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. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: Neurobiol Aging. 2020 Feb 8;90:84–92. doi: 10.1016/j.neurobiolaging.2020.02.002

Long-standing multiple sclerosis neurodegeneration: volumetric MRI comparison to Parkinson’s disease, mild cognitive impairment and Alzheimer’s disease and elderly healthy controls

Dejan Jakimovski 1, Niels Bergsland 1,2, Michael G Dwyer 1, Jesper Hagemeier 1, Deepa P Ramasamy 1, Kinga Szigeti 3, Thomas Guttuso 3, David Lichter 3, David Hojnacki 3,4, Bianca Weinstock-Guttman 3,4, Ralph HB Benedict 3,4, Robert Zivadinov 1,3,4,5
PMCID: PMC7166193  NIHMSID: NIHMS1568898  PMID: 32147244

Abstract

Recent studies show that multiple sclerosis (MS) patients exhibit significant neurodegeneration and brain atrophy-driven disability progression. No direct volumetric comparison between MS and other neurodegenerative diseases has been investigated. We compared the extent of neurodegeneration between long-standing MS, n=112, Parkinson’s disease (PD, n=37), amnestic mild cognitive impairment (aMCI, n=34), Alzheimer’s disease (AD, n=37) and healthy controls (HCs, n=184). On 3T MRI, global and regional brain volumes including whole brain (WBV), white matter (WMV), gray matter (GMV), cortical (CV), deep gray matter (DGM) and nuclei- specific volumes of thalamus, caudate, putamen, globus pallidus, and hippocampus were derived with SIENAX and FIRST software. Analysis of covariance (ANCOVA) adjusted for age- and sex-differences compared MRI-derived volumes. Post-hoc analyses utilizing a subset of age- and sex-matched subjects were conducted. WBV was not significantly different between diseases. MS had significantly lower WMV compared to other disease groups (p<0.021). Only AD patients had smaller GMV and CV when compared to MS patients (both p<0.001). The MS patients had smaller DGM volume than PD and aMCI (<0.001 and p=0.026, respectively). Lastly, the MS patients had lower thalamic volume when compared to all other neurodegenerative diseases (p<0.008). Long-standing MS patients exhibit comparable global atrophy patterns as seen in other classical neurodegenerative diseases. MS patients have lower WMV and thalamic volume when compared to other neurodegenerative diseases.

Keywords: neurodegeneration, multiple sclerosis, whole brain atrophy, Alzheimer’s disease, Parkinson’s disease, MRI, deep gray matter, thalamus

Introduction

Multiple sclerosis (MS) is a chronic neuroinflammatory disease and leading culprit for neurological disability in young and working population. Over the past 20 years, studies highlighted an underlying neurodegenerative processes and their strong association with physical disability and cognitive performance. (Jakimovski et al., 2018; Vaughn et al., 2019) Simultaneously, the success of MS disease-modifying treatments has resulted in significantly longer lifespans, improved long-term disability outcomes, and decreased mortality rates. (Vaughn et al., 2019) These changes lead to significant shift in the age-specific prevalence of MS worldwide. (Marrie et al., 2010) For example, in the region of Manitoba, the peak MS prevalence changed from persons aged 35 to 39 in 1984 to aged 55 to 60 only 20 years later. (Marrie et al., 2010) Further and greater changes towards an aging MS population (highest MS prevalence peaking at 60 to 69 years old) are also reported. (Grytten et al., 2016) Therefore, the current and evolving epidemiological MS profile opens previously uncommon age-related inquiries regarding potential comorbid Alzheimer’s disease (AD), cognitive aging, and differential symptom etiology. (Vaughn et al., 2019)

Multiple MS post-mortem studies show extensive axonal and neuronal loss, with as much as 40% cortical neuronal body loss and corresponding low cortical volumes. (Carassiti et al., 2017) Furthermore, the changes in cortical volume exhibit minimal associations with presence of inflammatory lesions, oligodendrocyte density, and cortical myelin. (Popescu et al., 2015) Comparable to other neurodegenerative disease, MS patients also demonstrate cortical atrophy patterns involving the bilateral temporal lobe and entorhinal cortex. (Steenwijk et al., 2016) Furthermore, MS patients also demonstrate a pattern of atrophy within the bilateral posterior cingulate, isthmus cingulate and anterior portion of the parahippocampal cortex which are also recognized as vulnerable AD brain regions. (Steenwijk et al., 2016) As such, use of volumetric, quantitative MRI can play an important role in monitoring of MS neurodegeneration. (Zivadinov et al., 2016)

Outside of the MS field, multiple MRI-based studies have determined specific spatial atrophy patterns that can differentiate neurodegenerative diseases from healthy elderly individuals, differentiate distinct neurodegenerative subtypes, or distinguish between cognitively impaired vs. non-impaired disease presentations. (Mak et al., 2015) For example, both progressive supranuclear palsy and multiple system atrophy demonstrate up to 3 times greater annualized brain atrophy when compared to Parkinson’s disease (PD). (Guevara et al., 2017) Furthermore, both greater whole brain atrophy and ventricular enlargement are proxy indicators of cognitive decline in PD patients, (Mak et al., 2017) and cortical/cerebellar pathology is associated with tremor severity. (Piccinin et al., 2017) In similar fashion, ventricular enlargement can be a convenient differentiating measure between Alzheimer’s disease (AD), amnestic mild cognitive impairment (aMCI) and healthy elderly individuals. (Nestor et al., 2008) Lastly, independent from cardiovascular diseases and cancer, a greater brain atrophy rate is associated with lower survival rate in elderly and non-demented population. (Olesen et al., 2011) Given the overlapping epidemiological profiles, MRI-derived brain atrophy assessment may assist in cases of potential ambivalent and isolated presentation of cognitive decline in elderly MS samples.

There are no previous studies that directly assess the magnitude of MS brain atrophy when compared to other classical neurodegenerative disorders. Based on this background, we aimed at determining the extent of neurodegeneration within elderly MS patients and comparing it with healthy controls (HCs), PD, aMCI, and AD patients.

Materials and Methods

Study populations:

Patients from three MRI studies performed in a single tertiary neurology center were included in this analysis. The inclusion criteria for this sub-analysis included 1) being diagnosed as MS, PD, aMCI or AD, or satisfying the criteria for being a HC, 2) age of 45 years old or older, and 3) presence of high-resolution 3D T1 MRI scan performed with standardized protocol. Contrarily, the exclusion criteria included 1) active clinical relapse, 2) use of intravenous corticosteroids 30 days before scanning, and 3) presence of additional concurrent or history of previous major neurological disorder. In particular, the aMCI diagnosis was derived based on the Petersen criteria. (Petersen et al., 1999) Similarly, the AD patients were diagnosed based on NINCDS-ADRDA criteria. (Blacker et al., 1994) Both groups were reviewed and confirmed after consensus review within the University at Buffalo – Alzheimer’s disease and Memory Disorders Center. The PD patients were evaluated by a neurologist specializing in movement disorders and all patients satisfied the UK Brain Bank Criteria for diagnosis. (Gibb and Lees, 1988) Lastly, MS patients were diagnosed based on the 2010 revision of the McDonald criteria and the disease course was categorized based on the 2013 Lublin criteria. (Lublin et al., 2014; Polman et al., 2011) The study flow chart for patient selection is shown in Figure 1. All participants provided written informed consent. The University at Buffalo Institutional Review Board (IRB) approved each specific study protocol and approval was obtained to combine the various studies for this analysis in order to study the aging effect on brain volumes in various neurological diseases.

Figure 1. Study flow chart.

Figure 1.

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MS – multiple sclerosis, PD – Parkinson’s disease, aMCI – amnestic mild cognitive impairment, AD – Alzheimer’s disease, HCs – healthy controls. [1] – post hoc analysis of 29 triples of age- and sex-matched MS, PD and HCs. [2] – post hoc analysis of 12 age and sex-matched subjects from each HC, MS, PD, aMCI and AD groups.

MRI acquisition and analysis:

All study participants underwent 3T MRI with the same Signa Excite 12 Twin-Speed scanner (General Electric, Milwaukee, WI, USA). No major hardware nor software changes occurred during the recruitment period. Detailed MRI parameters for 3D spoiled-gradient recalled (SPGR) high-resolution T1-weighted imaging and Fluid Attenuated Inversion Recovery (FLAIR) sequence are shown in the MRI Supplement Material. T2 lesion volumes (LV) were derived with semi-automated contouring/thresholding technique using Java Image Manipulation (JIM; www.xinapse.com, Xinapse Systems Ltd, Essex, UK). Global and regional brain volumes including whole brain (WBV), white matter (WMV), gray matter (GMV), cortical (CV), deep gray matter (DGM) and nuclei-specific volumes of thalamus, caudate, putamen, globus pallidus, and hippocampus were derived with single time-point Structural Image Evaluation, using Normalisation of Atrophy (SIENAX) and FMRIB’S Integrated Registration and Segmentation Tool (FIRST) software (https://fsl.fmrib.ox.ac.uk/fsl/fsl, FMRIB, Oxford, UK). Illustrated in Figure 2, is the FIRST-derived volume of the left and right DGM nuclei that were summed for the purpose of analyses. 3D T1-weigted images were lesion filled prior to segmentation to minimize the impact of T1 hypointensities on the analysis. All volumes were normalized with SIENAX scaling factor. In addition to volumetric comparisons, we calculated WMV to WBV and GMV to WBV ratios for each disease group.

Figure 2. Examples of SIENAX and FIRST-derived regions for MRI analysis.

Figure 2.

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SIENAX – Structural Image Evaluation, using Normalisation of Atrophy , FIRST - FMRIB’S Integrated Registration and Segmentation Tool, GM – gray matter, WM – white matter,

Statistical analysis:

All statistical analyses were performed with SPSS version 25.0 (IBM, Armonk, NY, USA). Demographic differences were derived with χ2 test for categorical and Student’s t-test for numerical variables. Analysis of covariance (ANCOVA) adjusted for age- and sex-differences compared the MRI-derived volumes. Post-hoc pair-wise comparison with Bonferroni correction compared estimated marginal means and p-values lower than 0.05 were considered statistically significant. Bar plots were used to visualize ANCOVA-based results (GraphPad Prism 8, La Jolla, CA, USA). Given the potential limitations of modeling brain volumes in a linear fashion, two secondary post-hoc analyses utilized one-way ANOVA to compare raw brain volumes in constructed age- and sex-matched groups. The first analysis utilized 29 triplets of age and sex-matched MS, PD, and HCs subjects, whereas the second post hoc analysis utilized 12 matched subject from each of the examined diseases (MS, PD, aMCI, AD, and HCs). Cohen’s d was used for determining the effect size. To assess whether MRI-derived volumes could separate the study groups, a multi-nominal logistic regression model was built where age and sex were used as force entry terms, and the main effects of tissue volumes as step-wise terms.

Results

The demographic and clinical characteristics of the study populations are shown in Table 1. In total, 184 HCs, 112 MS, 37 PD, 34 aMCI, and 37 AD subjects were considered for analysis. There were no differences in sex ratio nor in age between the HCs and MS patients (73.4% vs. 73.2% female χ2 p=1.000; and 59.5 vs. 60.3 years old, Student’s t-test p=0.339, respectively). When compared to MS patients, the PD group was significantly older (66.0 vs. 60.3 years old, Student’s t-test p<0.001), had lower percentage of females (43.2% vs. 73.2% χ2 p=0.001), and had shorter disease duration (9.7 vs. 24.8 years, Student’s t-test p<0.001). Due to the mean disease duration of 24.8 years and compared to multiple literature references, we hereafter refer the MS group as patients with long-standing MS. (Patani et al., 2007; Steenwijk et al., 2014) In terms of disease subtype, the MS group was composed of 59 (52.7%) relapsing-remitting MS (RRMS), 48 (42.9%) secondary progressive MS (SPMS) and 5 (4.5%) primary-progressive MS (PPMS) patients. As expected, both aMCI (74.9 years old) and AD (75.8 years old) patients were significantly older than the HCs, MS, and PD patients (all p<0.001). Lastly, both HC and MS groups had a greater proportion of females when compared to the aMCI group (χ2 p=0.035).

Table 1.

Study population demographic and estimated marginal means for MRI-derived brain volumes.

Demographic
characteristics		 HCs
(n=184)		 MS
(n=112)		 PD
(n=37)		 aMCI
(n=34)		 AD
(n=37)		 MS vs.
HCsa 		MS vs.
PDa 		MS vs.
aMCIa 		MS vs.
ADa
Female, n (%) 135 (73.4) 82 (73.2) 16 (43.2) 18 (52.9) 21 (56.8) 1.000 0.001 0.035 0.068
Age, mean, (SD) 59.5 (8.6) 60.3 (6.5) 66.0 (8.9) 74.9 (9.9) 75.8 (8.9) 0.339 <0.001 <0.001 <0.001
DDY, mean (SD) - 24.8 (9.9) 9.7 (5.8) - - - <0.001 - -
EDSS, median (IQR) - 3.5 (2.3-6.0) - - - - - - -
UPDRS, median (IQR)* - - 3.3 (0.0-10.0) - - - - - -
Estimated marginal means for global and regional MRI-derived brain volumes Bonferroni-adjusted p-values
WBV 1487.9 (5.9) 1407.1 (7.3) 1432.0 (12.7) 1432.1 (14.2) 1423.9 (13.8) <0.001 0.946 1.000 1.000
WMV 748.8 (3.9) 703.3 (4.9) 734.6 (8.6) 741.6 (9.6) 758.9 (9.4) <0.001 0.021 0.006 <0.001
GMV 731.9 (3.4) 703.8 (4.2) 697.5 (7.3) 690.5 (8.2) 664.9 (7.9) <0.001 1.000 1.000 <0.001
CV 595.1 (2.9) 573.8 (3.6) 563.9 (6.2) 554.8 (2.9) 531.7 (6.7) <0.001 1.000 0.181 <0.001
LVV 43.5 (1.5) 60.4 (1.9) 53.6 (3.3) 44.9 (3.7) 52.8 (3.6) <0.001 0.816 0.004 0.709
DGM 58.4 (0.6) 51.8 (0.5) 56.6 (0.9) 55.1 (0.9) 52.5 (0.9) <0.001 <0.001 0.026 1.000
Thalamus 19.6 (0.1) 17.0 (0.2) 19.4 (0.3) 19.1 (0.3) 18.3 (0.3) <0.001 <0.001 <0.001 0.008
Caudate 8.6 (0.07) 7.7 (0.1) 8.1 (0.2) 8.2 (0.2) 7.7 (0.2) <0.001 0.41 0.167 1.000
Putamen 12.2 (0.1) 11.1 (0.1) 11.7 (0.2) 11.5 (0.2) 10.8 (0.2) <0.001 0.126 1.000 1.000
Globus pallidus 4.5 (0.05) 3.9 (0.07) 4.5 (0.1) 4.1 (0.1) 3.9 (0.1) <0.001 <0.001 1.000 1.000
Hippocampus 9.3 (0.1) 8.3 (0.1) 8.8 (0.2) 8.0 (0.2) 7.6 (0.2) <0.001 0.475 1.000 0.034
WMV vs WBV ratio 0.506 (0.002) 0.5 (0.002) 0.513 (0.003) 0.519 (0.004) 0.534 (0.004) 0.197 0.007 <0.001 <0.001
GMV vs. WBV ratio 0.494 (0.002) 0.5 (0.002) 0.487 (0.003) 0.481 (0.004) 0.466 (0.004) 0.197 0.007 <0.001 <0.001
CV vs. WBV ratio 0.402 (0.001) 0.408 (0.002) 0.394 (0.003) 0.386 (0.004) 0.373 (0.003) 0.118 0.002 <0.001 <0.001
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HCs – healthy controls, MS – multiple sclerosis, PD – Parkinson’s disease, aMCI – amnestic mild cognitive impairment, AD – Alzheimer’s disease, DDY – disease duration in years, WBV – whole brain volume, WMV – white matter volume, GMV – gray matter volume, CV – cortical volume, LVV – lateral ventricular volume, DGM – deep gray matter.

All MRI-derived global and region-specific brain volumes are shown in milliliters (mL) and with estimated marginal mean (standard error).

*

Bradykinesia score composed of UPDRS questions 23, 24, 25, 26, and 31.

Age, sex, and disease duration was compared with Student’s t-test and χ2. Age and sex adjusted analysis of covariance (ANCOVA) is also. The covariates appearing in the model are evaluated as age of 63.1 years old and 67% female.

a

Pair-wise comparisons are calculated with Bonferroni-based post-hoc age- and sex-adjusted estimated marginal means. P-values after multiple comparison adjustment lower than 0.05 are considered statistically significant and shown in bold.

The estimated marginal means for age- and sex-adjusted MRI-derived global and regional brain volumes are shown in Table 1, whereas the raw brain volumes are shown in Table 2. After adjustment for the demographic differences and multiple comparison correction, MS patients had significantly lower estimated volumes compared to the HCs in each measured global and regional structure (all p<0.001). When compared to the remaining diseases, the MS patients had the lowest estimated WMV (703.3mL vs. 734.6mL vs. 741.6mL vs. 758.9mL for MS, PD, aMCI and AD, respectively and p<0.021) and lower estimated thalamus volume (17mL vs. 19.4mL vs. 19.1mL vs. 18.3mL, for MS, PD, aMCI and AD, respectively and p<0.008). Only AD patients had smaller estimated GMV, CV, and hippocampus volume when compared to MS patients (664.9mL vs. 703.8mL, p<0.001; 531.7mL vs. 573.8mL p<0.001; and 7.6mL vs. 8.3mL, p=0.034, respectively). MS patients did not differ in estimated WBV when compared to the other neurodegenerative diseases. Lastly, MS patients had lower estimated total DGM volume when compared to PD (51.8mL vs. 56.6mL, p<0.001) and aMCI patients (51.8mL vs. 55.1mL, p=0.026), but similar to AD. Differences in their estimated marginal means between all study groups for WBV, GMV, WMV, DGM, thalamus, and hippocampus are shown in Figure 3.

Table 2.

Absolute global and regional MRI-derived brain volumes.

Absolute MRI-
derived volumes		 HCs
(n=184)		 MS
(n=112)		 PD
(n=37)		 aMCI
(n=34)		 AD
(n=37)
T2-LV 0.1 (0.0-1.2) 11.8 (4.1-27.7) 0.9 (0.2-2.0) 3.4 (1.1-5.8) 3.8 (0.6-12.5)
WBV 1498.7 (85.6) 1420.9 (85.8) 1417.4 (83.9) 1373.7 (86.9) 1361.1 (90.4)
WMV 755.3 (58.4) 708.2 (46.8) 730.3 (44.4) 720.1 (47.5) 735.6 (63.1)
GMV 743.4 (48.5) 712.7 (62.9) 687.2 (52.1) 653.6 (58.2) 625.5 (44.2)
CV 604.9 (41.9) 581.5 (45.1) 554.5 (44.7) 528.8 (47.5) 497.8 (33.9)
LVV 39.6 (16.6) 57.4 (26.4) 57.4 (22.4) 57.6 (25.3) 66.2 (23.7)
DGM 59.3 (5.1) 52.5 (6.5) 55.7 (4.1) 52.1 (6.1) 49.3 (4.5)
Thalamus 19.9 (1.9) 17.3 (2.3) 19.1 (1.4) 18.0 (2.2) 17.2 (1.3)
Caudate 8.7 (1.1) 7.8 (1.2) 7.9 (0.6) 7.8 (0.9) 7.3 (0.7)
Putamen 12.4 (1.4) 11.2 (1.5) 11.6 (1.1) 10.9 (1.5) 10.2 (1.2)
Globus pallidus 4.6 (0.7) 3.9 (0.8) 4.5 (0.5) 3.9 (0.5) 3.8 (0.6)
Hippocampus 9.4 (1.1) 8.5 (1.2) 8.5 (1.2) 7.5 (1.4) 7.0 (1.3)
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HCs – healthy controls, MS – multiple sclerosis, PD – Parkinson’s disease, aMCI – amnestic mild cognitive impairment, AD – Alzheimer’s disease, LV – lesion volume, WBV – whole brain volume, WMV – white matter volume, GMV – gray matter volume, CV – cortical volume, LVV – lateral ventricular volume, DGM – deep gray matter.

T2-LV are shown as median (interquartile range). All brain volumes are shown as mean (standard deviation). All measures are shown in milliliters (mL).

Figure 3. Bar plot demonstrating differences in estimated marginal means.

Figure 3.

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HCs – healthy controls, MS – multiple sclerosis, PD – Parkinson’s disease, aMCI – amnestic mild cognitive impairment, AD – Alzheimer’s disease WBV – whole brain volume, GM – gray matter volume, WMV – white matter volume, DGM – deep gray matter.

The models evaluated differences based on estimated correction to mean age at 63.7 and 67% male population.

A – MS patients have comparable WBV with PD, AD and aMCI; B – MS patients have significantly lower WBV when compared to all other study groups; C – AD patients have significantly lower GMV when compared to MS patients; D – MS patients have comparable DGM volumes to AD and significantly lower when compared to PD, aMCI and HCs; E – MS patients have significantly lower thalamus volumes when compared to all other disease states; F – Only AD patients have smaller hippocampus when compared to MS patients.

In terms of regional brain ratios, MS patients had the lowest WBV to WBV ratio when compared to the other neurodegenerative diseases (50% vs. 51.3% for PD, p=0.007; 50% vs. 51.9% for aMCI, <0.001; and 50% vs. 53.4% for AD, p<0.001). Contrarily, greater cortical neurodegeneration in AD, aMCI and PD was showcased as significantly lower CV to WBV ratios when compared to MS patients (37.3% vs. 40.8%, p<0001. 38.6% vs. 40.8%, p<0.001, and 39.4% vs. 40.8%, p=0.002, respectively). There were no significant differences in regional brain ratios between MS patients and HCs.

After accounting for age and sex differences, the multi-nominal logistic regression model included and ordered thalamus volume (p<0.001), CV (p<0.001), hippocampus volume (p=0.003), LVV (p=0.003) and globus pallidus volume (p=0.004) as the most important differentiators between the four neurodegenerative diseases. The model was able to correctly predict 92.9% of all MS cases (Nagelkerke pseudo R2=0.759).

A direct comparison between 29 age- and sex-matched MS, PD and HC subjects is shown in Table 3. The estimated differences seen in Table 1 were corroborated by the post hoc analysis. When compared to HCs, MS patients had significantly lower global and regional brain volumes for all structures except for a statistical trend in CV (p=0.078). When compared to PD, the MS patients had lower WMV (703.2mL vs. 739.5mL, d=0.9, p=0.001), total DGM volume (51.9mL vs. 56.5mL, d= 0.86, p=0.002), and thalamus volume (16.9mL vs. 19.4mL, d=1.46, p<0.001). Furthermore, MS patients had lower putamen (11.0mL vs. 11.8mL, d=0.69, p=0.017) and globus pallidus (4.0mL vs. 4.5mL, d=0.59, p=0.04) volumes. When compared to HCs, PD patients had significantly lower WBV (1429.8mL vs. 1476.6mL, d=0.55, p=0.041), lower GMV (690.3mL vs. 732.0mL, d=0.87, p=0.002), lower CV (555.5mL vs. 593.6mL, d=0.93, p=0.001), lower caudate volume (8.1mL vs. 8.6mL, d=5.3, p=0.045) and lower hippocampus volume (8.6mL vs. 9.4mL, d=0.78, p=0.012).

Table 3.

Post hoc analysis of age and sex-matched MS, PD, and HCs.

Absolute MRI-
derived volumes		 HCs
(n=29)		 MS
(n=29)		 PD
(n=29)		 MS vs.
HCsa 		MS vs.
PDa 		PD vs.
HCsa
Female, n (%) 15 (51.7) 15 (51.7) 15 (51.7) 1.000 1.000 1.00
Age, mean (SD) 64.2 (8.0) 63.8 (7.2) 63.9 (8.5) 0.838 0.947 0.9
WBV 1476.6 (85.2) 1407.2 (81.7) 1429.8 (85.7) 0.002 0.308 0.041
WMV 744.6 (53.4) 703.2 (40.4) 739.5 (40.1) 0.002 0.001 0.683
GMV 732.0 (41.1) 703.9 (55.9) 690.3 (54.3) 0.034 0.35 0.002
CV 593.6 (33.5) 573.6 (49.2) 555.5 (47.4) 0.078 0.16 0.001
LVV 43.6 (18.6) 66.9 (32.1) 55.7 (24.2) 0.001 0.138 0.037
DGM 58.6 (4.9) 51.9 (6.3) 56.5 (4.2) <0.001 0.002 0.09
Thalamus 19.5 (1.9) 16.9 (1.9) 19.4 (1.5) <0.001 <0.001 0.727
Caudate 8.6 (1.2) 7.8 (0.9) 8.1 (0.6) 0.008 0.238 0.045
Putamen 12.2 (1.6) 11.0 (1.3) 11.8 (1.0) 0.005 0.017 0.324
Globus pallidus 4.6 (0.1) 4.0 (1.1) 4.5 (0.5) 0.039 0.04 0.603
Hippocampus 9.4 (0.8) 8.3 (1.3) 8.6 (1.2) <0.001 0.296 0.012
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HCs – healthy controls, MS – multiple sclerosis, PD – Parkinson’s disease, WBV – whole brain volume, WMV – white matter volume, GMV – gray matter volume, CV – cortical volume, LVV – lateral ventricular volume, DGM – deep gray matter.

a

Age and sex are compared with Student’s t-test and χ2. One-way ANOVA with pairwise post-hoc comparison is used to compare normalized brain volumes.

All brain volumes are shown as mean (standard deviation). All measures are shown in milliliters (mL). P-value lower than 0.05 was considered statistically significant and shown in bold.

Lastly, normalized brain volume comparisons between the smaller subset of 12 age- and sex-matched from each MS, PD, aMCI, AD and HCs groups are shown in Table 4. MS patients demonstrated lower total DGM volumes when compared to HCs (51.4mL vs. 56.2mL, d=0.94, p=0.031) and aMCI patients (51.4mL vs. 55.2mL, d=0.94, p=0.032). Furthermore, the MS patients had lower thalamus volume when compared to the HCs (16.9mL vs. 18.7mL, d=0.97. p=0.028), PD (16.9mL vs. 18.3mL, d=0.94, p=0.038), and aMCI patients (16.9mL vs. 19.0mL, d=1.27, p=0.005). On the other hand, both PD and AD patients had smaller GMV and CV when compared to the MS patients (d=0.99, p=0.024 and d=1.38, p=0.022 for PD; and d=1.0, p=0.003 and d=1.48, p=0.001 for AD, respectively). As previously shown, AD patients also had smaller hippocampus volume when compared to MS patients (7.1mL vs. 8.0mL, d=0.75, p=0.033).

Table 4.

Post hoc analysis of age and sex-matched MS, PD, aMCI, AD, and HCs.

Absolute MRI-
derived volumes		 HCs
(n=12)		 MS
(n=12)		 PD
(n=12)		 aMCI
(n=12)		 AD
(n=12)		 MS vs.
HCsa 		MS vs.
PDa 		MS vs.
aMCIa 		MS vs.
ADa
Female, n (%) 7 (58.3) 7 (58.3) 7 (58.3) 7 (58.3) 7 (58.3) 1.000 1.000 1.000 1.000
Age, mean (SD) 70.6 (5.1) 69.4 (3.9) 70.5 (5.0) 70.5 (5.0) 70.8 (5.6) 0.534 0.562 0.562 0.508
WBV 1425.4 (91.2) 1397.0 (59.4) 1361.0 (45.9) 1393.4 (57.8) 1382.1 (45.9) 0.377 0.111 0.882 0.639
WMV 715.6 (44.9) 705.3 (40.2) 712.0 (25.1) 727.6 (37.9) 749.4 (66.5) 0.557 0.626 0.176 0.062
GMV 709.7 (48.3) 691.8 (50.3) 649.0 (34.3) 665.9 (28.2) 632.7 (34.0) 0.382 0.024 0.134 0.003
CV 574.5 (35.3) 559.3 (45.4) 519.7 (32.4) 535.8 (23.4) 504.0 (26.9) 0.373 0.022 0.124 0.001
LVV 51.3 (22.2) 66.5 (28.8) 71.3 (27.5) 48.0 (22.4) 61.1 (24.1) 0.162 0.681 0.093 0.625
DGM 56.2 (5.4) 51.4 (4.8) 54.1 (3.7) 55.2 (3.1) 49.0 (3.1) 0.031 0.142 0.032 0.165
Thalamus 18.7 (1.9) 16.9 (1.8) 18.3 (1.1) 19.0 (1.5) 17.2 (1.1) 0.028 0.038 0.005 0.738
Caudate 8.5 (1.6) 7.8 (0.7) 7.8 (0.4) 7.9 (0.5) 7.3 (0.6) 0.22 0.713 0.667 0.086
Putamen 11.8 (1.9) 10.6 (1.2) 11.5 (1.1) 11.8 (1.0) 9.8 (1.2) 0.103 0.076 0.023 0.112
Globus pallidus 4.2 (0.4) 3.9 (0.7) 4.4 (0.7) 4.2 (0.3) 3.9 (0.6) 0.201 0.11 0.184 0.91
Hippocampus 9.0 (0.9) 8.0 (1.2) 8.3 (0.9) 7.9 (0.9) 7.1 (1.2) 0.088 0.923 0.521 0.033
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HCs – healthy controls, MS – multiple sclerosis, PD – Parkinson’s disease, aMCI – amnestic mild cognitive impairment, AD – Alzheimer’s disease, WBV – whole brain volume, WMV – white matter volume, GMV – gray matter volume, CV – cortical volume, LVV – lateral ventricular volume, DGM – deep gray matter.

a

Age and sex ratio is compared with Student’s t-test and χ2. One-way ANOVA with pairwise post-hoc comparison is used to compare normalized brain volumes.

All brain volumes are shown as mean (standard deviation). All measures are shown in milliliters (mL). P-value lower than 0.05 was considered statistically significant and shown in bold.

Discussion

This cross-sectional study demonstrates substantial MS neurodegeneration comparable to the classic neurodegenerative diseases. These findings were mainly driven by significantly lower WMV and lower DGM/thalamus volumes in MS compared to PD, aMCI, and AD.

As expected, the biggest differentiator unique to MS was the extent of WMV pathology. These differences can be attributed to known effects of inflammatory WM lesions causing axonal dissection, which leads to retrograde and antegrade/Wallerian neurodegeneration. (Louapre et al., 2017) The loss and thinning of the axonal bundles contribute to shrinking WMV and downstream atrophy of the connected GM structures. (Louapre et al., 2017) Therefore, long-standing MS disease would eventually lead to brain volumes that resemble a neurodegenerative phenotype. Another explanation for the WMV differences is highlighted by a recent study that outlined a potential link between greater central brain atrophy and the disappearance of some T2 lesions. (Dwyer et al., 2018) Specifically, a subset of periventricular MS lesions undergo neurodegenerative changes over time, ultimately resulting in being subsumed into the cerebrospinal fluid (CSF) space. (Dwyer et al., 2018) Furthermore, the amount of such atrophied LV is able to predict both disability progression and conversion into progressive MS phenotype. (Genovese et al., 2019) These findings present as highly probable explanation for previously reported discrepant trajectories of plateauing total lesion accumulation and greater brain atrophy that occurs in aging MS patients with long-standing disease. (Zivadinov et al., 2019) Conversely to the WMV differences, AD patients had an expected lower CV when compared to the remaining disease states. That being said, a combination of lower CV in AD patients and lower WMV in MS patients leads to a lack of significant differences in WBV between the disease states. These findings were further corroborated by our regional brain ratio analysis. The WBV of MS patients was equally proportions of WMV and GMV, whereas patients from the other neurodegenerative diseases showcased a greater amount of WMV relative to the WBV. Longitudinal studies derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database have consistently shown significant brain and cortex-specific atrophy as a differentiating factor between AD, aMCI and aging HCs. (Leung et al., 2013)

The second differentiator was thalamus volume, a major brain hub and relay station of intersecting tracts. As such, DGM atrophy, and more specifically, thalamus atrophy has been repeatedly shown as an independent neurodegenerative biomarker that can predict disability progression in various stages of MS. (Azevedo et al., 2018) Moreover, aging MS patients exhibit significant interaction between increasing age and accelerated expansion of LVV, a proxy measure of central brain atrophy. (Ghione et al., 2019) Furthermore, thalamus atrophy is consistently greater in MS patients when compared to age-matched HCs, a process which is present throughout the entire course of the disease. (Azevedo et al., 2018) The significantly lower thalamus volume in MS patients when compared to other neurodegenerative diseases comes as a surprise given that both aMCI, early sporadic AD, and familial AD patients display presymptomatic thalamus and caudate pathology/volume loss. (Pedro et al., 2012) As such, early thalamus and caudate changes can be detected approximately 5 years before the expected age of onset in patients that are carriers of AD autosomal dominant mutations. (Ryan et al., 2013) Older histopathological studies also suggest that certain thalamus nuclei within the anterior nuclear group are more susceptible to AD pathology when compared to others. (Xuereb et al., 1991) On the contrary, the same study showed that centromedian nucleus did not present neurofibrillary tangles and the pathology was equally seen within the HCs and PD patients alike. (Xuereb et al., 1991) These findings highlight the role of the anterior thalamus nuclei, a main component of the Papez circuit, and its association with processes of memory encoding and recall, the major cognitive impairment classically seen in AD patients. On the other hand, the inflammatory diseases like MS or neuromyelitis optica spectrum disorder (NMOSD) commonly affect the optic tract or spinothalamus tract and contribute to greater pathology within the lateral geniculate nucleus and ventral posterior nucleus, respectively. (Lin et al., 2018; Papadopoulou et al., 2019) Based on differential neuronal afferents that are affected, future thalamus subdivision and thalamic shape analysis may help with in vivo classification of thalamus nuclei pathology and discrepant cognitive presentations. Lastly, the greater MS pathology in areas bordering the CSF space may be attributed to CSF-mediated neurotoxicity and diffusion of toxic compounds that contribute to mitochondrial failure and neuronal death. (Wentling et al., 2019) Differentially, recent findings indicate more of a network-based conceptualization of aMCI and AD disease, with prion-like spread of the tau pathology. (Mito et al., 2018)

Contrarily to total DGM and thalamus volumes, AD patients present with smaller hippocampal volume when compared to MS and PD patients. Albeit significant, the effect size of these findings was not considerably large. These findings corroborate previous histopathological studies that demonstrate extensive demyelination of the MS hippocampus with no considerable neuronal loss when compared to control specimens. (Dutta et al., 2011) Hippocampal demyelination was instead associated with significant synaptic loss. (Dutta et al., 2011) Furthermore, several MRI studies have also examined the extent of hippocampal atrophy in MS patients. (Rocca et al., 2018) The lack of global hippocampal atrophy may be explained by a selective regional hippocampal vulnerability which specifically affects only the cornu ammonis (CA) 1 region. (Sicotte et al., 2008) This significant atrophy of CA1 has been suggested as a function of pathology spread through connections with dentate gyrus. (Planche et al., 2018) Lastly, the greater absolute size of the MS hippocampi may be further explained by findings of dentate gyrus enlargement which were initially associated with better cognitive performance in inflammatory active MS patients. (Rocca et al., 2015) In summary, attempts at imaging specific hippocampal subfields may further aid at understanding the potential differentiation between memory acquisition, storage, and retrieval discrepancies seen between MS and AD cognitive phenotypes. (Lafosse et al., 2013)

It is important to note that outside of total DGM and thalamic volumes, the 12 age- and sex-matched MS patients, did not exhibit significantly lower global brain volumes when compared to HCs. These findings can be potentially explained by two factors. Firstly, although the MS patients had numerically consistent lower brain volumes, the sample size may have not been powered enough to determine statistical significance. However, a recent and more powered study showed lack of WBV differences between MS patients and HCs within the 60-69 years old and 70-79 years old sub-analyses. (Ghione et al., 2019) Despite the lack of aging WBV group differences, the MS patients continue to retain significantly greater LVV when compared to HCs. (Ghione et al., 2019) Lastly, that work also demonstrated a significant aging interaction effect on the longitudinal expansion of LVV between MS patients and HCs, a finding that was not replicated for longitudinal changes of WBV. (Ghione et al., 2019)

Due to the retrospective nature of the study and inherit epidemiological differences between the disease states, we were not able to fully age-match the aforementioned five study populations. Therefore, AD and aMCI patients were significantly older when compared to the remaining study groups. Despite limitations regarding use of linear estimated means, the findings were further corroborated in smaller but age- and sex-matched post hoc analyses. Future comprehensive cortical characterization in tandem with neuropsychological assessment can potentially provide a better understanding of disease-specific atrophying regions and the resulting cognitive impairments. (Jakimovski et al., 2019) Since large part of our MS patients were diagnosed and treated during period when only first-line disease modifying treatments were available, half of our MS population was already classified as SPMS at the time of enrollment. Such subtype distribution is similar to earlier natural history studies which show that approximately 50% of MS patients will transition to SPMS over 20 years of disease duration. (Tremlett et al., 2008) In contrast, natural history studies of the newer treatment era show significant decrease in SPMS transition with only 10-15% after the same period. (University of California et al., 2016) Although the progressive phase may further accelerate the brain atrophy rate seen our MS patients, multiple studies have shown that the whole brain atrophy is largely independent of the disease subtype. (De Stefano et al., 2010; Vollmer et al., 2015) Another limitation is the known lack of precision from FIRST analysis. For example, newer methods utilizing quantitative susceptibility mapping may aid in more reliably segmenting DGM structures. The same technique can additionally provide a potential sub-thalamic segmentation. Lastly, a longitudinal design would allow more robust inferences regarding brain atrophy rates and allow comparison between the different neurodegenerative disease states.

In conclusion, long-standing MS patients exhibit global brain volumes comparable to commonly considered neurodegenerative diseases like AD and aMCI. Significantly lower WMV and thalamus volumes specifically characterize MS patients.

Supplementary Material

1

Highlights:

  • No direct volumetric comparison between multiple sclerosis (MS) and other neurodegenerative diseases has been investigated

  • MS had significantly lower white matter volume compared to other disease groups

  • Alzheimer’s disease patients had smaller gray matter and cortical volume when compared to MS patients

  • MS patients had lower thalamus volume when compared to all other neurodegenerative diseases

Disclosures:

Research reported in this publication was funded by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under award Number UL1TR001412. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

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Financial Relationships/Potential Conflicts of Interest:

Dejan Jakimovski, Niels Bergsland, Michael G. Dwyer, Jesper Hagemeier, Deepa P. Ramasamy and Kinga Sizgeti have nothing to disclose.

Thomas Guttuso, Jr. received personal compensation from Teva Pharmaceuticals for speaking and consulting services and from Duchesnay, Inc. for consulting services.

David Lichter received personal compensation for speaking from Teva Neuroscience and UCB Pharma.

Bianca Weinstock- Guttman received honoraria as a speaker and as a consultant for Biogen Idec, EMD Serono, Genentech, Novartis and Mallinckrodt. Dr. Weinstock-Guttman received research funds from Biogen Idec, Genentech, EMD Serono, and Novartis.

Ralph RH. Benedict received personal compensation from Neurocog Trials, Genentech, Roche, Takeda, Abbvie, Novartis, Sanofi and EMD Serono for speaking and consultant fees. He received financial support for research activities from Genzyme, Biogen, Mallinckrodt.

Robert Zivadinov received personal compensation from EMD Serono, Genzyme-Sanofi, Celgene and Novartis for speaking and consultant fees. He received financial support for research activities from Genzyme-Sanofi, Novartis, Celgene, Mapi Pharma and Protembis.

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