Arzu Ceylan Has Silemek1, Lukas Fischer1, Jana Pöttgen1,2, Iris-Katharina Penner3,4, Andreas K. Engel5, Christoph Heesen1,2, Stefan M. Gold1,6, and Jan-Patrick Stellmann1,2,7,8
1Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany, 2Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany, 3Klinik für Neurologie, Heinrich Heine Universität Düsseldorf, Düsseldorf, Germany, 4Zentrum für Angewandte Neurokognition und Neuropsychologische Forschung, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany, 5Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany, 6Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Berlin, Germany, 7CRMBM AMU-CNRS , Marseille, France, Marseille, France, 8CEMEREM, APHM, CHU Timone, Marseille France, Marseille, France
Synopsis
We utilized an approach combining DTI, RS-fMRI and graph-theory to
characterize the relation between cognitive profiles and global and local
network features in RRMS patients with mild to moderate disability. Closer
association of structural network metrics with cognitive abilities were
seen compared to standard-MRI outcomes and an interesting pattern of
associations with a slight predominance of nodes located in the default mode
network (DMN). While structural connectivity always showed a positive
correlation with performance, the number of functional connections of nodes was
mostly negatively correlated. DMN had inverse and direct relationships with
memory, possible indicating adaptive and maladaptive mechanisms.
Purpose
Multiple
Sclerosis (MS), which is a chronic inflammatory and neurodegenerative disease
of the central nervous system (CNS), can lead to severe cognitive impairment
over time1. To track MS
pathophysiology and its relation with clinical disability, magnetic
resonance imaging (MRI) is one of the best biomarker2. However, it is limited
to detect direct associations between symptoms and their underlying CNS
substrates by conventional MRI. In this study, we aimed to provide a comprehensive exploration of the association between neuropsychological (NP) performance and both structural and functional networks within global and nodal levels
using graph theory in mildly disabled relapsing remitting MS (RRMS) patients compared to healthy
controls (HC). Method
IRB
was taken and all participants gave signed informed consent form.
A
comprehensive NP test battery that included 20 different cognition tests was
administered in mildly disabled RRMS [n=33, F/M=20/13, age=40.9±9.7, median [Expanded Disability
Status Scale] (EDSS) = 2, range=0-4] and compared to closely matched
(age, sex and education level) HC [n=29, F/M=19/10, age=41.0±8.5]
(p>0.05).
MRI
Protocol: Diffusion tensor imaging (DTI) (single
shell, 32 independent direction, b=1000s/mm2, TR/TE=7200ms/90ms; voxel
size=1.9×1.9×2.0 mm, FOV=240mm, matrix=128x128, 54 axial sections, no gap) and resting state (RS) functional MRI (fMRI) [T2*-weighted (W) BOLD-sensitized echo
planer imaging sequence (TR/TE=2500ms/25ms; TI=900ms; 40 slices, voxel
size=2.7×2.7×3.0 mm, no gap, matrix=256x256, FOV=250mm, FA=90°, measurements=250)
were applied to all subjects on a 3T MRI (Siemens/Prisma). 3D T1W-MPRAGE (TR/TE=2500ms/2.12ms; TI=1100ms;
256 slices, voxel size=0.8×0.8×0.9 mm, no gap, matrix=288x288, FOV = 240mm) and
T2-W (TR/TE = 2800ms/90ms; 43 slices, voxel size 0.5×0.5×3.0 mm, no gap,
matrix=256x256, FOV = 240mm) sequences were also obtained. Neurological
assessments [EDSS3, the 9-Hole Peg Test (NHPT)4,5, and the Timed-25
food walk (T25FW)] and NP test battery were
applied in the same week with MRI assessment.
Data
Processing and Analysis:
MRITRIX36 was used to extract structural connectivity
based on probabilistic tractography as described in Beeson et al7. Individual functional networks were constructed based on wavelet
correlation from resting state time series as described8. To avoid the possible segmentation errors hypo-intense
lesion were filled on T1W images. Then, an automated procedure for the volume and thickness
measurement was performed for each subject using Freesurfer9. Segmentation correction was done manually, and volumes
were normalized by intracranial brain volume for each subject. Finally, gray-matter parcellation of 80 regions
(total=160) for each hemisphere was specified based on the Destrieux atlas
(2009)10 to perform the structural and functional
connectivity analysis. The location of each node in one of seven functional networks
(Yeo atlas) was determined on the FreeSurfer fsaverage11. As, graph metrics, we computed nodal strength
(i.e. the sum of edge weights in each node) for structural connectivity and nodal
degree (i.e. the sum of connections per node in each node) for functional
connectivity. The analyses were performed with statistics in R 3.2.3, including
the igraph12 and tnet13 packages. To investigate hub disruption based
on the continuum of network hierarchy14, group (RRMS and HC) level statistical analysis
of hub disruption were performed using linear mixed effects regression (LMER)15. The false discovery rate adjustment (FDR) was
applied.Results
Global graph strength
was decreased in RRMS compared to HC (p<0.001, FDR
corrected); however, there was no changed in global degree. The NP
battery was comprised as attention, processing speed, verbal and spatial
learning and memory, and executive function. While standard MRI metrics (e.g.
brain parenchymal fraction or lesion load) showed correlations with disease
duration (p=0.001), neurological exams (pEDSS = 0.003, pT25FW <0.001),
and TAP Alertness test (p = 0.04), structural network showed broader
associations with cognition (Table 1) on global level. Decreased global graph
strength was related with spatial memory specified by BVMT [Sum 1-3] and BVMT
[Recall], and with the processing speed specified by SDMT (p<0.05, FDR
corrected), while there was no association between these scores and functional
connectivity (Table 1). Nodal structural connectivity was decreased in all
subnetworks based on Yeo atlas in patients compared to HC (p<0.001) (Figure 1A); however, there was
no difference in nodal level of functional connectivity between the groups (Figure 1B). In
addition, nodal structural and functional connectivity had inverse relationship
mainly in visual and default mode network (DMN)s in patients, while it was
positive in controls (Figure 1C). Interestingly, poorer cognitive performance
was mostly correlated with higher functional connectivity but lower structural connectivity
in patients (Figure 2). Notable, higher functional connectivity in DMN had both
positive as well as negative associations with memory (Figure 3) and spatial memory
(Figure 4).Discussion and Conclusion
Cognitive
performance in early RRMS is related to a widespread disruption of structural
connectivity. In contrast, functional connectivity shows overall a putative
maladaptive profile such as higher connectivity indicated poorer performance. However,
the coexistence of positive and negative associations with memory and spatial
memory in DMN indicates a more complex and heterogenous pattern of adaptive and
maladaptive mechanisms. A longitudinal
study with a wide spectrum of clinical disabilities assessed by weighted
networks would be more useful to see the pattern of cognition within the long term
of the same cohort, and as well as the transition of MS cohorts. In
particular, assessment of dynamic change might give new insights in the
mechanisms behind the imbalanced associations in the DMN.Acknowledgements
This research received funding from
the NEUCONN grant which is supported by German Federal Ministry of Education
and Research (grant number 161A130).References
1. Filippi
M, Bar-Or A, Piehl F, et al. Multiple sclerosis. Nat Rev Dis Prim.
2018;4(1):43. doi:10.1038/s41572-018-0041-4
2.
Fox MD. Mapping Symptoms to Brain
Networks with the Human Connectome. N Engl J Med.
2018;379(23):2237-2245. doi:10.1056/NEJMra1706158
3.
Kurtzke JF. Rating neurologic
impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology.
1983;33(11):1444-1452. http://www.ncbi.nlm.nih.gov/pubmed/6685237. Accessed
November 13, 2018.
4.
Backman C, Cork S, Gibson D,
Parsons J. full-text. Can J Occup Ther. 1992;59(4):208-213.
5.
Chan T. An Investigation of Finger
and Manual Dexterity. Percept Mot Skills. 2000;90(2):537-542.
doi:10.2466/pms.2000.90.2.537
6.
Tournier J-D, Smith R, Raffelt D,
et al. MRtrix3: A fast, flexible and open software framework for medical image
processing and visualisation. Neuroimage. 2019;202:116137.
doi:10.1016/J.NEUROIMAGE.2019.116137
7.
Besson P, Dinkelacker V, Valabregue
R, et al. Structural connectivity differences in left and right temporal lobe
epilepsy. Neuroimage. 2014;100:135-144.
doi:10.1016/j.neuroimage.2014.04.071
8.
Wirsich J, Perry A, Ridley B, et
al. Whole-brain analytic measures of network communication reveal increased
structure-function correlation in right temporal lobe epilepsy. NeuroImage
Clin. 2016;11:707-718. doi:10.1016/J.NICL.2016.05.010
9.
Fischl B, Salat DH, Busa E, et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in
the human brain. Neuron. 2002;33(3):341-355.
http://www.ncbi.nlm.nih.gov/pubmed/11832223. Accessed November 13, 2018.
10.
Destrieux C, Fischl B, Dale A,
Halgren E. Automatic parcellation of human cortical gyri and sulci using
standard anatomical nomenclature. Neuroimage. 2010;53(1):1-15.
doi:10.1016/j.neuroimage.2010.06.010
11.
Thomas Yeo BT, Krienen FM, Sepulcre
J, et al. The organization of the human cerebral cortex estimated by intrinsic
functional connectivity. J Neurophysiol. 2011;106(3):1125-1165.
doi:10.1152/jn.00338.2011
12.
Csárdi G, Nepusz T. The Igraph
Software Package for Complex Network Research.
https://pdfs.semanticscholar.org/1d27/44b83519657f5f2610698a8ddd177ced4f5c.pdf.
Accessed June 19, 2019.
13.
Opsahl T. Structure and Evolution of
Weighted Networks. 2009.
https://ethos.bl.uk/OrderDetails.do;jsessionid=652A420A33195813CEE0717B838DAA3A?uin=uk.bl.ethos.507253.
Accessed November 13, 2018.
14.
Achard S, Delon-Martin C, Vértes PE,
et al. Hubs of brain functional networks are radically reorganized in comatose
patients. Proc Natl Acad Sci U S A. 2012;109(50):20608-20613.
doi:10.1073/pnas.1208933109
15.
Bates D, Mächler M, Bolker B, Walker
S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw.
2015;67(1):1-48. doi:10.18637/jss.v067.i01