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Brain functional plasticity at rest and during action in multiple sclerosis patients
Journal of Clinical Neuroscience, Volume 22, Issue 9, September 2015, Pages 1438 - 1443
We aimed to demonstrate that basal functional connectivity reorganization observed in a specific network at rest using resting state functional MRI (rs-fMRI) could be associated with functional cortical reorganization in such network during action (ta-fMRI) in a population of early multiple sclerosis (MS) patients. Altered basal functional connectivity has been previously reported in patients with MS but relationships with cortical reorganization during action have not been explored. Thirteen patients with early relapsing-remitting MS and 14 matched healthy controls were explored on a 3T MRI scanner at rest and during a motor task (conjugate finger flexion and extension movements of each hand). Hand motor networks were extracted from rs-fMRI data using group spatial independent component analysis. For the non-dominant motor network, patients presented a higher basal functional connectivity at rest and recruited a supplementary prefrontal cortical area during action compared to the controls. The levels of hyperconnectivity at rest and of activation in the recruited area during action were significantly correlated. No differences were demonstrated for the dominant motor network at rest and during action. The present study, combining rs-fMRI and ta-fMRI in non-disabled patients with early MS, revealed for the first time a direct association between functional reorganization depicted at rest and during action within the same system.
Keywords: Brain plasticity, Functional connectivity, Functional MRI, Motor system, Multiple sclerosis, Resting state.
Task-associated functional MRI (ta-fMRI) studies have widely demonstrated the phenomenon of brain functional plasticity in multiple sclerosis (MS) from the early stages of the disease , , , and . Brain plasticity may significantly counteract the functional impact of tissue injury, contributing to the lack of strong correlation demonstrated between structural MRI parameters and clinical deficits. However, assessment of brain plasticity remains difficult in clinical practice. Several factors limit the potential clinical application of ta-fMRI. First, this technique needs the active participation of the patient, which is influenced by motivation and/or disability. Second, the results obtained using functional MRI (fMRI) are highly influenced by the paradigm selected.
A promising alternative is to acquire fMRI data at rest and study correlations between spontaneous low frequency fluctuations of the cerebral blood oxygenation level-dependent (BOLD) signals extracted from remote cortical areas  . These fluctuations have shown strong temporal coherence between brain regions that represent functional systems like the sensorimotor network  and . With the recent optimization of post-processing methods such as independent component analysis (ICA), it is now possible to extract basal brain activity within the major neuronal networks from low frequency BOLD signal fluctuations  . Several studies performed in MS patients at all stages of the disease , , , , , , , and  have shown consistent changes in the level of functional connectivity in various resting state networks (RSN), suggesting that this technique is sensitive to brain functional reorganization  .
In the present study, we aimed to investigate whether functional connectivity reorganization at rest in the motor network could be associated with cortical reorganization assessed during a simple motor task in patients at the early stages of MS and without any motor disability. For this aim, we performed an fMRI protocol combining a resting state fMRI (rs-fMRI) and a ta-fMRI during a simple motor task in a single session for a homogenous group of non-disabled patients with early MS compared to healthy matched controls.
2. Materials and methods
Approval was received from the local Ethics Committee for human experimentation (Timone University Hospital, Marseille, France) and written informed consent was obtained from all subjects participating in the study.
A homogenous group of 13 patients with early relapsing remitting MS (median disease duration [range] = 8 months [3–40]; median age [range] = 32 years [21–43], 10 women and three men) was included in this study. A control group of 14 age and sex-matched healthy subjects was also included (median age [range] = 30 years [20–51], 10 women and four men). All were right-handed (determined using the Edinburgh handedness scale  ). All patients fulfilled MS diagnostic criteria according to McDonald, revised in 2010  . None of the patients had experienced a relapse or treatment with steroids in the preceding 3 months. Their disability level was rated using the Kurtzke expanded disability status scale  and the multiple sclerosis functional composite score, which is a three part composite of quantitative measures of ambulation (timed 8 meter walk), upper extremity function, (nine hole peg test [NHPT] with left and right hand) and cognitive function (paced auditory serial addition test, 3 seconds)  and .
2.2. Conventional MRI
MRI was performed on a 3T whole body MRI system (Verio; Siemens AG, Munich, Germany) using a 32 channel phased array head coil. The MRI protocol included localizer scout imaging, transverse fast spin-echo proton density-weighted and T2-weighted sequences (repetition time [TR] 8000, echo times [TE] 15 and 85 ms, 44 contiguous sections, 3 mm section thickness, field of view [FOV] 256 mm, matrix 2562, 1 mm × 1 mm × 3 mm resolution, acquisition time 3 min 54 s).
2.3. Functional MRI
Echo planar images (250 volumes) were acquired during resting state with a single shot gradient-echo echo planar imaging sequence (TR 3600 ms, TE 28 ms, 50 axial slices, thickness 2.5 mm, FOV 244 mm, matrix 1222, resolution 2 mm × 2 mm × 2.5 mm, acquisition time 15 min 12 s). Subjects were instructed to rest with their eyes closed, not fall asleep, and think of nothing in particular during this scan.
For the ta-fMRI, patients and controls performed a simple motor task consisting of successive finger flexion-extension movements of one hand in response to an acoustic signal (1 Hz). Movement amplitude was guided by a hard ball placed in the palm of the hand and held in place with the patient’s thumb and adhesive tape. Subjects alternated four periods of movement (two periods of dominant hand movement and two of non-dominant hand movement) with four periods of rest. An operator supervised the performance of the task to check that it was properly executed. We used a block design for the ta-fMRI acquisition. Each period of activation and rest lasted 30 seconds and consisted of 10 measurements (3 seconds/measurement).
The acquired measurements consisted of 80 volumes using single shot gradient-echo echo planar imaging sequences (TE = 30 ms, TR = 3000 ms, 36 contiguous slices, thickness 3 mm, FOV 192 mm, matrix 642, voxel resolution 3 mm × 3 mm × 3 mm, bandwidth 2.232 Hz/pixel).
2.4. Image processing
Sources of spurious or regionally non-specific variance related to physiological artifacts (cerebrospinal fluid pulsations, head motions) were removed by regression, including the signal averaged over the lateral ventricles and the signal averaged over a region centered in the deep cerebral white matter to reduce non-neuronal contributions to BOLD correlations , , and . The MELODIC toolbox of FSL (version 4.1.3; The Oxford Centre for Functional MRI of the Brain, Oxford, UK) was used to perform a concatenated group ICA to extract 51 different components, including the predefined RSN described in previous works  and . Images were corrected for acquisition delays (slice timing), realigned before spatial normalization (non-linear registration) and smoothed (8 mm). This data driven method allows for the extraction of distinct spatio-temporal patterns by identifying spatially independent and temporally synchronous brain regions  . Among all components obtained after ICA analysis, those related to the non-dominant and the dominant motor functional networks were selected by visual inspection according to the best correspondence with motor networks described by past studies , , , and . Next, a dual regression approach was applied using the independent component time course from each subject in order to obtain a connectivity map corresponding to each subject. Global connectivity indices were determined for each subject and each network from the mean value of regions corresponding to the significant clusters of the correlation maps. This index represents the magnitude of the correlation between all the regions composing the network  .
The connectivity maps of each subject were used to perform group analyses using SPM5 statistical parametric mapping software (Wellcome Trust Centre for Neuroimaging, London, UK). Connectivity maps of all subjects were made and comparisons between patients and controls were performed (p < 0.005; k = 20; corrected for cluster extent p < 0.05).
Images were post-processed using the SPM5 software. After realignment, images were normalized to Montreal Neurology Institute (MNI) coordinates, coregistered and smoothed with a 12 mm Gaussian filter. After obtaining a single image for each subject parameterizing the effect of interest, intra-group analysis was done (one sample t-test p < 0.005; k = 20; corrected at cluster level p < 0.05). Then, we performed inter-group analysis (two sample t-test p < 0.05; k = 20; corrected at cluster level p < 0.05). MNI coordinates were transformed into Talairach coordinates using a non-linear transformation method in order to locate activation clusters which were assigned to Brodmann areas. Correlations between global motor resting connectivity indexes, motor task activations and clinical scores were assessed using Spearman rank tests.
3.1. Clinical and conventional MRI characteristics
Demographic and clinical data of patients and controls are reported in Table 1 . Patients showed significantly decreased left manual dexterity (left NHPT score) compared to controls (p = 0.026). Other patient motor performances did not differ significantly from those of controls.
|Patients||Healthy controls||p value ⁎|
|n (female/male)||13 (10/3)||14 (10/4)|
|Age, years, median (range)||32 (21–43)||30 (20–51)||NS|
|Timed 8 meter walk, seconds, median (range)||5.4 (4.4–6.2)||5.0 (3.6–6.4)||NS|
|PASAT 3, median (range)||51 (31–59)||52 (29–58)||NS|
|NHPT, seconds, median (range; SD)||17.4 (15.3–31.5; 4)||17 (12.9–19.9; 2)||NS|
|MRC score, median (range)||5 (5–5)||5 (5–5)||NS|
|NHPT, seconds, median (range; SD)||20.2 (16.6–26.0; 3)||17.2 (14.4–21.7; 2)||0.026|
|MRC score, median (range)||5 (4–5)||5 (5–5)||NS|
|Duration of disease, months, median (range)||13.4 (3–40)||NA||NA|
|Presenting symptoms, n|
|Clinical conversion to MS, n||3||NA||NA|
|EDSS, median (range)||1 (0–3)||NA||NA|
|T2-weighted lesion load, cm3, median (range)||6.80 (0.04–24.08)||NA||NA|
⁎ Demographic, clinical and neuropsychological data of patients were compared with those of controls using a non-parametric Mann–Whitney U-test.
EDSS = expanded disability status scale, MRC = British Medical Research Council scale assessing muscle force, MS = multiple sclerosis, NA = not applicable, NHPT = nine hole peg test assessing hand motor function, NS = non-significant, PASAT 3 = paced auditory serial addition test, 3 seconds, SD = standard deviation.
3.2. Functional MRI analysis
Dominant and non-dominant motor networks at rest assessed in the whole population are displayed in Figure 1 . They include contralateral and ipsilateral sensorimotor cortex, contralateral secondary sensorimotor cortex, controlateral supplementary motor area and ipsilateral cerebellum. The two sample t-test comparing the connectivity maps of the dominant and non-dominant motor networks between patients and controls did not demonstrate any significant differences between the two groups (p < 0.005; k = 20; corrected at cluster level p < 0.05).
The levels of mean functional connectivity of the two components (non-dominant and dominant motor networks) were compared between patients and controls (Mann–Whitney U test). The comparison between patients and controls demonstrated that the level of mean functional connectivity of the non-dominant motor network was higher in patients compared to controls (p = 0.005). No differences were demonstrated between patients and controls in the dominant motor network.
3.2.2. Motor task
The non-dominant and dominant motor networks assessed by ta-fMRI in the patient group are displayed in Figure 2 . They include contralateral and ipsilateral sensorimotor cortex, contralateral secondary sensorimotor cortex, contralateral supplementary motor area and ipsilateral cerebellum.
The comparison between patients and controls demonstrated activation changes for the non-dominant hand motor task. During this task, compared to the controls the patients showed a supplementary activated area located in the right ventrolateral prefrontal cortex (VLPC; Brodmann’s area [BA] 47; Talairach coordinates 28, 32, −10; two sample t-test p < 0.005; k = 20; corrected at cluster level p < 0.05). This difference survived the inclusion of the NHPT performances as potential confounding covariates (p < 0.005; k = 20; corrected at cluster level p < 0.05). This region, which was recruited in patients, is displayed in Figure 3 . No differences were found from the motor task performed with the dominant hand (two sample t-test p < 0.005; k = 20; corrected at cluster level p < 0.05).
3.3. Correlation between resting state functional connectivity changes, motor task-associated activation and clinical data
In patients, the level of activation in the region recruited during the non-dominant hand motor task (right VLPC, BA 47) was significantly correlated with the level of synchronization at rest of the respective non-dominant motor network (Spearman rank correlation; rho = 0.81; p < 0.001; Fig. 4 ). No correlations were found for the other regions that were activated during the non-dominant hand motor task in the patient group, or between clinical variables and the level of synchronization at rest for the two networks.
The present study provides preliminary results suggesting that brain reorganization depicted at rest is associated with functional plasticity during action. Contrary to ta-fMRI, rs-fMRI is not influenced by the specific task performance of the patients and this technique will be more easily adaptable in clinical practice. In addition, rs-fMRI, using a unique acquisition session of 15 minutes, potentially allows us to assess the functional reorganization of the main neuronal networks. In contrast, ta-fMRI provides information only for the few networks involved in the task performed.
As first reported by Roosendaal et al., in patients at the onset of MS we found an increase in basal functional connectivity within the motor network at rest  . This pattern of reorganization may appear unexpected in a neurological disease characterized by diffuse brain injury that is expected to decrease functional connectivity. Nevertheless, this increase in functional connectivity seems to be dependent on disease stage, with hyperconnectivity observed only at the onset of MS and interpreted as a likely compensatory mechanism  . On the other hand, a decrease in functional connectivity was observed in patients at a more advanced stage of the disease  . The existence of such a mechanism has been largely described in ta-fMRI studies , , and . However, to date no study has combined these two approaches to assess the potential coherence of the pattern of reorganization obtained at rest and during action. Here, applying the two fMRI methods in the same population and during the same session, we demonstrated a coherent relationship between connectivity at rest and activation changes during action in a specific network. Firstly, with the two fMRI approaches, we observed cortical and connectivity reorganization only in the motor network of the non-dominant hemisphere. Indeed, ta-fMRI only depicted a supplementary activation in the right ventrolateral prefrontal cortex for the non-dominant hand motor task in patients compared to controls. Similarly at rest, only the non-dominant motor network showed significant increase in functional connectivity whereas the dominant motor network was not different compared to controls. Secondly, the level of synchronization of the non-dominant motor network (rs-fMRI) is highly correlated to the activation during action in the recruited cortical region (ta-fMRI). As synchronization of the motor network at rest was higher, so was the activation of the recruited area during the motor task. Therefore, brain functional plasticity depicted at rest predicts altered functional activation during action.
In the present study, no activation changes were observed during the dominant hand movement whereas the same task performed with the non-dominant hand induced recruitment of higher order sensorimotor areas. We speculate that movement of the non-dominant hand requires more cognitive effort compared to movement of the dominant hand and involves early compensatory mechanisms. This may involve a more widely distributed network for motor act preparation which may be more vulnerable to the subtle diffuse brain injury encountered at the onset of the disease, as described previously , , and . In line with this hypothesis, we observed altered performances of the NHPT performed with the non-dominant hand and not with the dominant hand in the patient group.
Furthermore, compared to controls, patients showed a supplementary activated area located in the right anterior VLPC (BA 47). This area is known to be involved in motor act preparation and control. Indeed, functional imaging research presented in a large meta-analysis has demonstrated that right VLPC activation may be attributed to motor inhibition and attentional orienting processes (reflexive reorienting)  . Despite this, the critical function performed by the anterior part of the right VLPC is not clearly established. In MS patients, movements of the non-dominant hand need recruitment of the right VLPC as one of the higher order sensorimotor areas.
The present study suffers from some limitations. First, the limited number of patients included prevents any potential generalization of the findings. This study only provides preliminary results that must be replicated in a larger group of patients. Second, the limited number of periods of hand movement during the task-related fMRI may reduce the sensitivity of the method and the ability to depict subtle activation changes for the dominant hand. Third, even if brain atrophy has not been taken into account, we can expect that loco-regional atrophy would induce artificial reduction of connectivity and not an increase of connectivity, as demonstrated in the present study.
In conclusion, the present study highlights that rs-fMRI, a paradigm free method, may be a promising marker of brain functional plasticity which is usually assessed by ta-fMRI. Further studies including a large number of patients, other neuronal networks and other diseases are required to confirm these preliminary findings.
Conflicts of Interest/Disclosures
The authors declare that they have no financial or other conflicts of interest in relation to this research and its publication.
-  P. Pantano, C. Mainero, G.D. Iannetti, et al. Contribution of corticospinal tract damage to cortical motor reorganization after a single clinical attack of multiple sclerosis. Neuroimage. 2002;17:1837-1843 Crossref
-  M.A. Rocca, B. Colombo, A. Falini, et al. Cortical adaptation in patients with MS: a cross-sectional functional MRI study of disease phenotypes. Lancet Neurol. 2005;4:618-626 Crossref
-  D.M. Mezzapesa, M.A. Rocca, M. Rodegher, et al. Functional cortical changes of the sensorimotor network are associated with clinical recovery in multiple sclerosis. Hum Brain Mapp. 2008;29:562-573 Crossref
-  A. Rico, W. Zaaraoui, J. Franques, et al. Motor cortical reorganization is present after a single attack of multiple sclerosis devoid of cortico-spinal dysfunction. MAGMA. 2011;24:77-84 Crossref
-  M.J. Lowe, M. Dzemidzic, J.T. Lurito, et al. Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuroimage. 2000;12:582-587 Crossref
-  M.D. Greicius, B. Krasnow, A.L. Reiss, et al. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253-258 Crossref
-  J.S. Damoiseaux, S.A. Rombouts, F. Barkhof, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103:13848-13853 Crossref
-  M.J. Lowe, M.D. Phillips, J.T. Lurito, et al. Multiple sclerosis: low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity, initial results. Radiology. 2002;224:184-192 Crossref
-  M.J. Lowe, E.B. Beall, K.E. Sakaie, et al. Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum Brain Mapp. 2008;29:818-827 Crossref
-  S.D. Roosendaal, M.M. Schoonheim, H.E. Hulst, et al. Resting state networks change in clinically isolated syndrome. Brain. 2010;133:1612-1621 Crossref
-  M.A. Rocca, P. Valsasina, M. Absinta, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology. 2010;74:1252-1259 Crossref
-  S. Bonavita, A. Gallo, R. Sacco, et al. Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Mult Scler. 2011;17:411-422 Crossref
-  A. Faivre, A. Rico, W. Zaaraoui, et al. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler. 2012;18:1251-1258 Crossref
-  M.A. Rocca, P. Valsasina, V. Martinelli, et al. Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology. 2012;79:1449-1457 Crossref
-  A.-M. Dogonowski, H.R. Siebner, P. Soelberg Sørensen, et al. Resting-state connectivity of pre-motor cortex reflects disability in multiple sclerosis. Acta Neurol Scand. 2013;128:328-335
-  M. Filippi, F. Agosta, E.G. Spinelli, et al. Imaging resting state brain function in multiple sclerosis. J Neurol. 2013;260:1709-1713 Crossref
-  R.C. Oldfield. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97-113 Crossref
-  C.H. Polman, S.C. Reingold, B. Banwell, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol. 2011;69:292-302 Crossref
-  J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444-1452
-  G.R. Cutter, M.L. Baier, R.A. Rudick, et al. Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain. 1999;122:871-882 Crossref
-  J.S. Fischer, R.A. Rudick, G.R. Cutter, et al. The Multiple Sclerosis Functional Composite Measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult Scler. 1999;5:244-250
-  A. Bartels, S. Zeki. Brain dynamics during natural viewing conditions–a new guide for mapping connectivity in vivo. Neuroimage. 2005;24:339-349 Crossref
-  D.J. Vincent, C.J. Bloomer, V.K. Hinson, et al. The range of motor activation in the normal human cortex using bold FMRI. Brain Topogr. 2006;18:273-280 Crossref
-  G. Bettus, E. Guedj, F. Joyeux, et al. Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp. 2009;30:1580-1591 Crossref
-  D. Mantini, M.G. Perrucci, S. Cugini, et al. Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis. Neuroimage. 2007;34:598-607 Crossref
-  G. Varoquaux, S. Sadaghiani, P. Pinel, et al. A group model for stable multi-subject ICA on fMRI datasets. Neuroimage. 2010;15:288-299 Crossref
-  V. Calhoun, T. Adali, J. Liu. A feature-based approach to combine functional MRI, structural MRI and EEG brain imaging data. Conf Proc IEEE Eng Med Biol Soc. 2006;1:3672-3675
-  C.F. Beckmann, M. DeLuca, J.T. Devlin, et al. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005;360:1001-1013 Crossref
-  M.D. Fox, A.Z. Snyder, J.L. Vincent, et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673-9678 Crossref
-  N. Gour, J.P. Ranjeva, M. Ceccaldi, et al. Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks. Neuroimage. 2011;58:687-697 Crossref
-  M.A. Rocca, D.M. Mezzapesa, A. Ghezzi, et al. A widespread pattern of cortical activations in patients at presentation with clinically isolated symptoms is associated with evolution to definite multiple sclerosis. AJNR Am J Neuroradiol. 2005;26:1136-1139
-  B.J. Levy, A.D. Wagner. Cognitive control and right ventrolateral prefrontal cortex: reflexive reorienting, motor inhibition, and action updating. Ann N Y Acad Sci. 2011;1224:40-62 Crossref
a Aix-Marseille University, National Center for Scientific Research, Center for Magnetic Resonance in Biology and Medicine UMR 7339, Faculté de Médecine, 27 Boulevard Jean Moulin, Marseille 13385, France
b Sainte-Anne Military Teaching Hospital, Department of Neurology, Toulon, France
c Assistance Publique Hopitaux de Marseille, Timone University Hospital, Department of Neurology and Clinical Neurosciences, Marseille, France
d Assistance Publique Hopitaux de Marseille, Timone University Hospital, Department of Medical Imaging, Marseille, France
⁎ Corresponding author. Tel.: +33 48 3162459; fax: +33 48 3162738.
1 These authors have contributed equally to the manuscript.
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