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Background: Efficient personalized therapy paradigms are needed to modify the disease course and halt grey (GM) and white matter (WM) damage in patients with multiple sclerosis (MS). Presently, promising disease-modifying drugs show impressive efficiency, however, tailored markers of therapy responses are required. Here, we aimed to detect in a real-world setting patients with a more favorable brain network response and immune cell dynamics upon dimethyl fumarate (DMF) treatment. Methods: In a cohort of 78 MS patients we identified two thoroughly matched groups, based on age, disease duration, disability status and lesion volume, receiving DMF (n = 42) and NAT (n = 36) and followed them over 16 months. The rate of cortical atrophy and deep GM volumes were quantified. GM and WM network responses were characterized by brain modularization as a marker of regional and global structural alterations. In the DMF group, lymphocyte subsets were analyzed by flow cytometry and related to clinical and MRI parameters. Results: Sixty percent (25 patients) of the DMF and 36% (13 patients) of the NAT group had disease activity during the study period. The rate of cortical atrophy was higher in the DMF group (-2.4%) compared to NAT (-2.1%, p < 0.05) group. GM and WM network dynamics presented increased modularization in both groups. When dividing the DMF-treated cohort into patients free of disease activity (n = 17, DMFR) and patients with disease activity (n = 25, DMFNR) these groups differed significantly in CD8+ cell depletion counts (DMFR: 197.7 ± 97.1/µl; DMFNR: 298.4 ± 190.6/µl, p = 0.03) and also in cortical atrophy (DMFR: -1.7%; DMFNR: -3.2%, p = 0.01). DMFR presented reduced longitudinal GM and WM modularization and less atrophy as markers of preserved structural global network integrity in comparison to DMFNR and even NAT patients. Conclusions: NAT treatment contributes to a reduced rate of cortical atrophy compared to DMF therapy. However, patients under DMF treatment with a stronger CD8+ T cell depletion present a more favorable response in terms of cortical integrity and GM and WM network responses. Our findings may serve as basis for the development of personalized treatment paradigms.
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ORIGINAL RESEARCH
published: 30 July 2019
doi: 10.3389/fimmu.2019.01779
Frontiers in Immunology | www.frontiersin.org 1July 2019 | Volume 10 | Article 1779
Edited by:
Robert Weissert,
University of Regensburg, Germany
Reviewed by:
Pamela Ann McCombe,
University of Queensland, Australia
Zsolt Illes,
University of
Southern Denmark, Denmark
*Correspondence:
Sergiu Groppa
segroppa@uni-mainz.de
These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Multiple Sclerosis and
Neuroimmunology,
a section of the journal
Frontiers in Immunology
Received: 08 January 2019
Accepted: 15 July 2019
Published: 30 July 2019
Citation:
Ciolac D, Luessi F,
Gonzalez-Escamilla G, Koirala N,
Riedel C, Fleischer V, Bittner S,
Krämer J, Meuth SG, Muthuraman M
and Groppa S (2019) Selective Brain
Network and Cellular Responses
Upon Dimethyl Fumarate
Immunomodulation in Multiple
Sclerosis. Front. Immunol. 10:1779.
doi: 10.3389/fimmu.2019.01779
Selective Brain Network and Cellular
Responses Upon Dimethyl Fumarate
Immunomodulation in Multiple
Sclerosis
Dumitru Ciolac 1,2,3 , Felix Luessi 1, Gabriel Gonzalez-Escamilla 1, Nabin Koirala 1,
Christian Riedel 4, Vinzenz Fleischer 1, Stefan Bittner 1, Julia Krämer 5, Sven G. Meuth 5,
Muthuraman Muthuraman 1† and Sergiu Groppa 1
*
1Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2),
University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany, 2Department of Neurology, Institute
of Emergency Medicine, Chisinau, Moldova, 3Laboratory of Neurobiology and Medical Genetics, Nicolae Testemi¸tanu State
University of Medicine and Pharmacy, Chisinau, Moldova, 4Department of Neuroradiology, University of Kiel, Kiel, Germany,
5Department of Neurology With Institute of Translational Neurology, University of Münster, Münster, Germany
Background: Efficient personalized therapy paradigms are needed to modify the
disease course and halt gray (GM) and white matter (WM) damage in patients with
multiple sclerosis (MS). Presently, promising disease-modifying drugs show impressive
efficiency, however, tailored markers of therapy responses are required. Here, we aimed
to detect in a real-world setting patients with a more favorable brain network response
and immune cell dynamics upon dimethyl fumarate (DMF) treatment.
Methods: In a cohort of 78 MS patients we identified two thoroughly matched groups,
based on age, disease duration, disability status and lesion volume, receiving DMF
(n=42) and NAT (n=36) and followed them over 16 months. The rate of cortical
atrophy and deep GM volumes were quantified. GM and WM network responses were
characterized by brain modularization as a marker of regional and global structural
alterations. In the DMF group, lymphocyte subsets were analyzed by flow cytometry
and related to clinical and MRI parameters.
Results: Sixty percent (25 patients) of the DMF and 36% (13 patients) of the NAT group
had disease activity during the study period. The rate of cortical atrophy was higher
in the DMF group (2.4%) compared to NAT (2.1%, p<0.05) group. GM and WM
network dynamics presented increased modularization in both groups. When dividing
the DMF-treated cohort into patients free of disease activity (n=17, DMFR) and patients
with disease activity (n=25, DMFNR) these groups differed significantly in CD8+cell
depletion counts (DMFR: 197.7 ±97.1/µl; DMFNR: 298.4 ±190.6/µl, p=0.03) and also
in cortical atrophy (DMFR:1.7%; DMFNR:3.2%, p=0.01). DMFRpresented reduced
longitudinal GM and WM modularization and less atrophy as markers of preserved
structural global network integrity in comparison to DMFNR and even NAT patients.
Ciolac et al. Brain Network Response to Immunomodulation
Conclusions: NAT treatment contributes to a reduced rate of cortical atrophy compared
to DMF therapy. However, patients under DMF treatment with a stronger CD8+T cell
depletion present a more favorable response in terms of cortical integrity and GM and WM
network responses. Our findings may serve as basis for the development of personalized
treatment paradigms.
Keywords: multiple sclerosis, structural integrity, gray matter networks, white matter networks, immunocellular
response, personalized therapy
INTRODUCTION
Loss of structural integrity driven by inflammation,
demyelination, and degeneration in multiple sclerosis
(MS) involves white matter (WM) and gray matter (GM)
compartments, the latter playing a key role in disability and
disease progression (14). Existing data suggest that CD4+
and CD8+T cells contribute to the damage of the cortical GM
(5,6). Evaluation of magnetic resonance imaging (MRI)-derived
parameters of GM structural alterations was incorporated
into studies to track the responses to disease-modifying drugs
(DMDs) (7) which have been proven to slow, to various extents,
the rate of GM tissue loss. We have recently shown that advanced
measures of brain network architecture closely mirror the disease
course and clinical impairment (8,9). Only an exact longitudinal
quantification of local and global GM and WM tissue properties
enables the development of precise disease course models,
thereby creating the basis for personalized therapeutic decisions.
Presently, for relapsing-remitting MS (RRMS) several
promising DMDs are available but the long-term benefits of
the therapeutic algorithms are still unclear. No unambiguous
personalized solutions to halt or ideally reverse the disease course
exist; however, first avenues for very efficient immunomodulatory
remedies arise. At the same time, markers predicting the
favorable response to a specific DMD are under meticulous
development but are not yet validated for clinical pathways.
Mainly, MRI parameters are regarded as surrogate measures of
treatment response to DMDs, although cerebrospinal fluid (CSF)
or peripheral blood immune response may also be a valuable
biomarker (10). In this respect, treatment response to dimethyl
fumarate (DMF) was reflected by reduced counts of CD4+
and CD8+T cells in patients without disease activity (1113).
Clinically and radiologically stable patients under DMF therapy
showed a more pronounced CD8+than CD4+T cell reduction
compared to active patients (11,13).
Here, we have postulated that the clinical and brain structural
response is tightly linked to the immune cell dynamics
under DMF treatment that could be reliably monitored by
analyzing peripheral blood lymphocyte subsets. Thus, we aimed
to recognize patients with no disease activity and structural
deterioration as mirrored by both cortical and subcortical
integrity and brain network changes and relate these to
immune cell dynamics. A second cohort of patients treated
with natalizumab (NAT) served as a reference group to
compare clinical, structural and network responses. To this end,
we computed regional rates of cortical atrophy, constructed
structural GM (from cortical thickness) and WM [from
probabilistic tractography (PT)] networks and correlated the
atrophy rates with longitudinal changes in lymphocyte subsets.
MATERIALS AND METHODS
Subjects
In this longitudinal study, 78 patients (mean age ±standard
deviation (SD) 32.7 ±8.7 years; 28 males; mean disease duration
of 51.1 ±37.8 months) were selected out of 1,156 patients
recruited at the Department of Neurology at the University
Medical Center of the Johannes Gutenberg University Mainz
in Germany according to the following inclusion criteria: (1)
patients aged 18–60 years, (2) patients diagnosed with RRMS, (3)
starting DMF or NAT treatment, (4) scanned with a standardized
MRI protocol (14), (5) serially acquired MRI scans at several time
points, (6) no corticosteroid use within 30 days prior to MRI,
(7) peripheral blood samples available at baseline and follow-
up time points for DMF-treated patients. Exclusion criteria:
(1) necessity in treatment escalation; (2) participation in any
interventional trial during the study period; (3) serious adverse
events requiring premature study termination; (4) patients with
primary or secondary MS progression. Forty-two patients (34.5
±9.0 years; 14 males) were identified on DMF treatment (DMF
group) and 36 patients (30.6 ±8.1 years; 14 males) on NAT
treatment (NAT group). All patients fulfilled the revised 2010
McDonald diagnostic criteria for RRMS (15). Prior to DMF
or NAT therapy patients were exposed to interferon β-1 β,
interferon β-1 αor glatiramer acetate. Patients’ characteristics are
included in Table 1.
Expanded Disability Status Scale (EDSS) score assessment was
obtained at treatment initiation and later at 3-month intervals.
MRI data were acquired at the time (1.9 ±1.7 months) of
treatment (DMF/NAT) onset, 6 months after the treatment and
then on an annual basis. To track more robust structural and
clinical changes, the longest follow-up MRI and EDSS performed
after 16 months (15.8 ±7.2 in the DMF group, 16.1 ±4.3 in
the NAT group) were considered for the analysis. Disease activity
was evaluated during the entire course of the study and was
based on MRI activity—appearance of new/enlarging T2 lesions
or gadolinium-enhancing lesions, and/or on clinical activity—
presence of relapse (new neurological symptom not associated
with fever/infection, lasting at least 24 h) and sustained disability
progression [increase in EDSS by 1.5 points if the baseline
EDSS score was 0, by 1.0 point if the baseline EDSS score
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Ciolac et al. Brain Network Response to Immunomodulation
TABLE 1 | Demographic, clinical, and brain volumetric characteristics of patient groups.
DMF group (n=42) NAT group (n=36)
Baseline Follow-up Baseline Follow-up
Age (years) 34.5 ±9.0 30.6 ±8.1
Gender (male/female) 14/28 14/22
Disease duration (months) 54.2 ±67.8 47.4 ±43.9
Follow-up duration (months) 15.8 ±7.2 16.1 ±4.3
EDSS 1.8 (0–6) 1.7 (0–6) 2.1 (0–6) 2.0 (0–6)
GM volume (mL) 620.0 ±75.4 617.6 ±76.1 632.7 ±73.8 622.4 ±71.1
WM volume (mL) 572.2 ±64.7 566.4 ±46.8 565.9 ±68.6 561.7 ±65.5
TB volume (mL) 1434.9 ±121.4 1427.2 ±111.9 1448.1 ±121.2 1433.3 ±112.3
T2 lesion volume (mL) 11.1 ±7.5 11.6 ±8.0 12.8 ±3.0 12.0 ±2.5
Variables are presented as means ±SD or median (range).
DMF, dimethyl fumarate; NAT, natalizumab; EDSS, expanded disability status scale; GM, gray matter; WM, white matter; TB, total brain.
No significant t-tests (p <0.05) for between or within group comparisons.
was 1.5, and by 0.5 points if the baseline EDSS score was >
5.0; (16)].
The study protocol was approved by institutional ethics
committee and patients gave written informed consent in
accordance with the Declaration of Helsinki.
Flow Cytometry
In the DMF group, absolute lymphocyte counts and lymphocyte
subsets (CD3+, CD4+, CD8+, CD56+, CD19+) were
quantified with flow cytometry. Blood samples were collected
at baseline (at treatment onset) and later repeatedly with a
6-month interval. Samples collected after almost one and a half
years follow-up (15.8 ±7.2 months) were used for the analysis.
Fresh blood samples were drawn into EDTA-containing tubes
and exposed to corresponding monoclonal antibodies (BD
Biosciences) at room temperature. After erythrocyte lysis and
double washing, absolute values of lymphocyte subsets were
counted with TruCount beads (BD Biosciences).
MRI Acquisition
Baseline and follow-up MRI scans were acquired in the
study setting with a 32-channel head coil 3T MRI scanner
(Magnetom Tim Trio, Siemens Healthcare) according to a
standardized protocol (14) comprising sagittal three-dimensional
(3D) T1-weighted magnetization prepared rapid gradient
echo (MP-RAGE), 3D T2-weighted fluid attenuated inversion
recovery (FLAIR) and diffusion tensor imaging (DTI) sequences.
Acquisition parameters of applied sequences were: T1 MP-
RAGE—repetition time (TR) =1,900 ms, echo time (TE) =
2.52 ms, inversion time (TI) =900 ms, echo train length (ETL)
=1, flip angle (FA) =9, matrix size =256 ×256, field of view
(FOV) =256 ×256 mm2, slice thickness (ST) =1 mm; T2-
FLAIR – TR =5000 ms, TE =388 ms, TI =1800 ms, ETL =
848, matrix size =256 ×256, FOV =256 ×256 mm2, ST =
1 mm; DTI – single-shot echo-planar readout, TR =9000 ms, TE
=102 ms, 30 gradients directions with b=900 s/mm2and one
no diffusion image with b=0 s/mm2, matrix size =128 ×128,
FOV =256 ×256 mm, 62 slices, in-plane resolution =2×2
mm2, ST =2.5 mm.
MRI Processing
Sequential study pipeline is shown in Figure 1.
Cortical Thickness: Longitudinal Analysis
Sagittal T1-weighted images were processed using FreeSurfer
(version 5.3.0, http://surfer.nmr.mgh.harvard.edu/) (17) for
cortical surface reconstruction and volumetric segmentation. The
longitudinal pipeline is based on creation of an unbiased within-
subject template space and image, using robust inverse consistent
registration. Initialization of processing steps—skull stripping,
Talairach transformations, atlas registration, and parcellation—
runs on the common information from the within-subject
template (1820). Cortical thickness (in mm) was quantified at
each vertex of the tessellated surface as the average of the shortest
distance between the GM-WM and the GM–CSF interface. All
cortical surfaces and subcortical segmentations were manually
checked for errors prior to the group analysis. To avoid lesion-
induced tissue misclassification errors, gray matter segmentation
was performed after filling of T1 hypointense lesions.
Longitudinal changes in cortical thickness between baseline
and follow-up were assessed by computing the percent change:
thickness at baseline MRI scan was subtracted from the thickness
at follow-up MRI scan and divided by the scan interval (in years)
and by the average thickness. The resulting surface maps were
smoothed with a full width at half maximum (FWHM) Gaussian
kernel of 10 mm.
DTI Analysis
Diffusion tensor computation and tractography analysis was
performed using inbuilt functionality in FSL (ver. 5.0.8,http://
www.fmrib.ox.ac.uk/fsl); details of this analysis can be found
elsewhere (21,22). In brief, the acquired diffusion data were
corrected for subjects’ head motion artifacts and eddy current
distortions, and subjected to skull and other non-brain tissues
removal. Thus, the pre-processed data were then used for
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Ciolac et al. Brain Network Response to Immunomodulation
FIGURE 1 | Data analysis pipeline. (A) The processed diffusion tensor images (DTI) were used for the derivation of probabilistic tractography. The number of
streamlines from each region of interest (ROI, according to the Automated Anatomical Labeling (AAL) atlas) to other ROIs was calculated and a connectivity matrix for
(Continued)
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Ciolac et al. Brain Network Response to Immunomodulation
FIGURE 1 | each subject was constructed. Subsequently, these white matter derived connectivity matrices were subjected to graph theoretical analysis and the
network measures were used in statistical analysis. (B) Using flow cytometry, absolute counts of lymphocyte subsets in patient blood samples were estimated and
their longitudinal changes correlated with the rates of cortical atrophy; differences in lymphocyte counts between the patient subgroups were analyzed. (C) From
T1-weighted magnetic resonance images (MRI), cortical thickness for each ROI according to the Desikan–Killiany atlas was calculated and used to estimate the rates
of cortical atrophy and construct the connectivity matrices. Subsequently, these gray matter derived connectivity matrices were subjected to graph theoretical analysis
and the network measures were used in statistical analysis.
computation of the tensor. The distribution of crossing fibers at
each voxel of the brain for the computation of PT was estimated
using BEDPOSTX (implemented in FSL) and the probability of
major and secondary fiber directions was calculated (23). All
images were aligned and affine-transformed into the Montreal
Neurological Institute (MNI)-152 space. At each voxel a multi-
fiber model was fit to the diffusion data, enabling to trace the
fibers through regions of crossing or complexity. To obtain
an estimate of the probability distribution of connections from
each seed voxel 5,000 streamline samples were drawn. The
generated tracts are volumes wherein the values at each voxel
represent the number of samples (or streamlines) that passed
through that particular voxel. Each tract from every seed mask
in the atlas was repeatedly sampled (5,000 times) and only
those tracts, which passed through at least one other seed mask
were retained. For the elimination of spurious connections,
tractography in individual subjects was thresholded to include
only voxels through which at least 10 percent of all streamline
samples had passed.
Brain Volumes and Lesion Segmentation
Quantification of GM and WM volumes at two time points
was done by using voxel-based morphometry (VBM) analysis
in Statistical Parametric Mapping (SPM8) software (http://www.
fil.ion.ucl.ac.uk/spm). Anatomical 3D T1 and T2-FLAIR images
were subjected for spatial normalization, tissue segmentation
and spatial smoothing to obtain GM, WM and total brain
(TB) volumes (24). Lesion segmentation tool (LST) (version
1.2.3; http://www.applied-statistics.de/lst.html) (25) was used to
compute the lesion volume (LV), details of which are mentioned
elsewhere (9). Briefly, 3D T2-FLAIR images were co-registered to
3D T1 images and lesion segmentation was run with 20 different
thresholds for the lesion growth algorithm (25).
Graph Theoretical Analysis
GM Network Construction
The entire cerebral cortex was parcellated into 68 bilateral
anatomical regions of interest (ROIs) (34 ROIs for each
hemisphere) based on the Desikan-Killiany atlas (26). Cortical
thickness from each cortical ROI was extracted and served for
the construction of GM connectivity matrices. These connectivity
matrices (size 68 ×68) for each group were obtained by
computing the Pearson correlation coefficient between the
anatomical regions across the group (8). Graph Analysis Toolbox
(GAT) was used to threshold the matrices into multiple densities
(ranged from 0.10 to 0.50) and compute the graph theoretical
network measures (27).
WM Network Construction
The obtained streamlines information from PT (as described
above) connecting each pair of ROIs (116—as defined in the
Automated Anatomical Labeling (AAL) atlas) was used to
construct the connectivity matrix for each subject (28). A more
detailed description of the network construction is presented in
our previous study (8). The obtained connectivity matrices were
included in the graph network analysis for the computation of
network properties using Brain Connectivity Toolbox (https://
sites.google.com/site/bctnet/) (29).
Network Measures
Topological organization of GM and WM networks was assessed
by computing the modularity. Modules are groups of nodes
forming a distinct subnetwork, where the within module
connection (correlation) is higher than the between module
correlation (30). Modularity (Q) represents the strength of
division of the network into modules and was calculated using
the Newman’s spectral algorithm (31). Since modularity is the
measure of networks’ segregation, higher modularity indicates
more isolated subnetworks within a given network. Hence,
with increasing modularity, the long-distance paths between
the modules decrease and the local interconnections within the
module increase.
Statistical Analysis
Statistical analyses were performed using SPSS software (version
23.0; IBM, Armonk, NY, USA.). Normal distribution of the
examined data was checked via Shapiro-Wilk test. For a
balanced matching of subjects, a multivariate model was tested
on our cohort of 1,156 patients with RRMS to select two
groups of patients matched upon demographical, clinical and
neuroimaging parameters at study entrance.
The H0 hypothesis: we assumed no association between
clinical and brain structural responses and immune cell dynamics
under DMF treatment. The between-group differences in disease
activity (MRI activity, clinical relapse) over the study period were
assessed by Pearson’s χ2-test.
The comparison of lymphocyte counts at baseline and follow-
up, and between baseline and follow-up time points in DMF-
treated patients, Mann–Whitney Uand Wilcoxon signed-rank
tests were used respectively.
To test if there are any differences in rates of cortical atrophy
between patient groups we performed general linear model
(GLM) analysis on vertex-by-vertex basis, accounting for the
effects of age and gender. Generated statistical parametric maps
of significant group differences were corrected for multiple
comparisons with Monte Carlo Z permutation cluster analysis
(10.000 iterations) at a threshold of Z =1.3 equivalent to a
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Ciolac et al. Brain Network Response to Immunomodulation
p-value of 0.05. In the DMF group, to assess if there is any
relationship between the rate of cortical atrophy and lymphocyte
subsets we applied separate GLM models, followed by Monte
Carlo Z correction for multiple comparisons as described above.
To examine whether the subcortical volumes vary in time
longitudinally as a function of group, a mixed-design repeated-
measures ANOVA was performed with hemisphere (left and
right) and time (baseline and follow-up) as within-subject factors
and group as a between-subject factor with two levels (DMF and
NAT), followed by Bonferroni correction.
The GM and WM network measures between baseline and
follow-up were compared by paired t-test.
RESULTS
Subjects
The multivariate analysis revealed no significant differences
between the DMF and NAT groups at baseline for age [F(1,76)
=2.75, p=0.10], disease duration [F(1,76) =0.26, p=
0.60], EDSS [F(1,76) =1.31, p=0.25], lesion volume [F(1,76)
=0.24, p=0.62], GM volume [F(1,76) =0.56, p=0.45),
WM volume [F(1,76) =0.17, p=0.67], MRI activity [F(1,76)
=2.55, p=0.11] and relapses [F(1,76) =1.37, p=0.24].
EDSS, GM, WM, TB and T2 lesion volumes at follow-up
did not differ significantly from baseline values within the
groups (p>0.05), indicating a stable disease period of 16
months on average in these patients despite a 4-year mean
disease duration. However, the subgroup analysis within the
patients with disease activity showed that the lesion volume
was significantly higher at follow-up (12.3 ±8.2 mL) than at
baseline (11.2 ±8.0 mL, p=0.04) only in the DMF group.
During the 16-month follow-up both groups were homogenous
in terms of MRI activity (χ2=2.534, p>0.05) and clinical
relapse (χ2=1.381, p>0.05). In the DMF group, 60% (25
patients) had disease activity [of them 60% (15)—MRI activity,
68% (17)—clinical relapse, 28% (7)—both] and were defined as
DMF non-responders (DMFNR), while 40% (17 patients) showed
no signs of disease activity (DMF responders, DMFR). In the
NAT group, 36% (13 patients) had disease activity [among them
19% (7) presented MRI activity, 28% (10) had clinical relapse,
11% (4) presented both] and 64% (23 patients) were without
disease activity.
During the entire study period, patients with disease activity
either from the DMF group or patients from the NAT group
didn’t have their DMD treatment escalated.
Peripheral Blood Cell Response Under
DMF Treatment
The follow-up period between the baseline and last blood samples
for DMFRwas 14.1 ±5.8 months and 16.1 ±4.9 months
for DMFNR. Examining the immunological profile of the DMF
subgroups, lymphocyte counts did not differ between the DMFR
and DMFNR at baseline. At follow-up, DMF-treated patients
showed lower counts of CD3+, CD4+, CD8+, CD56+and ALC,
and a higher CD4/CD8 ratio (all p<0.05) compared to baseline,
except CD19+, which did not reach significance in DMFNR (p>
0.05) (Supplementary Table 1). However, the DMFRsubgroup
displayed a stronger reduction in CD8+cells in comparison to
the DMFNR (197.7 ±97.1 /µl vs. 298.4 ±190.6 /µl; p=0.03)
(Figure 2) and a greater change (1) in CD8+cells (206 /µl vs.-
158 /µl; p=0.01). Similarly, a stronger drop in CD8+cells was
detected separately in patients without MRI activity compared to
those with (DMFR: 202.3 ±107.4 /µl vs. DMFNR: 312.7 ±125.0
/µl, p=0.02) and in patients without clinical activity compared
to patients with (DMFR: 211.1 ±98.7 /µl vs. DMFNR: 327.0 ±
144.5 /µl, p=0.03; Figure 3).
Cortical Atrophy Rates
The between-group comparison revealed greater rates of mean
cortical atrophy in the DMF group (2.4%) than in the NAT
group (2.1%, p<0.05), mostly within the frontal and temporal
lobes (Figure 4).
Within the DMF group, clusters of cortical atrophy were
identified mainly in the frontal, temporal and parietal lobes of
both hemispheres (Figure 5A). The NAT group showed clusters
of regional cortical atrophy only in the right inferior parietal and
rostral middle frontal areas (Figure 5B).
DMFRpatients had lower mean rates of cortical atrophy in
comparison to DMFNR (1.7% and 3.2%, respectively, p<
0.05). The areas showing a subgroup difference in atrophy rates
FIGURE 2 | Differences in CD8+, CD4+T cell counts and CD4/CD8 ratio at follow-up between the DMF responders and DMF non-responders subgroups. DMF
responders (DMFR) had lower counts of CD8+T cells at follow-up (after 14.1 ±5.8 months) in comparison to DMF non-responders (DMFNR); *p<0.05, ns, not
significant.
Frontiers in Immunology | www.frontiersin.org 6July 2019 | Volume 10 | Article 1779
Ciolac et al. Brain Network Response to Immunomodulation
were identified mainly in frontal, temporal and parietal lobes
(Figure 6).
Finally, we compared the DMFRsubgroup with the NAT
group in order to evaluate the differences in extent of structural
GM loss. Lower regional rates of cortical atrophy were identified
in the DMFRsubgroup in the following clusters: left lingual,
precuneus and right superior, and inferior parietal areas
(Figure 7).
Clusters of regional cortical atrophy rates are presented in
Supplementary Table 2.
Relation Between Lymphocyte Subsets
and Cortical Atrophy
In the DMF group, regression analysis disclosed the associations
between the cortical atrophy rates, and 1CD4+and 1CD8+
cells between baseline and follow-up. The change in CD4+cells
correlated with the atrophy rate in the right superior frontal area
(peak-vertex R2=0.383, p=0.012). The change in CD8+cells
correlated with the atrophy rate in the left superior parietal (peak-
vertex R2=0.490, p<0.0001), cuneus (peak-vertex R2=0.583, p
<0.0001), and rostral middle frontal (peak-vertex R2=0.489, p
<0.0001) and right anterior cingulate (peak-vertex R2=0.518, p
<0.0001), lateral occipital (peak-vertex R2=0.531, p<0.0001)
FIGURE 3 | Differences in follow-up CD8+T cell counts between patients
with and without MRI activity, and between patients with and without clinical
relapses under DMF treatment. Patients without MRI and clinical activity (DMF
responders, DMFR) had lower counts of CD8+T cells at follow-up (after 14.1
±5.8 months) in comparison to patients with MRI and clinical activity (DMF
non-responders, DMFNR); *p<0.05.
and operculum (peak-vertex R2=0.557, p<0.0001) (Figure 8
and Supplementary Table 3). The decrease in CD4+and CD8+
cells was associated with the lower cortical atrophy rate.
By correlating the rates of cortical atrophy and absolute
counts of CD4+and CD8+cells similar significant clusters could
be obtained.
Subcortical Structures
Subcortical structures showed no volumetric differences between
the DMF and NAT groups (effect of group,p>0.05). There
were no differences as well in subcortical volumes accounting
for time and hemisphere in both groups (time ×hemisphere ×
group interaction, p>0.05). The same was also true for the DMF
subgroups; subcortical volumes did not differ between DMFR
and DMFNR.
Gray Matter Network Measures
Longitudinal analysis of GM network measures in the DMF
group uncovered increased modularity (t=11.10, p<0.0001) at
follow-up (Figure 5A). The number of modules increased from
2 (at baseline) to 5 modules (at follow-up). In the NAT group,
modularity (t=5.73, p<0.0001) at follow-up as well was higher
than at baseline (Figure 5B) but with less modules at follow-up
(2) than at baseline (3).
DMFRsubgroup at follow-up displayed lower modularity (t=
5.20, p<0.0001) in comparison to baseline (Figure 6). Within
the DMFNR subgroup modularity (t=32.11, p<0.0001) at
follow-up was higher than at baseline.
White Matter Network Measures
In both the DMF and NAT groups, WM modularity at follow-
up did not differ (t=0.77, p=0.44 and t=1.44, p=0.15,
respectively) from the modularity at baseline (Figures 5A,B).
Within the DMF group, DMFRat follow-up exhibited lower
modularity (t=1.76, p=0.046) than at baseline (Figure 6).
DISCUSSION
Emerging immunomodulatory therapies considerably modify the
individual course of MS, possessing the potential to beneficially
influence neuroinflammation and diffuse damage to the GM and
FIGURE 4 | Comparison of cortical atrophy rates between the DMF and NAT groups. Cortical areas displaying the difference in cortical atrophy rates between the
DMF and NAT groups, mapped on lateral and medial pial surfaces of the left (LH) and right (RH) hemispheres. Negative values (blue spectrum) denote cortical areas
showing greater rates of cortical atrophy in the DMF group in comparison to the NAT group. Color bar indicates the significance levels in the clusters obtained from
Monte Carlo simulation at p<0.05 (Z=1.3).
Frontiers in Immunology | www.frontiersin.org 7July 2019 | Volume 10 | Article 1779
Ciolac et al. Brain Network Response to Immunomodulation
FIGURE 5 | Longitudinal changes in cortical thickness and associated network measures within the DMF and NAT groups. Left: cortical areas displaying the rates of
cortical atrophy (negative values, blue spectrum) and cortical thickening (positive values, red spectrum) in the (A) DMF and (B) NAT groups, mapped on lateral and
medial pial surfaces of the left (LH) and right (RH) hemispheres. Color bar indicates the significance levels in the clusters obtained from Monte Carlo simulation at p<
0.05 (Z=1.3). Right: modularity (Q) of gray matter (GM) and white matter (WM) networks at baseline (t0) and follow-up (t1) in the (A) DMF and (B) NAT groups; **p<
0.0001, ns, not significant.
WM. However, this therapeutic effectiveness comes at a price of
rare, but life-threatening side effects such as the development of
secondary immunologic disorders, hematopoietic diseases
or progressive multifocal leukoencephalopathy (PML).
Thus, individual patients’ stratification to therapy response
or failure is warranted. Importantly, easy to obtain and
measure immunological markers of therapy response are
extremely necessary to minimize tissue damage and long-
term functional impairment (10,32). Therefore, here the goal
was to stratify patients as responders to DMF therapy by
linking cellular responses to clinical, structural MRI and brain
network dynamics.
While overall the group of patients treated with NAT
displayed less cortical atrophy than patients receiving DMF, the
DMF-treated patients free of disease activity with a stronger
depletion of CD8+T cells showed even less GM loss in
comparison to the NAT-treated patients. Therefore, we highlight
the feasible potential of CD8+T cell subset monitoring as
a marker of individual treatment response. Previous own
work (11) and recent emerging data (3335) showed that
Frontiers in Immunology | www.frontiersin.org 8July 2019 | Volume 10 | Article 1779
Ciolac et al. Brain Network Response to Immunomodulation
FIGURE 6 | Comparison of cortical atrophy rates between the DMFRand DMFNR subgroups and associated network measures. Left: cortical areas displaying the
difference in cortical atrophy rates between the DMFRand DMFNR subgroups, mapped on lateral and medial pial surfaces of the left (LH) and right (RH) hemispheres.
Positive values (red spectrum) denote cortical areas showing lower rates of cortical atrophy in the DMFRsubgroup in comparison to the DMFNR subgroup. Color bar
indicates the significance levels in the clusters obtained from Monte Carlo simulation at p<0.05 (Z =1.3). Right: modularity (Q) of gray matter (GM) and white matter
(WM) networks at baseline (t0) and follow-up (t1) in the DMFRand DMFNR subgroups; *p<0.05, **p<0.0001, ns, not significant.
FIGURE 7 | Comparison of cortical atrophy rates between the DMFRand NAT groups. Cortical areas displaying the difference in cortical atrophy rates between the
DMFRand NAT groups, mapped on lateral and medial pial surfaces of the left (LH) and right (RH) hemispheres. Positive values (red spectrum) denote cortical areas
showing lower rates of cortical atrophy in the DMFRsubgroup in comparison to NAT group. Color bar indicates the significance levels in the clusters obtained from
Monte Carlo simulation at p<0.05 (Z=1.3).
lymphocyte subsets present varying susceptibility to DMF. It is
worth mentioning that the DMF-induced shifts in lymphocyte
subsets cannot be definitely considered as measures of treatment
response and currently the reduced counts of CD8+T and
other cells merely explain the DMF mechanism of action. Studies
with larger sample size and longer follow-up are required to
ascertain if depletion of CD8 (and other lymphocyte subsets) can
serve as guiding markers of DMF therapy response and support
treatment decisions.
Lymphocyte CD8+counts correlating with cortical atrophy in
patients upon DMF treatment points to the role of CD8+T cells
in ongoing inflammatory processes in GM. As it takes a longer
time for DMF in order to achieve a complete effect, a greater
loss of cortical GM and disease activity could emerge at first
months after DMF treatment onset. Higher cortical atrophy rates
in the DMF group could also be caused by inclusion of a higher
proportion of patients with disease activity. This is likely to be
due to selection bias, since our statistical method to match both
patient groups was based not on propensity score methods but on
a multivariate model that could possibly over-fit the model and
select patients with higher disease activity. Despite a relatively
high number of patients with disease activity in the DMF-treated
(60%) and in the NAT-treated group (36%), the lesion volume
and EDSS didn’t significantly change over time. Apparently, this
mismatch partly rises from the whole-group analysis because
DMF non-responders still showed a significant increase in lesion
volume at follow-up. In contrast, available studies evaluating
the efficacy of DMF on clinical/MRI activity and brain atrophy
measures report a 27% proportion of patients with new relapses,
a relative reduction by 21% of disability progression and T2 lesion
volume and a reduction by 21% of brain atrophy (36,37). On the
other hand, we are aware that the short follow-up period of our
Frontiers in Immunology | www.frontiersin.org 9July 2019 | Volume 10 | Article 1779
Ciolac et al. Brain Network Response to Immunomodulation
FIGURE 8 | Association between cortical atrophy rates and T cell subsets within the DMF group. Left: cortical areas displaying the correlation between cortical
atrophy rates and change (1) in (A) CD4+and (B) CD8+cells between baseline and follow-up, mapped on lateral and medial pial surfaces of the left (LH) and right
(RH) hemispheres. Color bar indicates the significance levels of the correlation in clusters obtained from Monte Carlo simulation at p<0.05 (Z=1.3). Right: scatter
plots showing the association between the cortical atrophy rate in the (A) right superior frontal cluster (at peak-vertex) and 1CD4+cells and (B) left superior parietal
cluster (at peak-vertex) and 1CD8+cells (dotted line represents the 95% confidence interval for the mean). The decrease in CD4+and CD8+T cell counts is
associated with the lower rates of cortical atrophy.
study precludes us to draw definite conclusions on delaying the
brain atrophy under both therapies, this being one of the study
limitations. As efficacy of DMDs is greater during the second and
following years after DMD treatment onset (3840), our study
period of almost one and half years could be relatively enough
to obtain approximate impressions on differences of cortical GM
and network responses to the DMF and NAT therapy.
In order to quantify discrete structural alterations and depict
local and global GM dynamics, we performed longitudinal brain
network analysis. This is an emerging tool to explore disease-
related reorganization processes that mirror the disease course
(8,9,4143). Modularity, a parameter reflecting long-range
disconnection and integration of functionally interacting brain
regions, is a very sensitive marker of structural integrity in
patients with MS (8,4446). As the disease progresses in patients
with RRMS, the brain circuits reorganize toward a topology
of higher modularity with long-range disconnections and local
structural homogeneity (9,44).
With the aid of modularity analysis we found that the GM
network dynamics is characterized by increased modularity
and long-range disconnections in both DMF- and NAT-
treated groups. Longitudinal brain network development
toward increased modularity presumably driven by cortical
reorganization processes could be an important structural
fingerprint mirroring functional impairments or even transition
into progressive forms of MS (9). In contrast, we observed an
inverse pattern of network topology with decreasing modularity
over time in DMF responders, and no differences between the
DMF non-responders and NAT patients. Here, we postulate
that DMF responders comprise a potentially DMF-induced
slowing of neuronal damage and reversal of local and global
reorganization processes in the given period.
In both NAT- and DMF-treated groups, WM network
topological characteristics did not change over time. On one
hand, the stability of WM network topology might be explained
by immunomodulatory treatment success. The beneficial impact
of NAT on the WM compartment can be assigned to positive
effects on myelination, stabilization of the blood-brain barrier
and less WM damage (47). On the other hand, the short study
period and the methodology applied could have been insufficient
to precisely track the WM pathology. The absence of structural
alterations in the WM and deep GM compartments possibly
suggests different effects of the studied therapies on GM and WM
compartments (48). The fact that only DMF responders showed
the characteristic decrease in modularity over time highlights a
distinct network response of WM networks to DMF exposure
as well. We must acknowledge that the conclusions drawn
from the WM network analysis need to be interpreted with
caution, since mapping of white matter connections by diffusion
MRI tractography has inherent sources of errors, artifacts and
biases that limit its anatomical accuracy and the validity of
obtained estimates (49). WM lesions may lead to inaccurate
Frontiers in Immunology | www.frontiersin.org 10 July 2019 | Volume 10 | Article 1779
Ciolac et al. Brain Network Response to Immunomodulation
tracking of termination sites of fibers or even cause deviations
of fiber bundles nearby the lesions (50). The DTI protocol and
computational algorithms used in this study can partly overcome
the constraints posed by the microstructural complexity of WM
tracts (fibers with crossing configurations, geometric distortion,
folding patterns) (51).
CONCLUSIONS
Our results indicate that NAT therapy opposed to DMF treatment
favors preservation of cortical and subcortical structural integrity
but with equivalent network responses. But within the patient
cohort treated with DMF, a more pronounced decline in
circulating CD8+T lymphocytes was associated with a favorable
clinical outcome and advantageous structural network responses.
Whether DMF could serve as a treatment strategy in NAT-
incompatible patients under conditions of rigorous T cell
monitoring should be further investigated.
ETHICS STATEMENT
The study protocol was approved by institutional
ethics committee and patients gave written informed
consent in accordance with the Declaration
of Helsinki.
AUTHOR CONTRIBUTIONS
Conceived and designed the study: SM, MM, and SG.
Acquired and analyzed patients’ data: DC, FL, GG-E,
NK, CR, VF, JK, and MM. Interpreted the data and
drafted the manuscript: DC, FL, SB, SM, MM, and SG.
Interpreted the data and revised the manuscript: VF, SM, MM,
and SG.
ACKNOWLEDGMENTS
We thank Cheryl Ernest for proofreading the manuscript. This
study was supported by a grant from German Research Council
(DFG; CRC-TR-128).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fimmu.
2019.01779/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Ciolac, Luessi, Gonzalez-Escamilla, Koirala, Riedel, Fleischer,
Bittner, Krämer, Meuth, Muthuraman and Groppa. This is an open-access article
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Frontiers in Immunology | www.frontiersin.org 12 July 2019 | Volume 10 | Article 1779
... Yet, higher values in network metrics from 7T-data may be directly related to the differences in CT and GWc obtained between 7T-and 3T-scanners. As network modules exhibit high intramodular dependencies and high intermodular independencies, increased modularity suggests increased segregation of neuronal circuitries within the cortical GM morphometric networks in MS. 20,41 Reorganization of cortical networks following disease onset indicates to act as a compensatory response of cortical GM tissue, associated with long-term clinical outcomes in MS. 20,41,42 Besides current measures of cortical GM integrity, network measures, closely reflecting ongoing tissue damage and repair processes, are promising markers for sensibly depicting disease progression. ...
... Yet, higher values in network metrics from 7T-data may be directly related to the differences in CT and GWc obtained between 7T-and 3T-scanners. As network modules exhibit high intramodular dependencies and high intermodular independencies, increased modularity suggests increased segregation of neuronal circuitries within the cortical GM morphometric networks in MS. 20,41 Reorganization of cortical networks following disease onset indicates to act as a compensatory response of cortical GM tissue, associated with long-term clinical outcomes in MS. 20,41,42 Besides current measures of cortical GM integrity, network measures, closely reflecting ongoing tissue damage and repair processes, are promising markers for sensibly depicting disease progression. ...
Article
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Objective: To determine the ability 7T-MRI for characterizing brain tissue integrity in early relapsing-remitting MS patients compared to conventional 3T-MRI and to investigate whether 7T-MRI improves the performance for detecting cortical grey matter neurodegeneration and its associated network reorganization dynamics. Methods: Seven early relapsing-remitting MS patients and seven healthy individuals received MRI at 7T and 3T, whereas 30 and 40 healthy controls underwent separate 3T- and 7T-MRI sessions, respectively. Surface-based cortical thickness (CT) and grey-to-white contrast (GWc) measures were used to model morphometric networks, analysed with graph theory by means of modularity, clustering coefficient, path length, and small-worldness. Results: 7T-MRI had lower CT and higher GWc compared to 3T-MRI in MS. CT and GWc measures robustly differentiated MS from controls at 3T-MRI. 7T- and 3T-MRI showed high regional correspondence for CT (r=0.72, p=2e-78) and GWc (r=0.83, p=5.5e-121) in MS patients. MS CT and GWc morphometric networks at 7T showed higher modularity, clustering coefficient and small-worldness than 3T, also compared to controls. Interpretation: 7T MRI allows to more precisely quantify morphometric alterations across the cortical mantle and captures more sensitively MS-related network reorganization. Our findings open new avenues to design more accurate studies quantifying brain tissue loss and test treatment effects on tissue repair
... Network measures have been robustly applied to closely depict progression of clinical 6 symptoms in MS 10 and therapeutic outcomes 25 . Despite these findings, attempts to inclusively model neuroinflammation and neurodegeneration to explain brain circuit functioning at a global scale have remained scarce. ...
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Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of MS pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations, and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair, and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.
... The sex-specific hippocampal network responses can be approached through graph theoretical analysis, which is a unique tool to investigate the alterations of brain networks in MS, 12 providing more sensitive metrics to MS pathology than conventional neuroimaging measures. [13][14][15] In light of the above, informing sex-specific signatures of hippocampal networks and regional structural integrity can offer valuable insights into the intrinsic hippocampal organization in MS that may underlie cognitive variability across sexes. Specifically, we test the following hypotheses: (i) morphometric network architecture of the hippocampal formation displays sex-specific differences in MS patients, (ii) regional structural integrity of the hippocampal formation follows the sex-specific network signatures and (iii) both network and regional properties distinctively relate to cognitive performance in female and male MS patients. ...
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The hippocampus is an anatomically compartmentalized structure imbedded in highly wired networks that are essential for cognitive functions. Hippocampal vulnerability has been postulated in acute and chronic neuroinflammation in multiple sclerosis, while the patterns of occurring inflammation, neurodegeneration or compensation have not yet been described. Besides focal damage to hippocampal tissue, network disruption is an important contributor to cognitive decline in multiple sclerosis patients. We postulate sex-specific trajectories in hippocampal network reorganization and regional integrity, and address their relation to markers of neuroinflammation, cognitive/memory performance, and clinical severity. In a large cohort of multiple sclerosis patients (n = 476; 337 females, age 35 ± 10 years, disease duration 16 ± 14 months) and healthy subjects (n = 110, 54 females; age 34 ± 15 years), we utilized MRI at baseline and at 2-year follow-up to quantify regional hippocampal volumetry and reconstruct single-subject hippocampal networks. Through graph analytical tools we assessed the clustered topology of the hippocampal networks. Mixed-effects analyses served to model sex-based differences in hippocampal network and subfield integrity between multiple sclerosis patients and healthy subjects at both time points and longitudinally. Afterwards, hippocampal network and subfield integrity were related to clinical and radiological variables in dependency of sex attribution. We found a more clustered network architecture in both female and male patients compared to their healthy counterparts. At both time points, female patients displayed a more clustered network topology in comparison to male patients. Over time, multiple sclerosis patients developed an even more clustered network architecture, though with a greater magnitude in females. We detected reduced regional volumes in most of the addressed hippocampal subfields in both female and male patients compared to healthy subjects. Compared to male patients, females displayed lower volumes of para- and presubiculum but higher volumes of molecular layer. Longitudinally, volumetric alterations were more pronounced in female patients, which showed a more extensive regional tissue loss. Despite a comparable cognitive/memory performance between female and male patients over the follow-up period, we identified a strong interrelation between hippocampal network properties and cognitive/memory performance only in female patients. Our findings evidence a more clustered hippocampal network topology in female patients with a more extensive subfield volume loss over time. A stronger relation among cognitive/memory performance and the network topology in female patients suggests a greater entrainment of brain’s reserve. These results may serve to adapt sex-targeted neuropsychological interventions.
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Background: To investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in MS patients with concomitant epilepsy. Methods: From 3T MRI scans of 30 MS patients with epilepsy (MSE; age 41±15 years, 21 females, disease duration 8±6 years, median Expanded Disability Status Scale (EDSS) 3), 60 MS patients without epilepsy (MS; age 41±12 years, 35 females, disease duration 6±4 years, EDSS 2), and 60 healthy subjects (HS; age 40±13 years, 27 females) regional volumes of GM lesions and of cortical, subcortical, and hippocampal structures were quantified. Network topology and vulnerability were modeled within the graph theoretical framework. The receiver operating characteristic (ROC) analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. Results: Higher lesion volumes within the hippocampus, mesiotemporal cortex, and amygdala were detected in MSE compared to MS (all p<0.05). MSE displayed lower cortical volumes mainly in temporal and parietal areas compared to MS and HS (all p<0.05). Lower volumes of hippocampal tail and presubiculum were identified in both MSE and MS patients compared to HS (all p<0.05). Network topology in MSE was characterized by higher transitivity and assortativity, and higher vulnerability compared to MS and HS (all p<0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67-0.91]) in discriminating between MSE and MS patients. Conclusions: High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity of epilepsy occurrence in MS.
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Background: Currently, no unequivocal predictors of disease evolution exist in patients with multiple sclerosis (MS). Cortical atrophy measurements are, however, closely associated with cumulative disability. Objective: Here, we aim to forecast longitudinal magnetic resonance imaging (MRI)-driven cortical atrophy and clinical disability from cerebrospinal fluid (CSF) markers. Methods: We analyzed CSF fractions of albumin and immunoglobulins (Ig) A, G, and M and their CSF to serum quotients. Results: Widespread atrophy was highly associated with increased baseline CSF concentrations and quotients of albumin and IgA. Patients with increased CSFIgA and CSFIgM showed higher functional disability at follow-up. Conclusion: CSF markers of blood–brain barrier integrity and specific immune response forecast emerging grey matter pathology and disease progression in MS
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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is nowadays an evidence-based state of the art therapy option for motor and non-motor symptoms in patients with Parkinson's disease (PD). However, the exact anatomical regions of the cerebral network that are targeted by STN-DBS have not been precisely described and no definitive pre-intervention predictors of the clinical response exist. In this study, we test the hypothesis that the clinical effectiveness of STN-DBS depends on the connectivity profile of the targeted brain networks. Therefore, we used diffusion-weighted imaging (DWI) and probabilistic tractography to reconstruct the anatomical networks and the graph theoretical framework to quantify the connectivity profile. DWI was obtained pre-operatively from 15 PD patients who underwent DBS (mean age = 67.87 ± 7.88, 11 males, H&Y score = 3.5 ± 0.8) using a 3T MRI scanner (Philips Achieva). The pre-operative connectivity properties of a network encompassing frontal, prefrontal cortex and cingulate gyrus were directly linked to the postoperative clinical outcome. Eccentricity as a topological-characteristic of the network defining how cerebral regions are embedded in relation to distant sites correlated inversely with the applied voltage at the active electrode for optimal clinical response. We found that network topology and pre-operative connectivity patterns have direct influence on the clinical response to DBS and may serve as important and independent predictors of the postoperative clinical outcome.
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Objective To examine the temporal profile of absolute and lymphocyte subset data from dimethyl fumarate (DMF) start and relationships to disease behavior. Methods A retrospective study performed on patients with an existing diagnosis of MS and a history of DMF exposure from a single MS center. Demographic, laboratory, and corresponding clinical relapse and MRI data were recorded from baseline and in 3–4-month intervals after treatment initiation extending to 3 years. The Spearman rank coefficient and mixed-effects models were used to assess longitudinal correlations between cell counts and measures of disease activity. Results A total of 292 patients with MS (228 women; median age at DMF initiation: 40.6 years, range: 16.1–66.7 years) were identified. An increased risk of disease activity was associated with higher absolute lymphocyte count (ALC) values at 3 months (p = 0.001, OR: 1.82) and at 6 months (p = 0.032, hazard ratio: 1.73). A reduced risk of disease evolution in patients with lower ALC values < 1,200 cells/μL compared with midtier (1,210–1,800 cells/μL) and the highest tertile (>1,810 cells/μL) was observed (p = 0.01). Conclusions Reductions in ALC values at months 3 and 6 after treatment initiation appear to be associated with improved clinical and radiologic outcomes. These data alone may help to provide a better understanding of both the safety and efficacy of DMF.
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Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Background: The effect of dimethyl fumarate (DMF) on circulating lymphocyte subsets and their contribution as predictors of clinical efficacy have not yet been investigated in multiple sclerosis (MS). Objective: To evaluate lymphocytes and lymphocyte subsets (analyzed 6 months after DMF start) in MS patients with and without disease activity after 1 year of treatment in a retrospective study. Methods: Peripheral blood lymphocyte subsets were analyzed by flow cytometry. Untreated MS patients ( n = 40) were compared to those 6 months after onset of DMF treatment ( n = 51). Clinical and magnetic resonance imaging (MRI) disease activity of DMF-treated patients were assessed in the first year under treatment. Results: Stable patients showed significantly lower lymphocytes, CD4+ and CD8+ T cells as well as CD19+ B cells compared to active patients under DMF treatment. Furthermore, an increased CD4/CD8 ratio ( p < 0.025) in stable patients indicated a disproportionate reduction of CD8+ T cells relative to CD4+ T cells. Reduced lymphocytes, CD8+ T cells, and CD19+ B cells 6 months after DMF start allowed prediction of the treatment response in the first year. Conclusion: DMF treatment response is reflected by lower circulating lymphocytes and specific lymphocyte subsets. Changes in the cellular immune profiles under DMF treatment are clinically relevant and might serve as a surrogate marker of treatment response.
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Multiple sclerosis (MS) is a chronic autoimmune disease caused by an insufficient suppression of autoreactive T lymphocytes. One reason for the lack of immunological control is the reduced responsiveness of T effector cells (Teff) for the suppressive properties of regulatory T cells (Treg), a process termed Treg resistance. Here we investigated whether the disease-modifying therapy of relapsing-remitting MS (RRMS) with dimethyl fumarate (DMF) influences the sensitivity of T cells in the peripheral blood of patients towards Treg-mediated suppression. We demonstrated that DMF restores responsiveness of Teff to the suppressive function of Treg in vitro, presumably by down-regulation of interleukin-6R (IL-6R) expression on T cells. Transfer of human immune cells into immunodeficient mice resulted in a lethal graft-versus-host reaction triggered by human CD4⁺ Teff. This systemic inflammation can be prevented by activated Treg after transfer of immune cells from DMF-treated MS patients, but not after injection of Treg-resistant Teff from therapy-naïve MS patients. Furthermore, after DMF therapy, proliferation and expansion of T cells and the immigration into the spleen of the animals is reduced and modulated by activated Treg. In summary, our data reveals that DMF therapy significantly improves the responsiveness of Teff in MS patients to immunoregulation.
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Personalized medicine is a medical model where treatment decisions are tailored to the patient in accordance with the specific characteristics of the individual and their disease. Typically, these decisions are based on the predicted treatment response of the patient, which is based on individual disease features. At present, treatment of multiple sclerosis (MS) relapses is far from personalized medicine: most patients receive 1 or 2 courses of corticosteroids, with treatment of unresponsive relapses escalating to apheresis (either plasma exchange or immunoabsorption).¹,2 Predictors of response to either corticosteroids or plasma exchange are limited.
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Network science provides powerful access to essential organisational principles of the human brain. It has been applied in combination with graph theory to characterise brain connectivity patterns. In multiple sclerosis (MS), analysis of the brain networks derived from either structural or functional imaging provides new insights into pathological processes within the grey and white matter. Beyond focal lesions and diffuse tissue damage, network connectivity patterns could be important for closely tracking and predicting the disease course. In this review, we describe concepts of graph theory, highlight novel issues of tissue reorganisation in acute and chronic neuroinflammation and address pitfalls with regard to network analysis in MS patients. We further provide an outline of functional and structural connectivity patterns observed in MS, spanning from disconnection and disruption on one hand to adaptation and compensation on the other. Moreover, we link network changes and their relation to clinical disability based on the current literature. Finally, we discuss the perspective of network science in MS for future research and postulate its role in the clinical framework.
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Background: The precise mechanism of action of dimethyl fumarate (DMF) treatment in MS remains unknown. Objective: To identify the changes in the blood lymphocyte profile of MS patients predicting no evidence of disease activity (NEDA) status after DMF treatment. Methods: We studied blood lymphocyte subsets of 64 MS patients treated with DMF at baseline and after 6 months of treatment by flow cytometry. NEDA (41 patients) or ongoing disease activity (ODA, 23 patients) were monitored after a year of follow-up. Results: During treatment, all patients experienced an increase in the naive T cells and a decrease in effector memory ones. However, only NEDA patients showed a significant reduction in central memory CD4+ and CD8+ T cells, memory B cells, CD4+ T cells producing interferon (IFN)-gamma, CD8+ T cells producing tumor necrosis factor-alpha (TNF-alpha), and IFN-gamma and B cells producing TNF-alpha. Additionally, they had an increase in regulatory CD56bright cells not observed in ODA group. After treatment, there was a negative correlation between CD56bright cells and CD8+ T cells producing IFN-gamma and TNF-alpha. Conclusion: A pro-tolerogenic shift in the blood leukocyte profile associates with an optimal response to DMF in MS.