diagnostics
Article
Cognitive Fatigue Is Associated with Altered
Functional Connectivity in Interoceptive and
Reward Pathways in Multiple Sclerosis
Michelle H. Chen 1,2 , John DeLuca 1,2 , Helen M. Genova 1,2 , Bing Yao 1,2 and
Glenn R. Wylie 1,2, *
1 Kessler Foundation, East Hanover, NJ 07936, USA; mchen@kesslerfoundation.org (M.H.C.);
jdeluca@kesslerfoundation.org (J.D.); hgenova@kesslerfoundation.org (H.M.G.);
byao@kesslerfoundation.org (B.Y.)
2 Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University,
Newark, NJ 07103, USA
* Correspondence: gwylie@kesslerfoundation.org
Received: 24 September 2020; Accepted: 6 November 2020; Published: 10 November 2020
Abstract: Cognitive fatigue is common and debilitating among persons with multiple sclerosis
(pwMS). Neural mechanisms underlying fatigue are not well understood, which results in lack of
adequate treatment. The current study examined cognitive fatigue-related functional connectivity
among 26 pwMS and 14 demographically matched healthy controls (HCs). Participants underwent
functional magnetic resonance imaging (fMRI) scanning while performing a working memory task
(n-back), with two conditions: one with higher cognitive load (2-back) to induce fatigue and one
with lower cognitive load (0-back) as a control condition. Task-independent residual functional
connectivity was assessed, with seeds in brain regions previously implicated in cognitive fatigue
(dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (vmPFC), dorsal anterior
cingulate cortex (dACC), insula, and striatum). Cognitive fatigue was measured using the Visual
Analogue Scale of Fatigue (VAS-F). Results indicated that as VAS-F scores increased, HCs showed
increased residual functional connectivity between the striatum and the vmPFC (crucial in reward
processing) during the 2-back condition compared to the 0-back condition. In contrast, pwMS
displayed increased residual functional connectivity from interoceptive hubs—the insula and the
dACC—to the striatum. In conclusion, pwMS showed a hyperconnectivity within the interoceptive
network and disconnection within the reward circuitry when experiencing cognitive fatigue.
Keywords: multiple sclerosis; functional connectivity; fatigue; neuroimaging; fMRI
1. Introduction
Fatigue is one of the most prevalent symptoms of multiple sclerosis (MS) [1,2], an immune-
mediated, neurodegenerative disorder characterized with demyelination, axonal injury, and brain
atrophy. Fatigue is a major clinical concern among persons with MS (pwMS), as it significantly disrupts
functional independence and quality of life [3]. A large-scale, retrospective study of the New York State
MS Consortium registry (n = 5428) found that baseline moderate to severe fatigue was a significant
predictor of decline in sustained neurologic disability and psychosocial limitations four years later [4].
Despite its high prevalence and debilitating impact on pwMS, the pathophysiology of fatigue is still
not well understood, which results in limited effective treatments for fatigue in MS [5]. Therefore, it is
imperative to understand the mechanisms underlying fatigue, in order to develop effective treatments
for fatigue and improve the lives of pwMS.
Diagnostics 2020, 10, 930; doi:10.3390/diagnostics10110930 www.mdpi.com/journal/diagnostics
Diagnostics 2020, 10, 930 2 of 22
One method to explore the mechanisms underlying fatigue is to utilize neuroimaging. Seminal
work by Chaudhuri and Behan [6] highlighted the basal ganglia’s role in central fatigue, which was
defined as “the failure to initiate and/or sustain attentional tasks (‘mental fatigue’) and physical
activities (‘physical fatigue’) requiring self-motivation.” Central fatigue is common in central nervous
system disorders such as MS and is qualitatively different from peripheral fatigue, which is more
characteristic of neuromuscular conditions such as myasthenia gravis. Peripheral fatigue primarily
affects physical activity such as exercise, sparing mental processes. There has been a body of literature
using neuroimaging to confirm the presence of central fatigue [7]. The current study examines cognitive
(or mental) fatigue, which is a type of central fatigue. Consistent with Chaudhuri and Behan [6]’s
model, several investigators have supported the hypothesis that cognitive fatigue is associated with
abnormalities in the cortical-striatal network, including the striatum of the basal ganglia, the thalamus,
and the ventromedial prefrontal cortex (vmPFC) [7–9]. Specifically, it is hypothesized that one’s effort
toward a task depends on the perceived reward from performing the task. In MS, it is suggested that
there is a mismatch between the perceived effort required for a task and the resulting benefit due
to abnormal reward processing in the cortico-striatal circuitry, which may lead to the experience of
cognitive fatigue [8].
Furthermore, recent evidence has suggested a link between fatigue and deficits in interoception,
or self-awareness of bodily internal states [10–12]. According to this framework, the subjective
feeling of fatigue may reflect the brain’s metacognitive interpretation of the body’s failure to control
internal states, due to disruptions in the interoceptive pathways. Consistent with this hypothesis,
Gonzalez Campo and colleagues [13] empirically identified interoceptive deficits among fatigued
pwMS, along with decreased gray matter volume and increased functional connectivity in the insula
and the anterior cingulate cortex (ACC)—both key regions involved in interoception; these behavioral
and brain alterations were not present in non-fatigued pwMS or healthy controls (HCs). Supporting
both reward processing and interoceptive hypotheses, a motivational fatigue network consisting of
the dorsal ACC (dACC), the dorsolateral prefrontal cortex (DLPFC), the vmPFC, and the insula have
been proposed in recent reviews [12,14]. This network monitors the internal bodily states and chooses
whether to continue exerting effort based on the perceived value of such effort (i.e., whether the effort
is “worth it”). Using this framework, the level of fatigue increases as effort is expended, and the
perceived value of effort declines; effort previously associated with high value becomes less rewarding,
which leads to decrements in motivation and task performance.
We previously demonstrated that cognitive fatigue may be the result of inefficient cerebral
activation when meeting increased task demands [15]. However, our previous study examined
activations of isolated brain regions without accounting for the inter-connections between them,
which precluded us from being able to implicate specific brain networks (and thus mechanisms)
involved in fatigue. To investigate neural mechanisms underlying MS-related cognitive fatigue,
the current study assessed functional connectivity among key brain regions underlying reward
processing and interoception. Participants underwent functional magnetic resonance imaging (fMRI)
scanning while performing a working memory task (n-back), which consisted of two conditions—one
with higher cognitive load (2-back) to induce fatigue, and one with lower cognitive load (0-back)
as a control condition. Task-independent functional connectivity in the error (residual) term was
used to isolate fatigue-related connectivity. We hypothesized that pwMS would exhibit alterations
in task-independent functional connectivity within reward processing and interoceptive networks
in the fatigue-inducing 2-back condition compared to the 0-back control condition, compared to
demographically matched HCs.
Diagnostics 2020, 10, 930 3 of 22
2. Materials and Methods
2.1. Participants
PwMS and HCs were recruited from the community by advertisements and word of mouth.
All participants were between the ages of 18 and 65 years, right-handed, and fluent English speakers.
Exclusionary criteria included: history of neurologic disorders (other than MS), serious mental illnesses
(e.g., bipolar disorder, schizophrenia), substance use disorders, or learning disabilities; current use
of steroids, benzodiazepines, or neuroleptics; and MS exacerbation/relapse within the past month.
All participants were screened for MRI contraindications (e.g., metals in body, medical contraindications
as determined by a physician, claustrophobia, pregnancy). All participants provided written informed
consent before enrollment. The study was conducted in accordance with the Declaration of Helsinki,
and all study procedures were approved by the Kessler Foundation institutional review board
(IRB number: R-663-10; 15 May 2010).
2.2. Procedures
Before the experimental paradigm, examples of mental fatigue were explained to the subjects,
and subjects were informed that they would be required to rate their level of mental fatigue at times
throughout the scan. All participants underwent fMRI scanning while performing a working memory
(n-back) task. They were asked to report their levels of mental fatigue before and after each run
(i.e., fMRI block).
2.3. N-Back Task Paradigm
The n-back working memory task was administered using the E-Prime software [16]. All stimuli
were presented on a screen located at the back of the magnet bore (a back-projection system was
used) that subjects viewed via a mirror that was attached to the head coil directly in their line of sight.
Subjects responded using a two-button button box (Cedrus Corp., San Pedro, CA, USA) with their right
hand. There were two conditions to the n-back task: a 0-back condition, designating a low working
memory load, and a 2-back condition, indicative of a higher working memory load. For the 0-back
condition, participants were required to press a button with their index finger when the target letter “K”
appeared on the screen. For the 2-back condition, participants were required to press the same button
if the target letter was identical to the letter two trials prior in the sequence. All participants practiced
both conditions prior to the scanning session. Each condition consisted of four runs (eight runs in
total), with 65 trials per run. All four runs of each condition were presented in a block, followed by the
four runs of the other condition. The order of the conditions was counterbalanced.
Task stimuli were in white and Arial 72-point font, with a black background. We chose 17 of the
26 English letters that have optimal discriminability from each other. The final stimuli set consisted of
A, B, C, D, F, H, J, K, M, N, P, Q, R, S, T, V, and Z. All letters were presented with equal frequency. Each
letter stimulus was shown for 1.5 s (and was not removed when participants responded), followed by
a 500 millisecond inter-trial interval. Each run lasted for 260 s. We used the Optseq2 program (part of
FreeSurfer; https://surfer.nmr.mgh.harvard.edu/optseq/) to jitter the interval between successive trials,
in order to deconvolve the data as an event related design. This was accomplished by inserting six
null events (duration was a multiple of the length of the trial) between successive trials. The average
inter-trial interval was 1587.87 milliseconds, and the standard deviation was 1769.7 milliseconds.
Accuracy and response time were dependent variables.
2.4. Fatigue Assessment
The Visual Analogue Scale of Fatigue (VAS-F) [17] was used to assess the level of state mental
fatigue. Participants were required to report their level of mental fatigue “right now, at this moment”,
on a scale from 0 (“not fatigued at all”) to 100 (“the most fatigue imaginable”), between fMRI runs.
There were five VAS-F ratings per n-back task condition (0-back and 2-back): one before the first run
Diagnostics 2020, 10, 930 4 of 22
of each condition and one after each of the four runs. We also asked participants to report levels
of happiness, sadness, pain, tension, and anger (which were not analyzed), to mask the purpose of
the study.
2.5. Neuroimaging Acquisition
Participants were scanned on a 3T Siemens (Malvern, Pennsylvania, USA) Allegra scanner.
Functional fMRI data was collected using a T2*-weighted pulse sequence (repetition time (TR) = 2 s;
echo time (TE) = 30 ms; flip angle = 80; field of view (FOV) = 220 mm; slice thickness = 4mm; number of
slices = 32; in-plane spatial resolution = 3.4 × 3.4 mm2 , matrix = 64 × 64). For each run of the two n-back
task conditions, 140 images were acquired. A T1-weighted image was also acquired (TE = 4.38 ms,
TR = 2 s, FOV = 220 mm, flip angle = 8◦ , slice thickness = 1 mm, NEX = 1, matrix = 256 × 256, in plane
resolution = 0.86 × 0.86 mm2 ) for functional localization and normalization into standard Montreal
Neurological Institute (MNI) space.
2.6. Neuroimaging Data Processing
The neuroimaging data was preprocessed using fMRIPrep version 1.4.1 [18] (RRID:SCR_016216),
which is based on Nipype v. 1.2.0 [19] (RRID:SCR_002502). For anatomical preprocessing,
the T1-weighted (T1w) image from each subject was corrected for intensity non-uniformity (INU)
with N4BiasFieldCorrection [20], distributed with ANTs v. 2.2.0 [21] (RRID:SCR_004757), and used as
T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype
implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target
template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter
(GM) was performed on the brain-extracted T1w using fast [22] (FSL 5.0.9; RRID:SCR_002823).
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed
through nonlinear registration with antsRegistration (ANTs v. 2.2.0), using brain-extracted versions of
both T1w reference and the T1w template. The following template was selected for spatial normalization:
ICBM 152 Nonlinear Asymmetrical template v. 2009c [23] (RRID:SCR_008796; TemplateFlow ID:
MNI152NLin2009cAsym).
For functional data preprocessing, the following preprocessing was performed on each of the eight
BOLD runs of fMRI data per subject (i.e., four runs of each condition). First, a reference volume and its
skull-stripped version were generated using a custom methodology of fMRIPrep. The BOLD reference
was then co-registered to the T1w reference using flirt [24] (FSL 5.0.9) with the boundary-based
registration [25] cost-function. Co-registration was configured with nine degrees of freedom to
account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the
BOLD reference (transformation matrices, and six corresponding rotation and translation parameters)
were estimated before any spatiotemporal filtering using mcflirt [26] (FSL 5.0.9). BOLD runs were
slice-time corrected using 3dTshift from AFNI (National Institute of Mental Health, Bethesda, MD,
USA) 20160207 [27] (RRID:SCR_005927). The BOLD time-series (including slice-timing correction when
applied) were resampled onto their original, native space by applying a single, composite transform
to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be
referred to as preprocessed BOLD in original space or just preprocessed BOLD. The BOLD time-series
were resampled into standard space, generating a preprocessed BOLD run in (MNI152NLin2009cAsym)
space. First, a reference volume and its skull-stripped version were generated using a custom
methodology of fMRIPrep. Several confounding time-series were calculated based on the preprocessed
BOLD: framewise displacement (FD), DVARS (the spatial root mean square of the data after temporal
differencing), and three region-wise global signals. FD and DVARS were calculated for each functional
run, both using their implementations in Nipype (following the definitions by [28]). The three
global signals were extracted within the CSF, the WM, and the whole-brain masks. Additionally,
a set of physiological regressors were extracted to allow for component-based noise correction [29]
(CompCor). Principal components were estimated after high-pass filtering the preprocessed BOLD
Diagnostics 2020, 10, 930 5 of 22
time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal
(tCompCor) and anatomical (aCompCor). tCompCor components were then calculated from the
top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask was
obtained by heavily eroding the brain mask, which ensured it did not include cortical GM regions.
For aCompCor, components were calculated within the intersection of the aforementioned mask and
the union of CSF and WM masks calculated in T1w space, after their projection to the native space of
each functional run (using the inverse BOLD-to-T1w transformation). Components were also calculated
separately within the WM and CSF masks. For each CompCor decomposition, the k components
with the largest singular values were retained, such that the retained components’ time series were
sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal).
The remaining components were dropped from consideration. The head-motion estimates calculated
in the correction step were also placed within the corresponding confounds file. The confound time
series derived from head motion estimates and global signals were expanded with the inclusion of
temporal derivatives and quadratic terms for each [30]. Frames that exceeded a threshold of 0.5 mm FD
or 1.5 standardized DVARS were annotated as motion outliers. All resamplings were performed with
a single interpolation step by composing all the pertinent transformations (i.e., head-motion transform
matrices, susceptibility distortion correction when available, and co-registrations to anatomical and
output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms
(ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels [31].
Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer, v. 7.1.0; Athinoula
A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA).
The resulting preprocessed data from the four runs of each n-back task condition were then
deconvolved in a single model. In the deconvolution, signal drift was modeled with a set of basic
functions; the motion parameters and their derivatives were used as regressors of no interest, as was
framewise displacement. Additionally, the signal from white matter, the ventricles and the global
signal, and the derivatives of each of these were included as regressors of no interest. Finally, a regressor
representing the onset time of each trial, convolved with a hemodynamic response function was
included to model task-related activation. Importantly, the task-related activation was modeled with
unit amplitude for all trials across the four runs with the result that only stable, invariant task-related
activation was modeled with this regressor. This was done for the 0-back and 2-back conditions
separately. The time-varying activation associated with cognitive fatigue was not modeled and was
therefore included in the error term. The time-series associated with the error term was saved and
used to assess connectivity between areas that have been shown to be related to cognitive fatigue.
Five seeds were chosen for the analyses, one in each of the following locations: the DLPFC,
the vmPFC, the dACC, the insula, and the striatum. Table 1 lists the coordinates of the center of a
4mm sphere used for each seed, and Figure 1 is a graphical representation of the seed locations. All
three-dimensional brain figures in this paper were created using BrainNet Viewer v. 1.7 [32]. For each
seed, the mean percent signal change was calculated within the seed for each volume of the error term
time-series. The correlation between this time-series of means and every voxel in the brain was then
computed, before being converted into z-scores with Fisher’s R-to-Z transformation [33]. The resulting
z-score maps were entered in the group-level analysis.
Diagnostics 2020, 10, 930 6 of 22
Table 1. Coordinates of center location of seed regions.
Location X Y Z
DLPFC 44 32 36
vmPFC −6 46 −6
dACC −4 20 46
Insula 34 22 0
Striatum 18 12 0
Abbreviations: DLPFC, dorsolateral prefrontal cortex. vmPFC, ventromedial prefrontal cortex. dACC, dorsal anterior
Diagnostics 2020, 10, x 6 of 21
cingulate cortex.
Figure 1. Graphical representation of seed locations. DLPFC, dorsolateral prefrontal cortex (red).
Figure 1.ventromedial
vmPFC, Graphical representation of seed
prefrontal cortex locations.
(yellow). DLPFC,
dACC, dorsolateral
dorsal prefrontal
anterior cingulate cortex
cortex (red).
(green).
vmPFC, ventromedial prefrontal cortex
Insula (light blue). Striatum (dark blue). (yellow). dACC, dorsal anterior cingulate cortex (green).
Insula (light blue). Striatum (dark blue).
2.7. Statistical Analysis Plan
2.7. Statistical Analysis Plan
Data analyses were conducted using the statistical package R (v. 4.0.2; R Foundation for Statistical
Data analyses
Computing, Vienna, were conducted using the statistical package R (v. 4.0.2; R Foundation for
Austria).
Statistical Computing, Vienna, Austria).
2.7.1. Demographic Characteristics
2.7.1.Group
Demographic Characteristics
differences in demographic characteristics were analyzed using independent-samples
T-tests for continuous
Group differencesvariables and Pearson’s
in demographic chi-squared
characteristics tests
were for binary
analyzed variables.
using independent-samples T-
tests for continuous variables and Pearson’s chi-squared tests for binary variables.
2.7.2. Behavioral Data
2.7.2.To
Behavioral Data the fMRI data, the amount of fatigue for each run was calculated as the mean
correlate with
of theTo
VAS-F scores
correlate before
with and after
the fMRI data,each run. Because
the amount we only
of fatigue wanted
for each runtowasinclude fMRI as
calculated signals that
the mean
reflected cognitive fatigue, we excluded data in which participants reported no fatigue
of the VAS-F scores before and after each run. Because we only wanted to include fMRI signals that for both before
and after each
reflected run (i.e.,
cognitive VAS-F
fatigue, we=excluded
0). Because
datathein VAS-F
which scores were skewed,
participants reportedwe notransformed the before
fatigue for both scores
using the Box Cox method. Transformed VAS-F scores and n-back task performance
and after each run (i.e., VAS-F = 0). Because the VAS-F scores were skewed, we transformed the scores (accuracy and
response time) were analyzed using linear mixed effects models, with restricted
using the Box Cox method. Transformed VAS-F scores and n-back task performance (accuracy and maximum likelihood
estimation (“lme4”
response time) werepackage
analyzedinusing
R). Degrees of freedom
linear mixed effectswere calculated
models, based on
with restricted the Satterthwaite’s
maximum likelihood
method (“lmerTest” package in R). Fixed factors included group (MS vs. HC), condition
estimation (“lme4” package in R). Degrees of freedom were calculated based on the Satterthwaite’s (0-back vs.
2-back),
method and run (runspackage
(“lmerTest” 1–4). Weinalso allowed
R). Fixed each included
factors subject togroup
have their
(MS own intercept
vs. HC), (random
condition effect).
(0-back vs.
Post hoc analyses for significant interaction terms were performed with the “emmeans”
2-back), and run (runs 1–4). We also allowed each subject to have their own intercept (random effect). package, using
Tukey
Post hocadjustment
analyses for
formultiple comparisons
significant interactionand thewere
terms Kenward–Roger
performed withmethodthe in calculatingpackage,
“emmeans” degrees
of freedom.
using Tukey adjustment for multiple comparisons and the Kenward–Roger method in calculating
degrees of freedom.
2.7.3. Neuroimaging Data
The contrast between the two conditions (2-back minus 0-back) was analyzed to isolate fatigue
induced by the additional task demand. Changes in residual functional connectivity (part of the error
Diagnostics 2020, 10, 930 7 of 22
2.7.3. Neuroimaging Data
The contrast between the two conditions (2-back minus 0-back) was analyzed to isolate fatigue
induced by the additional task demand. Changes in residual functional connectivity (part of the error
term as specified above) in the 2-back condition relative to the 0-back condition were interpreted as
related to fatigue. Separate linear mixed effects models (3dLME from the AFNI suite of processing tools)
were used for the data from each seed for each of the HC and MS groups. We chose to examine the
functional connectivity patterns by group separately because we wanted to see if there were overlapping
as well as differential connectivity patterns. Run (runs 1–4 of each condition) was included as a fixed
factor, and subject was included as a random factor (random intercept). The transformed VAS-F
scores were mean centered for each group and then included as a quantitative variable in the models.
The results of these analyses were corrected for multiple comparisons by using an individual voxel
probability threshold of p < 0.001 and a cluster threshold of 13 voxels (voxel dimension = 3 × 3 × 3 mm).
Monte Carlo simulations, using 3dClustSim (v. AFNI_17.2.16, compile date: 19 September 2017),
showed this combination to result in a corrected alpha level of p < 0.05.
3. Results
The sample consisted of 26 pwMS and 14 HCs. Table 2 summarizes the demographic and disease
characteristics. There were no significant differences between the two groups in demographic and
disease characteristics (p > 0.05).
Table 2. Demographic and disease characteristics.
MS (n = 26) HC (n = 14)
Characteristic MS vs. HC (p)
Mean (SD) Range Mean (SD) Range
Age: years 48.12 (10.09) 26–63 42 (12.87) 24–58 n.s.
Education: years 16.19 (2.19) 12–21 16.31 (1.97) 12–20 n.s.
Illness duration: years 11.05 (6.21) 1–28 - - -
Number Number
(%) (%)
Females 23 (88) - 13 (93) - n.s.
MS phenotype - - - - -
Relapsing-remitting 19 (73) - - - -
Primary progressive 2 (8) - - - -
Secondary progressive 3 (12) - - - -
Unknown 2 (7) - - - -
Column named “MS vs. HC (p)” denotes group comparisons in demographic variables using independent-samples
T-tests and Pearson’s chi-squared tests; p values are listed for significant variables; non-significant variables are
denoted as “n.s.” Abbreviations: MS, multiple sclerosis. HC, healthy control. SD, standard error.
3.1. VAS-F Data
Using the transformed VAS-F scores, as expected, the MS group reported significantly higher
levels of fatigue than the HC group across n-back conditions and runs (estimate = 1.136, standard
error (SE) = 0.315, p < 0.001; median VAS-F raw scores = 38.27 in MS vs. 12.41 in HC). There was
also a significant group × condition interaction (estimate = −0.385, SE = 0.200, p = 0.055). Tukey post
hoc pairwise comparisons revealed that while there were no differences in fatigue levels between
conditions for the HC group (p > 0.05), the MS group reported significantly higher levels of fatigue for
the 0-back condition than the 2-back condition (estimate = 0.120, SE = 0.043, p = 0.028; median VAS-F
raw scores = 39.96 in 0-back vs. 37.29 in 2-back). Differences in VAS-F scores were plotted using the
raw values to illustrate the actual differences among groups and conditions (Figure 2).
Diagnostics 2020, 10, 930 8 of 22
Diagnostics 2020, 10, x 8 of 21
Figure 2. Differences in VAS-F scores. The VAS-F raw scores are plotted as a function of group,
Figure 2. Differences in VAS-F scores. The VAS-F raw scores are plotted as a function of group,
condition, and rating. 0-back condition is denoted by circles, and the 2-back condition is denoted by
condition, and rating. 0-back condition is denoted by circles, and the 2-back condition is denoted by
triangles. Red represents the HC group, and turquoise represents the MS group. Abbreviations: VAS-F,
triangles. Red represents the HC group, and turquoise represents the MS group. Abbreviations: VAS-
Visual Analogue Scale of Fatigue. MS, multiple sclerosis. HC, healthy control.
F, Visual Analogue Scale of Fatigue. MS, multiple sclerosis. HC, healthy control.
3.2. N-Back Task Performance
3.2. N-Back Task Performance
3.2.1. Accuracy
3.2.1.There
Accuracy
was a significant group × condition × VAS-F (transformed) interaction (estimate = −0.03,
SE =There = 0.028).
0.01, pwas Tukey post
a significant hoc ×analyses
group conditionindicated
× VAS-F in (transformed)
the HC group, while there was
interaction no significant
(estimate = −0.03,
relationship between fatigue levels and accuracy for the 0-back condition, there was
SE = 0.01, p = 0.028). Tukey post hoc analyses indicated in the HC group, while there was no significant a non-statistically
significant trend
relationship for the fatigue
between 2-back condition
levels and suchaccuracy
that as fatigue
for thelevels increased,
0-back accuracy
condition, improved
there was a (slope
non-
= 0.021, confidence intervals [CI] = < −0.001 to 0.042). In the MS group, there was again
statistically significant trend for the 2-back condition such that as fatigue levels increased, accuracy no significant
relationship
improved between
(slope fatigue
= 0.021, levels and
confidence accuracy
intervals [CI]for
= <the 0-back
−0.001 condition;
to 0.042). however,
In the MS group,for the
there2-back
was
condition,
again accuracy significantly
no significant relationshipdeclined
betweenasfatigue
fatigue levels
levels and
increased (slope
accuracy for= the
−0.016; CI =condition;
0-back −0.030 to
−0.003). The
however, slopes
for the forcondition,
2-back the 2-backaccuracy
condition were significantly
significantly declineddifferent
as fatiguebetween the HC and
levels increased MS
(slope =
groups CI
−0.016; = −0.030=to
(estimate 0.037, SE =The
−0.003). 0.013, p = for
slopes 0.023). Figure condition
the 2-back 3 graphically
were represents the group
significantly × condition
different between
× VAS-F
the HC and (transformed)
MS groupsinteraction
(estimate =in0.037,
task accuracy.
SE = 0.013, p = 0.023). Figure 3 graphically represents the
group × condition × VAS-F (transformed) interaction in task accuracy.
Diagnostics 2020, 10, 930 9 of 22
Diagnostics 2020, 10, x 9 of 21
Figure 3. Group × condition × VAS-F (transformed) interaction for n-back task accuracy. For the 2-back
Figure 3. Group
condition, × condition
there was a positive× correlation
VAS-F (transformed) interaction
between fatigue levels for
andn-back task
accuracy in accuracy. For the
the HC group and2-a
back condition, there was a positive correlation between fatigue levels and accuracy in the HC
negative correlation in the MS group. There were no significant relationships between fatigue levels group
and
and aaccuracy
negativefor
correlation
the 0-backincondition.
the MS group. There
0-back were no
condition is significant
denoted byrelationships
dotted lines,between
and the fatigue
2-back
levels and accuracy for the 0-back condition. 0-back condition is denoted by dotted lines,
condition is denoted by solid lines. Abbreviations: MS, multiple sclerosis. HC, healthy control. and VAS-F,
the 2-
back
Visualcondition
Analogue is Scale
denoted by solid lines. Abbreviations: MS, multiple sclerosis. HC, healthy control.
of Fatigue.
VAS-F, Visual Analogue Scale of Fatigue.
3.2.2. Response Time
3.2.2. Response Time
Response time was significantly slower for the 2-back condition compared to the 0-back condition
Response
for both pwMStime was (estimate
and HCs = 147.88,
significantly = 27.57,
slowerSEfor p < 0.001),
the 2-back condition compared
but there were notohigher-order
the 0-back
condition forfor
interactions both pwMS time.
response and HCs (estimate = 147.88, SE = 27.57, p < 0.001), but there were no higher-
order interactions for response time.
3.3. Neuroimaging Data
3.3. Neuroimaging Data
3.3.1. DLPFC as Seed
3.3.1.Table
DLPFC as Seed
3 and Figure 4 summarize brain regions with significantly increased task-independent
residual functional
Table 3 and Figure connectivity
4 summarize with brain
the DLPFC
regionsaswith
VAS-F scores increased,
significantly increased in task-independent
either the 2-back
or 0-backfunctional
residual conditionconnectivity
among the with HC and MS groups.
the DLPFC In the
as VAS-F HC increased,
scores group, as in VAS-F
eitherscores increased,
the 2-back or 0-
task-independent residual functional connectivity was significantly increased
back condition among the HC and MS groups. In the HC group, as VAS-F scores increased, in the 2-back condition
task-
between the DLPFC
independent residualand left frontal
functional regions (superior
connectivity and inferior
was significantly frontalingyri)
increased and significantly
the 2-back condition
between the DLPFC and left frontal regions (superior and inferior frontal gyri) andfrontal
increased in the 0-back condition between the DLPFC and right frontal areas (middle gyrus,
significantly
vmPFC), the right precuneus, and the right cuneus. In the MS group, as VAS-F
increased in the 0-back condition between the DLPFC and right frontal areas (middle frontal gyrus, scores increased,
task-independent
vmPFC), the right residual
precuneus,functional
and the connectivity
right cuneus.was significantly
In the MS group,increased
as VAS-Fin the 2-back
scores condition
increased, task-
between the DLPFC and bilateral frontal regions (superior, middle, and inferior
independent residual functional connectivity was significantly increased in the 2-back condition frontal, precentral gyri),
the right superior
between the DLPFC parietal lobule (SPL),
and bilateral andregions
frontal the left(superior,
angular gyrus, andand
middle, it significantly increased
inferior frontal, in the
precentral
0-backthe
gyri), condition between
right superior the DLPFC
parietal lobule and bilateral
(SPL), frontal
and the regions (superior,
left angular gyrus, and middle, and orbital
it significantly frontal,
increased
precentral gyri), the right postcentral gyrus, the left SPL, the left hippocampus,
in the 0-back condition between the DLPFC and bilateral frontal regions (superior, middle, and and the left cerebellum.
orbital frontal, precentral gyri), the right postcentral gyrus, the left SPL, the left hippocampus, and
the left cerebellum.
Diagnostics 2020, 10, 930 10 of 22
Table 3. Task-independent (residual) fatigue-related functional connectivity with the dorsolateral
prefrontal cortex as seed.
Dorsolateral Prefrontal Cortex Seed
Location X Y Z Voxels Z Stat
HC
Increased Task-Independent Residual Connectivity in 2-back Condition
Superior Frontal Gyrus −30.7 −4.8 70 19 4.90
Inferior Frontal Gyrus −54.7 22.7 22 103 4.96
Increased Task-Independent Residual Connectivity in 0-back Condition
Middle Frontal Gyrus 27.8 39.9 46 27 −4.77
Ventromedial Prefrontal Cortex 14.0 12.4 −18 30 −5.13
Precuneus 14.0 −63.2 34 36 −4.30
Cuneus 0.3 −94.2 42 13 −5.05
MS
Increased Task-Independent Residual Connectivity in 2-back Condition
Superior Frontal Gyrus −16.9 70.8 18 14 3.77
Middle Frontal Gyrus 27.8 39.9 30 14 4.72
Middle Frontal Gyrus −41.0 43.3 30 29 5.56
Inferior Frontal Gyrus 62.1 12.4 14 16 3.91
Inferior Frontal Gyrus 58.7 39.9 10 25 4.72
Superior Medial Gyrus 0.3 53.7 22 61 6.21
Precentral Gyrus 38.1 2.1 34 15 5.00
Superior Parietal Lobule 20.9 −80.4 50 35 5.80
Angular Gyrus −37.6 −70.1 54 18 4.66
Increased Task-Independent Residual Connectivity in 0-back Condition
Superior Frontal Gyrus −23.8 9.0 70 54 −6.26
Middle Frontal Gyrus 45.0 9.0 58 15 −4.36
Superior Orbital Gyrus 7.1 60.5 −22 24 −4.73
Precentral Gyrus −41.0 −11.7 70 13 −4.59
Postcentral Gyrus 34.6 −28.9 78 21 −4.43
Postcentral Gyrus/Superior Parietal Lobule 45.0 −42.6 66 42 −4.53
Superior Parietal Lobule −37.6 −70.1 66 14 −6.01
Hippocampus −16.9 −35.7 14 15 −4.75
Cerebellum Crus I −51.3 −49.5 −38 45 −6.36
Cerebellum Crus I −37.6 −90.7 −30 15 −4.51
“X”, “Y”, and “Z” denote the three-dimensional peak coordinates of each significant clusters. Positive “X” values
represent the right hemisphere, and negative “X” values represent the left hemisphere. “Voxels” denote the number
of voxels in each cluster. “Z Stat” reflects the extent of functional connectivity between the seed region and the brain
region in each row; positive values represent increased functional connectivity in the 2-back condition, and negative
values represent increased functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis.
HC, healthy control.
Diagnostics 2020, 10, 930 11 of 22
Diagnostics 2020, 10, x 11 of 21
(a) (b)
Figure4.
Figure 4. Dorsolateral
Dorsolateral prefrontal
prefrontal cortex
cortex connectivity
connectivity 3-dimensional
3-dimensional rendering. (a) Task-independent,
fatigue-related connectivity
fatigue-related connectivity in in the
the healthy
healthy control
control group;
group; (b)
(b) Task-independent,
Task-independent, fatigue-related
fatigue-related
connectivity
connectivity in in the
the multiple
multiple sclerosis
sclerosis group.
group. The seed is denoted
denoted by by green
green spheres;
spheres; regions
regions with
with
increased connectivity to the seed in the 2-back condition are denoted by yellow spheres,
increased connectivity to the seed in the 2-back condition are denoted by yellow spheres, and regions and regions
with
withincreased
increasedconnectivity
connectivityto tothe
theseed
seedininthe
the0-back
0-backcondition
conditionarearedenoted
denotedby byred
redspheres.
spheres. Cerebellar
Cerebellar
regions
regionswere
wereomitted.
omitted. Anterior
Anterior orientation
orientation is is in
in the
the front
front of
of the
the figure;
figure; right
right hemisphere
hemisphere isis on
on the
the left
left
side,
side,and
andleft
lefthemisphere
hemisphere is on
is the
on right side. side.
the right Abbreviations: SFG, superior
Abbreviations: frontal frontal
SFG, superior gyrus. MFG,
gyrus.middle
MFG,
frontal
middlegyrus.
frontalIFG, inferior
gyrus. IFG, frontal
inferiorgyrus.
frontalvmPFC,
gyrus. ventromedial prefrontalprefrontal
vmPFC, ventromedial cortex. SMG, superior
cortex. SMG,
medial gyrus. PrG, precentral gyrus. PoG, postcentral gyrus. SOG, superior
superior medial gyrus. PrG, precentral gyrus. PoG, postcentral gyrus. SOG, superior orbital gyrus. orbital gyrus. SPL,
superior parietal
SPL, superior lobule.
parietal AG, angular
lobule. gyrus.gyrus.
AG, angular
3.3.2. vmPFC as Seed
3.3.2. vmPFC as Seed
Table 4 and Figure 5 summarize brain regions with significantly increased task-independent
Table 4 and Figure 5 summarize brain regions with significantly increased task-independent
residual functional connectivity with the vmPFC as VAS-F scores increased, in either the 2-back or 0-back
residual functional connectivity with the vmPFC as VAS-F scores increased, in either the 2-back or 0-
condition among MS and HC groups. In the HC group, as VAS-F scores increased, task-independent
back condition among MS and HC groups. In the HC group, as VAS-F scores increased, task-
residual functional connectivity was significantly increased in the 2-back condition between the vmPFC
independent residual functional connectivity was significantly increased in the 2-back condition
and left inferior frontal areas (middle orbital, and inferior), left middle cingulate cortex (MCC), bilateral
between the vmPFC and left inferior frontal areas (middle orbital, and inferior), left middle cingulate
superior temporal, the left putamen, and the right cerebellum, and it significantly increased in the
cortex (MCC), bilateral superior temporal, the left putamen, and the right cerebellum, and it
0-back condition between the vmPFC and primarily left frontal areas (superior, middle, and inferior)
significantly increased in the 0-back condition between the vmPFC and primarily left frontal areas
and bilateral temporal areas (middle and inferior temporal gyri, temporal pole). In the MS group, as
(superior, middle, and inferior) and bilateral temporal areas (middle and inferior temporal gyri,
VAS-F scores increased, task-independent residual functional connectivity was significantly increased
temporal pole). In the MS group, as VAS-F scores increased, task-independent residual functional
in the 2-back condition between the vmPFC and right frontal regions (inferior frontal and supplemental
connectivity was significantly increased in the 2-back condition between the vmPFC and right frontal
motor areas), the left insula, bilateral temporal regions (middle and inferior), the right supramarginal
regions (inferior frontal and supplemental motor areas), the left insula, bilateral temporal regions
gyrus, the right middle occipital gyrus, and the left cerebellum, and it significantly increased in
(middle and inferior), the right supramarginal gyrus, the right middle occipital gyrus, and the left
the 0-back condition between the vmPFC and the right inferior temporal gyrus and the left inferior
cerebellum, and it significantly increased in the 0-back condition between the vmPFC and the right
occipital gyrus.
inferior temporal gyrus and the left inferior occipital gyrus.
Table 4. Task-independent (residual) fatigue-related functional connectivity with the ventromedial
prefrontal cortex as seed.
Ventromedial Prefrontal Cortex Seed
Location X Y Z Voxels Z Stat
HC
Increased Task-independent Residual Connectivity in 2-back Condition
Middle Orbital Gyrus −10.1 60.5 −10 110 5.45
Inferior Frontal Gyrus −41.0 36.5 −18 68 5.45
Middle Cingulate Cortex −3.2 −25.4 46 30 4.84
Diagnostics 2020, 10, 930 12 of 22
Table 4. Task-independent (residual) fatigue-related functional connectivity with the ventromedial
prefrontal cortex as seed.
Ventromedial Prefrontal Cortex Seed
Location X Y Z Voxels Z Stat
HC
Increased Task-independent Residual Connectivity in 2-back Condition
Middle Orbital Gyrus −10.1 60.5 −10 110 5.45
Inferior Frontal Gyrus −41.0 36.5 −18 68 5.45
Middle Cingulate Cortex −3.2 −25.4 46 30 4.84
Superior Temporal Gyrus/Insula −47.9 5.5 −10 21 4.78
Superior Temporal Gyrus 69.0 −28.9 22 20 4.72
Putamen −20.4 −1.4 −6 16 5.11
Cerebellar Vermis 7.1 −77.0 −18 20 5.29
Increased Task-independent Residual Connectivity in 0-back Condition
Superior Frontal Gyrus/Superior Orbital Gyrus −27.2 67.4 −2 19 −5.10
Middle Frontal Gyrus −47.9 36.5 34 208 −6.12
Middle Frontal Gyrus 34.6 15.8 62 18 −4.64
Inferior Frontal Gyrus −20.4 26.1 −22 24 −4.32
Temporal Pole −54.7 19.3 −22 22 −4.26
Middle Temporal Gyrus −58.2 2.1 −26 27 −4.06
Inferior Temporal Gyrus 58.7 −39.2 −18 19 −4.00
MS
Increased Task-independent Residual Connectivity in 2-back Condition
Inferior Frontal Gyrus 45.0 12.4 6 40 5.20
Inferior Frontal Gyrus 38.1 9.0 30 21 4.42
Supplemental Motor Area 14.0 12.4 66 39 5.10
Insula −37.6 19.3 6 13 4.43
Middle Temporal Gyrus 65.6 −49.5 2 31 4.14
Inferior Temporal Gyrus −61.6 −56.4 −6 13 4.15
Supramarginal Gyrus 58.7 −25.4 46 31 4.30
Middle Occipital Gyrus 34.6 −63.2 34 53 5.42
Cerebellum Crus I −51.3 −49.5 −30 37 5.38
Increased Task-independent Residual Connectivity in 0-back Condition
Inferior Temporal Gyrus 31.2 2.1 −50 20 −5.51
Inferior Occipital Gyrus −54.7 −73.6 −18 21 −4.66
“X”, “Y”, and “Z” denote the three-dimensional peak coordinates of each significant clusters. Positive “X” values
represent the right hemisphere, and negative “X” values represent the left hemisphere. “Voxels” denote the number
of voxels in each cluster. “Z Stat” reflects the extent of functional connectivity between the seed region and the brain
region in each row; positive values represent increased functional connectivity in the 2-back condition and negative
values represent increased functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis.
HC, healthy control.
“X” values represent the right hemisphere, and negative “X” values represent the left hemisphere.
“Voxels” denote the number of voxels in each cluster. “Z Stat” reflects the extent of functional
connectivity between the seed region and the brain region in each row; positive values represent
increased functional connectivity in the 2-back condition and negative values represent increased
functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis. HC, healthy
Diagnostics 2020, 10, 930 13 of 22
control.
(a) (b)
Figure 5. Ventromedial prefrontal cortex connectivity 3-dimensional rendering. (a) Task-independent,
fatigue-related connectivity in the healthy control group. (b) Task-independent, fatigue-related
connectivity in the multiple sclerosis group. The seed is denoted by green spheres; regions with
increased connectivity to the seed in the 2-back condition are denoted by yellow spheres, and regions
with increased connectivity to the seed in the 0-back condition are denoted by red spheres. Cerebellar
regions were omitted. Anterior orientation is in the front of the figure; right hemisphere is on the
left side, and left hemisphere is on the right side. Abbreviations: SFG, superior frontal gyrus. MFG,
middle frontal gyrus. IFG, inferior frontal gyrus. vmPFC, ventromedial prefrontal cortex. MCC, middle
cingulate cortex. SMA, supplementary motor area. SOG, superior orbital gyrus. MOG, middle orbital
gyrus. SMG, supramarginal gyrus. STG, superior temporal gyrus. MTG, middle temporal gyrus. ITG,
inferior temporal gyrus. MOcG, middle occipital gyrus. IOcG, inferior occipital gyrus.
3.3.3. dACC as Seed
There were no fatigue-related changes in task-independent residual functional connectivity in the
HC group. Table 5 and Figure 6 summarize brain regions with significantly increased task-independent
residual functional connectivity with the dACC as VAS-F scores increased, in either the 2-back or
0-back condition in the MS group. In the MS group, as VAS-F scores increased, task-independent
residual functional connectivity was significantly increased in the 2-back condition between the dACC
and the left inferior frontal gyrus, right parietal regions (SPL, precuneus), and bilateral caudate nuclei,
and it significantly increased in the 0-back condition between the dACC and left postcentral gyrus and
the right cerebellum.
Diagnostics 2020, 10, 930 14 of 22
Table 5. Task-independent (residual) fatigue-related functional connectivity with the dorsal anterior
cingulate cortex as seed.
Dorsal Anterior Cingulate Cortex Seed
Location X Y Z Voxels Z Stat
MS
Increased Task-independent Residual Connectivity in 2-back Condition
Inferior Frontal Gyrus −47.9 5.5 30 50 4.64
Superior Parietal Lobule 24.3 −73.6 58 43 4.82
Precuneus 7.1 −63.2 66 30 4.63
Caudate Nucleus −16.9 9.0 18 24 4.14
Caudate Nucleus 14.0 22.7 14 18 4.38
Increased Task-independent Residual Connectivity in 0-back Condition
Postcentral Gyrus −41.0 −28.9 62 46 −5.34
Cerebellum Crus 1 41.5 −83.9 −34 16 −4.98
“X”, “Y”, and “Z” denote the three-dimensional peak coordinates of each significant clusters. Positive “X” values
represent the right hemisphere, and negative “X” values represent the left hemisphere. “Voxels” denote the number
of voxels in each cluster. “Z Stat” reflects the extent of functional connectivity between the seed region and the brain
region in each row; positive values represent increased functional connectivity in the 2-back condition and negative
values represent increased functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis.
Diagnostics 2020, 10, x 14 of 21
HC, healthy control.
Figure 6. Dorsal anterior cingulate cortex connectivity 3-dimensional rendering. Figure represents
Figure 6. Dorsal anterior cingulate cortex connectivity 3-dimensional rendering. Figure represents
task-independent, fatigue-related connectivity in the multiple sclerosis group; there were no significant
task-independent, fatigue-related connectivity in the multiple sclerosis group; there were no
differences between conditions in the healthy control group. The seed is denoted by a green sphere;
significant differences between conditions in the healthy control group. The seed is denoted by a
regions with increased connectivity to the seed in the 2-back condition are denoted by yellow spheres,
green sphere; regions with increased connectivity to the seed in the 2-back condition are denoted by
and regions with increased connectivity to the seed in the 0-back condition are denoted by red spheres.
yellow spheres, and regions with increased connectivity to the seed in the 0-back condition are
Cerebellum was omitted. Anterior orientation is in the front of the figure, right hemisphere is on the
denoted by red spheres. Cerebellum was omitted. Anterior orientation is in the front of the figure,
left side, and left hemisphere is on the right side. Abbreviations: dACC, dorsal anterior cingulate
right hemisphere is on the left side, and left hemisphere is on the right side. Abbreviations: dACC,
cortex. IFG, inferior frontal gyrus. PoG, postcentral gyrus. SPL, superior parietal lobule.
dorsal anterior cingulate cortex. IFG, inferior frontal gyrus. PoG, postcentral gyrus. SPL, superior
parietal lobule.
3.3.4. Insula as Seed
Table 6 and Figure 7 summarize brain regions with significantly increased task-independent
residual functional connectivity with the insula as VAS-F scores increased, in either the 2-back or 0-
back condition among the MS and HC groups. In the HC group, as VAS-F scores increased, task-
independent residual functional connectivity was significantly increased in the 2-back condition
between the insula and the left inferior/orbital frontal gyri and the right superior temporal gyrus, and
Diagnostics 2020, 10, 930 15 of 22
3.3.4. Insula as Seed
Table 6 and Figure 7 summarize brain regions with significantly increased task-independent
residual functional connectivity with the insula as VAS-F scores increased, in either the 2-back or 0-back
condition among the MS and HC groups. In the HC group, as VAS-F scores increased, task-independent
residual functional connectivity was significantly increased in the 2-back condition between the insula
and the left inferior/orbital frontal gyri and the right superior temporal gyrus, and it significantly
increased in the 0-back condition between the insula and the right MCC, the left paracentral lobule
and postcentral gyrus, the left SPL, the right precuneus, bilateral occipital regions (superior and
middle occipital and calcarine), and the right cerebellum. In the MS group, as VAS-F scores increased,
task-independent residual functional connectivity was significantly increased in the 2-back condition
between the insula and the right middle orbital gyrus, the left superior medial gyrus, the right insula,
and the right caudate nucleus, and it significantly increased in the 0-back condition between the
insula and the left vmPFC, the right fusiform gyrus, primarily right occipital regions (inferior occipital,
calcarine, cuneus, and lingual), and the left cerebellum.
Table 6. Task-independent (residual) fatigue-related functional connectivity with the insula as seed.
Insula Seed
Location X Y Z Voxels Z Stat
HC
Increased Task-independent Residual Connectivity in 2-back Condition
Inferior Frontal Gyrus −51.3 26.1 −2 51 5.45
Inferior Frontal Gyrus −47.9 22.7 30 18 3.81
Middle Orbital Gyrus −6.6 70.8 −14 15 4.18
Superior Temporal Gyrus 55.3 −25.4 2 18 4.96
Increased Task-independent Residual Connectivity in 0-back Condition
Middle Cingulate Cortex 14.0 −32.3 42 20 −4.47
Paracentral Lobule −10.1 −39.2 82 13 −5.33
Postcentral Gyrus −41.0 −46.1 66 17 −4.84
Superior Parietal Lobule −30.7 −42.6 74 22 −4.46
Precuneus 7.1 −63.2 66 51 −4.60
Superior Occipital Gyrus 24.3 −70.1 46 16 −3.95
Middle Occipital Gyrus −37.6 −77.0 34 34 5.46
Calcarine Gyrus 20.9 −77.0 14 13 −4.60
Cerebellum lobule VIII 24.3 −35.7 −50 22 −4.74
MS
Increased Task-independent Residual Connectivity in 2-back Condition
Middle Orbital Gyrus 0.3 64.0 −10 68 5.09
Superior Medial Gyrus −6.6 57.1 46 21 5.11
Insula 45.0 5.5 −6 26 4.24
Caudate Nucleus 10.6 12.4 18 13 5.91
Diagnostics 2020, 10, 930 16 of 22
Table 6. Cont.
Insula Seed
Location X Y Z Voxels Z Stat
Increased Task-independent Residual Connectivity in 0-back Condition
Ventromedial Prefrontal Cortex −6.6 5.5 −22 15 −6.21
Fusiform Gyrus 27.8 −66.7 −6 13 −4.09
Inferior Occipital Gyrus 31.2 −87.3 −2 19 −3.70
Calcarine Gyrus 3.7 −70.1 18 15 −4.03
Cuneus 14.0 −73.6 30 26 −5.39
Lingual Gyrus −6.6 −83.9 −2 39 −4.88
Cerebellum Crus I −54.7 −56.4 −38 15 −4.62
Cerebellum Lobule VI −20.4 −66.7 −14 14 −4.85
Cerebellum Lobule VIII −16.9 −63.2 −62 26 −4.46
“X”, “Y”, and “Z” denote the three-dimensional peak coordinates of each significant clusters. Positive “X” values
represent the right hemisphere, and negative “X” values represent the left hemisphere. “Voxels” denote the number
of voxels in each cluster. “Z Stat” reflects the extent of functional connectivity between the seed region and the brain
region in each row; positive values represent increased functional connectivity in the 2-back condition, and negative
Diagnostics
values2020, 10, x increased functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis.
represent 16 of 21
HC, healthy control.
(a) (b)
Figure
Figure 7. 7. Insula
Insula connectivity
connectivity 3-dimensional
3-dimensional rendering.
rendering. (a) (a) Task-independent,
Task-independent, fatigue-related
fatigue-related
connectivity
connectivityininthe thehealthy
healthycontrol
control group.
group.(b) (b)
Task-independent,
Task-independent, fatigue-related connectivity
fatigue-related in thein
connectivity
multiple sclerosis
the multiple group.
sclerosis TheThe
group. seedseed
is denoted
is denotedby by
green spheres;
green regions
spheres; regionswith
withincreased
increasedconnectivity
connectivity
toto the
the seed
seed in
in the
the 2-back
2-back condition
condition are are denoted
denoted by by yellow
yellow spheres,
spheres, andand regions
regions with
with increased
increased
connectivitytotothe
connectivity theseed
seedininthe
the0-back
0-backcondition
conditionare aredenoted
denotedby byred
redspheres.
spheres.Cerebellar
Cerebellarregions
regionswere
were
omitted.Anterior
omitted. Anteriororientation
orientationisisininthe
thefront
frontofofthe
thefigure;
figure;right
righthemisphere
hemisphereisison onthetheleft
leftside,
side,and
andleft
left
hemisphereisison
hemisphere on the
the right
right side.
side. Abbreviations:
Abbreviations: IFG, IFG,inferior
inferiorfrontal
frontal gyrus.
gyrus. vmPFC,
vmPFC,ventromedial
ventromedial
prefrontal cortex.
prefrontal cortex. SMG,
SMG,superior
superiormedial
medial gyrus.
gyrus. MCC,MCC,middle
middle cingulate
cingulate cortex.
cortex. PcL,
PcL,paracentral
paracentral
lobule.PoG,
lobule. PoG,postcentral
postcentralgyrus.
gyrus.MOG,MOG,middlemiddleorbital
orbitalgyrus.
gyrus.STG,
STG,superior
superiortemporal
temporalgyrus.
gyrus. SPL,
SPL,
superior parietal lobule. SOcG, superior occipital gyrus. MOcG, middle occipital
superior parietal lobule. SOcG, superior occipital gyrus. MOcG, middle occipital gyrus. IOcG, inferiorgyrus. IOcG, inferior
occipitalgyrus.
occipital gyrus.
3.3.5. Striatum as Seed
There were no fatigue-related changes in task-independent residual functional connectivity in
the HC group. Table 7 and Figure 8 summarize brain regions with significantly increased task-
independent residual functional connectivity with the striatum as VAS-F scores increased, in either
the 2-back or 0-back condition in the MS group. In the MS group, as VAS-F scores increased, task-
Diagnostics 2020, 10, 930 17 of 22
3.3.5. Striatum as Seed
There were no fatigue-related changes in task-independent residual functional connectivity in the
HC group. Table 7 and Figure 8 summarize brain regions with significantly increased task-independent
residual functional connectivity with the striatum as VAS-F scores increased, in either the 2-back or
0-back condition in the MS group. In the MS group, as VAS-F scores increased, task-independent
residual functional connectivity was significantly increased in the 2-back condition between the
Diagnostics 2020, 10, x 17 of 21
striatum and the left SPL and the right angular gyrus/SPL. There were no regions with increased
task-independent residual functional
Table 7. Task-independent connectivity
(residual) in the
fatigue-related 0-backconnectivity
functional condition with
with the
the striatum in the
striatum as
MS group.
seed.
Striatumfunctional
Table 7. Task-independent (residual) fatigue-related Seed connectivity with the striatum as seed.
Location X Y Z Voxels Z Stat
Striatum Seed
MS
Location
Increased Task-independent ResidualX Connectivity
Y Z
in 2-back Voxels
Condition Z Stat
Superior Parietal Lobule MS −30.7 −80.4 58 16 4.73
Angular Gyrus/Superior Parietal Lobule
Increased Task-independent 41.5
Residual Connectivity −80.4
in 42
2-back Condition 16 4.58
“X”, “Y”,Superior
and “Z”Parietal
denote the three-dimensional−30.7
Lobule peak coordinates
−80.4 of each 58significant16clusters. Positive
4.73
“X” values represent the right hemisphere, and negative “X” values represent the left hemisphere.
Angular Gyrus/Superior Parietal Lobule 41.5 −80.4 42 16 4.58
“Voxels” denote the number of voxels in each cluster. “Z Stat” reflects the extent of functional
“X”, “Y”, and “Z” denote the three-dimensional peak coordinates of each significant clusters. Positive “X” values
connectivity
represent between
the right the seed
hemisphere, region “X”
and negative andvalues
the brain region
represent in each
the left row; positive
hemisphere. valuesthe
“Voxels” denote represent
number
increased
of functional
voxels in each connectivity
cluster. “Z Stat” reflectsin
thethe 2-back
extent condition,
of functional and negative
connectivity betweenvalues represent
the seed region andincreased
the brain
region in eachconnectivity
functional row; positive in
values
the represent increased functional
0-back condition. connectivity
Abbreviations: MS,inmultiple
the 2-backsclerosis.
condition,HC,
and negative
healthy
values represent increased functional connectivity in the 0-back condition. Abbreviations: MS, multiple sclerosis.
control.
HC, healthy control.
Figure 8.8. Striatum connectivity
Figure connectivity 3-dimensional
3-dimensional rendering. Figure represents task-independent,
rendering. Figure task-independent,
fatigue-related
fatigue-related connectivity
connectivity in in the
the multiple
multiple sclerosis
sclerosis group;
group; there
there were
were nono significant
significant differences
differences
between
between conditions
conditions in in the
the healthy
healthy control
control group.
group. TheThe seed
seed isis denoted
denoted byby aa green
green sphere,
sphere, and
and regions
regions
with
withincreased
increased connectivity
connectivity to to the
the seed
seed in
in the
the2-back
2-back condition
condition are are denoted
denoted by by yellow
yellow spheres;
spheres; there
there
were
wereno noregions
regionswith
withincreased
increasedconnectivity
connectivitytotothe theseed
seedinin
thethe
0-back
0-backcondition.
condition.Anterior
Anterior orientation
orientationis
in
is the front
in the of the
front figure;
of the right
figure; hemisphere
right hemisphere is onisthe
on left
the side, and left
left side, andhemisphere is on is
left hemisphere the
onright side.
the right
Abbreviations: SPL, superior
side. Abbreviations: parietal
SPL, superior lobule.lobule.
parietal AG, angular gyrus.gyrus.
AG, angular
4. Discussion
4. Discussion
The current study examined cognitive fatigue-related functional connectivity (independent of
The current study examined cognitive fatigue-related functional connectivity (independent of
task-related activations) in a heterogeneous sample of pwMS compared to demographically matched
task-related activations) in a heterogeneous sample of pwMS compared to demographically matched
HCs. Results indicated that as state cognitive fatigue increased, HCs showed a left lateralized
HCs. Results indicated that as state cognitive fatigue increased, HCs showed a left lateralized
connectivity pattern and increased connectivity between the striatum of the basal ganglia and the
vmPFC (which are crucial in reward processing) during the fatigue-inducing, higher cognitive load
condition (2-back) compared to the lower cognitive load control condition (0-back). In contrast,
pwMS displayed a more bilateral connectivity pattern and increased connectivity from interoceptive
Diagnostics 2020, 10, 930 18 of 22
connectivity pattern and increased connectivity between the striatum of the basal ganglia and the
vmPFC (which are crucial in reward processing) during the fatigue-inducing, higher cognitive load
condition (2-back) compared to the lower cognitive load control condition (0-back). In contrast,
pwMS displayed a more bilateral connectivity pattern and increased connectivity from interoceptive
hubs—the insula and the dACC—to the striatum in the 2-back condition relative to the 0-back condition.
Taken together, pwMS’s more diffused functional connectivity pattern associated with increased task
demands (designed to induce fatigue) may have been inefficient (as evident in their decline in task
accuracy), resulting in increased cognitive fatigue and interoceptive efforts to control bodily states
(in order to combat fatigue). On the other hand, HCs appeared successful in modulating fatigue
(and even improve task accuracy as fatigue level rose) through the reward circuitry.
Our identification of interoceptive mechanisms in MS-related fatigue is consistent with recent
evidence of interoceptive deficits and hyperconnectivity of interoceptive hubs (the ACC and the insula)
among fatigued pwMS [13]. Specifically, we found that the dACC and the insula were hyperconnected
to the striatum, a structure most commonly implicated in fatigue [7–9]. These findings suggest that
pwMS may have difficulty with regulating their bodily states as fatigue level rises during a taxing
activity, which urges them to direct additional neural resources towards monitoring their internal
signals. On the contrary, HCs appear to be actively processing the costs and benefits of exerting
additional effort through the reward pathway, which facilitates task performance. Our results regarding
the reward pathway are somewhat consistent with previous functional connectivity studies, which have
identified negative correlations between fatigue measures and frontal-striatal functional connectivity
among fatigued pwMS compared to non-fatigued pwMS and HCs [34,35]. In the current study, only
HCs showed a positive correlation between fatigue and frontal-striatal functional connectivity, and no
such correlation was found in pwMS. The current study extends previous findings by establishing
functional connectivity patterns associated with state fatigue during a cognitively demanding task.
Previous studies generally correlated resting-state functional connectivity with trait fatigue (e.g.,
questionnaires asking about fatigue symptoms over the past week or month), which may be more
reflective of brain reorganization due to disease pathology, rather than the experience of feeling fatigued.
The current study’s analysis of the task-independent signals associated with reports of in-the-moment
fatigue during a challenging cognitive task allowed us to characterize in vivo relationships among brain
regions, as state fatigue level rose. These results support a pattern of functional disconnection within
the reward pathway in MS-related fatigue, which may be used as a target for interventions. Indeed,
we previously discovered that progressive resistance exercise training could increase frontal-striatal
functional connectivity and reduce the everyday impact of fatigue [36]. Moreover, we have successfully
decreased on-task fatigue using a behavioral gambling task aimed to stimulate the frontal-striatal
network (with monetary rewards) in pwMS [37].
Present findings of a more diffused pattern of functional connectivity in MS are consistent with
the existing literature, which has posited that functional connectivity changes occur as a response to
combat structural damage [38–41]. Such functional reorganization in MS has long been demonstrated
with task-based activations. A recurrent finding is that pwMS often recruit bilateral brain regions
to support functions typically subserved by more lateralized networks in HCs, such as in a finger
tapping task [42,43]. MS fatigue research, specifically, has repeatedly demonstrated bilateral functional
recruitment among fatigued pwMS compared to non-fatigued pwMS and HCs (who exhibit a
more lateralized pattern of activation) during challenging (therefore fatiguing) cognitive and motor
tasks [44,45]. Our results concur with these observations, with pwMS displaying bilateral connectivity
while HCs showing primarily left lateralized connectivity during the more mentally challenging
condition. Whether such reorganization is adaptive or maladaptive is up for debate in the field [46]
and in actuality may depend on the specific context of the situation (e.g., patient demographic and
disease characteristics, types of experimental tasks involved). For example, one of our previous papers
demonstrated that altered functional connectivity may be compensatory in earlier stages of the disease
(maintaining behavioral performance) but may be maladaptive (and does not improve performance)
Diagnostics 2020, 10, 930 19 of 22
as the disease progresses [38]. The current study supports a maladaptive framework, as changes
in functional connectivity were associated with a decline in task performance. It also extends our
previous work, which found an association between cognitive fatigue and an inefficient pattern of
task-based activations in pwMS relative to HCs [15].
The current study has a few limitations which should be considered when interpreting its
findings. First, although the sample size is comparable to many fMRI studies, it is still relatively
small for generalization to the overall population. Therefore, future investigations should confirm
these findings with a larger sample size. A larger sample size would also allow for analysis into the
roles of disease phenotype and duration. Unfortunately, our small sample size and predominance
of the relapsing–remitting course of pwMS precluded us from being able to determine if various MS
phenotypes would show a similar connectivity pattern. Second, since the MS group reported much
higher levels of fatigue than the HC group overall and started out reporting higher fatigue (first rating),
it is unclear whether our observations reflected pathological fatigue in MS or higher levels of fatigue in
general. Future research may consider including patient groups of varying pathologies and specific HC
groups with high levels of fatigue (e.g., medical residents with long work hours) to test this question.
For example, a recent study identified functional connectivity in the insula and the putamen (part of
the striatum) as important hubs for cognitive fatigue across multiple cognitive tasks among healthy
older adults [10]. Therefore, it is possible that our observations reflect a general fatigue network rather
than a MS-specific fatigue network.
5. Conclusions
The current study identified altered cognitive fatigue-related functional connectivity in the
interoceptive and reward pathways among pwMS. Specifically, pwMS showed a hyperconnectivity
within the interoceptive network and disconnection within the reward circuitry. Such alterations may
be the result of inefficient brain connectivity when meeting increased task demands.
Author Contributions: Conceptualization, H.M.G. and G.R.W.; methodology, G.R.W.; formal analysis, G.R.W.
and M.H.C.; data curation, H.M.G. and G.R.W.; writing—original draft preparation, M.H.C.; writing—review
and editing, J.D., H.M.G., B.Y., and G.R.W.; visualization, M.H.C.; funding acquisition, H.M.G., G.R.W., and J.D.
All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by NATIONAL MULTIPLE SCLEROSIS SOCIETY (NMSS), grant number
RG 4232A1/1 (PI: Helen Genova); NMSS, grant number MB-1606-08779 (PI: John DeLuca); NMSS, grant number
CA1069-A-7 (PI: John DeLuca), and KESSLER FOUNDATION.
Conflicts of Interest: J.D. has served on advisory boards for Biogen, Celgene and Novartis; has been a speaker for
Biogen; and has received funding from Biogen. All other authors declare no conflict of interest.
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