Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

Seongjin Choi; Margaret Spini; Jun Hua; Daniel M Harrison

doi:10.1371/journal.pone.0249973

. 2021 Apr 26;16(4):e0249973. doi: 10.1371/journal.pone.0249973

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT₁ mapping

Seongjin Choi ¹, Margaret Spini ², Jun Hua ^3,⁴, Daniel M Harrison ^1,^5,^*

Editor: Niels Bergsland⁶

PMCID: PMC8075220 PMID: 33901207

Abstract

Although the blood-brain barrier (BBB) is altered in most multiple sclerosis (MS) lesions, gadolinium enhancement is seen only in acute lesions. In this study, we aimed to investigate gadolinium-induced changes in T₁ relaxation time in MS lesions on 7-tesla (7T) MRI as a means to quantify BBB breakdown in non-enhancing MS lesions. Forty-seven participants with MS underwent 7T MRI of the brain with a magnitude-prepared rapid acquisition of 2 gradient echoes (MP2RAGE) sequence before and after contrast. Subtraction of pre- and post-contrast T₁ maps was used to measure T₁ relaxation time change (ΔT₁) from gadolinium. ΔT₁ values were interrogated in enhancing white matter lesions (ELs), non-enhancing white matter lesions (NELs), and normal appearing white matter (NAWM) and metrics were compared to clinical data. ΔT₁ was measurable in NELs (median: -0.139 (-0.304, 0.174) seconds; p < 0.001) and was negligible in NAWM (median: -0.001 (-0.036, 0.155) seconds; p = 0.516). Median ΔT₁ in NELs correlated with disability as measured by Expanded Disability Status Scale (EDSS) (rho = -0.331, p = 0.026). Multiple measures of NEL ΔT₁ variability also correlated with EDSS. NEL ΔT₁ values were greater and more variable in patients with progressive forms of MS and greater in those not on MS treatment. Measurement of the changes in T₁ relaxation time caused by contrast on 7T MP2RAGE reveals clinically relevant evidence of BBB breakdown in NELs in MS. This data suggests that NEL ΔT₁ should be evaluated further as a potential biomarker of persistently disrupted BBB in MS.

Introduction

Gadolinium enhancement of white matter lesions (WMLs) has long been used as a surrogate marker of active inflammation and blood-brain barrier (BBB) breakdown in multiple sclerosis (MS) [1]. The presence of enhancing WMLs can be used clinically to meet diagnostic criteria for dissemination in time [2] and as a marker of relapsing disease activity [3]. Reductions in the number of enhancing WMLs on MRI has been used as a treatment effect outcome measure in most clinical trials [4], and the availability of this tool has arguably led to more rapid development of MS therapeutics.

While gadolinium-enhanced MRI is a useful tool for differentiation of acutely inflamed WMLs from those that are not, the inflammatory status of non-enhancing lesions (NELs) are more difficult to elucidate. Although no longer practical due to toxicity concerns, early triple-dose gadolinium studies demonstrated an increase in the number of enhancing lesions visualized compared to standard-dose gadolinium in MS patients [5, 6]. Further, enhancing lesions were found in 42% of patients with primary progressive MS (PPMS)–a subtype of MS typically not associated with acute relapsing activity [7]. It is still unclear if these findings represent milder acutely inflamed lesions or more chronic disruptions of the BBB in older, possibly chronically inflamed WMLs. Regardless, it is clear that qualitative evaluation of WML contrast enhancement is an insufficient method to fully characterize WMLs and more quantifiable methods are needed.

Measurements of vascular permeability by dynamic contrast-enhanced (DCE) MRI are a candidate method for measuring BBB breakdown in MS. In fact, prior DCE MRI studies suggest increased permeability in non-enhancing WMLs and NAWM in patients with MS [8]. Alterations in T₁-relaxation time, which is shortened in the presence of gadolinium [9], have also been used as a measure of BBB integrity. Multiple studies have found T₁ shortening in both enhancing and non-enhancing WMLs [10].

Despite the clear potential for these techniques to measure BBB integrity, their clinical application has been limited by practical considerations. For example, permeability measurements require complex post-acquisition processing, which make this impractical for use in large studies or in the clinic. Echo-planar imaging (EPI) techniques for measurement of T₁ relaxation time suffer from distortions and partial volume averaging that reduce the accuracy of T₁ measurements in small structures. Spoiled gradient-echo techniques for T₁ measurement are more robust but require multiple acquisitions that must be co-registered before pre and post contrast images can be compared, which increases scan time and decreases the accuracy and reproducibility of measurement.

More recently, the magnetization-prepared rapid acquisition of 2 gradient echoes (MP2RAGE) technique has been used for T₁ relaxation time measurement [11]. This technique, which produces both a T₁-weighted (T₁-w) MPRAGE-like image and a quantitative T₁ map, provides several advantages over previous methods. MP2RAGE acquires all necessary images in a simultaneous acquisition, and thus co-registration is not required. High spatial resolutions are possible in clinically acceptable scan times without partial volume averaging effects. MP2RAGE images are also inherently B1 field inhomogeneity corrected, making them ideal for application at higher magnetic fields [11]. The promise of MP2RAGE as a useful tool in the study of MS has already been evaluated, with early results showing robust and reproducible T₁ measurements that correlate with disability and increases in the ability to visualize white and gray matter lesions [12, 13].

In this study, we aimed to take advantage of MP2RAGE’s ability to provide robust, high resolution maps of T₁ relaxation time to measure gadolinium-induced T₁ signal change as a biomarker of BBB leakage in non-enhancing lesions in MS. We looked to evaluate this at 7T, where images can be acquired at fine resolution and the absolute T₁ relaxation time difference between tissues are greater [13, 14], further enhancing the potential accuracy of measurement over prior, similar studies.

Materials and methods

Participants

Volunteers aged 18 to 65 with diagnoses of relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and progressive MS (PPMS) according to revised 2010 McDonald Criteria [2] were recruited from the Johns Hopkins Multiple Sclerosis Center and the University of Maryland Center for Multiple Sclerosis Treatment and Research. Participants were excluded for contraindications to MRI (i.e. metallic foreign bodies) or gadolinium contrast (i.e. previous allergy to contrast, renal failure). Demographic and clinical characteristics of the study population is provided in Table 1.

Table 1. Demographic and clinical characteristics of the study population.

Characteristic	Full Cohort (n = 47)	Relapsing-Remitting MS Subjects (n = 34, 72.3%)	Progressive MS Subjects (n = 13, 27.7%)
Age, mean (SD) in years	46.2 (11.2)	44.3 (11.4)	51.2 (9.4)
Sex, female (%)	30 (63.8%)	23 (67.7%)	7 (53.9%)
Symptom duration, mean (SD) in years	11.5 (8.3)	11.4 (8.8)	11.9 (7.2)
On MS treatment (%)	34 (72.3%)	27 (79.4%)	7 (53.9%)
Interferon-beta	6 (12.8%)	4 (11.8%)	2 (15.4%)
Glatiramer acetate	7 (14.9%)	7 (20.6%)	0 (0%)
Natalizumab	4 (8.5%)	3 (8.8%)	1 (7.7%)
Teriflunomide	1 (2.1%)	1 (2.9%)	0 (0%)
Fingolimod	4 (8.5%)	3 (8.8%)	1 (7.7%)
Dimethyl Fumarate	11 (23.4%)	9 (26.5%)	2 (15.4%)
Rituximab	1 (2.1%)	0 (0%)	1 (7.7%)
EDSS score, median (range)	3.0 (1, 6.5)	3.0 (1.0, 6.0)	6.0 (4.0, 6.5)
SDMT # correct, mean (SD)	53.2 (12.3)	55.7 (12.8)	46.5 (7.9)
PASAT # correct, mean (SD)	44.8 (12.8)	45.9 (13.4)	42.1 (11.3)
MFIS score, mean (SD)	39.5 (20.4)	35.5 (21.6)	49.9 (12.3)
9HPTDOM, mean (SD)	45.3 (111)	25.3 (15.7)	40.9 (21.3)
9HPTNONDOM, mean (SD)	32.3 (22.5)	27.1 (18.6)	46.1 (26.6)
25FTW, mean (SD)	8.66 (13.2)	5.5 (3.4)	17.0 (23.2)
# of WML, mean (SD)	87.4 (68.5)	77.6 (51.8)	113.1 (98.1)
Volume of WML, mean (SD) in mm3	6099.8 (5790.4)	5753.9 (5193.7)	7004.3 (7288.5)
# of cases with enhancing lesions (%)	5 (10.6%)	4 (8.5%)	1 (2.1%)
# of enhancing lesions seen in participants with enhancement, median (range)	1 (1, 3)	1 (1, 3)	1 (1, 1)

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SD = standard deviation; # = number; MS = multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; PMS = progressive multiple sclerosis; EDSS = Expanded Disability Status Scale; SDMT = Symbol Digit Modalities Test; PASAT = Paced Auditory Serial Addition Test; MFIS = Modified Fatigue Impact Scale; 9HPTDOM = 9-hole peg test for dominant hand; 9HPTNONDOM = 9-hole peg test for non-dominant hand; 25FTW = timed 25-foot walk.

MRI acquisition

All participants underwent MRI of the whole brain on a 7T MRI scanner (Philips, Achieva, The Netherlands) with a volume transmit head coil and a 32-channel receiver coil (Nova Medical Inc.) as part of a multi-modal brain MRI study. MP2RAGE and magnetization prepared fluid attenuated inversion recovery (MPFLAIR) sequences were used in this analysis. MP2RAGE sequence parameters are as follows: MP2RAGE TR = 8500 ms, TR = 6.9 ms, TE = 2.1 ms, inversion times = 1000/3000 ms, flip angles = 5/5 degrees, Turbo factor = 252, Field-of-view = 220x220 mm², near-isotropic resolution of 0.7x0.688x0.688 mm³, SENSE acceleration factor = 2x2, total acquisition time = 9 min 46 sec. Sequences were obtained before and approximately 10 minutes after intravenous gadolinium contrast agent (0.1 mmol/kg) administration. Detailed MPFLAIR sequence parameters are as follows: TR = 8000 ms, TE = 400 ms, TI = 2,077 ms, flip angle = 90 degrees, SENSE acceleration factor = 2x3, total acquisition time = 10 min 48 sec.

Image processing

All MRI data were processed for T₁-w images and a quantitative T₁ relaxation time map (T₁ map), as previously described [11]. This was accomplished using custom software written in Matlab (Mathworks, Inc., Natick, MA), which was based on publicly available code provided by the developers of this sequence (https://github.com/JosePMarques/MP2RAGE-related-scripts). A denoised T₁-w image was created by multiplying the second inversion image (after N4 bias correction) [15] and the T₁-w image (after adding 0.5 to make its intensity range positive) to suppress the background noise of MP2RAGE T₁-w image [16]. The corresponding denoised T₁-w image was used for skull stripping and co-registration. After T₁ map processing and linear registration of post-contrast images to the pre-contrast space, T₁ difference maps, which we term “delta T₁” (ΔT₁) maps, were generated by subtracting pre-contrast from post-contrast T₁ maps. Masks were manually created for all WMLs by reviewing MPFLAIR and MP2RAGE images in tandem; MPFLAIR images were used as guidance and masks drawn on the pre-contrast T₁ map. Our process for WML masking on 7T MP2RAGE is previously described in Spini et al (2020) [17]. Masks were separately drawn for lesions with visually apparent contrast enhancement on T₁-w images. Non-enhanced lesion (NEL) masks were created by subtraction of enhancing lesions (ELs) from the overall WML mask (example in Fig 1).

Fig 1 — A: Lesioned areas (indicated by blue arrows) appeared hypointense on pre-contrast T₁-weighted image. B: EL area (indicated by a red arrow) on post-contrast T₁-weighted image while NELs remains unenhanced. C: White matter lesions seen on T₁ map (EL in red, NEL in blue). D: White matter lesions seen on ΔT₁ map (EL in red, NEL in blue, units: s). The color bar displays the full range of ΔT₁ (units: s) values only in this participant. EL = enhancing lesion; NEL = non-enhancing lesion; T₁w = T₁-weighted.

A custom processing pipeline was created in the Java Image Science Toolkit (JIST, version 3.0, https://www.nitrc.org/projects/jist) [18] environment using tools from the CBS Tools processing package for high resolution, ultra-high field MRI [19] and the Lesion-TOADS segmentation algorithm [20] for brain segmentation. Segmentation was performed after lesion filling based on manually drawn WML masks. A normal-appearing white matter (NAWM) mask was created by subtracting WML masks from cerebral WM masks. SC, MS, DMH delineated WML and DMH reviewed and finalized them. SB delineated EL, and DMH reviewed and finalized them. SC counted lesions, and DMH reviewed and finalized them.

Disability measures

Disability was characterized through use of the Kurtzke Expanded Disability Status Scale (EDSS) score [21]. Upper extremity function was measured using the 9-hole peg test (9HPT) and gait function was assessed through the timed 25-foot walk (25FTW) [22]. The Symbol Digit Modalities Test (SDMT) and paced auditory serial addition test (PASAT) were used to assess cognitive functioning [22]. The modified fatigue impact scale (MFIS) was used to assess MS-related fatigue [23].

Statistical analysis

Differences between pre-contrast and post-contrast T₁ metrics were assessed by paired t-test and matched pairs Wilcoxon test. Group differences for EDSS, MS phenotype, and treatment effect were assessed by Mann-Whitney U-test. To allow for statistical analysis using dichotomous variables, we divided subjects into groups of ≤ or > the median EDSS score, which would compare mild and moderate disability. We also performed a logistic regression model with adjustment for MS phenotype to ensure any treatment effect findings were not influenced by MS phenotype (due to propensity for progressive patients to be untreated). Correlations were assessed by Spearman or Pearson correlations, as appropriate. R (version 4.0.3, R Core Team, Vienna, Austria, https://www.R-project.org/) was used to perform all statistical analysis. Python (version 3.7.1) was used to visualize data in part. P-values lower than 0.05 were considered statistically significant in all analyses. The false discovery rate (FDR) method was used to correct for multiple comparisons. However, as overcorrection for false discovery can often mask true positive findings in small cohorts (such as this study) and inhibit hypothesis-generating research, we have reported both unadjusted and FDR-adjusted p-values to allow readers to come to their own conclusions [24].

Results

Demographics of the recruited cohort are shown in Table 1. There were forty-seven (47) participants with MS, of which 34 (72.3%) were of the RRMS phenotype and 13 (27.7%) were of progressive phenotypes (SPMS or PPMS). Most subjects in this cohort (72.3%) were on disease modifying therapies at the time of their scan. Visual review of scans in this cohort revealed only 5 participants (10.6%) with visually enhancing WMLs, with a range of 1–3 enhancing lesions on those scans.

Table 2 shows data derived from T₁ maps. Consistent with prior literature [25], T₁ relaxation time (units: seconds) in MS lesions (median (range): 2.430 (2.097, 2.806)) was prolonged (P<0.001) on pre-contrast T₁ maps compared to NAWM (median (range): 1.300 (1.16, 1.403)). As expected, ELs showed a significantly shorter (P = 0.017) post-contrast T₁ relaxation times when we compared the means of per subject median. Although the absolute difference between the inter-subject median of pre- and post- contrast median T₁ in NAWM was very small, resulting in a ΔT₁ of -0.004 (-0.035, 0.146), this difference was significant (p = 0.006). However, no appreciable difference was seen in comparing the inter-subject mean of NAWM T₁ (p = 0.411). Although NELs appeared unenhanced on T₁-w images, T₁ relaxation time was significantly shorter (P<0.001) in NELs on post-contrast T₁ maps (median (range): 1.784 (1.286, 2.591)) compared to pre-contrast (median (range): 1.912 (1.478, 2.681)). Consequently, an appreciable ΔT₁ value was found for NELs (median (range): -0.134 (-0.281, 0.134)), which was of a far greater magnitude than seen for NAWM. A small, but significant difference was also noted between pre- and post-contrast T₁ values in cortical gray matter (cGM).

Table 2. T₁ metrics from pre- and post-contrast T₁ maps.

Tissue	Measure^*	Median pre-contrast T₁ (units: s)	Median post-contrast T₁ (units: s)	Difference significance	Median ΔT₁ (units: s)
WML	Mean (SD)	1.938 (0.27)	1.819 (0.274)	P < 0.001^a	-0.129 (0.072)
WML	Median (range)	1.915 (1.478, 2.681)	1.784 (1.286, 2.591)	P < 0.001^b	-0.135 (-0.281, 0.134)
NEL	Mean (SD)	1.937 (0.27)	1.820 (0.274)	P < 0.001^a	-0.129 (0.072)
NEL	Median (range)	1.912 (1.478, 2.681)	1.784 (1.286, 2.591)	P < 0.001^b	-0.134 (-0.281, 0.134)
EL^**	Mean (SD)	2.444 (0.253)	1.477 (0.544)	P = 0.017^a	-0.957 (0.589)
EL^**	Median (range)	2.430 (2.097, 2.806)	1.217 (0.988, 2.069)	P = 0.063^b	-1.100 (-1.604, -0.314)
NAWM	Mean (SD)	1.290 (0.056)	1.286 (0.057)	P = 0.411^a	0.004 (0.029)
NAWM	Median (range)	1.300 (1.16, 1.403)	1.290 (1.188, 1.461)	P = 0.006^b	-0.004 (-0.035, 0.146)
cGM	Mean (SD)	1.897 (0.055)	1.805 (0.049)	P < 0.001^a	-0.09 (0.041)
cGM	Median (range)	1.898 (1.666, 2.025)	1.818 (1.68, 1.872)	P < 0.001^b	-0.096 (-0.143, 0.097)

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^aPaired t-test (two-tailed);

^bMatched-pairs Wilcoxon test (two-tailed); SD = standard deviation; WML = white matter lesion; NEL = non-enhancing lesion; EL = enhancing lesion; NAWM = normal-appearing white matter; cGM = cortical gray matter.

* All measures in the second column indicate mean and median values of 47 individual intra-subject median T₁ values collected within each tissue.

** Gd-enhanced lesions were only confirmed in five out of forty-seven participants, so this analysis is in only those 5 sujects.

To determine if central tendency measures for ΔT₁ in NELs were influenced heavily by a small number of lesions on each scan or if this reflected a more diffuse process, heat maps of ΔT₁ in NEL masks were visually reviewed for each subject (examples in Fig 2). In all cases, ΔT₁ appeared to represent a scattered phenomenon, as none showed large deviations in ΔT₁ focal to a small number of lesions along with a neutral ΔT₁ in all other lesions.

Fig 2 — Shown are images from three separate participants (A: SPMS/female/53, B: RRMS/female/52, C: SPMS/female/61). Color maps of ΔT₁ (units: s) in NEL’s were overlaid upon T₁-weighted images. Each color bar (not scaled) displays the full range of ΔT₁ only within NEL of the corresponding participant. Three representative participants shown for display purposes (analysis was in all subjects). Each row shows three consecutive image slices of a single participant. SPMS = secondary progressive multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; BBB = blood-brain barrier; NEL = non-enhancing lesion.

ΔT₁ values in NELs, NAWM, and cGM were also evaluated for their relationship with other measures of lesion severity. Per-subject mean and median ΔT₁ did not correlate with mean and median pre-contrast T₁ (rho = -0.207, P = 0.16 and rho = -0.286, P = 0.05, respectively) in NELs. In full cohort, voxel-wise analysis, all ΔT₁ and pre-contrast T₁ (Fig 3B) voxels in NELs were only weakly correlated (rho = -0.261, P<0.001; Fig 3B). Comparatively, ΔT₁ and pre-contrast T₁ in all voxels in NAWM and cGM showed stronger correlations (rho = -0.348, P<0.001 in Fig 3E; rho = -0.368, P<0.001 in Fig 3H, respectively). Fig 3A, 3D and 3G show the diversity of distribution of ΔT₁ in NEL, NAWM, and cGM, respectively, in the study cohort using overlaid kernel density estimation (KDE) and Fig 3C, 3F and 3I show the same pre-contrast T₁ for NEL, NAWM, and cGM. In general, pre-contrast T₁ showed a more diverse set of distributions, including many of which were bimodal and skewed, whereas ΔT₁ was more normally distributed in all tissues with different extents of diversity. Compared to NAWM and GM, voxel-wise values in NELs had a wider range of ΔT₁ and pre-contrast T₁ values. Magnitude and variability measures of NEL ΔT₁ showed no significant correlation with overall or average WML lesion volume per subject (Table 3). The magnitude of NEL ΔT₁ did not correlate with WML count, but NEL ΔT₁ variance and IQR did correlate with WML count (rho = 0.582, P<0.001 and rho = 0.489, P<0.001, respectively). The correlation between lesion count and lesion ΔT₁ variance and IQR were significant even after controlling for age, sex, and symptom duration and corrected for non-conservative multiple comparisons (false discovery rate) (S1 Table).

Fig 3 — A: Overlaid kernel density estimations (KDEs) of ΔT₁ in NEL. Each colored line represents the smoothed histogram using KDE for all voxels in one subject. Blue vertial line indicates voxel-wise mean ΔT₁ value (-0.116 s), and green line denotes voxel-wise median ΔT₁ value (-0.105 s). B: Per voxel Spearman’s rho coefficient for the relationship of pre-contrast T₁ and ΔT₁ of all NEL voxels of the entire cohort shows a weak correlation, which indicates that observed ΔT₁ is not solely driven by initial T₁ before contrast agent administration. C: Overlaid KDEs of pre-contrast T₁ in NEL D: Overlaid KDEs of ΔT₁ in NAWM. E: Per voxel Spearman’s rho coefficient in NAWM shows a stronger correlation than NEL (blue line, mean ΔT₁ of 0.011 s; green line, median ΔT₁ of 0.002 s). F: Overlaid KDEs of pre-contrast T₁ in NAWM. G: Overlaid KDE of ΔT₁ in cGM (blue line, mean ΔT₁ of -0.107 s; green line, median ΔT₁ of -0.091 s). H: Per voxel Spearman’s rho coefficient in cGM also shows a stronger correlation than NEL. I: Overlaid KDEs of pre-contrast T₁ in cGM. NEL = non-enhancing lesion, NAWM = normal-appearing white matter, cGM = cortical gray matter.

Table 3. Correlation of ΔT₁ with WML volume and count.

Tissue	T₁ metric (per subject)	WML volume			WML count			WML volume/count
Tissue	T₁ metric (per subject)	r	p	adjusted p	r	p	adjusted p	r	p	adjusted p
WML	mean ΔT₁	0.194	0.191	0.372	-0.181	0.223	0.372	0.264	0.073	0.230
	variance ΔT₁	0.182	0.220	0.372	0.590	< 0.001	< 0.001	-0.150	0.315	0.429
	median ΔT₁	0.160	0.284	0.426	-0.272	0.064	0.230	0.261	0.077	0.230
	IQR ΔT₁	0.221	0.135	0.338	0.493	< 0.001	0.003	-0.081	0.589	0.680
	kurtosis ΔT₁	0.106	0.479	0.599	-0.010	0.949	0.949	0.042	0.777	0.833
NEL	mean ΔT₁	0.218	0.140	0.312	-0.175	0.240	0.404	0.273	0.063	0.226
	variance ΔT₁	0.139	0.353	0.481	0.582	< 0.001	< 0.001	-0.170	0.254	0.404
	median ΔT₁	0.164	0.269	0.404	-0.270	0.066	0.226	0.262	0.075	0.226
	IQR ΔT₁	0.216	0.146	0.312	0.489	< 0.001	0.004	-0.082	0.583	0.673
	kurtosis ΔT₁	0.005	0.975	0.975	-0.126	0.400	0.500	0.055	0.712	0.763
NAWM	mean ΔT₁	0.175	0.240	0.450	0.203	0.172	0.392	0.011	0.941	0.998
	variance ΔT₁	0.244	0.099	0.392	0.341	0.019	0.286	0.000	0.998	0.998
	median ΔT₁	0.210	0.156	0.392	0.231	0.118	0.392	0.002	0.991	0.998
	IQR ΔT₁	0.198	0.183	0.392	0.235	0.111	0.392	0.028	0.851	0.998
	kurtosis ΔT₁	-0.154	0.300	0.500	-0.141	0.343	0.514	-0.073	0.626	0.853
cGM	mean ΔT₁	0.397	0.006	0.043	0.339	0.020	0.074	0.159	0.287	0.391
	variance ΔT₁	0.261	0.076	0.163	0.423	0.003	0.043	-0.069	0.647	0.746
	median ΔT₁	0.309	0.035	0.104	0.229	0.122	0.229	0.142	0.340	0.425
	IQR ΔT₁	0.263	0.074	0.163	0.356	0.014	0.070	-0.031	0.838	0.838
	kurtosis ΔT₁	-0.173	0.244	0.387	-0.168	0.258	0.387	0.047	0.755	0.809

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WML = white matter lesion; NEL = non-enhancing lesion; NAWM = normal-appearing white matter; cGM = cortical gray matter.

All p-values are given with actual numbers except values < 0.001. Coefficients with p-values less than 0.05 are shown in bold face. Partial correlations after controlling for age, sex, and symptom duration are provided in S1 Table.

Correlation analysis was used to examine the clinical significance of gadolinium-induced T₁ shortening (Table 4). In bivariate correlation analysis, median ΔT₁ in NELs correlated with EDSS (rho = -0.365, P = 0.01) scores. The interquartile range (IQR), variance, and kurtosis of ΔT₁ values in NELs also all correlated with EDSS scores (Table 4). These measures indicate that wider spread (larger IQR, variance and smaller kurtosis) in NEL ΔT₁ values, likely related to regions of larger lesion ΔT₁ in the background of overall small ΔT₁ are seen in cases with more advanced disability. Similar associations between variability in NEL ΔT₁ and disability were seen for non-dominant hand 9HPT and SDMT scores (Tables 4 and 5) When adjusted for covariates of age, sex, and symptom duration, most of these correlations remained significant (S2 Table). However, adjustment of p-values for multiple comparisons resulted in loss of significance for all these relationships. IQR and kurtosis of ΔT₁ in NAWM showed bivariate correlation with EDSS scores. The IQR of ΔT₁ in cGM correlated with EDSS, 9HPTDOM, and 25FTW. However, nearly all the correlations noted between NAWM and cGM values and disability scores were no longer significant after adjustment for covariates and correction for multiple comparisons (S2 Table).

Table 4. Correlation of per subject ΔT₁ with clinical measures (Part I).

Tissue	T₁ metric (per subject)	EDSS			SDMT			PASAT			MFIS
Tissue	T₁ metric (per subject)	rho	p	adjusted p	r	p	adjusted p	r	p	adjusted p	r	p	adjusted p
WML	mean ΔT₁	-0.231	0.118	0.393	-0.098	0.514	0.719	0.204	0.173	0.463	-0.174	0.249	0.518
	variance ΔT₁	0.286	0.051	0.257	-0.115	0.441	0.648	0.031	0.836	0.910	0.230	0.124	0.393
	median ΔT₁	-0.366	0.011	0.081	0.018	0.905	0.931	0.220	0.141	0.411	-0.116	0.444	0.648
	IQR ΔT₁	0.365	0.012	0.081	-0.312	0.033	0.192	-0.030	0.841	0.910	0.172	0.252	0.518
	kurtosis ΔT₁	-0.388	0.007	0.081	0.384	0.008	0.081	0.049	0.745	0.899	-0.002	0.989	0.989
NEL	mean ΔT₁	-0.231	0.118	0.365	-0.059	0.692	0.824	0.214	0.153	0.383	-0.134	0.375	0.597
	variance ΔT₁	0.324	0.027	0.153	-0.186	0.211	0.485	0.009	0.952	0.952	0.156	0.301	0.527
	median ΔT₁	-0.365	0.012	0.081	0.024	0.875	0.901	0.221	0.140	0.377	-0.109	0.472	0.672
	IQR ΔT₁	0.367	0.011	0.081	-0.316	0.031	0.153	-0.032	0.834	0.885	0.165	0.272	0.501
	kurtosis ΔT₁	-0.387	0.007	0.081	0.411	0.004	0.081	0.048	0.752	0.824	-0.269	0.070	0.273
NAWM	mean ΔT₁	0.088	0.558	0.906	0.018	0.903	0.929	0.185	0.218	0.695	-0.002	0.990	0.990
	variance ΔT₁	0.280	0.057	0.495	-0.209	0.158	0.695	-0.025	0.870	0.923	0.088	0.559	0.906
	median ΔT₁	0.047	0.753	0.906	0.036	0.810	0.906	0.188	0.210	0.695	0.033	0.829	0.906
	IQR ΔT₁	0.292	0.046	0.495	-0.232	0.116	0.678	-0.056	0.711	0.906	0.124	0.413	0.808
	kurtosis ΔT₁	-0.290	0.048	0.495	0.152	0.308	0.771	0.143	0.343	0.801	-0.284	0.055	0.495
cGM	mean ΔT₁	0.020	0.896	0.896	-0.047	0.754	0.896	0.252	0.091	0.366	0.024	0.876	0.896
	variance ΔT₁	0.252	0.087	0.366	-0.163	0.272	0.502	-0.119	0.431	0.580	0.100	0.507	0.657
	median ΔT₁	-0.050	0.739	0.896	0.027	0.859	0.896	0.263	0.078	0.366	-0.027	0.857	0.896
	IQR ΔT₁	0.278	0.058	0.366	-0.224	0.131	0.382	-0.119	0.430	0.580	0.167	0.269	0.502
	kurtosis ΔT₁	-0.243	0.100	0.366	0.244	0.098	0.366	0.166	0.271	0.502	-0.166	0.270	0.502

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WML = white matter lesion; NEL = non-enhancing lesion; NAWM = normal-appearing white matter; cGM = cortical gray matter; IQR = inter-quartile range; rho = Spearman’s rho coefficient; r = Pearson’s correlation coefficient; p = p-values; adjusted p = p-value corrected for multiple comparison (false discovery rate: FDR); EDSS = Expanded Disability Status Scale; SDMT = Symbol Digit Modalities Test; PASAT = Paced Auditory Serial Addition Test; MFIS = Modified Fatigue Impact Scale

Table 5. Correlation of per subject ΔT₁ with clinical measures (Part II).

Tissue	T₁ metric (per subject)	9HPTDOM			9HPTNONDOM			25FTW
Tissue	T₁ metric (per subject)	r	p	adjusted p	r	p	adjusted p	r	p	adjusted p
WML	mean ΔT₁	0.147	0.329	0.564	-0.034	0.820	0.910	-0.086	0.567	0.764
	variance ΔT₁	0.266	0.074	0.322	0.027	0.858	0.910	0.143	0.338	0.564
	median ΔT₁	0.051	0.737	0.899	-0.073	0.628	0.814	-0.164	0.271	0.520
	IQR ΔT₁	0.398	0.006	0.081	0.131	0.382	0.607	0.255	0.084	0.325
	kurtosis ΔT₁	-0.199	0.185	0.463	-0.160	0.282	0.520	-0.190	0.201	0.469
NEL	mean ΔT₁	0.128	0.397	0.604	-0.055	0.713	0.824	-0.099	0.508	0.684
	variance ΔT₁	0.301	0.042	0.184	0.063	0.673	0.824	0.166	0.266	0.501
	median ΔT₁	0.048	0.754	0.824	-0.076	0.613	0.794	-0.166	0.266	0.501
	IQR ΔT₁	0.399	0.006	0.081	0.133	0.372	0.597	0.256	0.083	0.290
	kurtosis ΔT₁	-0.184	0.222	0.485	-0.106	0.480	0.672	-0.227	0.125	0.365
NAWM	mean ΔT₁	0.118	0.435	0.808	-0.034	0.822	0.906	-0.041	0.782	0.906
	variance ΔT₁	0.238	0.112	0.678	0.054	0.718	0.906	0.118	0.428	0.808
	median ΔT₁	0.117	0.439	0.808	-0.034	0.819	0.906	-0.041	0.785	0.906
	IQR ΔT₁	0.196	0.191	0.695	0.056	0.711	0.906	0.174	0.242	0.705
	kurtosis ΔT₁	-0.160	0.288	0.771	-0.061	0.682	0.906	-0.197	0.184	0.695
cGM	mean ΔT₁	0.032	0.831	0.896	-0.180	0.226	0.502	-0.134	0.370	0.577
	variance ΔT₁	0.265	0.075	0.366	0.131	0.379	0.577	0.198	0.183	0.492
	median ΔT₁	0.030	0.842	0.896	-0.142	0.340	0.577	-0.185	0.212	0.502
	IQR ΔT₁	0.273	0.066	0.366	0.118	0.429	0.580	0.240	0.105	0.366
	kurtosis ΔT₁	-0.235	0.116	0.370	-0.140	0.348	0.577	-0.245	0.097	0.366

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WML = white matter lesion; NEL = non-enhancing lesion; NAWM = normal-appearing white matter; cGM = cortical gray matter; IQR = inter-quartile range; rho = Spearman’s rho coefficient; r = Pearson’s correlation coefficient; p = p-values; adjusted p = p-value corrected for multiple comparison (false discovery rate: FDR); 9HPTDOM = 9-hole peg test for dominant hand; 9HPTNONDOM = 9-hole peg test for non-dominant hand; 25FTW = timed 25-foot walk.

Median EDSS was 3.0 (range 1.0–6.5). ΔT₁ in NELs was greater (P<0.0001) in participants with whose EDSS score was greater than the median (-0.168, range: -0.272 to 0.050) than those with EDSS scores at or below the median (-0.109, range: -0.281 to 0.134) (Fig 4A and Table 6). A wider IQR and lower positive kurtosis (thus, a wide-tailed distribution) for ΔT₁ in NELs was also seen in those with EDSS scores above the median (Table 6). Gadolinium also induced greater changes in mean and median T₁ relaxation time of NELs in participants with progressive MS (SPMS and PPMS) phenotypes (median ΔT₁: -0.177, range: -0.272 to -0.118) than those with RRMS (median ΔT₁: -0.110, range: -0.281 to 0.134; P<0.001) (Fig 4B and Table 6). The variance and IQR were also elevated in those with progressive phenotypes (Table 6). Mean and median ΔT₁ in NELs were greater in subjects not on MS treatment (Fig 4C and Table 6). In further analysis of treatment effect using logistic regression, ΔT₁ significantly predicted whether a participant was on treatment (P = 0.01), even when adjusted for MS phenotype (P = 0.045) (Table 7).

Fig 4 — A: NEL median ΔT₁ comparison for EDSS scores that were > median (3.0) vs. ≤ median. Gadolinium induced greater changes in T₁ in MS participants with higher EDSS scores. B: NEL median ΔT₁ comparison for MS Type, PMS vs. RRMS. Gadolinium induced greater changes in T₁ in participants with PMS. C: NEL median ΔT₁ comparison for multiple sclerosis immunomodulatory treatment, Yes vs. No. Gadolinium induced greater changes in T₁ in participants not on MS treatment. Per subject median ΔT₁ values were used in the Mann-Whitney U-test. NEL = non-enhancing lesion; EDSS = Expanded Disability Status Scale; MS = multiple sclerosis; PMS = progressive multiple sclerosis (PPMS + SPMS); PPMS = primary progressive multiple sclerosis; SPMS = secondary progressive multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis.

Table 6. Group differences in ΔT₁ metrics.

NEL T₁ metric	EDSS		MS type		Treatment
NEL T₁ metric	≤ Median Score	> Median Score	PMS	RRMS	No	Yes
mean ΔT₁	-0.120	-0.186 *	-0.187	-0.122 **	-0.192	-0.126 **
mean ΔT₁	(-0.304, 0.174)	(-0.238, 0.100)	(-0.238, -0.062)	(-0.304, 0.174)	(-0.304, -0.049)	(-0.231, 0.174)
median ΔT₁	-0.109	-0.168 ***	-0.177	-0.110 ***	-0.162	-0.122 *
median ΔT₁	(-0.281, 0.134)	(-0.272, 0.050)	(-0.272, -0.118)	(-0.281, 0.134)	(-0.281, -0.045)	(-0.201, 0.134)
variance ΔT₁	0.081	0.118 ***	0.119	0.082 **	0.111	0.083
variance ΔT₁	(0.026, 0.297)	(0.006, 0.366)	(0.048, 0.366)	(0.006, 0.297)	(0.006, 0.366)	(0.040, 0.337)
IQR ΔT₁	0.272	0.382 **	0.357	0.272 *	0.337	0.297
IQR ΔT₁	(0.142, 0.439)	(0.118, 0.702)	(0.229, 0.702)	(0.118, 0.469)	(0.118, 0.702)	(0.193, 0.689)
kurtosis ΔT₁	6.425	5.102 **	5.255	6.205	5.837	6.023
kurtosis ΔT₁	(4.459, 14.589)	(2.636, 8.803)	(3.843, 8.803)	(2.636, 14.589)	(2.636, 14.589)	(3.843, 11.825)

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Inter-subject median (range) is shown for each metric. Significance for group difference tested by Mann-Whitney U-test shown. Statistically significances (*: P < 0.05, **: P < 0.01, ***: P < 0.001) are shown in bold.

NEL = non-enhancing lesion; MS = multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; PMS = progressive MS (primary progressive + secondary progressive multiple sclerosis); IQR = inter-quartile range

Table 7. Multiple logistic regression analysis of significance of treatment effect on ΔT₁.

Model	β ₀	β ₁	β ₂
Model	p-value	p-value	p-value
Tx = β₀ + β₁ × ΔT₁	3.846^**	19.176^*	N/A
Tx = β₀ + β₁ × ΔT₁	P = 0.002	P = 0.013	N/A
Tx = β₀ + β₁ × ΔT₁ + β₂ × (MS type)	3.869^**	17.967^*	-0.129
Tx = β₀ + β₁ × ΔT₁ + β₂ × (MS type)	P = 0.002	P = 0.045	P = 0.80

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Simple logistic regression model in first row, followed by multiple logistic regression including MS Type (relapsing-remitting versus progressive). Tx = MS treatment status (1 = yes, 0 = no); MS = multiple sclerosis, N/A = not applicable;

*: P < 0.05;

**: P < 0.01.

Discussion

This study demonstrates that a shift in T₁ relaxation time caused by gadolinium can be measured in WMLs in MS by MP2RAGE on 7T MRI, even when visible contrast enhancement cannot be observed. Further, the magnitude and variability of this T₁-shift (termed ΔT₁ in this study) is associated with the severity of MS-related disability and MS-phenotype and ΔT₁ magnitude is associated with use of immunomodulatory MS treatment. These findings have potential implications towards measurement of disease activity and treatment effect monitoring in MS.

In order to place these findings into proper context, the underlying mechanism leading to an appreciable ΔT₁ in lesions warrants further exploration. Gadolinium-based contrast agents (GBCAs) are utilized in MR imaging because of their extracellular distribution and reduced T₁ relaxation rates [26]. In a healthy central nervous system, gadolinium does not leak into the extravascular space or accumulate in tissues due to the actions of the BBB. Focal disruption of the BBB leads to gadolinium accumulation, which is an established marker of acute inflammation in MS lesions [27]. However, the process of BBB disruption in MS is not exclusive to acutely inflamed WMLs. The BBB, which is composed of a glycolax layer, non-fenestrated endothelium with tight junctions, basal lamina, and astrocytic end feet, may be breached through both disruptive and non-disruptive processes [28]. Transient inflammatory changes are typically non-disruptive, resulting in increases in the concentration and activity of cellular adhesion molecules and transporters, along with changes to astrocyte activity [26]. More profound BBB injury can result in disruptive changes to the BBB, including degeneration of the glycolax layer, damage to endothelium and glia limitans, breakdown of tight junctions, and astrocytopathy [28]. Histopathologic examination of MS lesions confirms BBB disruption in acutely inflamed MS lesions, including basal lamina degradation and deposition of vascular fibrinogen within lesions [29, 30]. These findings are not exclusive to acutely inflamed lesions, however, as chronic-active lesions also show fibrinogen deposition, in addition to deposition of IgG, endothelial cell changes, and leukocyte activity in widened Virchow-Robin spaces [29, 31]. To a lesser degree, chronic-inactive lesions also show similar signs of BBB breakdown on histopathology [29].

Given evidence of BBB breakdown in WMLs of all types, extravasation of gadolinium into lesioned tissue is likely the mechanism by which ΔT₁ changes occurred in this study. Although an increase in blood flow could potentially cause a ΔT₁ shift through increased presence of intravascular gadolinium, all histopathologic and MRI evidence points towards reduced vascularity and blood flow in the white matter and WMLs of patients with MS [32]. Further, the mean distribution half-life of most GBCAs are approximately 10 minutes, and thus by the time of acquisition of MP2RAGE images in this study the plasma concentration of gadolinium has markedly decreased, whereas tissue concentrations are peaked [33].

Measurement of ΔT₁ in NELs in this study is thus revealing of BBB breakdown in WMLs without visible contrast enhancement. The minimal ΔT₁ in NAWM shows that this is specific to lesions and correlations with disability and phenotype confirm clinical relevancy. These findings are in line with prior data performing measurements of BBB integrity in MS lesions by other neuroimaging methodologies. Previous work using T₁ maps calculated from combination of a T₁-weighted spin-echo sequence and a PD-weighted gradient-echo sequence before and after triple-dose gadolinium (0.3 mmol/kg) consistently showed a T₁ shift with contrast in NELs in MS, which was not apparent in NAWM [10, 34]. The difference between NELs and NAWM was not profound at 5 minutes after contrast injection, but became more pronounced later and was sustained at time points as far out as 60 minutes. Our study did not include multiple post-gadolinium acquisition time points, so such a comparison cannot be made. However, although these prior studies had similar findings, T₁-shortening in NELs was not related to disability levels or phenotype, and piloting of this technique in a placebo-controlled trial of natalizumab showed no treatment effect [35]. DCE MRI is also capable of measuring BBB disruption in NELs, with findings of increased permeability in NELs in MS patients with recent relapses and decreased permeability in those on disease modifying drugs [8]. Similar findings were seen in this study, with participants on MS treatment having a smaller ΔT₁ in NELs. The significance of this finding is unclear. While it is possible this was influenced by a bias towards refraining from disease modifying therapy in very advanced, especially progressive MS phenotypes, adjustment for MS phenotype did not alter the relationship. Given that the treatment effect of some MS therapies, such as interferon-beta, is partially mediated by improvements in BBB function [36], it is quite possible that the measurement of ΔT₁ in this study revealed less BBB breakdown as a function of immunomodulatory therapy. Such findings indicate the potential applicability of this technique as a clinical trial outcome measure.

Although similar to prior work, our proposed concept of MP2RAGE ΔT₁ mapping as a means by which to study BBB integrity in MS stands out because of potential greater applicability of the technique to clinical trials and clinical practice than other methods. Unlike some prior methodologies, MP2RAGE ΔT₁ mapping does not require triple-dose gadolinium. Widespread use of triple-dose gadolinium is impractical given safety concerns such as allergic reactions, nephrogenic systemic sclerosis, and recent evidence showing deposition of gadolinium in the dentate nucleus and globus pallidus [37]. Further, unlike other methods, creation of T₁ maps from MP2RAGE does not require multiple image acquisitions with later synthesis. This property allows rapid and robust production of T₁ maps, which can readily be created on-scanner with automated, manufacturer-provided tools on some scanning platforms. Analysis is also simple, with direct subtraction of T₁ maps and interpretation of the absolute change after gadolinium administration. This is in direct contrast to the complex mathematical modeling required to derive and interpret permeability measurements from DCE MRI [38]. MP2RAGE T₁ maps are also likely more reliable than other T₁ mapping techniques, as the inhomogeneity correction inherent to processing of MP2RAGE images removes the influence of scanner field inhomogeneity and the acquisition technique minimizes the influence of T₂* and proton density effects, resulting in a more ‘pure’ T₁ map [11]. It is likely that these properties, along with the higher resolution affordable at 7T, allowed us to quantify ΔT₁ in NELs in a manner that was more-clinically relevant than prior work. Although 7T was used in this study, it should be noted that the MP2RAGE technique is not exclusive to 7T and can easily be performed and processed on clinical scanners. Further, given that the r1 relaxivity of some GBCAs is reduced at higher magnetic fields [39], it is possible that similar, or possibly more robust findings can be seen at 1.5T or 3T. This effect may be counter-balanced, however, by the greater separation of T₁ relaxation values between tissue types and increased resolution at 7T [11, 14]. Given potential equipoise regarding field strength, future attempts at replication of our findings should aim to perform direct comparisons of contrast-enhanced MP2RAGE at 3T and 7T. An inability to confirm our findings on 3T would clearly limit potential clinical applications and use in clinical trials.

The need for alternate means by which to subclassify MS lesions is clear, as imaging-pathology correlations reveal that differentiation of active, chronic-active, and chronic-inactive lesions by T₂ signal characteristics alone is not possible [29]. Although visible contrast enhancement differentiates active from chronic lesions, differentiation of chronic lesions into those that have continued inflammation versus those that do not is more difficult. Multiple putative imaging measures for such chronic inflammatory lesion changes have been proposed, including slowly expanding lesions and lesions with paramagnetic rims [40]. If chronic BBB breakdown is indicative of chronic inflammation, ΔT₁ measurement may be a means by which to quantify chronic inflammatory changes in MS lesions and differentiation of such lesions from those that are chronic-inactive. This hypothesis could potentially be studied in the future by applying the ΔT₁ technique in a multi-modal study including susceptibility-weighted images, with comparisons between lesions with and without paramagnetic rims. Establishing a unique link between ΔT₁ and paramagnetic rims. Establishing a unique link between ΔT₁ and paramagnetic rims (as a surrogate for chronic-active inflammation could potentially provide additional tools for) quantification of disease activity in SPMS and PPMS, in whom contrast enhancing lesions are rarely seen and chronic-active lesions are more pathologically predominant [41, 42]. Despite the rarity of visually enhancing lesions in progressive phenotypes, BBB breakdown clearly occurs, as studies of triple-dose gadolinium and delayed acquisition of images after contrast reveal significant increases in the number of enhancing lesions visible in PPMS [41]. Given our findings of a more profound and variable ΔT₁ in NELs seen in those with progressive MS, we propose further evaluation of this technique as a means by which to monitor progression.

It is unclear if the ΔT₁ changes seen here are due to non-disruptive BBB changes, perhaps occurring in the setting of increased cytokine release and microglial activity due to chronic inflammation, versus more permanent disruptive changes to the BBB that occurred at lesion formation. Differentiation between these two processes could be attempted by comparing lesions with and without paramagnetic rims, but ultimate proof will likely require in vivo imaging followed by post-mortem histopathology. However, given that prolonged T₁ relaxation times (seen as darker ‘black holes’ on T₁-w images) strongly correlate with the degree of tissue damage within WMLs [43], our results showing only a weak correlation between pre-contrast T₁ and ΔT₁ values suggest the severity of a lesion is not the sole determinant of BBB leak. Further, the correlation between pre-contrast T₁ and ΔT₁ values in NELs was weaker than the same correlation evaluated in NAWM or cGM—further supporting ΔT₁ as a metric with more specificity to BBB disruption as a function of lesional-pathology rather than widespread tissue alterations. These data together suggest that lesion-based ΔT₁ provides additional information beyond T₁ alone, warranting further investigation as an MS outcome measure.

In addition to a lack of histopathologic correlations, there are a few other potential limitations to consider when evaluating the conclusions of this work. The lack of healthy control participants, a limitation in this study, precluded comparison of NAWM of MS brains and healthy brains. Including healthy participants in the future study would help with the interpretation of the patient data. Although our sample size is relatively large in the context of 7T research in MS, larger sample sizes will be needed to confirm these findings and evaluate widespread applicability. It is quite likely that the small sample size of the study is responsible for reductions in the number of significant correlations with correction for multiple comparisons and/or with adjustment for co-variates. Greater statistical power is necessary to determine if the relationships seen in univariate analysis are due to false discovery or covariance with other factors. We hope that the initial findings of this work are hypothesis stimulating and that our group and others can work to either refute or confirm the findings with larger, future work.

This study is also limited by its cross-sectional nature, and determination as to whether these findings track with disability progression or are modified by treatment will likely require longitudinal analyses. Additionally, as lesion masks were drawn as one volume for each subject, our study does not permit the specificity of lesion-by-lesion analysis within subjects beyond visual inspection of heat maps. Individual lesion analyses are beyond the scope and capability of this dataset but could be evaluated in the future. However, individual lesion analyses are potentially fraught with limitations of their own, such as unclear lesion borders in regions of large, confluent periventricular demyelination. An additional limitation of our paper due to the lack of lesion-by-lesion masks is the inability to separate out the influence of any non-demyelinating (i.e. microvascular ischemic) lesions on our results. We expect that any such contribution would be extremely small, but cannot rule out this possibility, especially in older subjects in the cohort. Finally, although the MP2RAGE technique employed does help to overcome receive field (B₁^-) inhomogeneity, it has been noted that the accuracy of T₁ estimation can also be influenced by transmit field (B₁⁺) inhomogeneity. Such inhomogeneities are especially relevant for T₁ estimation at ultra-high field. The influence of this homogeneity, along with rare voxel misalignments in co-registration likely explain the small proportion of positive ΔT₁ voxels seen in this work, which is similar to prior evaluations of T₁ change with gadolinium [34]. To overcome field-induced limitations in T₁ mapping, more recent work has evaluated measurement of B₁⁺ field maps by techniques such as the Sa2RAGE sequence, with integration of these maps into T₁ map calculations [44]. Despite our lack of B₁⁺ field map acquisition significant and clinically relevant findings were obtained by our protocol. Thus, future work including Sa2RAGE or other similar methods would only likely improve upon the ability to measure ΔT₁ in NELs in MS.

Conclusion

Despite limitations, the findings of this study lead to a conclusion that T₁ mapping by MP2RAGE shows promise as a novel means by which to assess MS disease severity and potentially to monitor treatment effect. Integration of clinically feasible evaluations of BBB integrity in MS will likely help give greater insight into MS pathology, and may provide an opportunity to evaluate chronic lesion inflammation.

Supporting information

S1 Table. Partial correlation of ΔT₁ with WML volume and count.

(DOCX)

Click here for additional data file.^{(18.5KB, docx)}

S2 Table. Partial correlation of per subject ΔT₁ with clinical measures.

(DOCX)

Click here for additional data file.^{(24.7KB, docx)}

Acknowledgments

The investigators are grateful to research nurses Kerry Naunton and Julie Fiol and MRI technicians Terri Brawner, Kathleen Kahl, and Ivana Kusevic—all of whom were critical to implementation of the study.

Data Availability

Data cannot be shared publicly because of potentially identifying information. Data may be made available to researchers who meet criteria for access to confidential data by arranging a data transfer agreement with the University of Maryland, Baltimore. Requests for such an agreement can be initiated by contacting the Sponsored Projects office at the University of Maryland Baltimore at team-red@ordmail.umaryland.edu.

Funding Statement

This work was funded by grants from the NIH - National Institute of Neurological Disorders and Stroke (NINDS 1K23NS072366-01A1 and 1R01NS104403-01; PI: DMH) and EMD Serono, Inc (PI: DMH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. https://www.ninds.nih.gov/ https://www.emdserono.com/us-en/company.html.

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PLoS One. doi: 10.1371/journal.pone.0249973.r001

Decision Letter 0

Niels Bergsland

19 Nov 2020

PONE-D-20-33106

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

PLOS ONE

Dear Dr. Harrison,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In addition to the comments raised by the Reviewers, please also address the following comments:

1. The word “gender” is used throughout the manuscript. Usually, “sex” (the biological designation) is meant. “Gender” is the social construct and is rarely relevant in neurologic disease. Please revise the text to use “sex” rather than “gender” throughout.

2. The "Symbol Digit Modalities Test" is incorrectly referred to as the "symbol digits modalities test" (note the lack of pluralization of digits and capitalization in the correct spelling).

3. The point about potentially inhibiting hypothesis-generating research due to multiple comparisons is understood, some correction is warranted in the context of your manuscript. Table 3 and Table 4 have 45 and 105 tests, respectively. While Bonferroni correction would indeed be overly conservative, something like false discovery rate correction be considered.

4. P-values, by definition, cannot be equal to 0.0, as reported in Table 4. Please update accordingly.

5. While the comments about the benefit of the MP2RAGE sequence with respect to not requiring co-registration for T1 measurements is acknowledged, this point is somewhat weakened later on when you refer to the subtraction of T1 maps in the context of the current manuscript, which does require co-registration. Consider mentioning this.

Please submit your revised manuscript by Jan 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Niels Bergsland

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PLOS ONE

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Dr Choi and colleagues report a cross-sectional study to investigate if there is a BBB disruption in non-enhancing lesions (NEL) evaluating changes in T1 relaxation time in pre-and post-contrast MRI using MP2RAGE at 7T. They found that subtraction of pre-and post-contrast T1 maps, ΔT1, was greater in NEL than in NAWM and correlated with EDSS. While it is a novel approach, there are former articles at lower resolution MRI showing that NELs have increased permeability compared to NAWM.

I have major concerns.

• Abstract: The authors propose that NEL ΔT1 should be further evaluated as a biomarker for disease severity and treatment effect in MS. I would rather say that it should be evaluated as a possible biomarker of persistently disrupted BBB.

• Introduction: They state: -The existence of chronic-active lesions, along with the relative inability to differentiate these from inactive lesions on standard MRI sequences, demonstrates need additional in vivo markers for chronic inflammation in MS-. However, there are articles that showed in vivo and postmorten paramagnetic rims in susceptibility weighted imaging at 7, that correlated with chronic active lesions in pathological analysis. The presence of paramagnetic RIMS was also assessed at 3T MRI. A paragraph describing these results and data on slowly expanding lesions should be added.

• Materials and methods: Was MP2RAGE the only sequence acquired?

• Results: Data on the disease duration should be included. What percentage of progressive patients where under treatment? These data are important for the interpretation of the results.

• Discussion:

I. Paragraph-4 Authors claim that the magnitude and variability of ΔT1 is associated with the severity of MS-related disability, MS-phenotype and the use of immunomodulatory MS treatment. To better analyze the results, it is important to include disease duration and percentage of progressive MS under treatment.

II. Paragraph-5 As they found that ΔT1 revealed less BBB breakdown as a function of immunomodulatory therapy, they highlight the potential applicability of this technique as an outcome measure in clinical trials, and in clinical practice. The authors should deeply clarify that MP2RAGE at 7T is not currently feasible in clinical trials setting or in clinical practice. As they mention in the last sentence of that paragraph, their findings need to be replicated first at 3T.

III. Paragraph-7 They propose that changes in ΔT1 may be due to increased cytokine release and microglial activity due to chronic inflammation, or more permanent disruptive changes to the BBB that occurred at lesion formation. They claim that the differentiation between these two processes would likely require in vivo imaging followed by post-mortem histopathology. However, I think that a good approach to elucidate possible mechanisms involved would be to see if there is a gradient of ΔT1 in individual lesions, if a greater ΔT1 is detected at the edge of the lesion, this would be in favor of chronic inflammation in the border of the lesion. Furthermore, if susceptibility-weighted images show paramagnetic rims in NEL with greater ΔT1, it would suggest chronic inflammation in the edge of the lesion. Could the authors do a more detailed analysis?

• Conclusion: I would focus the conclusion on that using MP2RAGE at 7T, persistent disruption of the BBB was found. The last sentence mentions that there are currently no stablished biomarkers, I would remove it, there are promising biomarkers.

Reviewer #2: Thank you for this interesting study.

Please reformat Table 1 to show the variables subdivided by groups of MS category. It is important to know the relative ages and disability levels

The average age of this cohort was 46.2 (11.2). WM lesions of vascular origin may have also occurred in this cohort. Did you make an attempt to distinguish these from MS lesions?

Why did you chose EDSS = 5.0 as a threshold for binarization? This is not particularly clinically meaningful. Since 3.0 was the median in your cohort (and 6.5 the max), and since this is more meaningful (mild vs moderate disability), this might be a more appropriate threshold.

The phrase ‘inter-subject mean of per subject median’ is not well explained. Suggest rephrase or explain more fully in methods.

You found a pre/post-contrast difference in NAWM when using the median but not the mean – how do you explain this?

You report that delta T1 was significantly greater in NELs than in NAWM. But NELs also had a higher pre-contrast T1 than NAWM (as expected). Therefore, if both NELs and NAWM had the same permeability, and accumulated the same concentration of contrast, NELs would have a higher delta T1 simply because of the higher pre-contrast T1 (about double – though you find a bigger change than this). Presumably in controls you find a higher delta T1 in GM vs WM? You should report delta T1 for NAGM in this cohort and compare to NAWM. I am concerned that using delta T1 to make inferences about BBB permeability is difficult. You report a weak correlation between pre-contrast T1 and delta T1 – this deserves more discussion and a figure. Advise redo the correlation but within tissue types – i.e. within NAWM, GM, etc. If the correlation is weak in NELs, then this may be due to heterogeneity in some other factor – like permeability (or vascular surface area). This would fit with your other findings of lesional heterogeneity.

What was the rationale for a 10 minute gap between injection and post-contrast scan? Would a longer gap have influenced results?

I do not see that you acquired a T2-weighted or FLAIR sequence for lesion detection. If lesion masks were created using T1 information only, it is likely that some lesions were misclassified as NAWM, and that the lesions mask was biased towards black holes. You report the median T1 of lesions to be 2430 ms – this is very long and suggests that a significant number of black holes were present. The leakage behaviour of black holes is unlikely to be representative of T1-isointense lesions.

Has the raw data been provided? I cannot see in this manuscript. I assume this will follow in a supplementary material.

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PLoS One. 2021 Apr 26;16(4):e0249973. doi: 10.1371/journal.pone.0249973.r002

Author response to Decision Letter 0

9 Mar 2021

Please see the attached "Response to Reviewers" document attached to the submission. Full response could not be pasted here due to formatting, figures, etc.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0249973.r003

Decision Letter 1

Niels Bergsland

23 Mar 2021

PONE-D-20-33106R1

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

PLOS ONE

Dear Dr. Harrison,

Please see below for one outstanding issue. I agree with the Reviewer that this point would be beneficial to discuss.

Please submit your revised manuscript by May 07 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Response to comment from previous round:

We thank you for the suggestion. Unfortunately, we suggest that such an analysis would be well beyond the scope of this paper and also may not provide any definitive conclusion. Regarding scope, as GRE images were not reviewed for paramagnetic rims and paramagnetic rim lesions were not separately masked, this would add multiple additional processing/identification/masking/registration/analysis steps – all of which would have to be performed, described, and evaluated in detail on their own in this already large paper (both in size and concept). As a first description of the ΔT1 concept requires much more detail on MP2RAGE ΔT1 methods and analysis, we feel there is not sufficient room to do so at this time. We do agree that, perhaps, in a future analysis, once the ΔT1 concept is described in this paper and this paper can be referred to, a multi-modal analysis of the type suggested could be done. Further, it should be noted that while lesions with paramagnetic rims do indeed correlate well with chronic-active lesions at autopsy, the finding of paramagnetic rims also has been described in acutely inflamed lesions, so specificity may complicate interpretation. Regarding the spatial analysis suggested – unfortunately that is not possible with the current WML masks created for this paper. As described in the methods, each individual WML was not mapped on its own. Rather, a full comprehensive WML mask was created for each individual subject. The full 3D volume of this mask and an enhancing WML mask for each subject were subtracted to create the full 3D volume for NELs for each subject. The analysis suggested would require each individual lesion in each individual subject to separated into individual volumes and then creation of a geometric methodology for separating the inner and outer portions of those volumes. While that could be the goal of a later undertaking and report, the idea for which we thank the reviewer, this would not be possible at this time with this dataset. We should note that our original Discussion section (last paragraph) acknowledges the limitation of the full volume approach, rather than a lesion-by-lesion analysis, but also discusses the biases/limitations of lesion-by-lesion analyses.

Although this does not entirely answer the reviewer’s request, though, it should be noted that a qualitative review of ΔT1 patterning was performed and is described in the methods and results, and a clear inner vs outer pattern was not clearly seen (examples in Figure 2). However, a new, comprehensive, lesion-by-lesion quantitative volumetric study could and should be done in the future, which will answer

your question.

Comment to the author

I appreciate the detailed response.

I accept that this analysis is beyond the scope of this paper, however, it would be helpful if you could add a brief comment on how susceptibility-weighted imaging (presence of paramagnetic RIM) could help elucidate whether changes in ΔT1 in NEL may be due to chronic disrupted BBB or due to chronic inflammation.

As you mention, enhancing lesions may show a transient paramagnetic RIM, however, here, you want to discriminate the possible underlying processes in the ΔT1 changes seen in NEL.

The authors have addressed my other comments.

Reviewer #2: The authors have addressed all the comments clearly and I recommend that the manuscript be accepted.

**********

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Reviewer #1: No

Reviewer #2: Yes: Aravinthan Varatharaj

PLoS One. 2021 Apr 26;16(4):e0249973. doi: 10.1371/journal.pone.0249973.r004

Author response to Decision Letter 1

25 Mar 2021

Please see attached response to reviewer file

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PLoS One. doi: 10.1371/journal.pone.0249973.r005

Decision Letter 2

Niels Bergsland

29 Mar 2021

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

PONE-D-20-33106R2

Dear Dr. Harrison,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0249973.r006

Acceptance letter

Niels Bergsland

14 Apr 2021

PONE-D-20-33106R2

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT₁ mapping

Dear Dr. Harrison:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

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Supplementary Materials

S1 Table. Partial correlation of ΔT₁ with WML volume and count.

(DOCX)

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S2 Table. Partial correlation of per subject ΔT₁ with clinical measures.

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Data Availability Statement

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PERMALINK

Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

Seongjin Choi

Margaret Spini

Jun Hua

Daniel M Harrison

Roles

Abstract

Introduction

Materials and methods

Participants

Table 1. Demographic and clinical characteristics of the study population.

MRI acquisition

Image processing

Fig 1. Lesion masking for ELs and NELs as seen on pre- and post-contrast T1w images, T1 map, and ΔT1 map.

Disability measures

Statistical analysis

Results

Table 2. T1 metrics from pre- and post-contrast T1 maps.

Fig 2. Magnitude of ΔT1 in non-enhanced lesion areas.

Fig 3. ΔT1 and Pre-contrast T1 in NEL, NAWM, cGM of entire cohort.

Table 3. Correlation of ΔT1 with WML volume and count.

Table 4. Correlation of per subject ΔT1 with clinical measures (Part I).

Table 5. Correlation of per subject ΔT1 with clinical measures (Part II).

Fig 4. Group differences in median ΔT1 in non-enhancing white matter lesions.

Table 6. Group differences in ΔT1 metrics.

Table 7. Multiple logistic regression analysis of significance of treatment effect on ΔT1.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Niels Bergsland

Roles

Author response to Decision Letter 0

Decision Letter 1

Niels Bergsland

Roles

Author response to Decision Letter 1

Decision Letter 2

Niels Bergsland

Roles

Acceptance letter

Niels Bergsland

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT₁ mapping

Fig 1. Lesion masking for ELs and NELs as seen on pre- and post-contrast T₁w images, T₁ map, and ΔT₁ map.

Table 2. T₁ metrics from pre- and post-contrast T₁ maps.

Fig 2. Magnitude of ΔT₁ in non-enhanced lesion areas.

Fig 3. ΔT₁ and Pre-contrast T₁ in NEL, NAWM, cGM of entire cohort.

Table 3. Correlation of ΔT₁ with WML volume and count.

Table 4. Correlation of per subject ΔT₁ with clinical measures (Part I).

Table 5. Correlation of per subject ΔT₁ with clinical measures (Part II).

Fig 4. Group differences in median ΔT₁ in non-enhancing white matter lesions.

Table 6. Group differences in ΔT₁ metrics.

Table 7. Multiple logistic regression analysis of significance of treatment effect on ΔT₁.