with WM SAs, n (%) Subjects with WM JC SAs, n (%) Subjects with WM PVL SAs, n (%) Subjects with WM IT SAs, n (%) Subjects with DWM SA, n (%) Subjects with ≥9 WM SAs, n (%) WM-SAN mean (SD) median WM JC SAN mean (SD) median WM PVL SAN mean (SD) median WM IT SAN mean (SD) median WM DWM SAN mean (SD) median WM- SAV, mean (SD) median DAWM-SAV, mean (SD) median WM-SAV + DAWM-SAV mean (SD) median WM SA, MD mean (SD) median (×10−4 ) WM SA, RD mean (SD) median (×10−4 ) WM SA, AD mean (SD) median (×10−4 )
13 (32.5) 2 (5.0) 4 (10.0) 0 13 (32.5) 3.0 (7.5) 1.9 (4.5) 0 0.1 (0.3) 0 0.1 (0.5) 0 0.0 (0) 0 1.7 (4.1) 0 81.7 (201.6) 0 656.3 (594.7) 438.0 738.0 (710.2) 507.0 10.3 (1.7) 9.6 8.8 (1.8) 8.2 13.4 (1.5) 12.7
fHC (N = 28) 10 (35.7) 4 (14.3) 7 (25) 1 (3.5) 9 (32.1) 4.0 (14.3) 3.0 (5.1) 0 0.2 (0.568) 0 0.5 (0.922) 0 0.1 (0.189) 0 2.3 (4.2) 0 222.8 (414.3) 0 592.6 (382.6) 579.1 815.5 (674.3) 712.4 10.3 (1.7) 10.0 8.9 (1.9) 8.5 13.2 (1.5) 12.7
p value 0.492 0.185 0.094 0.412 0.594 0.305 0.544 0.188 0.091 0.232 0.736 0.460 0.636 0.350 0.763 0.880 0.564
non-fHC non – familial healthy controls; fHC – healthy relatives of multiple sclerosis patient; SD – standard deviation; SAs – signal abnormalities; SAN – signal abnormality number; JC – juxtacortical; PVL – periventricular; IT – infratentorial, DWM – deep white matter, SAV – signal abnormality volume; DAWM – dirty appearing white matter; DTI – diffusion tensor imaging; MD – mean diffusivity; RD – radial diffusivity; AD – axial diffusivity. The differences between the groups were compared using the chi-square test or Mann–Whitney U-test. The SAV is expressed in millimeter cubes (mm3 ).
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Table 3 Regression analysis of MRI variables and vascular risk factors with gender as covariate in group of healthy relatives with multiple sclerosis patients. RNFL thickness ˇ ≥9 WM SA WM SAN WM SAV WM SA DTI MD WM SA DTI AD WM SA DTI RD DAWM−SAV Hypertension Smoking Obesity (BMI>30) Heart disease
−0.572 −0.615 −0.633 −0.086 −0.144 −0.063 −0.348 −0.387 0.291 −0.308 0.022
TMV p value *
0.003 0.002* 0.001* 0.753 0.600 0.818 0.077 0.058 0.316 0.159 0.860
ˇ
p value
−0.373 −0.332 −0.360 −0.494 −0.487 −0.491 −0.066 −0.419 0.513 −0.263 −0.041
0.067 0.124 0.089 0.053 0.060 0.055 0.746 0.038 0.061 0.231 0.753
SAN – signal abnormalities number; SAV – signal abnormality volume; WM – white matter; DTI – diffusion tensor imaging; MD – mean diffusivity; AD – axial diffusivity; RD – radial diffusivity; DAWM – dirty appearing white matter. Multiple linear regression was done with gender as covariate. p Values were corrected using Benjamini–Hochberg analysis. * Represents values that remain significant after Benjamini–Hochberg analysis (p < 0.05).
3.3. MRI DTI characteristics of the groups MRI DTI WM SA measures including MD, RD and AD were included in the analysis and are listed in Table 2. In all three DTI subgroup analyses, there were no significant differences between the two HC groups. 3.4. Relationship between MRI parameters, OCT and vascular risk factors In multivariate regression analysis, the OCT variables (RNFL thickness and TMV) were used as dependent variables, MRI outcomes and vascular risk factors as independent variables and gender as covariate. Multivariate regression analysis in the fHC group is shown in Table 3. In the non-fHC group, no significant associations were observed in regression analysis with any of the used variables. When RNFL thickness was used as the dependent variable, a significant association was observed with the following MRI variables: ≥9 T2 WM SA, WM SAN and WM SAV. When TMV was used as the dependent variable, no significant associations were observed in the explored MRI variables. A similar regression analysis model using vascular risk factors as independent variables did not show an association with OCT measures. 4. Discussion The present study is the first to evaluate the relationship between OCT and 3T MRI outcomes in healthy relatives of MS patients. No significant differences were found between the non-fHC and the fHC groups in RNFL thickness or TMV. While periventricular localization and increased WM SA disease burden were more frequent in fHC, this difference did not reach a level of significance compared to non-fHC. No WM SA DTI differences between the two groups were found. Interestingly, in regression analysis, there was a significant association between conventional MRI variables (>9 T2 WM SA, WM SAV and WM SAN) and decreased RNFL thickness only in the fHC group. After being introduced in research studies almost a decade ago, OCT has been widely adopted and was recently proposed
as an outcome measure in remyelinating as well as neuroprotective clinical trials in MS [13,33]. OCT showed a relationship with the clinical stage of the disease, lesion burden and brain atrophy [9,12,18–21,34]. It is an inexpensive, well-tolerated and highly reproducible method, which potentially allows for monitoring disease progression. OCT also revealed RNFL thinning suggestive of clinically silent axonal loss ongoing in MS patients, independently of the optic neuritis history [35,36]. A recent meta-analysis of several OCT studies by Petzold et al. [14] suggested that the silent loss of RNFL thickness supports the growing evidence of parallel neurodegenerative and inflammatory process in MS [37,38]. In the present study, no differences were found in explored OCT parameters between the two HC groups indicating that there was not a greater loss of RNFL thickness or TMV ongoing in fHC compared to nonfHC. MRI monitoring of disease progression and response to therapy is one of the most important goals in MS clinical trials [4,12,13], but it is known that conventional MRI measures are not sensitive enough to differentiate between demyelination and neurodegeneration [3]. Conventional MRI analysis in our study showed increased WM SAV in the fHC group yet this difference did not reach a level of significance. Previous studies using conventional brain MRI showed an increase in WM SAs consistent with MS in the healthy relatives of MS patients [24,25,39]. Although advanced MRI techniques, showed better correlations with clinical [5,40] and visual disability [21,22,41], the present study did not reveal any differences in DTI parameters between the study groups. These findings should be interpreted with caution though due to a small number of participants. To the best of our knowledge, DTI has not yet been studied in healthy relatives of MS patients. Previous non-conventional MRI studies of asymptomatic relatives of MS patients showed significant magnetization transfer ratio (MTR) changes in WM SAs, indicative of demyelinating pathology [42,43], although some other studies showed no differences in MTR of WM in the siblings of MS patients [44]. In multivariate regression analysis, a significant correlation was observed between decreased RNFL thickness and the following conventional MRI variables: ≥9 T2 lesions, WM SAN and WM SAV, only in the fHC group. This is, indeed, an interesting finding. Similar results using conventional MRI were described previously, showing a strong correlation between increased T2 lesion burden and decreased RNFL thickness in MS patients [20,21]. It was also described that RNFL thickness loss indicates more severe WM than GM damage in early MS [45], which is also in line with our results of increased WM SAV in asymptomatic relatives of MS patients, and its association with OCT in regression analysis. Healthy relatives of MS patients are under increased risk of developing the disease [23]. The risk of developing MS in a firstdegree relative of the affected patient is 30–50 fold higher than in the general population [46], while second and third degree relatives also show an increased risk for developing MS [47]. It could be useful to evaluate healthy subjects who are at higher risk for developing the disease using sensitive methods proven efficient in monitoring MS patients such as OCT. An important limitation of our study is the small number of subjects, especially in the fHC group, that could have limited our ability to show differences between the two groups. Therefore, further studies with a larger sample of healthy relative of MS patients are necessary. In conclusion, we found an association between decreased RNFL thickness on OCT and increased WM injury in the HCs of familial MS relatives. Further studies should explore the pathophysiology of these findings.
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Conflict of interest statement Study funding: The authors have nothing to disclose. Disclosures: Tereza Gabelic, Rebecca Melia, Cheryl Kennedy, Deepa Ramasamy, Ellen Carl, Niels Bergsland, Muhammad W. Masud, Norah Lincoff and Vesna Brinar have nothing to disclose. Dr. Bianca Weinstock-Guttman has participated in speaker’s bureaus and served as a consultant for Biogen Idec, Teva Neurosciences, EMD Serono, Pfizer, Novartis, Genzyme, and Acorda. She also has received grant/research support from the agencies listed above as well as ITN, Questcor and Shire. No other industry financial relationships exist. Murali Ramanathan received research funding or consulting fees from EMD Serono, Biogen Idec, Allergan, Netezza, Pfizer, Novartis, the National Multiple Sclerosis Society, the Department of Defense, Jog for the Jake Foundation, the National Institute of Health and National Science Foundation. He received compensation for serving as an Editor from the American Association of Pharmaceutical Scientists. These are unrelated to the research presented in this report. Robert Zivadinov received personal compensation from Teva Pharmaceuticals, Biogen Idec, EMD Serono, Novartis and SanofiGenzyme for speaking and consultant fees. Dr. Zivadinov received financial support for research activities from Biogen Idec, Teva Pharmaceuticals, EMD Serono, Novartis and Sanofi-Genzyme. Acknowledgements Support from the National Multiple Sclerosis Society (RG3743 and RG4836-A-5) and the Department of Defense Multiple Sclerosis Program (MS090122) to the Ramanathan laboratory is gratefully acknowledged. References [1] Silber E, Sharief MK. Axonal degeneration in the pathogenesis of multiple sclerosis. J Neurol Sci 1999;170:11–8. [2] Lassmann H. Axonal and neuronal pathology in multiple sclerosis: what have we learnt from animal models. Exp Neurol 2010;225:2–8. [3] Poloni G, Minagar A, Haacke EM, Zivadinov R. Recent developments in imaging of multiple sclerosis. Neurologist 2011;17:185–204. [4] Zivadinov R. Can imaging techniques measure neuroprotection and remyelination in multiple sclerosis? Neurology 2007;68:S72–82 (discussion S91–76). [5] Rovaris M, Gass A, Bammer R, Hickman SJ, Ciccarelli O, Miller DH, et al. Diffusion MRI in multiple sclerosis. Neurology 2005;65:1526–32. [6] Filippi M, Iannucci G, Cercignani M, Assunta Rocca M, Pratesi A, Comi G. A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging. Arch Neurol 2000;57:1017–21. [7] Rocca MA, Cercignani M, Iannucci G, Comi G, Filippi M. Weekly diffusionweighted imaging of normal-appearing white matter in MS. Neurology 2000;55:882–4. [8] Tavazzi E, Dwyer MG, Weinstock-Guttman B, Lema J, Bastianello S, Bergamaschi R, et al. Quantitative diffusion weighted imaging measures in patients with multiple sclerosis. Neuroimage 2007;36:746–54. [9] Harrison DM, Shiee N, Bazin PL, Newsome SD, Ratchford JN, Pham D, et al. Tractspecific quantitative MRI better correlates with disability than conventional MRI in multiple sclerosis. J Neurol 2013;260:397–406. [10] Rovaris M, Iannucci G, Falautano M, Possa F, Martinelli V, Comi G, et al. Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis: an exploratory study with diffusion tensor MR imaging. J Neurol Sci 2002;195:103–9. [11] Benedict RH, Bruce J, Dwyer MG, Weinstock-Guttman B, Tjoa C, Tavazzi E, et al. Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Mult Scler 2007;13:722–30. [12] Frohman E, Costello F, Zivadinov R, Stuve O, Conger A, Winslow H, et al. Optical coherence tomography in multiple sclerosis. Lancet Neurol 2006;5: 853–63. [13] Barkhof F, Calabresi PA, Miller DH, Reingold SC. Imaging outcomes for neuroprotection and repair in multiple sclerosis trials. Nat Rev Neurol 2009;5:256–66. [14] Petzold A, de Boer JF, Schippling S, Vermersch P, Kardon R, Green A, et al. Optical coherence tomography in multiple sclerosis: a systematic review and metaanalysis. Lancet Neurol 2010;9:921–32.
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