Cognitive decline in multiple sclerosis
van Geest, Q.
2018
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van Geest, Q. (2018). Cognitive decline in multiple sclerosis: A multifaceted dynamic approach.
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Chapter 3.1
Is impaired information
processing speed a matter
of structural or functional
damage in multiple sclerosis?
NeuroImage: Clinical (submitted)
Abstract
Objective Cognitive deficits, especially those of information processing speed (IPS), are common in multiple sclerosis (MS), however, the underlying neurobiological mechanisms remain poorly understood. In this study, we examined structural and functional brain changes separately, but also in an integrative manner, in relation to IPS performance.Methods IPS was measured using the symbol digit modalities test (SDMT) in 330 MS patients and 96 controls. Patients with IPS impairment (IPS-I, Z-score < -1.5) were compared to patients with preserved IPS performance (IPS-P) on volumetric measures, white matter integrity loss (using diffusion tensor imaging) and the severity of functional connectivity changes (using resting-state fMRI). Significant predictors of IPS performance were used to create groups of mild or severe structural and/or functional damage to determine the relative effect of structural and/or functional changes on IPS.
Results IPS-I patients, compared to IPS-P patients, showed lower deep gray matter volume and lower WM integrity, but stronger increases in functional connectivity. Patients with predominantly structural damage had worse IPS (Z-score = -1.49) than patients with predominantly functional changes (Z-score = -0.84), although both structural and functional measures remained significant in a regression model. Patients with severe structural and functional changes had worst IPS (Z-score = -1.95).
91
Introduction
Multiple sclerosis (MS) is a progressive, inflammatory, and neurodegenerative disease of the central nervous system characterized by demyelination and
neuronal loss.1 In addition to physical disabilities, cognitive deficits are common,
affecting approximately 40-70% of the MS patients.2,3 Among cognitive deficits,
problems with information processing speed (IPS) are frequently seen and already
present early in the disease.2,4–6
In MS, previous imaging studies have shown that IPS deficits are related to
structural or functional brain abnormalities,5,7–13 including deep gray matter
(DGM) atrophy14 and white matter (WM) integrity loss,11 as well as changes in
functional connectivity.12,13 Unfortunately, studies that have integrated structural
and functional measures to explain IPS deficits are currently lacking. Although structural and functional brain characteristics are intertwined to a certain extent,
there is no one-to-one relation between these two.15 Therefore, it might be that
structural damage may occur in the presence of minor functional changes but may also involve severe functional changes. As complex cognitive functions such as IPS arise from an efficient interplay between the brain's functional and structural architecture, varying levels of structural and/or functional damage may
also result in different levels of IPS impairment in MS.16 This emphasizes the need
to consider both structural and functional measures simultaneously to be able to better understand IPS deficits.
We hypothesize that to increase our understanding of the underlying neurobiology of IPS deficits an integrated measure of functional and structural brain changes
is essential, instead of studying either one or the other.17 Therefore, we integrated
advanced functional and structural MRI measures to examine the relative and joint impact of functional and structural brain changes in explaining IPS performance.
Methods
Participants
All participants with complete functional and structural imaging protocols (see
below) of the Amsterdam MS Cohort18,19 were included, resulting in 330 MS
Neuropsychological testing
All subjects underwent neuropsychological evaluation using an expanded Brief
Repeatable Battery of Neuropsychological tests, as previously described.20 Of
these assessments, we formed groups based on IPS only, as measured with the
symbol digit modalities test (SDMT),21 which was corrected for effects of sex, age
and education.3 These scores were subsequently converted to Z-scores based
on the mean and standard deviation (SD) of the HC and used to categorize MS patients into either IPS impaired (IPS-I, Z-score ≤ -1.5 on SDMT) or IPS preserved (IPS-P, Z-score > -1.5 on SDMT). For descriptive purposes, similar cut-off scores were applied to the remaining cognitive domains.
MR imaging
MR imaging was performed on a 3T scanner (GE Signa HDxt, Milwaukee, WI, USA) using an eight-channel head-coil. The structural imaging protocol included a 3D T1-weighted inversion-prepared fast spoiled gradient recall sequence (FSPGR, TR
7.8 ms, TE 3 ms, TI 450ms, FA 12º, sagittal 1.0 mm sections, 0.94 × 0.94 mm2
in-plane resolution) for volumetric measurements, a 3D fluid-attenuated inversion-recovery sequence (FLAIR, TR 8000 ms, TE 125 ms, TI 2350 ms, sagittal 1.2 mm slices,
0.98 × 0.98 mm2 in-plane resolution) for lesion detection and a diffusion tensor
imaging (DTI) sequence covering the entire brain using five volumes without directional weighting (i.e. b0) and 30 volumes with non-collinear diffusion gradients
(EPI, b = 1000 s/mm2, TR 13000 ms, TE 91 ms, FA 90º, 2.4 mm contiguous axial slices,
2 × 2 mm2 in-plane resolution). Brain function was assessed using resting-state
functional MRI with whole-brain coverage using 202 volumes, of which the first two were discarded (EPI, TR 2200 ms, TE 35 ms, FA 20º, 3 mm contiguous axial
slices, 3.3 × 3.3 mm2 in-plane resolution).
Volumetric measures
Hyperintense lesions were automatically segmented on the FLAIR images22 and
filled on the T1 using LEAP23 to minimize the impact of lesions on volumetric
measures and registration algorithms. Normalized gray matter (NGMV) and WM (NWMV) volumes were calculated with SIENAX (part of FSL 5). FIRST was used to segment deep gray matter (DGM) structures and the volume of these structures were computed, summed and normalized for head size, resulting in normalized DGM volume (NDGMV). Normalized cortical volumes (NCGMV) were computed by subtracting FIRST segmentations from the SIENAX-based GM
93
A
B
C
Lesion load White matter
segmentation matter segmentationCortical gray Deep gray mattersegmentation Mean FA
Power atlas Individual
FC matrix control FC matrixAverage healthy deviation matrixIndividual FC
ROI RO I 0 0 0.23 0.23 -0.08 -0.08 0 0.17 0.17 0 0.12 0.12 -0.15 -0.15 0.47 0.47 0 0 0.02 0.02 -0.38 -0.38 0 0.05 0.05 0 0.18 0.18 0.07 0.07 0.16 0.16 0 0 0.21 0.21 0.30 0.30 0 0.12 0.12 0 -0.06 -0.06 -0.22 -0.22 0.34 0.34
-
=
Structural damage Functional damage median severe mild mild severe Increased functionalconnectivity Decreased functionalconnectivity
S F
S F
S F
S F
Figure 3.1.1 Data analysis fl owchart
Severity of fractional anisotropy-based damage
DTI data were pre-processed using FSL 5, including motion- and eddy current correction on images and gradient vectors, followed by diffusion tensor fitting. Since fractional anisotropy (FA) is the most commonly examined diffusion measure in MS, we focused on whole-brain FA as a measure of WM integrity, also to limit the number of dependent variables. To obtain skeletonised FA maps,
the default tract-based spatial statistics (TBSS) pipeline was used.24 Mean FA scores
of the WM skeleton were extracted for each subject as a measure of whole-brain WM integrity.
Processing of functional images
Pre-processing of fMRI data was carried out using the default pipeline of MELODIC, consisting of motion correction, removal of non-brain tissue, spatial smoothing using a 5 mm full-width-at-half-maximum Gaussian kernel and high-pass temporal filtering to cut off frequencies below 0.01 Hz. All resting-state fMRI scans were checked for artifacts, excessive motion and registration errors. The individual level of motion was calculated based on the average frame-to-frame motion. The amount of motion was not different between HC and MS (p = 0.34) and no subject moved more than 0.3 mm. To remove signal originating from residual non-brain tissue as well as from voxels sensitive to EPI-distortions, voxels with a signal intensity in the lowest quartile of the robust range were
excluded.20,25 For this study we used the power atlas,26 which was specifically
designed to study brain networks and consists of 264 GM regions. This atlas was registered to 3DT1 space with inverted non-linear registration parameters, using nearest neighbour interpolation. The atlas was then multiplied with the SIENAX and FIRST segmentations to include GM only. Subsequently, inverted boundary-based registration matrices were used to register the atlas to each individual fMRI scan using nearest neighbour interpolation. Regions of interest (ROIs) were excluded if these contained missing values in more than 10% of the subjects after the final registration to fMRI, leaving 238 GM regions in the final atlas for which mean time series were calculated. Functional connectivity matrices were formed by calculating Pearson correlations between the time series of all these pairs of nodes. Since the controversial nature of negative connectivity values, only positive
correlation coefficients were considered.27,28 Since the average level of functional
connectivity is known to be highly variable, which could hamper between-group
comparisons,29 these raw correlation coefficients were converted to relative
connectivity Z-scores by subtracting each individual's mean connectivity and
95
Severity of functional network changes
While global structural measures (e.g. NGMV, NWMV and whole-brain FA) are commonly computed, there is no such equivalent with regard to functional measures. Studies usually address regional changes in functional connectivity. After computing global structural measures, however, we subsequently aimed to design a whole-brain functional network measure representing the severity of functional connectivity changes in each individual. In other words, we needed an individual quantification of the amount of deviation from normal in functional connectivity levels. To determine "normal" connectivity levels for each link, we constructed an average normalized HC matrix (based on all 96 HC). Subsequently, we subtracted each individual normalized connectivity matrix from the aforementioned average HC matrix, resulting in a deviation score per element of the connectivity matrix. The values of these deviation matrices could thus either be positive, reflecting an increase in functional connectivity, or negative, reflecting
a decrease in functional connectivity (Figure 3.1.1B). For each individual matrix,
the average connectivity level of increased and decreased links was used as two measures of the severity of functional network changes for subsequent analyses.
Function versus structure
Together, this pipeline resulted in four measures of structural damage (i.e. NDGMV, NCGMV, lesion volume, and severity of WM integrity loss) and two measures of functional damage (i.e. the severity of increased and decreased functional connectivity changes). First, we compared these measures between IPS-I and IPS-P patients. Subsequently, in the entire MS group, a backward regression model was conducted to determine which of these significant functional and/or structural variables were the main independent predictors of IPS performance. Finally, to be able to integrate structural and functional measures, structural predictors were paired with functional predictors to determine the combined amount of damage for each patient individually. Four groups of patients, classified based on median splits of functional and structural damage, were examined in relation to IPS performance, namely:
1. Patients with mild functional damage and mild structural damage
2. Patients with severe functional damage but only mild structural damage, from here on referred to as "predominantly functional damage"
3. Patients with mild functional damage but severe structural damage, from here on referred to as "predominantly structural damage"
Mild damage was defined as scores lower than the median score (based on the MS group), whereas severe damage was defined as scores higher than the median score. In all groups the relation between the severity of structural and functional damage with IPS performance was investigated. To limit the number of comparisons, only consecutive groups were compared on IPS performance.
Statistical analysis
Statistical analyses were conducted in SPSS version 22.0 (Chicago, IL, USA). All variables were checked for normal distributions by histogram inspection and normality tests. Before conducting statistical analyses, it was checked whether the required assumptions were met. General linear models were used to compare measures of interest between groups including age, sex, and education as covariates. The linear regression model used to determine independent predictors of IPS followed a backward selection procedure. To examine how functional and structural measures were interrelated, Pearson correlations were calculated for normally distributed variables, or Spearman's Rank-Order correlations for non-normally distributed variables. Test statistics were considered significant with
p-values < 0.05 (Bonferroni corrected).
Results
Demographics and cognitive profiles: IPS-I versus IPS-P
Of all MS patients, 130 (39%) were defined as IPS-I, and 200 (61%) as IPS-P (see
Table 3.1.1). No difference in sex was found for the two groups. However, IPS-I patients were older and had a lower educational level compared to IPS-P patients. The IPS-I group consisted of a higher percentage of progressive patients (36% versus 20%, p = 0.002) and had higher EDSS scores (EDSS: 3.5 versus EDSS: 3.0,
p = 0.01) compared to IPS-P patients. The disease duration was longer in IPS-I
patients compared to IPS-P patients. Of the IPS-I patients, 33% showed impairment on one additional cognitive domain, 15% on two cognitive domains, 14% on three cognitive domains and 14% on more than three cognitive domains. Of the IPS-P patients, 24% showed impairment on one cognitive domain, 14% on two cognitive domains, 6% on three cognitive domains and 6% on more than three cognitive domains.
Functional and structural damage: IPS-I versus IPS-P
97
Table 3.1.1 Demographics IPS impaired patients, IPS preserved patients and healthy controls
IPS impaired IPS preserved HC p
n 130 200 96 –
Age, years 50.01 (11.33) 46.93 (10.74) 45.87 (10.45) 0.01a,c
Female/male 85/45 140/60 56/40 0.14 Educational level, years* 4.00 (3.00 – 6.00) 5.00 (4.00 – 6.00) 6.00 (4.00 – 7.00) 0.002a,b
RRMS/SPMS/PPMS 83/31/16 160/20/20 – 0.002 Symptom duration, years* 15.82 (7.8 – 21.59) 9.80 (6.63 – 20.32) – 0.01 EDSS* 4.0 (3.0 – 6.0) 3.0 (2.00 – 4.00) – 0.04 SDMT 37 (8.6) 58 (8.9) 61 (9.81) 0.001a,b,c
EDSS: expanded disability status scale; HC: healthy controls; IPS: information processing speed; RRMS: relapsing remitting multiple sclerosis; PPMS: primary progressive multiple sclerosis; SDMT: symbol digit modalities test; SPMS: secondary progressive multiple sclerosis
* Not normally distributed data for which median (interquartile range) are provided
a Significant difference between IPS impaired and HC b Significant difference between IPS preserved and HC c Significant difference between IPS impaired and IPS preserved
Table 3.1.2 Structural and functional MRI characteristics of IPS impaired patients, IPS preserved patients and healthy controls
IPS impaired IPS preserved HC p
n 130 200 96 – NGMV (ml) 758.81 (60.42) 799.98 (59.04) 818.54 (53.13) < 0.001a,b,c NWMV (ml) 657.55 (35.95) 675.98 (32.87) 697.09 (31.29) < 0.001a,b,c NDGMV (ml) 52.76 (7.49) 58.39 (5.28) 62.91 (37.35) < 0.001a,b,c NCGMV (ml) 726.33 (56.77) 763.64 (47.06) 779.41 (52.27) < 0.001a,b,c Normalized TLL (ml)* 21.67 (10.71 – 35.59) 10.51 (5.62 – 17.83) – < 0.001 FA whole brain 0.39 (0.03) 0.40 (0.02) 0.42 (0.02) < 0.001a,b,c
Absolute FC 0.93 (1.02) 0.53 (1.03) 0.00 (1.00) < 0.001a,b,c
Decreased FC -0.70 (0.95) -0.42 (0.97) 0.00 (1.00) < 0.001a,b
Increased FC 0.95 (1.13) 0.52 (1.12) 0.00 (1.00) < 0.001a,b,c
For descriptive purposes, functional connectivity values were converted to Z-scores based on the mean and SD of the HC
FA: fractional anisotropy; FC: functional connectivity; HC: healthy controls; IPS: information processing speed; NCGMV: normalized cortical gray matter volume; NDGMV: normalized deep gray matter volume; NGMV: normalized gray matter volume; NWMV: normalized white matter volume; TLL: total lesion load * Not normally distributed data for which median (interquartile range) are provided
IPS-P patients (p = 0.01; Figure 3.1.2A). In the entire MS group, a stronger increase in functional connectivity was associated with lower NCGMV and NDGMV (r = -0.218 and r = -0.179, p < 0.001), as well as higher lesion load and loss of WM integrity (ρ = 0.180 and r = -0.231, p < 0.001).
Which measure is the best predictor of IPS deficits in MS?
The final model (R2 = 0.454, p < 0.001) contained the following predictors for
worse IPS performance: lower NDGMV (β = 0.374, p < 0.001), older age (β = -0.206,
p < 0.001), lower education (β = 0.180, p < 0.001), loss of WM integrity (β = 0.152, p = 0.012), male sex (β = -0.116, p = 0.010), and increased functional connectivity
(β= -0.102, p = 0.021). Based on this regression model, NDGMV, loss of WM integrity, and increased functional connectivity were selected as structural and functional measures of interest. To obtain the individual effect sizes of these measures, separate models were conducted containing demographic variables and the measure of interest. As a result, NDGMV volume explained 42% of the variance in IPS (p < 0.001), WM integrity 37% (p < 0.001), and increased functional connectivity 24% (p < 0.001).
Impact of different severities of functional and structural damage
After identifying NDGMV, loss of WM integrity, and functional connectivity as independent predictors of IPS, four groups were created based on the severity
of functional and structural damage (Supplementary Table 3.1.1): mild structural
and functional damage (group 1), predominantly functional damage (group 2), predominantly structural damage (group 3), and functional as well as structural damage (group 4). Since two structural measures appeared to be predictors of IPS, NDGMV and WM integrity were combined in one composite score by first creating
Z-scores relative to the healthy controls for both measures separately and then
add these Z-scores. In this way, NDGMV and loss of WM integrity were averaged
into one structural damage composite score (Figure 3.1.2B). As expected, group
4 had worst IPS performance compared to all other groups (Z-score: -1.95 ± 1.41; group 4 vs 3: p = 0.02). Group 1 had best IPS performance (Z-score: -0.40 ± 1.10; group 1 vs 2: p = 0.02). Additionally, group 3 had worse IPS performance (Z-score: -1.49 ± 1.12) than group 2 (Z-score: -0.84 ± 1.10; p = 0.002). Groups 2 and 4 did not differ on the severity of functional connectivity, while groups 1 and 3 did not differ on the level of structural damage.
Impact of DGM atrophy and loss of WM integrity separately
99
in IPS performance was observed between patients with predominantly WM integrity loss and those with predominantly functional damage (p > 0.05;
Figure 3.1.2D).
Figure 3.1.2 IPS status is reflected by different severities of functional and structural changes
IPS impaired (IPS-I) patients showed more severe increases in functional connectivity than IPS preserved (IPS-P) patients (A). A similar stepwise deterioration in IPS performance was observed for functional changes combined with a composite score of structural damage (B), functional changes combined with DGM volume loss (C) and functional changes combined with WM integrity loss (D). In general, mild functional and mild structural changes were associated with the best IPS performance. Severe functional changes were associated with better IPS than severe structural changes, whereas severe functional changes combined with severe structural changes were associated with the worst IPS.
Discussion
Using innovative and integrated measures for functional and structural damage, we were able to demonstrate that different severities of functional and structural damage reflect stepwise worsening of IPS. MS patients with mild functional and mild structural damage had the best, although lower than HC, IPS. A further decline in IPS was found in MS patients with predominantly functional damage. MS patients with predominantly structural damage had worse IPS than the previous two groups, whereas MS patients with both severe functional and severe structural damage were worst off. The severity of functional network changes seemed to have an additive effect on IPS performance, as a similar degree of structural damage can be accompanied with either mild or severe functional network changes, resulting in different levels of IPS.
Until now, functional connectivity studies mostly focused on a few selected
regions.19,30,31 In this study, we defined an innovative whole-brain functional
connectivity measure to assess the severity of connectivity changes in one single measure, which allows to map widespread functional connectivity changes. Both increased and decreased functional connectivity were observed in MS patients compared to HC, but only increased functional connectivity changes discriminated IPS-P from IPS-I patients. Several studies have reported increased levels of functional connectivity during rest as a correlate of cognitive
deficits.19,20,32,33 This increase in functional connectivity could indicate altered
functional network activities, including increased levels of network synchrony
and abnormal oscillatory rhythm.34 In the current study, changes in whole-brain
measures were related to IPS performance. The rationale for investigating IPS was based on its high frequency and early presence in the disease. In addition, this domain is likely to depend on the interaction across many distant brain regions and cannot be assigned to one single brain region, and therefore differences in performance might reflect changes in whole-brain structural and functional measures. However, since IPS scores might influence and might be influenced by deficits in other cognitive domains, it is unfortunately not feasible to examine
IPS deficits in isolation.35,36 This is also shown by our own data since more cognitive
deficits were detected in the IPS-I group.
101
predominantly functional damage (Z-score: -0.84). Larger cognitive consequences of structural damage were also shown by the regression models, that is, structural measures possessed a stronger predictive value. The strong influence of structural measures on IPS performance was previously reported as well. Not only loss of
WM integrity,9,11 but also loss of DGM volume8,14 was associated with worse IPS.
This might be explained by the relatively rigid nature of the brain's structural architecture, whereas the functional network might be better able to circumvent
damage due to its dynamic and more flexible properties.16
Among the structural measures, it seemed that DGM volume had a stronger effect than WM integrity loss. Contrary to what we observed for DGM volume, there was no significant difference in IPS performance between patients with predominantly WM integrity loss and those with predominantly functional damage. Like fMRI changes, WM integrity loss is likely to reflect more subtle damage, especially in
the so-called normal appearing WM,37,38 while a measure like DGM atrophy is
an MRI marker for (substantial) neurodegeneration,39,40 possibly explaining the
larger impact of the latter on IPS. It is important, however, to note that we focused on whole-brain measures, while certain focal structural and functional changes might influence the results differently.
Although structural damage was a strong predictor for IPS performance, investigating the joint impact of structural and functional measures showed that some subgroups with a similar degree of structural damage (i.e. both group 1 and group 2, as well as group 3 and group 4) had different degrees (i.e. mild or severe) of functional changes. For example, in some patients, severe structural damage occurred simultaneously with severe functional changes (group 4), associated with worse IPS scores than when accompanied with mild functional changes (group 3). One could hypothesize that in this subgroup the functional network "suffers" from the structural changes. The absence of a strict one-to-one relation between the level of structural and functional damage, emphasizes the value of integrating both measures. Our findings showed that adding information about the severity of functional changes is needed to distinguish between different levels of IPS performance in patients with similar degrees of structural damage. This might indicate that the functional network acts as mediating factor between the level of structural damage and IPS performance. Additionally, the resilience of the functional brain network might also limit cognitive consequences, as observed in patients with severe structural damage and only mild functional damage. More resilient networks might theoretically be able to cope with a larger amount
103
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Supplementary Table 3.1.1 Demographics and MRI characteristics of the groups with mild and severe structural and/or functional changes
Mild S & F Mild S & severe F Severe F & mild S Severe F & S p
Functional & WM changes
n
Age
Sex (female/male) Education
Disease duration, years FA NDGMV Increased FC 97 43.09 (8.46)b,c,d 52/45b,c,d 6 (4 – 6) 10.48 (5.91)c,d 0.41 (0.01)c,d 59.54 (4.58)c,d 0.65 (0.03)b,d 68 46.15 (11.91)a,c,d 47/21 4 (3 – 6) 12.00 (7.24)c,d 0.41 (0.01)c,d 59.25 (4.72)c,d 0.76 (0.03)a,c 67 49.70 (9.41)a,b,d 48/19 5 (4 – 6) 16.74 (8.09)a,b,d 0.38 (0.02)a,b 52.86 (6.90)a,b 0.66 (0.03)b,d 96 54.13 (0.59)a,b,c 76/20 5 (3 – 6) 19.35 (8.97)a,b,c 0.37 (0.02)a,b 52.43 (7.19)a,b 0.76 (0.03)a,c < 0.001 0.002 n.s. < 0.001 < 0.001 < 0.001 < 0.001
Functional & GM changes
n
Age
Sex (female/male) Education
Disease duration, years FA NDGMV Increased FC 91 42.40 (8.65)a,b,c 56/35 6 (4 – 6) 10.75 (6.06)c,d 0.41 (0.02)b,c,d 61.42 (3.30)c,d 0.65 (0.03)b,d 75 47.59 (12.20)a,d 64/11a,c,d 3 (4 – 6) 12.71 (6.96)c,d 0.40 (0.02)a,c,d 61.21 (2.85)c,d 0.76 (0.03)a,c 74 49.82 (8.71)a,d 45/29 5 (4 – 6) 15.76 (8.19)a,b,d 0.39 (0.02)a,b,d 51.35 (4.98)a,b 0.67 (0.03)b,d 90 53.55 (10.79)a,b,c 60/30 5 (3 – 6) 19.33 (9.50)a,b,c 0.38 (0.02)a,b,c 50.24 (15.59)a,b 0.76 (0.06)a,c < 0.001 0.003 n.s. < 0.001 < 0.001 < 0.001 < 0.001
Functional & combined structural changes
n
Age
Sex (female/male) Education
Disease duration, years FA NDGMV Increased FC 100 41.98 (8.96)b,c,d 58/42b,d 5 (4 – 6) 10.32 (5.51)c,d 0.41 (0.01)c,d 60.55 (4.05)c,d 0.65 (0.03)b,d 67 45.50 (12.17)a,c,d 54/13 3 (5 – 6) 11.73 (6.77)c,d 0.41 (0.01)c,d 61.03 (3.44)c,d 0.75 (0.03)a,c 69 50.58 (8.03)a,b,d 46/23 5 (4 – 6) 16.78 (8.21)a,b,d 0.38 (0.02)a,b 51.81 (5.79)a,b 0.66 (0.02)b,d 94 54.38 (10.17)a,b,c 67/27 5 (3 – 6) 19.50 (9.17)a,b,c 0.37 (0.03)a,b 51.06 (6.09)a,b 0.76 (0.03)a,c < 0.001 0.0200 n.s. < 0.001 < 0.001 < 0.001 < 0.001 F: functional; FA: fractional anisotropy; FC: functional connectivity; GM: gray matter NDGMV: normalized deep gray matter; S: structural
Abstract
Objective To explore the added value of dynamic functional connectivity (dFC) of the default mode network (DMN) during resting-state (RS), during an information processing speed (IPS) task, and the within-subject difference between these conditions, on top of conventional brain measures in explaining IPS in people with multiple sclerosis (pwMS).Methods In 29 pwMS and 18 healthy controls, IPS was assessed with the Letter Digit Substitution Test and Stroop Card 1 and combined into an IPS-composite score. White matter (WM), grey matter (GM), and lesion volume were measured using 3T MRI. WM integrity was assessed with diffusion tensor imaging. During RS and task-state fMRI (i.e. symbol digit modalities task, IPS), stationary functional connectivity (sFC; average connectivity over the entire time series) and dFC (variation in connectivity using a sliding window approach) of the DMN was calculated, as well as the difference between both conditions (i.e. task-state minus RS; ΔsFC-DMN and ΔdFC-DMN). Regression analysis was performed to determine the most important predictors for IPS.
Results Compared to controls, pwMS performed worse on IPS-composite (p = 0.022), had lower GM volume (p < 0.05) and WM integrity (p < 0.001), but no alterations in sFC and dFC at the group level. In pwMS, 52% of variance in IPS-composite could be predicted by cortical volume (β = 0.49, p = 0.01) and ΔdFC-DMN (β = 0.52, p < 0.01). After adding dFC of the DMN to the model, the explained variance in IPS increased with 26% (p < 0.01).
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Introduction
Up to 50% of people with multiple sclerosis (pwMS) suffer from problems with information processing speed (IPS), also known as "cognitive slowing". Deficits in IPS are among the first cognitive symptoms in pwMS and related to reduced
quality of life.1–4 The search for neural correlates of IPS deficits resulted in several
structural and functional brain measures. These include white and grey matter
damage (e.g. lesions, atrophy, and reduced tissue integrity),5–7 but also changes
in activation and functional connectivity (FC) during an IPS task or during
resting-state (RS).8–10 Although these measures do explain IPS to a certain extent,
there is still room to improve the relationship between brain measures and IPS. For example, intuitively IPS depends on the ability of the brain to rapidly transfer information within its functional network. As FC measures have previously been averaged over the entire scanning session (i.e. time series), from here on referred to as stationary FC (sFC), the variability in FC over time has not been taken into account. With this latter measure, the changes in connectivity strength during a time series are obtained, from here on referred to as dynamic FC (dFC). As dFC
seems to be behaviorally relevant with respect to cognition in healthy subjects11–13
or symptoms in neurological disorders,14–16 we argue that dFC could also be of
importance for maintained IPS in pwMS, as it could reflect the fast-changing
connectivity patterns within the brain that cannot be captured with sFC.11
Brain networks
The brain's functional network has an intrinsic organization, namely various (interconnected) RS networks that can be identified with RS functional magnetic resonance imaging (fMRI). This intrinsic organization of the brain has been linked to cognitive functioning, as it is thought enable the flow of activity (i.e. information)
during task performance.17,18 An important brain network related to cognitive
(dys)functioning is the default mode network (DMN).19 This network consists of
several core regions, including the medial temporal lobe, medial prefrontal cortex,
posterior cingulate cortex, and inferior parietal cortex.19 Recent studies have
shown task-related 'responsivity' in sFC of the DMN, that is, the ability to change the connection strength upon task demands, to enable information integration
throughout the brain.20,21
Dynamics of the DMN
Previous studies have linked DMN dynamics during RS or task-state to cognitive functioning, such as executive functioning, cognitive flexibility, concept formation, and (working) memory, in healthy subjects and individuals with neurological
disorders (e.g. temporal lobe epilepsy and MS).16,22–28 Furthermore, one study in
frontoparietal network during task-state relative to RS was related to better
cognitive flexibility outside the scanner (i.e. Stroop task).23 Together with studies
showing differences in dFC between RS and task-state, the change in dFC of the DMN between RS and task-state might reflect the ability of the brain to adapt as task demands change, in order to optimally execute the task at hand (i.e. increased
information processing throughout the brain).11,29–31
To investigate whether dFC of the DMN is indeed a neural correlate of IPS in MS, and relevant next to previously identified correlates, we explored its incremental value when explaining IPS variance on top of conventional measures of brain abnormalities (defined as: brain atrophy, lesions, white matter integrity, and sFC of the DMN). We hypothesized that dFC of the DMN, and especially the difference in dFC between RS and task-state, that is, the ability of the brain to adapt upon task demands, would explain unique variance in IPS.
Materials and methods
Subjects and study design
In this prospective observational study, all pwMS (n = 33) and healthy controls (HCs; n = 19) met the following inclusion criteria: (1) aged 18–65 years, (2) no contra-indications for MRI, (3) no psychiatric or neurological disease (for pwMS: other than MS). For pwMS, additional inclusion criteria were: (4) a diagnosis of relapsing-remitting MS, and (5) without relapse or steroid treatment for at least four weeks prior to study measurements. Subjects performing below chance level (< 50% correct, n = 3 pwMS) on the fMRI paradigm were excluded from the entire study, as well as subjects with many frame-to-frame head displacements (> 0.5 mm for > 20% of frames, n = 1 pwMS and n = 1 HC) during fMRI to minimize
motion effects on dFC measures.32 The study was approved by the local institutional
111
Clinical measures
All subjects underwent neuropsychological testing, including, among others, the Letter Digit Substitution Task (LDST; oral version, 90 seconds), which is an
equivalent of the Symbol Digit Modalities Test (SDMT),33 and the Stroop Test (for all
tests see Supplementary Methods).34 Scores on all neuropsychological tests were
converted into a Z-score relative to HCs. Scores on the LDST and Stroop Card 1 were averaged into one IPS composite Z-score. Anxiety and depression levels were
assessed with the Hospital Anxiety and Depression Scale.35 Fatigue was measured
using the Checklist of Individual Strength.36 Additionally, physical disability was
assessed by a trained physician using the Expanded Disability Status Scale (EDSS).37
MRI acquisition
All subjects were examined using a 3T whole-body MRI scanner (GE Signa-HDxt, Milwaukee, WI, USA) with a 32-channel head coil. The protocol included the following sequences: three-dimensional (3D) T1-weighted fast spoiled gradient echo for volume measurements (repetition time (TR): 8.22 ms, echo time (TE): 3.22 ms, inversion time (TI): 450 ms, flip angle 12 degrees, 1.0 mm sagittal slices,
0.94 × 0.94 mm2 in-plane resolution); 3D fluid-attenuated inversion recovery
(FLAIR; TR: 8000 ms, TE: 128 ms, TI: 2343 ms, 1.2 mm sagittal slices, 0.98 × 0.98
mm2 in-plane resolution) for white matter (WM) lesion detection; and diffusion
tensor imaging (DTI; TR: 7200 ms, TE: 83 ms, flip angle 90 degrees, 57 axial slices with an isotropic 2.0 mm resolution) with 5 volumes without directional weighting
and 30 volumes with non-collinear diffusion gradients (b-value: 1000 s/mm2) to
assess WM integrity. To correct for echo planar imaging (EPI) induced artifacts, two scans with reversed phase-encode blips were acquired for DTI. Furthermore, RS fMRI (eyes closed; EPI, 202 volumes, TR: 2200 ms, TE: 35 ms, flip angle 80 degrees,
3 mm contiguous axial slices, 3.3 × 3.3 mm2 in-plane resolution) and task-related
(i.e. task-state) fMRI (IPS paradigm; EPI, 460 volumes, TR: 2000 ms, TE: 30 ms, flip
angle 80 degrees, 4 mm contiguous axial slices, 3.3 × 3.3 mm2 in-plane resolution)
were performed to measure sFC and dFC.
Structural MRI measures
Whole-brain and lesion volume
Lesions were automatically segmented on FLAIR images and filled on the 3DT1
images using LEAP.38,39 WM and grey matter (GM) volumes were measured using
SIENAX.40 Volumes of deep GM structures were measured using FIRST (FSL v5.0.9,
fmrib.ox.ac.uk/fsl). Cortical GM volume was measured by subtracting the FIRST
Severity and extent of WM damage
The susceptibility-induced off-resonance field was estimated for the diffusion weighted sequence using a method described previously, and the two images
were combined into a single corrected one (and used for further processing).41
Motion- and eddy-current correction was performed, followed by diffusion tensor fitting (FMRIB's Diffusion Toolbox, FSL). Tract-based spatial statistics with
default settings was used to obtain skeletonized fractional anisotropy (FA) maps.42
In order to obtain individual measures of whole-brain WM integrity damage, we quantified the severity and extent of WM damage using a method that has
been described previously.43 In short, each subject's skeleton was voxelwise
expressed as a Z-score relative to HCs. The severity of WM damage in a subject was subsequently calculated by computing average normalized FA score within the skeleton. The extent of WM damage was calculated by counting the number
of voxels exceeding the threshold of Z < -3.1 (p < 0.001) in each subject.43 Finally,
the whole-brain average FA of the white matter was obtained for each subject.
Functional MRI
Task-state fMRI paradigm
Task-state fMRI was obtained by examining IPS using the modified version of the
SDMT (mSDMT) inside the scanner.8 Briefly, in the stimulus condition a panel of
nine paired stimulus boxes were presented. The boxes in the upper row contained a symbol, while the boxes in the lower row contained a digit (1 to 9). Below this panel, a symbol-digit pair was presented, and the subject had to indicate via a button box whether or not this symbol-digit pair matched one of the pairs in the upper panel. The timing of the stimuli was pre-specified. Accuracy was used for the analyses of behavioral task data. Reaction time for correct trials was not included, as this can be influenced by motor problems in pwMS. The paradigm lasted approximately 15 minutes.
Preprocessing of fMRI data
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Atlas construction
In this study, we used the Brainnetome atlas44 (210 regions) to parcellate the brain.
By overlaying the Yeo7 RS network atlas45 on the Brainnetome atlas, we were
able to identify which Brainnetome regions belonged to the DMN based on two criteria: 1) > 50% of voxels of a Brainnetome region had to overlap with the DMN from the Yeo7 atlas, and 2) the overlap with the DMN should be the highest and the differences in overlap with the second highest RS network should be > 15%. In total, 38 regions of the Brainnetome atlas were considered part of the DMN. After preprocessing of RS and task-state images, the Brainnetome atlas in standard space was non-linearly registered to each subject's 3D T1 image, and subsequently masked for GM (binarized SIENAX segmentation). Next, the subcortical regions derived by FIRST were added, resulting in an atlas containing 224 regions. This novel atlas was then linearly registered to RS and task-state fMRI space, where areas known to be prone to artifacts were removed (e.g. orbitofrontal cortex), by excluding voxels with a signal intensity in the lowest quartile of the robust intensity range (i.e. the minimum and maximum if the outer tails of the intensity distribution are ignored). Finally, the average time series for each brain region was obtained during RS and task-state fMRI, and imported into Matlab R2012a (Natick, Massachusetts, USA) for further analysis.
Stationary FC
To obtain sFC, Pearson correlation coefficients between all brain regions (i.e. region of interest-wise analysis) over the entire time series (absolute values) were
calculated for RS and task-state fMRI separately (see Figure 3.2.1A).
Dynamic FC
For dFC, a sliding-window approach was used with settings that were selected
based on previous studies (see Figure 3.2.1A), resulting in 35 and 86 sliding windows
for RS and task-state time series, respectively.28,46 For RS time series the window
length was 27 volumes (59.4 s) with a shift of 5 volumes (11 s). For task-state time series, the window length was 30 volumes (60 s) with a shift of 5 volumes (10 s). Note that the differences in window length and shift were caused by differences in TR. Next, absolute Pearson correlation coefficient was calculated between all 224 regions for each window. Finally, the absolute difference in FC was calculated between each consecutive window and subsequently summed per matrix cell, resulting in a 224 by 224 dFC matrix.
DMN connections
and dFC measure was divided by its corresponding whole-brain average sFC and dFC. Furthermore, we calculated the difference in sFC of the DMN (ΔsFC-DMN) between RS and task-state as follows: task-state sFC DMN minus RS sFC DMN. The differences in dFC of the DMN (ΔdFC-DMN) was calculated similarly: task-state dFC DMN minus RS dFC DMN. Positive values indicate an increase in sFC or dFC in
task-state relative to RS. See Figure 3.2.1B and Figure 3.2.1C for a schematic overview of
functional measures.
Statistical analysis
Statistical analyses were performed in SPSS version 22 (Armonk, NY, USA). Normality of variables was investigated using the Kolmogorov-Smirnov test and visual inspection of histograms. NLV and the extent of WM damage were log-transformed to obtain normally distributed data. EDSS, educational level, questionnaires for anxiety and depression, and mSDMT accuracy were not transformed and tested with Mann-Whitney U tests. Univariate and multivariate general linear models were constructed to assess group differences in behavioral measures (IPS, questionnaires, and task performance), structural MRI (atrophy, extent and severity of white matter damage), sFC, and dFC (with average head motion as covariate to further limit its effect on the analyses). We performed a two-tailed one-sample t-test in each group to test whether ΔsFC-DMN and ΔdFC-DMN differed from zero (indicating a change). The differences in ΔsFC-DMN and ΔdFC-DMN between groups were analyzed with a univariate general linear model. Additionally, we explored the relationship between dFC and conventional brain measures using Pearson correlation coefficients. Because of multiple testing regarding brain measures and the correlation analyses, Benjamini-Hochberg
false discovery rate corrected p-values (corr. p) are reported for these analyses.47
115
Post hoc specifi city analysis
To investigate the specificity of the relationship between dynamic brain measures that were significantly related to IPS, we performed a correlation analysis (Spearman's rank correlation) with other outcome variables, including EDSS score, fatigue, depression, and all neuropsychological tests.
Time Br ain a cti va tion Time Br ain a cti va tion sFC Timeseries FC FC FC FC
window 1window 2window 3
window 4 etc.
Resting-state Task-state (IPS paradigm)
1 2 3 4 5 6 7 8 9 4
|ΔFC| |ΔFC| |ΔFC|
∑
dFC =
Stationary functional connectivity Dynamic functional connectivity A B C Timeseries RS sFC RS dFC task-state sFC task-state dFC ΔdFC-DMN (task-state – RS) ΔsFC-DMN (task-state – RS)
Figure 3.2.1 Schematic overview of functional measures
Stationary (s) functional connectivity (FC) was calculated with Pearson correlation coeffi cients over the entire time series, whereas for dynamic (d) FC the time series were divided into sliding windows (A). For each sliding window, FC was calculated and subsequently the absolute diff erence between each consecutive window was calculated and summed as a measure of dFC. For both resting-state (RS) and task-state fMRI, sFC and dFC of the default mode network was obtained (B). Additionally, the diff erence in sFC and dFC between task-state and RS was calculated (C).
Results
Demographics and clinical measures
The final sample size included 29 pwMS (18 women, mean age: 41.3 ± 9.3 years, mean disease duration: 11.1 ± 7.1 years) and 18 HCs (11 women, mean age: 40.7 ±
13.3 years; see Table 3.2.1). No differences between groups were found for age,
sex, and educational level. Anxiety and depression scores were similar between groups, but pwMS reported more fatigue than controls (pwMS: 72.0 ± 33.6, HCs: 47.1 ± 18.3, p = 0.007). Compared to HCs, pwMS performed worse on the LDST (Z-score: -1.1 ± 1.4, p = 0.005), but not on the Stroop Card 1 (Z-score: -0.5 ± 1.4, p = 0.186). The IPS composite Z-score was lower in pwMS (Z-score: -0.8 ± 1.3) compared to HCs (Z-score: 0.0 ± 0.8, p = 0.022). Furthermore, compared to HCs, pwMS performed worse on tests for visuospatial memory, executive
functioning, working memory, and verbal fluency (see Supplementary Table
3.2.1). In total, 6 pwMS met the criteria for cognitive impairment (scoring at least 2 SD below that of HCs on at least 2 tests). Accuracy on the IPS task inside the scanner did not differ between groups.
Structural brain changes
NWMV was similar between groups (see Table 3.2.2), while pwMS displayed
lower NCGMV (corr. p = 0.009) and NDGMV (corr. p = 0.002). Additionally, the severity and extent of WM damage was worse in pwMS compared to HCs (corr.
p < 0.001 for both). Furthermore, whole brain FA was lower in pwMS than in HCs
(corr. p < 0.001).
Stationary and dynamic FC
No group differences were observed for RS and task-state sFC and dFC of the DMN
(see Table 3.2.3). Additionally, in both groups, ΔsFC-DMN did not differ significantly
from zero (mean ΔsFC-DMN pwMS: -0.02 ± 0.06, t(28) = 1.70, corr. p = 0.133; mean ΔsFC-DMN HCs: -0.02 ± 0.07, t(17) = 1.06, corr. p = 0.304), suggesting no difference in sFC between RS and task-state. Furthermore, ΔsFC-DMN did not differ between pwMS and HCs.
In pwMS and HCs, ΔdFC-DMN differed significantly from zero (mean ΔdFC-DMN pwMS: 0.02 ± 0.02, t(28) = 3.63, corr. p = 0.004; mean ΔdFC-DMN HCs: 0.03 ± 0.01,
t(17) = 2.81, corr. p = 0.024), suggesting an increase in dFC of the DMN during
117
Relationship between dFC and conventional brain measures
After correction for multiple testing, none of the correlation coefficients between dynamic and conventional brain measures were statistically significant in pwMS or HCs (corr. p > 0.251).
Table 3.2.1 Demographics and clinical measures
pwMS (n = 29) HCs (n = 18) p Age 41.25 (9.34) 40.68 (13.29) 0.863 Sex (female/male) 18/11 11/7 0.948 Educational levela 6.00 (5.00 – 7.00) 6.00 (5.00 – 7.00) 0.098 Disease duration 11.05 (7.11) – – EDSSa 3.00 (1.00 – 6.00) – – HADS-Aa 5.50 (0.00 – 12.00)b 4.00 (2.00 – 13.00) 0.170 HADS-Da 3.00 (0.00 – 14.00)b 1.50 (0.00 – 6.00) 0.229 CIS20r 71.97 (33.58)b 47.06 (18.29) 0.007 Z-score LDST -1.11 (1.38) 0.00 (1.00) 0.005
Z-score Stroop Card 1 -0.51 (1.41) 0.00 (1.00) 0.186
IPS composite Z-score -0.81 (1.31) 0.00 (0.78) 0.022
mSDMT performance (inside scanner)
Accuracy (%)a 95.45 (77.27 – 100.00) 96.36 (86.36 – 100.00) 0.307
Displayed data are mean (standard deviation)
CIS20r: Checklist of Individual Strength, revised; EDSS: Expanded Disability Status Scale; HCs: healthy controls; HADS: Hospital Anxiety and Depression Scale; A: Anxiety; D: Depression; IPS: information processing speed; LDST: Letter Digit Substitution Test; mSDMT: modified Symbol Digit Modalities Test; pwMS: people with multiple sclerosis
Table 3.2.2 Brain volumes and white matter damage
pwMS (n = 29) HCs (n = 18) Effect size (η2) p p corr.
NWMV (ml) 682.55 (46.91) 699.07 (39.36) 0.033 0.219 0.219 NCGMV (ml) 780.47 (76.20) 839.46 (59.41) 0.148 0.008 0.009 NDGMV (ml) 58.80 (6.88) 65.08 (4.59) 0.207 0.001 0.002 NLV (ml) 22.46 (15.99) – – – –
WM damage
Whole brain average FA
Severity score
Average Z-score skeleton
Extent score
Number of affected voxels (%) 0.33 (0.03)a -0.61 (0.50)a 3430.30 (5115.74)a (2.87%) 0.36 (0.02) 0.00 (0.34) 21.90 (55.86) (0.02%) 0.335 0.321 0.822 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
Displayed data are mean (standard deviation)
HCs: healthy controls; NCGMV: normalized cortical grey matter volume; NDGMV: normalized deep grey matter volume; NLV: normalized lesion volume; NWMV: normalized white matter volume; WM: white matter; p corr. = false discovery rate corrected p-values; pwMS = people with multiple sclerosis
a n = 27
Predicting IPS
mSDMT accuracy
In pwMS, mSDMT accuracy inside the scanner could not be predicted by
conventional brain measures (block 1 – 4; Table 3.2.4). However, when adding
block 5, the model predicted 23% of the variance in mSDMT accuracy, effectively by ΔdFC-DMN only (β = 0.51, p = 0.006). In HCs, 23% of variance in mSDMT accuracy could be explained by RS dFC of the DMN only (β = 0.53, p = 0.025).
For both pwMS and HCs, Figure 3.2.2A displays the relationship between the final
model and the outcome measure. IPS composite Z-score
In pwMS, confounders and conventional brain measures could explain 26% of variance in IPS composite Z-score (block 1 – 4) with NCGMV (β = 0.47, p = 0.050)
as predictor (Table 3.2.4). When adding dFC of the DMN, the amount of explained
variance increased to 52% (R2 change = 0.25, p = 0.001). The predictors now
included NCGMV (β = 0.49, p = 0.013) and ΔdFC-DMN (β = 0.52, p = 0.001) In HCs, none of the predictors were significantly related to IPS outside the scanner.
119
Table 3.2.3 Task-state and resting-state stationary and dynamic functional connectivity
pwMS (n = 29) HCs (n = 18) Effect size (η2) p p corr.
Average head motion
Resting-state (mm) Task-state (mm) 0.068 (0.035) 0.091 (0.042) 0.068 (0.028) 0.068 (0.036) < 0.001 0.075 0.989 0.063 0.989 0.167 Resting-state sFC DMN dFC DMN 0.959 (0.051) 1.018 (0.030) 0.952 (0.052) 1.019 (0.024) 0.005 < 0.001 0.647 0.893 0.989 0.989 Task-state sFC DMN dFC DMN 0.940 (0.048) 1.034 (0.024) 0.935 (0.040) 1.049 (0.027) < 0.001 0.035 0.957 0.213 0.989 0.167 Difference
Task-state minus resting-state
ΔsFC-DMN ΔdFC-DMN -0.019 (0.060) 0.016 (0.023) -0.017 (0.068) 0.029 (0.044) < 0.001 0.042 0.917 0.168 0.989 0.167
Displayed data are mean (standard deviation)
dFC: dynamic functional connectivity; DMN: default mode network; HCs: healthy controls; p corr.: false discovery rate corrected p-values; pwMS: people with multiple sclerosis; sFC: stationary functional connectivity
Post hoc specificity analysis
Within both groups, no significant relationship was found between ΔdFC-DMN and EDSS score (pwMS: corr. p = 0.860), fatigue (pwMS: corr. p = 0.768; HC: corr.
p = 0.712), depression (pwMS and HCs: corr. p = 0.712) or any neuropsychological
test (pwMS and HCs corr. p > 0.712).
Post hoc exploration: effect of switching medication
No differences were found between switchers (n = 13) and non-switchers (n = 19) with respect to demographics, disease characteristics, IPS composite Z-score, or
mSDMT accuracy (Supplementary Table 3.2.2). Furthermore, no significant group
differences were found regarding structural MRI, sFC and dFC of the DMN.
No effect of switching was found on mSDMT accuracy (β = -0.14, p = 0.395). Furthermore, adding the interaction between switching with ΔdFC-DMN to the
model did not increase the amount of explained variance (R2 change = 0.02,
p = 0.463), resulting in a final model explaining 23% of variance in mSDMT by
ΔdFC-DMN (β = 0.52, p = 0.005). Similar findings were obtained for IPS composite
Z-score: no effect of switchers/non-switchers was found on this dependent variable
(β = -0.16, p = 0.263). Additionally, adding the interaction terms between switchers/ non-switchers with ΔdFC-DMN and NCGMV did not improve the model in terms
of explained variance (R2 change = 0.04, p = 0.358). In total, 54% of variance could
Table 3.2.4 Hierarchical regression models for predicting mSDMT performance and IPS composite Z-score
Adjusted R2 Standardized β Test statistic p
mSDMT accuracy pwMS Full model: block 1 – 4 N/A N/A N/A
Full model: block 1 – 5
ΔdFC-DMN 0.23 0.51 8.83a 2.97b 0.006 0.006
HCs Full model: block 1 – 4 N/A N/A N/A
Full model: block 1 – 5
RS dFC-DMN 0.23 -0.53 6.10a -2.47b 0.025 0.025 IPS composite Z-score
pwMS Full model: block 1 – 4
Age NCGMV 0.26 -0.14 0.47 5.65a -0.60b 2.07b 0.010 0.554 0.050
Full model: block 1 – 5
Age NCGMV ΔdFC-DMN 0.52 < 0.01 0.49 0.52 10.20a < 0.01b 2.69b 3.67b < 0.001 0.998 0.013 0.001
HCs Full model: block 1 – 4 N/A N/A N/A
Full model: block 1 – 5 N/A N/A N/A
dFC: dynamic functional connectivity; DMN: default mode network; HCs: healthy controls; IPS: information processing speed; mSDMT: modified Symbol Digit Modalities Test; N/A: not applicable; pwMS: people with multiple sclerosis; RS: resting-state; sFC: stationary functional connectivity
a F-value b t-value
Discussion
121
Conventional MRI and IPS
Problems with IPS in MS have typically been explained as a consequence of WM damage, such as lesions and decreased integrity, but also of cortical and deep
GM atrophy.5–7 Although WM damage or deep GM atrophy were not identified as
the most important predictors for IPS in our sample, we did observe that NCGMV could explain up to 26% of variance in IPS outside the scanner. With respect to sFC, we did not observe differences between pwMS and controls, whereas previous studies did show differences in DMN effective and stationary FC at rest and during
an IPS task, both related to IPS.9,10 These contradictory findings might be explained
by methodological differences, such as sample size and operationalization of FC. With respect to the latter, a previous study used a seed-based approach instead
of an atlas-based approach on RS data only.10 The other study investigated the
directionality of FC (i.e. effective FC) during IPS, which provides differential
information than sFC, and is therefore difficult to compare.9 Furthermore, we
normalized FC measures for whole-brain average FC, as this average FC can vary greatly between subjects and potentially drive differences between groups, which
-3 -2 -1 0 1 2 Healthy controls mSDMT accuracy (%) Standardized residual mSDMT accuracy (%) -3 -2 -1 0 1 2 100 95 90 85 80 75
People with multiple sclerosis
Standardized residual 100 97.5 95 92.5 90 87.5 IPS composite Z-score -3 -2 -1 0 1 2 3
People with multiple sclerosis
Standardized residual 2 0 -2 -4 A
B Figure 3.2.2 Relationship between the regression
model and outcome measures
For both accuracy on the modified symbol digit modalities test (A) and information processing speed composite Z-score (B), the standardized residuals of the final regression model, including dynamic functional connectivity of the default mode network, is plotted against performance for people with MS and healthy controls separately.
is not always performed in other studies. Applying this normalization step allowed us to deal with individual differences in FC and can thereby more accurately reflect possible changes in FC between groups.
Dynamics and IPS
Although no differences between pwMS and HCs were observed in dFC of the DMN, adding dFC to the regression model, next to confounding variables and conventional MRI measures, increased the explained variance by the model for IPS inside and outside the scanner. Furthermore, we did not observe a significant relationship between ΔdFC-DMN and EDSS score, fatigue, depression or any other neuropsychological test score, suggesting that our findings are specific for IPS. Changes in RS DMN dynamics have been observed in other neurological disorders, including schizophrenia, autism, attention deficit hyperactivity disorder, depression, and epilepsy. Often, these changes were
related to the severity of symptoms.14–16,48 A large challenge to directly compare
between study results is differences in operationalization of DMN dynamics (e.g. standard deviation of FC, non-overlapping windows, or spectrum analysis) but also variation in types of pathology. Nevertheless, the present measure of dFC was able to pick up individual differences in pwMS and HCs regarding dFC of the DMN and IPS. This is in line with a recent study that was able to identify individuals based on spatial patterns of dynamic characteristics of FC (i.e. 'fingerprinting'), which was a significant predictor for higher order cognitive functions (i.e. fluid
intelligence and executive functions).22
DMN responsivity
In both pwMS and HCs, an increase in dFC of the DMN was observed from RS to task-state, suggesting that this might be a response upon increasing cognitive demands. Interestingly, a larger increase in dFC from RS to task-state in pwMS was the only significant predictor for IPS inside the scanner, as well as a significant predictor for IPS outside the scanner (next to NCGMV). These findings suggest that during IPS, the DMN seems to change its FC pattern more often than during RS,
possibly reflecting increased information flow throughout the network.25 In HCs,
however, lower RS dFC of the DMN was related to better IPS inside the scanner, whereas no predictors were found for IPS outside the scanner (probably explained by limited variation in performance and obtained brain measures). Previous studies in healthy subjects have linked both lower and higher brain dynamics during RS to
better cognitive functioning.12,23,27 These varying results illustrate the complexity
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The behavioral relevance of increasing DMN dynamics during task-state relative to RS is in line with a previous study in healthy subjects on cognitive flexibility
(i.e. Stroop task) and dFC of the DMN with the frontoparietal network.23 That is,
higher dFC of the DMN with frontoparietal network during task-state and lower dFC during RS in isolation were related to better cognitive flexibility outside the
scanner.23 However, a larger increase in task-state relative to RS dFC explained even
more variance in cognitive flexibility.23 Another study found that the frontoparietal
network was more dynamic during working memory compared to a control condition, which could be related to better working memory and executive
functions.29 For sFC, the increase in DMN connectivity with respect to increasing
task-load has been described previously in healthy subjects.20,21 In turn, this DMN
responsivity could be related to maintained task performance.20,21 Metaphorically,
this task-evoked responsivity of the brain might be similar as performing a
challenging physical exercise that exposes certain cardiac conditions.13 Hence,
one could speculate that combining RS with task-state functional measures might (partly) capture the ability of a patient's functional network to adapt upon task
demands, which might explain a patient's cognitive abilities.13
Switching medication
The present MS sample consisted of pwMS switching to fingolimod treatment and non-switchers continuing with first-line therapy. The status of pwMS (switchers/ non-switchers) did not relate to IPS or mediated the relationship between ΔdFC-DMN and NCGMV with IPS. These findings suggest that switchers were statistically similar to non-switchers. However, one should keep in mind that more than half of the switchers used first-line therapy prior to fingolimod and changed therapy because of disease activity (n = 7), whereas positivity for John Cunningham virus, and therefore at risk for developing progressive multifocal leukoencephalopathy, was the main reason for pwMS to switch from natalizumab to fingolimod (n = 6) and not so much disease activity. Future studies should explore possible changes in brain dynamics under MS treatment over time in relationship to disease activity and cognitive functioning.
