Imaging correlates of visual function in multiple sclerosis.

No single neuroimaging technique or sequence is capable of reflecting the functional deficits manifest in MS. Given the interest in imaging biomarkers for short- to medium-term studies, we aimed to assess which imaging metrics might best represent functional impairment for monitoring in clinical trials. Given the complexity of functional impairment in MS, however, it is useful to isolate a particular functionally relevant pathway to understand the relationship between imaging and neurological function. We therefore analyzed existing data, combining multiparametric MRI and OCT to describe MS associated visual impairment. We assessed baseline data from fifty MS patients enrolled in ReBUILD, a prospective trial assessing the effect of a remyelinating drug (clemastine). Subjects underwent 3T MRI imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI), myelin content quantification, and retinal imaging, using OCT. Visual function was assessed, using low-contrast letter acuity. MRI and OCT data were studied to model visual function in MS, using a partial, least-squares, regression analysis. Measures of neurodegeneration along the entire visual pathway, described most of the observed variance in visual disability, measured by low contrast letter acuity. In those patients with an identified history of ON, however, putative myelin measures also showed correlation with visual performance. In the absence of clinically identifiable inflammatory episodes, residual disability correlates with neurodegeneration, whereas after an identifiable exacerbation, putative measures of myelin content are additionally informative.

Introduction

Functional impairment can take a long time to manifest in multiple sclerosis (MS) [1, 2]. Shorter-term clinical trials are needed to assess therapeutic efficacy over reasonable time scales. Imaging methods have been employed for this purpose [3, 4], but the correlation between imaging metrics and functional impairment remains uncertain and the specific imaging techniques that are best suited for measurement have not yet been defined [5, 6]. This is particularly important for investigations of neuroprotective and reparative therapies.

The visual pathway is anatomically well defined and functionally discrete, with well-validated measures that are both quantifiable and reliable. For these reasons, the visual pathway has long been used as a model for understanding pathological processes (e.g. inflammation, demyelination, and neurodegeneration) that underlie neurological dysfunction in the disease [7, 8].

ReBUILD, a double-blind, placebo-controlled trial, demonstrated the effectiveness of clemastine fumarate in improving visual-evoked, potential latency and visual function [9]. To further explore the relationship between imaging and function, and in particular to overcome the mismatch between conventional MRI and clinical deficit in MS [6], we performed an analysis based on optical coherence tomography (OCT) and advanced MRI assessments from the baseline assessment of the ReBUILD trial cohort [9]. To describe visual impairment in MS, we specifically implemented a multimodal approach, combining OCT and novel MRI techniques capable of estimating both metrics for myelin content, such as myelin water fraction (MWF) [10, 11] and metrics for neurite organization (neurite density (NDI) and orientation dispersion indexes (ODI)) [12].

Methods

Subjects and clinical evaluation

Fifty relapse-remitting (RRMS) patients underwent baseline assessments as part of the ReBUILD trial prior to the initiation of treatment (32 females, average age of 40.1 years ± 10.3 SD, median EDSS 2.0 (range 0 to 5.5), and median disease duration of 3.2 years (range 0.4 to 30.4). Either a clinically evident, optic neuritis that had occurred within 6 months before screening or more than 5 years prior to enrollment in the qualifying eye for the Rebuild study was an exclusion criterion. For the purpose of this study we considered all patients’ eyes for which data was available, including those eyes with ON older than 5 years (i.e. eyes that were not basis for qualification in the study). Visual impairment was measured by low contrast letter acuity (LCLA; Sloan 2.5% low contrast vision chart; Precision Vision, La Salle, IL, USA). The single eye measures were averaged with the fellow eye ones to have a single value for each patient. The study was approved by the UCSF Institutional Review Board and all participants provided informed consent. The trial was registered at ClinicalTrials.gov (number NCT02040298) before initiation of patient enrolment. All research was performed in accordance with relevant guidelines/regulations.

MRI acquisition

Each subject underwent brain MRI acquisitions on a 3T Siemens Skyra scanner. The MRI protocol included standard sagittal T1-weigthed, 3D MPRAGE (1 mm3 cubic voxel), a two-shell, neurite-orientation dispersion and density imaging (NODDI) [12] protocol (30 & 64 directions at b = 700 & 2000 s/mm2, 2.2 mm3 cubic voxel) and a multi-echo gradient-echo (MEGE) sequence for myelin content quantification [10, 11]. Details of the MRI protocol were previously reported [9].

MRI analysis

Automated parcellation of T1-weighted volumes was performed using Freesurfer Image Analysis Suite Version 5.6. Total cortex, primary visual area (V1, Brodman area 17), thalami and cerebellar cortex were parcellated as volumes of interest (VOIs) from T1-weigthed, 3D MPRAGE image (Fig 1) [13-15]. Computed volumes were normalized by intracranial volume. Optic radiation (OR) VOIs were obtained from the JHU DTI-based (MNI space) white-matter atlases [16] and co-registered to the single subject space using linear and nonlinear transformations (FLIRT/FNIRT) [17, 18]. After correcting for distortions due to eddy current and head motion, maps of mean diffusivity (MD) were calculated by fitting the diffusion tensor model within each voxel, using dipy fit tensor [19]. The NODDI model was fitted to the diffusion datasets in MATLAB (http://mig.cs.ucl.ac.uk/Tutorial.NODDImatlab) and maps of ODI were therefore computed [12]. MEGE data were instead processed to obtain quantification of the MWF [10, 11]. MD, ODI and MWF maps were co-registered to T1 space, using linear and nonlinear transformations (FLIRT/FNIRT) [17, 18]. The mean value of each MRI metric was calculated for each VOI (V1, thalami and cerebellar cortex) averaging left and right hemispheres.

Fig 1

MRI analysis methods: Volume of interests.

A: Freesurfer pipeline was used to segment specific gray matter regions belonging to the visual network, specifically thalamus, cerebellar cortex and V1 (primary visual area). Region of interest were identified on each subject. B: probabilistic map of optic radiation from the Juelich histological atlas (on MNI space).

Lesion burden

An expert neuroradiologist (EC) assessed the number of occipital cortical lesions and also segmented the total white-matter-lesion burden within the OR, using the available FLAIR and T1-weigthed 3D MPRAGE images. We confirmed the location of lesions by using occipital lobes masks derived by Freesurfer and JHU DTI-based OR registered in the single subject space.

OCT

Retinal imaging was performed using spectral-domain OCT (Heidelberg Engineering, Heidelberg, Germany, eye explorer software version 1.9.10.0) as previously described [7]. Peripapillary, retinal-nerve-fiber-layer (pRNFL) thickness and macular volume (MV) were obtained. Intra-retinal-layer segmentation was executed to quantify the Ganglion Cell Layer (GCL) through the Viewing Module 6.0. We followed the APOSTEL guidelines for reporting OCT studies (see S1 and S2 Tables) [20].

Statistical analysis

Using a two-tailed t-test, we tested comparisons of demographic characteristics between ON negative and ON positive groups. To describe the variability in MS-associated visual impairment (LCLA), we performed a PLS to model baseline LCLA. using demographic (age at disease onset, and at MRI), MRI (normalized volumes, MD, myelin, ODI) and OCT metrics (GCL, pRNFL, MV), separately considering negative and positive history of ON. The significance of each predictor was established once the “Variable Importance in the Projection” (VIP) parameter was greater than 0.8 [21]. Random-forest approach was also used to test the reliability of the variable selected in the model. K-fold, cross-validation analysis was performed to test the reliability of each identified model by 4-fold cross-validation with 100 repeats between least absolute shrinkage and selection operator (LASSO) and PLS. The predictive accuracy was measured by root-mean-square error (RMSE) on both training data and test data. Model selection, using PLS has the advantage of avoiding co-linearity-related data inflation, although this approach lacks an efficient method for subset selection. LASSO was therefore applied to confirm the importance of the identified variables.

Results

Twenty-eight patients had a prior history of optic neuritis (ON), whereas 22 did not. Demographic and clinical characteristics are reported in Table 1. LCLA scores for the 2 groups (22.9±10.1 and 23.1±9.2) were lower than the LCLA values for healthy controls in literature (45.48±11.22) [22]. There was a difference in age between the ON negative and ON positive groups (p <0.05). MRI and OCT metrics data are reported in S1 Table.

Table 1

Demographic and clinical characteristics of the groups with negative and positive history of optic neuritis (ON).

	ON negative history group	ON positive history group
N#	22	28
Age	43.9 (9.7)	37.1 (9.9)
Sex	14 F (64%)	18 F (64%)
Disease duration (years)	3.5 (0.7 to 14.2)	2.9 (0.45 to 30.4)
Time from ON (years)	/	3.0 (0.2 to 15.8)
EDSS	2.0 ± (0 to 5.5)	2.0 (0 to 4)
LCLA	22.9 (10.1)	23.1 (9.2)
VEP	125.7 (8.6)	129.0 (11.4)

Demographic data, LCLA abd VEP are reported as mean (SD) or n (%). Disease duration, time from ON and EDSS are reported at median (range).

Using partial least squares regression analysis (PLS), we identified model predictors of LCLA with R-squared values up to 0.43 and 0.39 with or without ON respectively (Table 2). GCL proved to be the best OCT-related, partial predictor in particularly in cases of a negative history of ON (Variable Importance in the Projection (VIP) = 1.12 and 0.84 without and with previous ON, respectively) (S1 and S2 Tables). Lesion burden and OCT metrics appeared to be more informative for patients with no history of ON. Measures of cortical neurodegeneration seemed equally informative in both negative and positive history of optic neuritis, contributing to clinical disability. Thalamic volume was more informative in patients without ON. Measures of myelin content (MWF) within the OR and thalamus instead gained importance when we evaluated patients with ON. A random-forest approach confirmed all variables of the two models except for the whole-brain, cortical, gray matter in the ON positive history model.

Table 2

Schematic representations of the LCLA model results in subjects with negative and positive history of optic neuritis.

LCLA MODEL		Variable	VIP	Scale Coefficient
LCLA IN NEGATIVE HISTORY OF ON	R squared 42.8	GCL	1.12	0.2364
		OR lesion volume	1.01	-0.2123
		pRNFL	1.00	0.2105
		Thalamic volumes	0.93	0.1961
		V1 volume	0.92	0.1929
LCLA IN POSITIVE HISTORY OF ON	R squared 39.3	Thalamic MWF	1.19	0.22
		OR MWF	1.11	0.21
		Cortical GM volume	0.96	0.18
		V1 volume	0.86	-0.16
		GCL	0.84	0.16

We reported the R squared of each model. The significance of each variable selected by PLS is reported as “Variable Importance in the Projection” (VIP). The scale coefficient, representative of the effect size for each variable, is also shown. ON = optic neuritis; MWF = myelin water fraction; GCL = Ganglion Cell Layer; OR = optic radiation; pRNFL = peripapillary retinal nerve fiber layer; GM = gray matter.

Model cross-validation analysis

PLS models showed low RMSE variability on both training data and test data, suggesting that they yielded stable predictions. Among the two PLS models, 9 out of 10 predictors selected by PLS also survived in the LASSO models’ subset selection.

Discussion

Evaluating the ReBUILD baseline data set, we identified MRI and OCT metrics-based models that showed a strong correlation with visual performance and demonstrated good reliability. Our results employ a more complete analysis of a single pathway because of the depth of data acquisition, the focus on myelin metrics and the capacity to assess structural injury to the visual system.

These data indicate that in patients with a history of ON, myelin loss is the main contributor to clinical, visual disability in MS. In this group, both MRI markers for putative myelin content (MWF) and neurodegeneration are significantly associated with visual function. By contrast, in patients without prior episodes of optic neuritis, axonal neurodegeneration appears to be responsible for most of the clinical disability, together with white matter lesions within the optic radiations.

There are different potential explanations for these observations. One is that separate processes underlie neurodegeneration and myelin damage; i.e. there is a degenerative process acting in MS that is independent of myelin loss. Alternatively, myelin loss and neuroaxonal loss could be time-disassociated (i.e. neurodegeneration may follow myelin loss by a prolonged period—even years - and therefore functional impairment from an asymptomatic episode in the more distant past may be more likely to be related to neuroaxonal loss). The differences between the models might also be due to damage in the posterior pathway (OR lesions, V1 neurodegeneration), contributing to visual disability particularly in patients without ON.

The strength of our models is their demonstrated robustness. A weakness of our study, however, is that some of the data may have been influenced by the criteria for inclusion in the clinical trial [9]. For example, patients with mild visual disability or dysfunction were likely excluded.

We attempted to address limitations related to sample size and lack of an independent dataset for validation by using a nested cross-validation of the identified models. The size of the cohort also forced us to select a subset of the available MRI metrics before performing our analysis to minimize the risk of redundancy (e.g. using diffusion MRI ODI versus fractional anisotropy). This means that other similar metrics may perform similarly or better in future cohorts evaluating the same outcomes. Our failure to identify GM lesion burden as a meaningful factor might also be due to the study design that did not include dedicated cortical MRI sequences [23] and therefore resulted in poor sensitivity in detecting cortical lesions [24]. In order to be applied in observational and clinical studies, the obtained predictive models need to be replicated in larger populations.

In conclusion, we have demonstrated that combinations of advanced multiparametric MRI sequences within the visual network and OCT correlate with visual performance, adding to our understanding of the underlying pathological mechanisms responsible for clinical dysfunction. These measures should be candidates for observational and clinical studies in the visual pathways in MS, as our field shifts its focus on regenerative medicine.

Schematic representations of the LCLA model results looking at the entire cohort.

We reported the R squared. The significance of each variable selected by PLS is reported as “Variable Importance in the Projection” (VIP). The scale coefficient, representative of the effect size for each variable, is also shown. ON = optic neuritis; MWF = myelin water fraction; GCL = Ganglion Cell Layer; OR = optic radiation; pRNFL = peripapillary retinal nerve fiber layer; GM = gray matter.

(DOCX)

Click here for additional data file.

OCT and MRI metrics values are reported for subject with negative and positive history of ON.

Data are mean (SD). Volumes are reported as (mm3). MD 10−3 mm2/s.

(DOCX)

Click here for additional data file.

(XLSX)

Click here for additional data file.

7 Apr 2020

PONE-D-20-00559

Imaging correlates of visual function in Multiple Sclerosis

PLOS ONE

Dear Dr. Green,

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First, I would like to thank you for your patience during the first round of revisions; it's was very much appreciated.

While there are a number of issues to address from both Reviewers, most of them are relatively minor; both suggested that only a minor revision is required. Regarding the point raised by Reviewer 1 and the reporting of the OCT data with respect to the APOSTEL guidelines: I would prefer that this is not dropped, but rather the full information is reported as per these guidelines.

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Reviewer #1: In this original research article entitled “Imaging correlates of visual function in Multiple Sclerosis” by Caverzasi et al, a comprehensive structural analysis of the afferent visual pathway was performed, in order to identify the measures that best reflect visual disability in patients with MS. The inclusion of multiple imaging measures, including retinal OCT metrics (pRNFL and GCL) as well as MRI measures of myelin- and neuroaxonal loss along the visual pathway (in thalamus, optic radiations, V1 cortex) is the most important advantage of this study. Moreover, low-contrast letter acuity was used as a more sensitive measure than high-contrast LA, to reflect visual disability in MS.

Patients with previous (recent, i.e. between 5 years and 6 months prior to baseline) ON and patients without clinical ON episodes were included in the study. These two groups were surprisingly were very similar regarding their visual disability.

The results suggested that several measures along the visual pathway (not only retinal OCT-metrics) are associated with visual dysfunction both in patients with- and without previous ON. While in patients with previous ON measures reflecting myelin loss along the visual pathway (in thalamus, and in OR) were most informative reg. visual dysfunction, in patients without previous ON measures of neuroaxonal loss (in the retina and in thalamus and V1) as well as OR lesions seemed most relevant for visual dysfunction.

I find these results very interesting and the article appropriate to be published in the journal. However, I have some questions/suggestions for the authors:

Abstract:

-Please define NODDI

- “Measures of neurodegeneration described most of the observed variance in visual disability”. I would propose to also mention that these neurodegenerative measures were along the entire visual pathway (from retina to V1), if the restriction in word count allows.

- I would suggest to change the sentence: “Absent clinically identifiable inflammatory episodes, residual disability correlates with neurodegeneration…” to : “In absence of clinically identifiable….”

Methods

- Page 5: The average (SD) EDSS is given; I would suggest to use the median (range) instead, due to the non-normal distribution of this ordinal measure.

- It would be helpful to have the descriptive statistics (demographic characteristics of patients, ON/no ON, time since ON, EDSS etc.) summarized in a table, if possible. Moreover, it should be briefly mentioned if all patients had RRMS or if also patients with progressive MS were included in this study.

- Page 5: “….was an exclusion criteria for this study.”: Please correct to: “ was an exclusion criterion for this study”

- Please briefly explain the rationale of excluding patients with ON more than 5 years ago

- Page 6: “An expert neuroradiologist (EC) assessed the number of occipital, cortical lesions and also segmented the total, white-matter-lesion burden within the optic radiation, using the available FLAIR and T1-weigthed 3D MPRAGE images». Could you please briefly explain how this was done? Were the occipital lobe and the OR localized by merely the “eyes” of the rater or you used a combination of the FLAIR-lesion masks and the V1-Free-surfer segmentation/the atlas-based OR VOI to confirm that lesions were indeed part of the V1/the OR? If the first was the case, I would briefly mention this “manual” character of the analysis as a minor limitation in the discussion.

OCT:

- Please briefly explain why GCL was used instead of GCIPL (since the latter is used more widely, due to data suggesting that the segmentation of the GCIPL is more reproducible than of the GCL alone).

- Was the GCL averaged between both eyes for the models? (also for ON+ patients?)

- It is stated, that: “We followed the APOSTEL guidelines for reporting OCT studies”. However, no details reg. the OCT protocol, excluded scans etc. are provided in the paper. I would either delete this sentence, or provide all the details according to “APOSTEL” as supplementary material.

- Due to the large amount of metrics used in the study, many of which do not appear in the results, if they were not predictive enough in the models, I believe that a Table summarizing all the metrics used (from retina to V1) and their mean values at baseline, would be helpful for the reader (perhaps as supplementary material?). Alternatively, I would at least suggest to specifically name the metrics that later appear in the results (such as “thalamic myelin” etc.) also in the Methods (e.g. In the sentence: “two-shell,neurite-orientation dispersion and density imaging (NODDI) [12] protocol (30 & 64 directions at b = 700 & 2000 s/mm2, 2.2 mm3 cubic voxel) and a multi-echo gradient-echo (MEGE) sequence “for myelin content quantification…” you could add which metrics derived from this method (e.g.: “ to assess the average thalamic myelin” etc.)

Results

In general, since especially in patients without ON many measures might reflect global CNS neurodegeneration rather than damage restricted in the visual pathway, it may be useful to include (i.e. to correct for) normalised brain volume in the models.

- Page 7: Similarly to my comment above reg. the EDSS, I would assume that EDSS and disease duration are probably not normally distributed, thus better the median (range) instead of mean (SD) should be provided.

- “Subjects with no history of ON showed an average low contrast letter acuity (LCLA) score of 23 ± 10 SD. Subjects with a history of ON scored an average of 23 letters ± 9 SD on LCLA testing”.:

It is surprising for the reader, that patients with ON do not have worse LCLA than patients without ON. You did not mention in “Methods” weather LCLA was measured mono- or binocularly, but I suppose (seeing these results) the latter was the case. Please clarify this in “Methods”.

Moreover, it should be mentioned in “Methods” or “Results” if ON was unilateral in all cases. If this was indeed the case, wouldn’t it be preferable to perform the LCLA ON+ model only for the affected eye (if of course data from mono-ocular LCLA testing are available)?

In such a model the LCLA of the affected eye would be the DV, similarly the OCT parameters only from the affected eye would be included in this model, while the other measures (OR, thalamus, V1) would remain as they are.

I would expect that in such a model unilateral GCL thickness would be a better predictor as in the current ON+ model (currently surprising for me that GCL is a better predictor of visual acuity in the ON- vs. the ON+ model).

Page 8: “Measures of neurodegeneration (e.g. volumes of gray matter structures) seemed equally informative in both negative and positive history of optic neuritis, contributing to clinical disability.”. In table 1, thalamic volumes appear only to be important for patients with negative ON history. Moreover, V1 Volume is mentioned once as “V1 GM volume” (in the ON- model) and once “V1 VOIs volumes” (in the ON+ model)--> is this the same parameter? If yes, I would reformulate the above sentence, e.g.:

“The volume of cortical gray matter structures (particularly V1) seemed equally informative in both negative and positive history of optic neuritis, while thalamic volume was more informative in patients without ON.”

However, it should be noted, that the scale coefficient for “V1 VOIs volumes” in the ON+ model is negative, which –to my understanding- would mean that the thicker the V1 cortex the worse the vision. Please confirm and/or comment.

Were the DTI-measures of the OR (mean MD) less informative than the OR-Myelin in the ON+ model?

Discussion

In general, I believe that in such an analysis, using several measures of the visual pathway, which are not independent from each other (e.g. the association of thalamic- and OR-myelin with LCLA in the ON+ model might well be due to an anterograde transsynaptic degeneration after ON thus these measures may be well correlated with GCL thickness in these patients), the interpretation of the results should be made with more caution. I propose to briefly comment on this in the discussion.

- As also mentioned above I find it surprising that GCL is less informative than e.g. thalamic myelin in patients with positive ON history, since several previous papers showed GCIPL to be the best-correlated measure with LCLA, visual quality of life etc, in patients with MS-ON. I believe that the authors should comment on this (and the possible reasons) in the discussion (see also my comment above reg. monocular vs. binocular LCLA testing).

- It is mentioned that: “neurodegeneration may follow myelin loss by a prolonged period - even years- and therefore functional impairment from an episode in the more distant past may be more likely to be related to neuroaxonal loss).”

If this would explain the results of the current paper, it would also imply that patients with negative ON history in this study may have had an ON episode in the more distant past. Can this be the case? (I understood from Methods and exclusion criteria that patients with ON never had ON). Alternatively, please re-formulate this sentence.

- Having said this, I believe that another explanation for the differences between the ON- and ON+ models might be that damage in the posterior pathway (OR lesions, V1 neurodegeneration) may contribute to visual disability particularly in patients without ON (OR lesion volume remained only in the ON- model, V1 Volume is more informative in the ON- model). Please briefly discuss. Moreover, the sentence: “By contrast, in patients without prior episodes of optic neuritis, only axonal neurodegeneration appears to be responsible for clinical disability.”, is not completely true, since also OR lesion volume, which is not directly a neurodegenerative measure seems relevant in these patients.

- As weakness of this study it is mentioned that : “some of the data may have been influenced by the criteria for inclusion in the clinical trial [9]. For example, patients with mild visual disability or dysfunction were likely excluded.” Please also briefly comment on excluded patients that had ON > 5 years ago and on patients with progressive MS (if indeed excluded).

Further, I believe that the lack of healthy controls is a drawback, since it cannot be concluded which metrics were indeed abnormal compared to HC and also the grade of visual disability of the patients cannot be directly appreciated. I propose to mention this in the discussion and also, if possible, to add whether the included patients could be seen as visually impaired (according to indirect comparison of their LCLA results with LCLA values of healthy controls in the literature).

Reviewer #2: The aim of this work was to describe visual impairment in MS, using a multimodal approach, combining quantitative MRI (qMRI) techniques and OCT.

Clinical, qMRI and OCT data comes from the baseline assessments of the ReBUILD study, a double blind, placebo-controlled phase 2 trial that showed the efficacy of Clemastine in improving VEP latencies and visual function, by stimulating remyielination.

The design and methodology, as well as, results of the study are clear and sufficiently exhaustive.

I have just a few minor comments:

Abstract: the sentence "absent clinical..." is not clear and should be rephrased

Methods:

1) how the authors managed possible/expected parcellation errors made by Freesurfer?

2) I'm a little surprised to see that ON+ patients had the same LCLA score as ON- subjects. In order to better characterize the two subgroups of subjects, the authors might also briefly show VEP latencies and OCT results.

3) if permitted by the publisher, I'd like to see a table reporting, for each group of subjects, qMRI and OCT measured metrics that were included in the predictive models.

4) since LCLA scores were substantially equal in the two subgroups of subjects, did the authors look at a LCLA model including the whole population? I think it would be interesting to look also at the results of such an analysis.

Results/Discussion:

I would suggest underpinning in the discussion that in order to be applied in observational and clinical studies, the (obtained) predictive model needs to be replicated in other/larger populations and, possibly, simplified.

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Reviewer #1: No

Reviewer #2: No

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28 May 2020

Please see response to review document

Reviewer #1

In this original research article entitled “Imaging correlates of visual function in Multiple Sclerosis” by Caverzasi et al, a comprehensive structural analysis of the afferent visual pathway was performed, in order to identify the measures that best reflect visual disability in patients with MS. The inclusion of multiple imaging measures, including retinal OCT metrics (pRNFL and GCL) as well as MRI measures of myelin- and neuroaxonal loss along the visual pathway (in thalamus, optic radiations, V1 cortex) is the most important advantage of this study. Moreover, low-contrast letter acuity was used as a more sensitive measure than high-contrast LA, to reflect visual disability in MS.

Patients with previous (recent, i.e. between 5 years and 6 months prior to baseline) ON and patients without clinical ON episodes were included in the study. These two groups were surprisingly very similar regarding their visual disability. The results suggested that several measures along the visual pathway (not only retinal OCT-metrics) are associated with visual dysfunction both in patients with- and without previous ON. While in patients with previous ON measures reflecting myelin loss along the visual pathway (in thalamus, and in OR) were most informative reg. visual dysfunction, in patients without previous ON measures of neuroaxonal loss (in the retina and in thalamus and V1) as well as OR lesions seemed most relevant for visual dysfunction. I find these results very interesting and the article appropriate to be published in the journal. However, I have some questions/suggestions for the authors:

A: We thank the reviewer for his/her positive comments.

Abstract:

-Please define NODDI

- “Measures of neurodegeneration described most of the observed variance in visual disability”. I would propose to also mention that these neurodegenerative measures were along the entire visual pathway (from retina to V1), if the restriction in word count allows.

- I would suggest to change the sentence: “Absent clinically identifiable inflammatory episodes, residual disability correlates with neurodegeneration…” to : “In absence of clinically identifiable….”

A: We modified the abstract based on the excellent suggested edits – including defining NODDI.

Methods

- Page 5: The average (SD) EDSS is given; I would suggest to use the median (range) instead, due to the non-normal distribution of this ordinal measure.

A: We now report the EDSS as median (range)

- It would be helpful to have the descriptive statistics (demographic characteristics of patients, ON/no ON, time since ON, EDSS etc.) summarized in a table, if possible. Moreover, it should be briefly mentioned if all patients had RRMS or if also patients with progressive MS were included in this study.

A: We thank the reviewer for his/her comment. We now mention that all the subject enrolled in the study had RRMS. We also added a table (now Table 1) summarizing the demographics and clinical information for the two groups (negative and positive history of optic neuritis).

- Page 5: “….was an exclusion criteria for this study.”: Please correct to: “ was an exclusion criterion for this study”.

A: We have edited accordantly.

- Please briefly explain the rationale of excluding patients with ON more than 5 years ago

A: We thank the reviewer for this comment. This was an exclusion criterion for the REBUILD trial and we appreciate the need for clarification in the current manuscript. We modified accordingly. For the REBUILD trial, the “qualifying eye” could not have had an ON more than 5 years ago because the purpose of the trial was to induce remyelination using clemastine as a remyelinating treatment. We considered lesions older than 5 years to have less remyelinating potential. However both eyes were examined, even the eyes with ON more than 5 years prior the study and we utilized all data for the current analysis. We now mention this in the Methods (line 48 of the revised manuscript with track changes).

- Page 6: “An expert neuroradiologist (EC) assessed the number of occipital, cortical lesions and also segmented the total, white-matter-lesion burden within the optic radiation, using the available FLAIR and T1-weigthed 3D MPRAGE images». Could you please briefly explain how this was done? Were the occipital lobe and the OR localized by merely the “eyes” of the rater or you used a combination of the FLAIR-lesion masks and the V1-Free-surfer segmentation/the atlas-based OR VOI to confirm that lesions were indeed part of the V1/the OR? If the first was the case, I would briefly mention this “manual” character of the analysis as a minor limitation in the discussion.

A: We thank the reviewer to highlight the missing information. We did use the Freesurfer and JHU atlas based mask to confirm the location the cortical and white matter lesions. We addressed this in the Methods (line 111 of the revised manuscript with track changes).

OCT:

- Please briefly explain why GCL was used instead of GCIPL (since the latter is used more widely, due to data suggesting that the segmentation of the GCIPL is more reproducible than of the GCL alone).

A: Thanks for raising this important point. The rationale for the use of GCIPL is based on the reported failure of intra-retinal layer segmentation of macular OCT when distinguishing the boundary between GCL and IPL. This concept is true for some devices, however with more recent software updates the Heidelberg Spetralis is successful in distinguishing these 2 layers. We recently performed a test-retest study on 20 subjects in a separate project (unpublished data available on request) showing no meaningful differences in terms of coefficient of variance between GCL, IPL and INL. As a consequence, we utilized GCL as layer which reflects cell soma of the RGCs.

- Was the GCL averaged between both eyes for the models? (also for ON+ patients?)

A: This is correct. GCL (as all the other measures) were averaged between both eyes

It is stated, that: “We followed the APOSTEL guidelines for reporting OCT studies”. However, no details reg. the OCT protocol, excluded scans etc. are provided in the paper. I would either delete this sentence, or provide all the details according to “APOSTEL” as supplementary material.

A: We now prepared an “APOSTEL” table included in the supplemental material.

- Due to the large amount of metrics used in the study, many of which do not appear in the results, if they were not predictive enough in the models, I believe that a Table summarizing all the metrics used (from retina to V1) and their mean values at baseline, would be helpful for the reader (perhaps as supplementary material?). Alternatively, I would at least suggest to specifically name the metrics that later appear in the results (such as “thalamic myelin” etc.) also in the Methods (e.g. In the sentence: “two-shell,neurite-orientation dispersion and density imaging (NODDI) [12] protocol (30 & 64 directions at b = 700 & 2000 s/mm2, 2.2 mm3 cubic voxel) and a multi-echo gradient-echo (MEGE) sequence “for myelin content quantification…” you could add which metrics derived from this method (e.g.: “ to assess the average thalamic myelin” etc.)

A: We thank again the reviewer for her/his comment. In addition to conventional MRI metrics (volumes and lesion burden), we decided to focus the analysis on three specific metrics: MD (DTI derived), ODI (NODDI based) and MWF specific for myelin content (MEGE sequence). We now reported this more clearly in section “MRI analysis” of the Methods (line 89 of the revised manuscript with track changes) and we now report more clearly within the entire manuscript that myelin water fraction (MWF) was used as putative myelin metric. In the Supplementary material we now report each metric (OCT or MRI) mean values.

Results

In general, since especially in patients without ON many measures might reflect global CNS neurodegeneration rather than damage restricted in the visual pathway, it may be useful to include (i.e. to correct for) normalised brain volume in the models.

A: The authors agree on this point. All brain volumes were normalized (using total intracranial volume) (line 83 of the revised manuscript with track changes).

- Page 7: Similarly to my comment above reg. the EDSS, I would assume that EDSS and disease duration are probably not normally distributed, thus better the median (range) instead of mean (SD) should be provided.

A: We now report EDSS, disease duration and time from ON as median (range).

- “Subjects with no history of ON showed an average low contrast letter acuity (LCLA) score of 23 ± 10 SD. Subjects with a history of ON scored an average of 23 letters ± 9 SD on LCLA testing”.:

It is surprising for the reader, that patients with ON do not have worse LCLA than patients without ON. You did not mention in “Methods” weather LCLA was measured mono- or binocularly, but I suppose (seeing these results) the latter was the case. Please clarify this in “Methods”.

A: The reason for the similar LCLA results within the two groups is due to the inclusion criteria of the Rebuild trial. To be included the patients needed to have a visual deficit (i.e. VEP p100 > 118 ms in at least one eye). The reported LCLA data were measured monocularly and averaged for the model analysis. We now clarified this in Methods (line 52 of the revised manuscript with track changes).

Moreover, it should be mentioned in “Methods” or “Results” if ON was unilateral in all cases. If this was indeed the case, wouldn’t it be preferable to perform the LCLA ON+ model only for the affected eye (if of course data from mono-ocular LCLA testing are available)?

In such a model the LCLA of the affected eye would be the DV, similarly the OCT parameters only from the affected eye would be included in this model, while the other measures (OR, thalamus, V1) would remain as they are.

I would expect that in such a model unilateral GCL thickness would be a better predictor as in the current ON+ model (currently surprising for me that GCL is a better predictor of visual acuity in the ON- vs. the ON+ model).

A: This is an interesting point. We agree with the reviewer about the expected results. We didn’t perform this analysis because our cohort contained patients with bilateral ON.

Page 8: “Measures of neurodegeneration (e.g. volumes of gray matter structures) seemed equally informative in both negative and positive history of optic neuritis, contributing to clinical disability.”. In table 1, thalamic volumes appear only to be important for patients with negative ON history. Moreover, V1 Volume is mentioned once as “V1 GM volume” (in the ON- model) and once “V1 VOIs volumes” (in the ON+ model)--> is this the same parameter? If yes, I would reformulate the above sentence, e.g.:

“The volume of cortical gray matter structures (particularly V1) seemed equally informative in both negative and positive history of optic neuritis, while thalamic volume was more informative in patients without ON.”

A: We modified the sentence following the reviewer’s comment: “Measures of cortical neurodegeneration seemed equally informative in both negative and positive history of optic neuritis, contributing to clinical disability. Thalamic volume was more informative in patients without ON” (line 158 of the revised manuscript with track changes). We now consistently refer to V1 volume both in the text as well as in tables.

However, it should be noted, that the scale coefficient for “V1 VOIs volumes” in the ON+ model is negative, which –to my understanding- would mean that the thicker the V1 cortex the worse the vision. Please confirm and/or comment.

A: We thank the reviewer for his comment. The V1 volume scale coefficient showed opposite direction (positive in ON- history and negative in ON+ history). This “predictor” of the LCLA models survived also the cross-validation (survived also in LASSO, cross validation is a method to test model RMSE stability). This apparent inconsistency can be related to the nature of the PLS analysis and how the model is constructed. We have to consider the predictor as part of the model rather than one single variable predicting LCLA. It could represent a correction within the model for the observed contribution of the other variables to LCLA, specifically the model in ON+ history was overestimating the V1 contribution to the model.

Were the DTI-measures of the OR (mean MD) less informative than the OR-Myelin in the ON+ model?

A: Absolutely. DTI derived MD was not selected in either models and in particular in the ON+ one, whereas thalamic and OR myelin derived measure (MWF) were the most informative.

Discussion

In general, I believe that in such an analysis, using several measures of the visual pathway, which are not independent from each other (e.g. the association of thalamic- and OR-myelin with LCLA in the ON+ model might well be due to an anterograde transsynaptic degeneration after ON thus these measures may be well correlated with GCL thickness in these patients), the interpretation of the results should be made with more caution. I propose to briefly comment on this in the discussion.

- As also mentioned above I find it surprising that GCL is less informative than e.g. thalamic myelin in patients with positive ON history, since several previous papers showed GCIPL to be the best-correlated measure with LCLA, visual quality of life etc, in patients with MS-ON. I believe that the authors should comment on this (and the possible reasons) in the discussion (see also my comment above reg. monocular vs. binocular LCLA testing).

A: We thank the reviewer for his comment. PLS analysis should limit collinearity across the studied features. Moreover a cross-validation analysis (LASSO) was also performed to test the validity of the identify “predictors” of LCLA in both models and 9 out of 10 (as reported in the manuscripts) predictors were confirmed. Regarding the contribution of GCL we now added in the supplementary material the model that includes the entire cohort. Across models GCL is the most informative. It is true that there appears to be less “informative” power in the ON+ history compared to MWF measurements. As previously stated, we need to look at these predictors within the model and as part of the model. It may be that part of the informative contribution of GCL was “addressed” already by other metrics (e.g. cortical GM and V1 volumes).

- It is mentioned that: “neurodegeneration may follow myelin loss by a prolonged period - even years- and therefore functional impairment from an episode in the more distant past may be more likely to be related to neuroaxonal loss).”

If this would explain the results of the current paper, it would also imply that patients with negative ON history in this study may have had an ON episode in the more distant past. Can this be the case? (I understood from Methods and exclusion criteria that patients with ON never had ON). Alternatively, please re-formulate this sentence.

A: The reviewer understood correctly. Patients without ON never had clinically evident ONs. But the inclusion criteria for the REBUILD clinical trial were designed to ensure that patients had demyelinating injury in the visual pathway (i.e. VEP P100 latency in at least one eye of 118 ms). For this reason, these patients likely presented paucisymptomatic inflammatory episodes within the visual pathway. We changed the sentence in the Discussion (line 195 of the revised manuscript with track changes)..

- Having said this, I believe that another explanation for the differences between the ON- and ON+ models might be that damage in the posterior pathway (OR lesions, V1 neurodegeneration) may contribute to visual disability particularly in patients without ON (OR lesion volume remained only in the ON- model, V1 Volume is more informative in the ON- model). Please briefly discuss. Moreover, the sentence: “By contrast, in patients without prior episodes of optic neuritis, only axonal neurodegeneration appears to be responsible for clinical disability.”, is not completely true, since also OR lesion volume, which is not directly a neurodegenerative measure seems relevant in these patients.

A: Thanks for your comment. We modified the sentence in the Discussion (line 178 of the revised manuscript with track changes).

- As weakness of this study it is mentioned that : “some of the data may have been influenced by the criteria for inclusion in the clinical trial [9]. For example, patients with mild visual disability or dysfunction were likely excluded.” Please also briefly comment on excluded patients that had ON > 5 years ago and on patients with progressive MS (if indeed excluded).

A: The authors believe that having clarified the sentence about the exclusion criteria this cannot be considered a limitation of the work. We added the disease course of the patients included in the trial in methods.

Further, I believe that the lack of healthy controls is a drawback, since it cannot be concluded which metrics were indeed abnormal compared to HC and also the grade of visual disability of the patients cannot be directly appreciated. I propose to mention this in the discussion and also, if possible, to add whether the included patients could be seen as visually impaired (according to indirect comparison of their LCLA results with LCLA values of healthy controls in the literature).

A: The authors agree on this point. Unfortunately, given the study design, no healthy controls were analyzed for this study. In this recent publication, “Temporal visual resolution and disease severity in MS” by Noah Ayadi, et al. (Neurol Neuroimmunol Neuroinflamm 2018), the average Sloan 2.5% score for 62 healthy control eyes was 45.48±11.22. We added this paper in the bibliography.

Reviewer #2

The aim of this work was to describe visual impairment in MS, using a multimodal approach, combining quantitative MRI (qMRI) techniques and OCT.

Clinical, qMRI and OCT data comes from the baseline assessments of the ReBUILD study, a double blind, placebo-controlled phase 2 trial that showed the efficacy of Clemastine in improving VEP latencies and visual function, by stimulating remyielination.

The design and methodology, as well as, results of the study are clear and sufficiently exhaustive.

A: We thank the reviewer for his/her positive comments.

I have just a few minor comments:

Abstract: the sentence "absent clinical..." is not clear and should be rephrased

A: We addressed this in the abstract.

Methods:

1) how the authors managed possible/expected parcellation errors made by Freesurfer?

A: Freesurfer pipeline segmentation and parcellation was visually checked by an expert neuroradiologist.

2) I'm a little surprised to see that ON+ patients had the same LCLA score as ON- subjects. In order to better characterize the two subgroups of subjects, the authors might also briefly show VEP latencies and OCT results.

A: We now report VEP and OCT results of the two subgroups in the Supplementary material.

3) if permitted by the publisher, I'd like to see a table reporting, for each group of subjects, qMRI and OCT measured metrics that were included in the predictive models.

A: We now added this data in Supplementary material.

4) since LCLA scores were substantially equal in the two subgroups of subjects, did the authors look at a LCLA model including the whole population? I think it would be interesting to look also at the results of such an analysis.

A: We now looked at the entire population and reported in the Supplementary material.

Results/Discussion:

I would suggest underpinning in the discussion that in order to be applied in observational and clinical studies, the (obtained) predictive model needs to be replicated in other/larger populations and, possibly, simplified.

A: We now included this comment in the discussion (line 209 of the revised manuscript with track changes).

Submitted filename: Response to Reviewers_V1.3 Final .docx

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19 Jun 2020

Imaging correlates of visual function in Multiple Sclerosis

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22 Jul 2020

PONE-D-20-00559R1

Imaging correlates of visual function in Multiple Sclerosis

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