Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Number of MRI T1-hypointensity corrected by T2/FLAIR lesion volume indicates clinical severity in patients with multiple sclerosis

  • Tetsuya Akaishi ,

    Roles Conceptualization, Software, Visualization, Writing – original draft

    t-akaishi@med.tohoku.ac.jp

    Affiliations Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan, Department of Education and Support for Regional Medicine, Tohoku University Hospital, Sendai, Japan

    ⨯
  • Toshiyuki Takahashi,

    Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft

    Affiliations Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan, Department of Neurology, National Hospital Organization Yonezawa National Hospital, Yonezawa, Japan

    ⨯
  • Kazuo Fujihara,

    Roles Data curation, Methodology, Supervision, Writing – review & editing

    Affiliation Department of Multiple Sclerosis Therapeutics, Fukushima Medical University School of Medicine, Fukushima, Japan

    ⨯
  • Tatsuro Misu,

    Roles Conceptualization, Investigation, Methodology, Validation, Writing – review & editing

    Affiliation Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan

    ⨯
  • Shunji Mugikura,

    Roles Data curation, Investigation, Methodology, Writing – review & editing

    Affiliation Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Japan

    ⨯
  • Michiaki Abe,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Education and Support for Regional Medicine, Tohoku University Hospital, Sendai, Japan

    ⨯
  • Tadashi Ishii,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Education and Support for Regional Medicine, Tohoku University Hospital, Sendai, Japan

    ⨯
  • Masashi Aoki,

    Roles Project administration, Supervision, Writing – review & editing

    Affiliation Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan

    ⨯
  • Ichiro Nakashima

    Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

    Affiliation Department of Neurology, Tohoku Medical and Pharmaceutical University, Sendai, Japan

    ⨯

Abstract

Introduction

Progressive brain atrophy, development of T1-hypointense areas, and T2-fluid-attenuated inversion recovery (FLAIR)-hyperintense lesion formation in multiple sclerosis (MS) are popular volumetric data that are often utilized as clinical outcomes. However, the exact clinical interpretation of these volumetric data has not yet been fully established.

Methods

We enrolled 42 consecutive patients with MS who fulfilled the revised McDonald criteria of 2010. They were followed-up for more than 3 years from onset, and cross-sectional brain volumetry was performed. Patients with no brain lesions were excluded in advance from this study. For the brain volumetric data, we evaluated several parameters including age-adjusted gray-matter volume atrophy, age-adjusted white-matter volume atrophy, and T2-FLAIR lesion volume. The numbers of T1-hypointense and T2-FLAIR-hyperintense areas were also measured along the same timeline. The clinical data pertaining to disease duration, expanded disability status scale (EDSS), and MS severity score (MSSS) at the timing of volumetry were collected.

Results

Among the 42 patients with MS and brain lesions, the number of T1-hypointensity (rho = 0.51, p<0.001), gray-matter atrophy (rho = 0.40, p<0.01) and white-matter atrophy (rho = 0.49, p<0.001) correlated with the EDSS. T1-hypointensity count divided by FLAIR lesion volume correlated with the MSSS (rho = 0.60, p<0.001). Meanwhile, counts or volumes of FLAIR-hyperintense lesions were associated only with the times of past relapses, and did not correlate with present neurological disability level or ongoing disease activity. These findings were consistent regardless of the presence of spinal cord lesions.

Conclusion

Numbers of T1-hypointensities and brain atrophy equally indicated the current neurological disability in MS. The number of T1-hypointensities divided by FLAIR lesion volume represented the clinical severity. The size or number of FLAIR lesions reflected earlier relapses but was not a good indicator of neurological disability or clinical severity.

Citation: Akaishi T, Takahashi T, Fujihara K, Misu T, Mugikura S, Abe M, et al. (2020) Number of MRI T1-hypointensity corrected by T2/FLAIR lesion volume indicates clinical severity in patients with multiple sclerosis. PLoS ONE 15(4): e0231225. https://doi.org/10.1371/journal.pone.0231225

Editor: Glenn R. Wylie, Kessler Foundation, UNITED STATES

Received: November 20, 2019; Accepted: March 18, 2020; Published: April 3, 2020

Copyright: © 2020 Akaishi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Ichiro Nakashima is funded by JSPS KAKENHI Grant Number 17K09772.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ARR, annual relapse rate; EDSS, expanded disability status scale; FL, T2-FLAIR-hyperintense lesions; GM, gray matter; MS, multiple sclerosis; MSSS, MS severity score; PPMS, primary-progressive MS; RRMS, relapsing-remitting MS; SPMS, secondary-progressive MS; WM, white matter

Introduction

Multiple sclerosis (MS) is an autoimmune-related demyelinating disease involving the central nervous system [1–3]. Many risk factors associated with MS have been suggested, including Epstein-Barr virus infection [4, 5], vitamin D deficiency [6, 7], and smoking [8, 9], but the direct cause or exact pathological mechanism of MS is still unknown [10, 11]. Patients with typical MS usually show multiple demyelinating lesions present in the brain (i.e., juxta-cortical, peri-ventricular), optic nerves, or spinal cord [12–14]. Demyelinating lesions are present in T2-fluid-attenuated inversion recovery (FLAIR)-hyperintense areas, which sometimes contain T1-hypointense areas (i.e., T1-black hole) [15, 16]. The diagnostic criteria of MS generally comprise of the following two components: dissemination in time and dissemination in space [12, 17]. These components are evaluated based on the distribution of T2-FLAIR-hyperintense lesions (FL) and the presence of gadolinium enhancement [13, 18]. In addition to these demyelinating lesions, accelerated brain atrophy in gray matter (GM), as well as white matter (WM), is also known to occur in patients with MS [19–22].

With the establishment of brain volumetric methods, many volume-related variables (e.g., GM volume, WM volume, FL volume, T1-hypointense volume) are measured in MS patients. The primary imaging parameter for the diagnosis of MS is thought to be FL; however, its role as a surrogate parameter to reflect neurological disability or prognosis is uncertain [23]. Some previous reports indicated that brain atrophy or T1-hypointense areas (i.e., T1-black hole) may better reflect neurological disability—including cognitive impairment—and predict eventual neurological prognosis [24, 25]. The development of T1-hypointense lesions may result from localized hypo-perfusion and could be irreversible in cases of structural brain damage [26, 27]. Approximately 20% of contrast-enhancing lesions later evolve into persistent T1-hypointense areas [28]. Several reports have suggested that among MRI-related parameters, brain atrophy and especially gray-matter atrophy, has the best potential to predict clinical disability and cognitive impairment [29, 30]. However, it remains unknown whether brain atrophy or amount of T1-black hole would better reflect the concurrent neurological disability [21]. Therefore, the need for these data in routine clinical practice is still unmet [19, 31]. To maximize the clinical usability of brain volumetry, clarifying the clinical significance of each volumetric variable is essential. In this study, we enrolled a feasible number of patients with MS and brain lesions. Each patient underwent brain volumetry and was followed-up for more than 3 years. These efforts comprised an attempt to clarify the clinical utility of each volumetric data for the management of patients with MS.

Methods

Enrollment

This study enrolled 42 consecutive patients with MS (14 males and 28 females) who were treated in a single university hospital in Japan, and followed-up for more than 3 years until 2017. Each patient underwent brain volumetry once between 2016 and 2017. All patients fulfilled the revised McDonald criteria of 2010. Because this study tried to clarify the clinical significance of brain volumetry, those with no brain lesions (n = 15) were excluded in advance from this study. Patients with a radiologically isolated syndrome (n = 3), who were incidentally found to have T2-hyperintense brain lesions, were also excluded in advance. All the enrolled patients were confirmed to be negative for serum anti-aquaporin 4 (AQP4) antibody and anti-myelin oligodendrocyte glycoprotein (MOG) antibody in advance by utilizing a live cell-based assay (CBA) method [32, 33].

Clinical variables

The demographic data of the enrolled patients, sex, onset age, and disease duration at the time of brain volumetry were collected. Concerning the data related to disease severity and activity, the expanded disability status scale (EDSS) [34], MS severity score (MSSS) [35], and number of past clinical relapses at the time of volumetry were collected. MSSS allows cross-sectional comparison of clinical severity between patients with different disease durations. The score is assigned to each patient according to the present EDSS and disease duration. To adjust for the effect of the site of the responsible lesions (i.e., brain, optic nerves, or spinal cord), the site mainly contributing to the EDSS level in each patient was evaluated.

Brain magnetic resonance imaging data

MR imaging was performed on a 3 Tesla whole-body clinical system (IngeniaCX or Achieva dStream, Philips Healthcare, Best, The Netherlands) using a 32-channel anterior and posterior phased-array coil. The protocol contained two 3D sequences: a fat-saturated 3D FLAIR (TR: 4800 ms, TE: 268 ms, TI: 1650 ms, 240 × 240 mm2 field of view (FOV), 180 sagittal slices, 1.0 × 1.0 × 1.0 mm3 voxel resolution) and a 3D T1-weighted fast field echo (FFE) sequence (TR: 10 ms, TE: 5.7 ms, FA: 8°, 240 × 240 mm2 FOV, 180 sagittal slices, 1.0 × 1.0 × 1.0 mm3 voxel resolution). Brain volumetry was determined by uploading the DICOM data (3-D FLAIR and 3-D T1 MP-RAGE images) to the Icometrix website (Leuven, Belgium; Chicago, USA), as reported previously [21, 36]. The method utilized both the T1-weighted and the FLAIR images in an iterative process to obtain tissue (i.e. WM, GM, cerebrospinal fluid) and lesion segmentations. A lesion-filled T1-weighted image was created by replacing lesion voxels with values matching the WM tissue intensities. Thus, in the final lesion-filled T1-weighted image segmentation, FL lesions were included in the WM segment. Brain GM volume, WM volume, and FL volume were provided in the analysis report. Age-adjusted GM atrophy and age-adjusted WM atrophy were cross-sectionally estimated for each patient by subtracting the age-adjusted mean of volume in normal controls, which was provided by Icometrix, from the measured volume in each patient. In addition to these data, the number of T1-hypointense areas (T1-count) and FL (FL-count) were simultaneously counted in each patient. The volumes of T1-hypointense areas were not included in the volumetric data analysis in this study. As described later in the results section, the T1-count and FL-volume showed a moderate-to-strong positive correlation, but only T1-count showed a significant correlation with EDSS or MSSS. Thus, we also calculated the ratio of T1-count and FL-volume (T1-count/FL-volume), and checked the correlations between the ratio and the studied clinical variables.

Statistical analyses

The correlation between each volumetric and clinical data parameter was evaluated by the Spearman’s correlation coefficient (rho) because of the non-normal distribution of the evaluated variables, followed by a test of no correlation. The multiple regression analysis comparing the contribution to the MSSS level was performed using the present age, GM atrophy, and T1-count/FL-volume as the explanatory variables. Because multiple comparisons were performed, p-values less than 0.01 were considered statistically significant. Statistical analyses were conducted using the SPSS Statistics Base 22 software (IBM, Armonk, NY, USA) and MATLAB R2015a (MathWorks, Natick, MA, USA).

Ethics statement

This study was approved by the Institutional Review Board of Tohoku University Graduate School of Medicine (THK 2010589). Written informed consent was obtained from all the participants enrolled in this study. The study processes were performed according to standard regulations.

Results

Patients’ backgrounds

The mean ± standard deviation (SD) of onset age in the enrolled 42 patients was 26.7 ± 8.3 years, and the mean disease duration at the time of brain volumetry was 12.4 ± 7.5 years. Concerning the disease modifying therapies during brain volumetry, 4 were untreated, 28 were treated with fingolimod, 8 with interferon-beta injection, 2 with dimethyl fumarate, 1 with glatiramer acetate, and 1 with natalizumab. The clinical types of MS at the timing of volumetry were as follows: 30 had relapsing-remitting MS, 10 had secondary-progressive MS, and 2 had primary-progressive MS.

The median and interquartile range (IQR, 25–75 percentiles) of EDSS during brain volumetry were 2.0 (1.0–3.5). The mean ± SD of MSSS during brain volumetry was 3.37 ± 2.60. All EDSS data in this study were evaluated in the chronic phase, at least 3 months after the last clinical relapse. Details of each enrolled patient are summarized in S1 Table.

Measured volumetric data

Mean ± SD of the age-adjusted GM atrophy was 106 ± 51 [cc], and that of age-adjusted WM atrophy was 44 ± 43 [cc]. Median (IQR) of FL-volume was 15.0 (9.3–25.0) [cc], the median T1-count was 5 (2–12), and median FL-count was 18 (12–32). Mean ± SD of the T1-count/FL-volume was 0.42 ± 0.30 [/cc], and the mean T1-count/FL-count was 0.32 ± 0.24.

Correlations between magnetic resonance imaging-based variables

A correlation matrix between the raw magnetic resonance imaging (MRI)-derived variables is shown in Table 1. GM atrophy and WM atrophy showed a weak positive correlation. Both FL-volume and T1-count showed moderate positive correlations with WM atrophy; however, FL-volume and T1-count showed a strong positive correlation. T1-count showed significant correlations with the levels of EDSS (Fig 1A) and MSSS, but it did not correlate with the ARR. Meanwhile, FL-volume had a significant correlation with ARR but not with EDSS (Fig 1B) or MSSS.

thumbnail
Download:
Fig 1. Scatter plots of MRI-derived variables, and neurological disability.

(a) Scatter plot of the number of T1-hypointense areas and EDSS. (b) Scatter plot of FL volume and EDSS. (c) Scatter plot of the ratio of T1-hypointense count/FL-volume and MSSS. The grey linear lines are the approximate straight lines. Abbreviations: FL, FLAIR-hyperintense lesion; MSSS, multiple sclerosis severity scores.

https://doi.org/10.1371/journal.pone.0231225.g001

thumbnail
Download:
Table 1. A correlation matrix with volumetric data in the enrolled 42 patients.

https://doi.org/10.1371/journal.pone.0231225.t001

T1-hypointense count adjusted by FLAIR lesion volume

Because the T1-count showed a strong positive correlation with FL-volume, we calculated the ratio of T1-count to FL-volume (T1-count/FL-volume). The correlation matrix employing this T1-count/FL-volume is shown in Table 2, together with the matrix for other four types of MRI-derived data divided by FL-count or -volume. As can be seen, T1-count/FL-volume more significantly reflects the level of MSSS than the other duration-adjusted MRI data. A scatter plot with these two variables is shown in Fig 1C. The present age, onset age, or disease duration did not affect the correlation between T1-count/FL-volume and MSSS.

thumbnail
Download:
Table 2. Correlation matrix between volumetry-derived variables and disease severity.

https://doi.org/10.1371/journal.pone.0231225.t002

Multiple regression analysis

Next, we compared the significance of age-adjusted GM atrophy and T1-count/FL-volume for the level of MSSS by performing a multiple regression analysis. Here, only the ratio of T1-count/FL-volume (F(3,38) = 24.46, p<0.0001) showed a statistically significant contribution to MSSS. In contrast, GM atrophy (F(3,38) = 3.03, p = 0.0896) and present age (F(3,38) = 0.17, p = 0.68) did not significantly contribute to MSSS.

Presence of spinal cord lesions

Finally, we analyzed the correlation coefficients between the MRI-derived variables and clinical variables only among patients who had no spinal cord lesions (n = 24). The achieved correlation matrix is shown in Table 3. The results obtained in the previous sections were true even when analyzed among the patients who had no spinal cord lesions.

thumbnail
Download:
Table 3. Correlation matrix among the patients without spinal cord lesions (n = 24).

https://doi.org/10.1371/journal.pone.0231225.t003

Discussion

Many MRI imaging-derived parameters have been evaluated previously as indicators or predictors of neurological disability in patients with MS. Previous studies demonstrated that accelerated GM atrophy in patients with MS drives neurological disability progression in this population [29]. Other reports have suggested that the accumulation of T2-FLAIR-hyperintense WM lesions could reflect the cognitive dysfunction in MS, although the results differed among the various studies [37]. Other reports suggested that T1-hypointense areas are more important than T2-FLAIR-hyperintense lesions for reflecting the ongoing neurological damage and determining the eventual neurological prognosis. Although the clinical importance of each of these imaging parameters has been separately shown, which of the parameters should be the most clinically important, and what exactly each of the parameters clinically indicates in the management of patients with MS has not been fully elucidated. In this study, we demonstrated the importance of the amount of T1-hypointense areas, together with the brain atrophy, as a parameter of concurrent neurological disability. When the count of T1-hypointense areas was divided by FL-volume, it well represented the clinical severity in patients with MS. Meanwhile, the volume of T2/FLAIR-hyperintense lesions did not show a significant correlation with current neurological disability or the ongoing disease activity.

Another notable finding of this study was that a simple T1-count could efficiently reflect the clinical severity and disease activity. Because T1-count can be manually counted without specific volumetric software, this simple information would be quite useful in routine clinical practice to estimate the ongoing disease activity in each MS patient. If the brain volumetric data can be achieved concurrently, these data would surely add more clinically useful information. In some previous reports, in addition to the lesion load, the location of T2/FLAIR lesions (e.g. corticospinal tract, superior longitudinal fasciculus, inferior fronto-occipital fasciculus) was also shown to impact the neurological disability level and the progression rate [38, 39]. Considering both the amount of T1-hypointense areas within T2/FLAIR lesions and the location of them may further facilitate the clinical utility of MRI-derived parameters.

There were some limitations to this study. The major limitation was that we did not evaluate the volume of T1-hypointense areas. Since this study was strongly based on MRI measures of T1-hypointense lesions, this limitation does not allow us to answer which of brain atrophy and development of T1-black hole would better reflect the concurrent neurological disability and clinical severity. Further research that measures the volume of T1-hypointense areas is needed to conclude this point. Another limitation is was that our study cohort was relatively small, and consisted entirely of Asian patients. Studies with larger sample sizes, which include patients of other ethnicities, are needed to confirm whether our conclusions can be generalized to Caucasian and African-American patients with MS. The final limitation was that we did not assess cognitive impairment in this study. Thus, which of the T1-hypointense area and GM atrophy may more efficiently reflect the cognitive impairment is still unknown. As a future perspective, comprehensive comparisons between the above-described imaging parameters and clinical variables other than EDSS (e.g., cognitive impairment, fatigue) should be targets of future clinical research.

Conclusions

The number of T1-hypointense areas, corrected for T2-FLAIR-hyperintense lesion volume, reflects the disease severity and activity in patients with MS. Further research is needed to determine the importance of regular follow ups of the number and volumes of T1-hypointense areas, along with FL-volume, to evaluate treatment efficacy or to estimate the eventual neurological prognosis in patients with MS.

Supporting information

S1 Table. Clinical and imaging variables of the enrolled patients with MS.

https://doi.org/10.1371/journal.pone.0231225.s001

(XLSX)

References

  1. 1. Goldenberg MM. Multiple sclerosis review. P t. 2012;37(3):175–84. Epub 2012/05/19. pmid:22605909; PubMed Central PMCID: PMC3351877.
  2. 2. Dobson R, Giovannoni G. Multiple sclerosis—a review. Eur J Neurol. 2019;26(1):27–40. Epub 2018/10/10. pmid:30300457.
  3. 3. Noseworthy JH, Lucchinetti C, Rodriguez M, Weinshenker BG. Multiple sclerosis. N Engl J Med. 2000;343(13):938–52. Epub 2000/09/28. pmid:11006371.
  4. 4. Pakpoor J, Disanto G, Gerber JE, Dobson R, Meier UC, Giovannoni G, et al. The risk of developing multiple sclerosis in individuals seronegative for Epstein-Barr virus: a meta-analysis. Multiple sclerosis (Houndmills, Basingstoke, England). 2013;19(2):162–6. Epub 2012/06/29. pmid:22740437.
  5. 5. Guan Y, Jakimovski D, Ramanathan M, Weinstock-Guttman B, Zivadinov R. The role of Epstein-Barr virus in multiple sclerosis: from molecular pathophysiology to in vivo imaging. Neural Regen Res. 2019;14(3):373–86. Epub 2018/12/13. pmid:30539801; PubMed Central PMCID: PMC6334604.
  6. 6. Sintzel MB, Rametta M, Reder AT. Vitamin D and Multiple Sclerosis: A Comprehensive Review. Neurol Ther. 2018;7(1):59–85. Epub 2017/12/16. pmid:29243029; PubMed Central PMCID: PMC5990512.
  7. 7. Pierrot-Deseilligny C, Souberbielle JC. Vitamin D and multiple sclerosis: An update. Mult Scler Relat Disord. 2017;14:35–45. Epub 2017/06/18. pmid:28619429.
  8. 8. Wingerchuk DM. Smoking: effects on multiple sclerosis susceptibility and disease progression. Ther Adv Neurol Disord. 2012;5(1):13–22. Epub 2012/01/26. pmid:22276073; PubMed Central PMCID: PMC3251901.
  9. 9. Zhang P, Wang R, Li Z, Wang Y, Gao C, Lv X, et al. The risk of smoking on multiple sclerosis: a meta-analysis based on 20,626 cases from case-control and cohort studies. PeerJ. 2016;4:e1797. Epub 2016/03/26. pmid:27014514; PubMed Central PMCID: PMC4806598.
  10. 10. Garg N, Smith TW. An update on immunopathogenesis, diagnosis, and treatment of multiple sclerosis. Brain Behav. 2015;5(9):e00362. Epub 2015/10/09. pmid:26445701; PubMed Central PMCID: PMC4589809.
  11. 11. Ghasemi N, Razavi S, Nikzad E. Multiple Sclerosis: Pathogenesis, Symptoms, Diagnoses and Cell-Based Therapy. Cell J. 2017;19(1):1–10. Epub 2017/04/04. pmid:28367411; PubMed Central PMCID: PMC5241505.
  12. 12. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292–302. Epub 2011/03/10. pmid:21387374; PubMed Central PMCID: PMC3084507.
  13. 13. Filippi M, Rocca MA, Ciccarelli O, De Stefano N, Evangelou N, Kappos L, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016;15(3):292–303. Epub 2016/01/30. pmid:26822746; PubMed Central PMCID: PMC4760851.
  14. 14. Barkhof F, Filippi M, Miller DH, Scheltens P, Campi A, Polman CH, et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain. 1997;120 (Pt 11):2059–69. Epub 1997/12/16. pmid:9397021.
  15. 15. Bagnato F, Jeffries N, Richert ND, Stone RD, Ohayon JM, McFarland HF, et al. Evolution of T1 black holes in patients with multiple sclerosis imaged monthly for 4 years. Brain. 2003;126(Pt 8):1782–9. Epub 2003/06/25. pmid:12821527.
  16. 16. Truyen L, van Waesberghe JH, van Walderveen MA, van Oosten BW, Polman CH, Hommes OR, et al. Accumulation of hypointense lesions ("black holes") on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology. 1996;47(6):1469–76. Epub 1996/12/01. pmid:8960729.
  17. 17. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73. Epub 2017/12/26. pmid:29275977.
  18. 18. Mantero V, Abate L, Balgera R, La Mantia L, Salmaggi A. Clinical Application of 2017 McDonald Diagnostic Criteria for Multiple Sclerosis. J Clin Neurol. 2018;14(3):387–92. Epub 2018/07/05. pmid:29971979; PubMed Central PMCID: PMC6031991.
  19. 19. Rocca MA, Battaglini M, Benedict RH, De Stefano N, Geurts JJ, Henry RG, et al. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology. 2017;88(4):403–13. Epub 2016/12/18. pmid:27986875; PubMed Central PMCID: PMC5272969.
  20. 20. Sanfilipo MP, Benedict RH, Weinstock-Guttman B, Bakshi R. Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology. 2006;66(5):685–92. Epub 2006/03/15. pmid:16534104.
  21. 21. Akaishi T, Nakashima I, Mugikura S, Aoki M, Fujihara K. Whole brain and grey matter volume of Japanese patients with multiple sclerosis. J Neuroimmunol. 2017;306:68–75. Epub 2017/04/08. pmid:28385190.
  22. 22. Azevedo CJ, Cen SY, Jaberzadeh A, Zheng L, Hauser SL, Pelletier D. Contribution of normal aging to brain atrophy in MS. Neurol Neuroimmunol Neuroinflamm. 2019.
  23. 23. van Walderveen MA, Barkhof F, Hommes OR, Polman CH, Tobi H, Frequin ST, et al. Correlating MRI and clinical disease activity in multiple sclerosis: relevance of hypointense lesions on short-TR/short-TE (T1-weighted) spin-echo images. Neurology. 1995;45(9):1684–90. Epub 1995/09/01. pmid:7675227.
  24. 24. Giorgio A, De Stefano N. Cognition in multiple sclerosis: relevance of lesions, brain atrophy and proton MR spectroscopy. Neurological sciences: official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology. 2010;31(Suppl 2):S245–8. Epub 2010/07/17. pmid:20635111.
  25. 25. Lazeron RH, Boringa JB, Schouten M, Uitdehaag BM, Bergers E, Lindeboom J, et al. Brain atrophy and lesion load as explaining parameters for cognitive impairment in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2005;11(5):524–31. Epub 2005/10/01. pmid:16193889.
  26. 26. Narayana PA, Zhou Y, Hasan KM, Datta S, Sun X, Wolinsky JS. Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2014;20(3):365–73. Epub 2013/07/10. pmid:23836878; PubMed Central PMCID: PMC3844029.
  27. 27. Rocca MA, Comi G, Filippi M. The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis. Frontiers in neurology. 2017;8:433. Epub 2017/09/21. pmid:28928705; PubMed Central PMCID: PMC5591328.
  28. 28. van den Elskamp IJ, Lembcke J, Dattola V, Beckmann K, Pohl C, Hong W, et al. Persistent T1 hypointensity as an MRI marker for treatment efficacy in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2008;14(6):764–9. Epub 2008/07/10. pmid:18611989.
  29. 29. Eshaghi A, Prados F, Brownlee WJ, Altmann DR, Tur C, Cardoso MJ, et al. Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. 2018;83(2):210–22. Epub 2018/01/14. pmid:29331092; PubMed Central PMCID: PMC5838522.
  30. 30. Messina S, Patti F. Gray matters in multiple sclerosis: cognitive impairment and structural MRI. Multiple sclerosis international. 2014;2014:609694. Epub 2014/03/04. pmid:24587905; PubMed Central PMCID: PMC3920616.
  31. 31. Marciniewicz E, Podgorski P, Sasiadek M, Bladowska J. The role of MR volumetry in brain atrophy assessment in multiple sclerosis: A review of the literature. Adv Clin Exp Med. 2019;28(7):989–99. Epub 2019/02/08. pmid:30729761.
  32. 32. Takahashi T, Fujihara K, Nakashima I, Misu T, Miyazawa I, Nakamura M, et al. Establishment of a new sensitive assay for anti-human aquaporin-4 antibody in neuromyelitis optica. Tohoku J Exp Med. 2006;210(4):307–13. Epub 2006/12/06. pmid:17146196.
  33. 33. Sato DK, Callegaro D, de Haidar Jorge FM, Nakashima I, Nishiyama S, Takahashi T, et al. Cerebrospinal fluid aquaporin-4 antibody levels in neuromyelitis optica attacks. Ann Neurol. 2014;76(2):305–9. Epub 2014/07/01. pmid:24977390; PubMed Central PMCID: PMC4173125.
  34. 34. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444–52. Epub 1983/11/01. pmid:6685237.
  35. 35. Roxburgh RH, Seaman SR, Masterman T, Hensiek AE, Sawcer SJ, Vukusic S, et al. Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology. 2005;64(7):1144–51. Epub 2005/04/13. pmid:15824338.
  36. 36. Fujimori J, Fujihara K, Ogawa R, Baba T, Wattjes M, Nakashima I. Patterns of regional brain volume loss in multiple sclerosis: a cluster analysis. J Neurol. 2019. Epub 2019/10/28. pmid:31654116.
  37. 37. Mollison D, Sellar R, Bastin M, Chandran S, Wardlaw J, Connick P. The clinico-radiological paradox of cognitive function and MRI burden of white matter lesions in people with multiple sclerosis: A systematic review and meta-analysis. PLoS One. 2017;12(5):e0177727. Epub 2017/05/16. pmid:28505177; PubMed Central PMCID: PMC5432109.
  38. 38. Bodini B, Battaglini M, De Stefano N, Khaleeli Z, Barkhof F, Chard D, et al. T2 lesion location really matters: a 10 year follow-up study in primary progressive multiple sclerosis. Journal of neurology, neurosurgery, and psychiatry. 2011;82(1):72–7. Epub 2010/07/16. pmid:20627965; PubMed Central PMCID: PMC3002838.
  39. 39. Sastre-Garriga J, Tintore M. Multiple sclerosis: Lesion location may predict disability in multiple sclerosis. Nature reviews Neurology. 2010;6(12):648–9. Epub 2010/12/07. pmid:21131911.