Altered functional and structural connectivities in patients with MS
A 3-T study
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Abstract
Objective: To determine the functional and structural substrates of motor network dysfunction in patients with relapsing-remitting multiple sclerosis (RRMS).
Methods: Using a 3-T scanner, in 12 right-handed RRMS patients and 14 matched controls, we acquired diffusion tensor (DT) MRI and functional MRI during the performance of a simple motor task with the right (R) hand. Using DT MRI tractography, we calculated DT-derived metrics from several motor and nonmotor white matter (WM) fiber bundles. Functional connectivity analysis was performed using SPM2.
Results: Compared with control, MS patients had abnormal DT MRI metrics of all the WM bundles studied. Compared with controls, MS patients had more significant activations of the left (L) supplementary motor area (SMA), the L primary sensorimotor cortex (SMC), and the R cerebellum. They also had increased functional connectivity between the R primary SMC and the R cerebellum (p = 0.01) and the L SMA and the L primary SMC (p = 0.04). Coefficients of altered connectivity were correlated with structural MRI metrics of tissue damage of the corticospinal and the dentatorubrothalamic tract (r values ranging from −0.73 to 0.85).
Conclusions: The correlations found between measures of functional connectivity and structural damage to some of the major brain motor white matter bundles suggest an adaptive role of functional connectivity changes in limiting the clinical consequences of structural damage in patients with relapsing-remitting multiple sclerosis. Combining measures of altered functional and structural connectivities of specific brain networks is a promising tool to elucidate the mechanisms responsible for clinical manifestations of CNS damage.
Glossary
- 9-HPT=
- nine-hole peg test;
- CC=
- corpus callosum;
- CST=
- corticospinal tract;
- DCM=
- dynamic causal model;
- DRT=
- dentatorubrothalamic;
- DT=
- diffusion tensor;
- EDSS=
- Expanded Disability Status Scale;
- FA=
- fractional anisotropy;
- fMRI=
- functional MRI;
- FOV=
- field of view;
- LL=
- lesion load;
- MD=
- mean diffusivity;
- MS=
- multiple sclerosis;
- OR=
- optic radiation;
- ROI=
- region of interest;
- RRMS=
- relapsing-remitting MS;
- SFOF=
- superior fronto-occipital fasciculus;
- SLF=
- superior longitudinal fasciculus;
- SMA=
- supplementary motor area;
- SMC=
- sensorimotor cortex;
- TE=
- echo time;
- TR=
- repetition time;
- WM=
- white matter.
Profound and rapid advances in MRI technology are providing novel instruments that are useful not only for obtaining detailed in vivo anatomic information on the structure of the brain, but also on its functions. This has been achieved thanks to the development of new acquisition techniques, including functional MRI (fMRI) and diffusion tensor (DT) MRI, and the refinement of the methods of analysis. All of this is providing important insights into the pathophysiology of various neurologic conditions, including multiple sclerosis (MS).
Clinically, MS is characterized by heterogeneous patterns of manifestations that can result in the accumulation of fixed disability over time.1 Although the application of different MR techniques has demonstrated the presence of widespread damage in the brain and cord of these patients, which is not limited to T2-visible lesions but also involves the normal-appearing white and gray matter, the quantification of overall brain and cord damage does not allow full explanation of the set of clinical manifestations and the mechanisms responsible for the accumulation of disability in MS.2 Results obtained by the use of fMRI for the assessment of brain function in these patients indicate that the presence and effectiveness of brain plasticity might have a role in limiting the clinical consequences of MS-related damage, at least at the beginning of the disease and in clinically stable patients, thus helping to explain part of the well-known clinical MRI paradox.3
Previous fMRI studies have mainly focused on the evaluation of differences of activation between patients and healthy controls in terms of extent of activations and differences between the regions recruited during the performance of a given task.3 However, the functional and structural substrates of such changes have not been investigated. It is tempting to speculate that dysfunction of neuronal connections among cortical areas possibly related to structural damage of specific brain pathways might help explain these functional abnormalities. Preliminary studies have shown abnormal functional and effective connectivities in MS patients during cognitive tasks4,5 and during the resting state.6,7 However the correlations of such changes with underling structural damage have not been investigated previously.
The aim of this study was to determine the functional and structural substrates of motor network dysfunction in patients with relapsing-remitting (RR) MS without overt motor impairment, using an analysis of functional connectivity and MR tractography.
METHODS
Patients.
We studied 12 consecutive right-handed8 patients with RRMS.9 There were 11 women and 1 man; their mean age was 36.0 years (range = 25–46 years), their median disease duration was 8 years (range = 3–10 years), and their median Expanded Disability Status Scale (EDSS) score10 was 1.5 (range = 1.0–2.5). At the time MRI was performed, all patients had been relapse- and steroid free for at least 6 months. The main inclusion criteria were the absence of clinical involvement of the right upper limbs and no previous relapses involving the right upper limb. All patients were treated with immunomodulatory drugs (two of them were treated with glatiramer acetate, and the remaining 10 patients received one of the three available formulations of interferons beta). None of them complained of treatment-related side effects, such as fatigue, at the time of fMRI acquisition. Fourteen sex- and age-matched right-handed8 healthy volunteers with no previous history of neurologic dysfunction and a normal neurologic exam served as controls (11 women and 3 men, mean age = 33.7 years, range = 24–42 years). All subjects were assessed clinically by a single neurologist, who was unaware of the MRI and fMRI results. The mean laterality quotient at the Edinburgh Handedness Inventory8 was 0.96 (range = 0.90–100) in healthy volunteers and 0.97 (range = 0.90–100) in MS patients. Local ethical committee approval and written informed consent from all subjects were obtained before study initiation.
Functional assessment.
Upper-right limb motor functional assessment was performed for all individuals on the same day MRI was acquired, using the nine-hole peg test (9-HPT) and the maximum finger-tapping frequency. The maximum finger-tapping rate was observed for two 30-second trial periods outside the magnet, and the mean frequency to the nearest 0.5 Hz entered the analysis. No difference was found in the performance of these tests between MS patients and healthy controls (time to complete the 9-HPT: mean = 19.8 seconds, SD = 2.5 seconds for controls; mean = 21.4 seconds, SD 2.6 seconds for patients; finger-tapping rate: mean = 3.4 Hz, SD = 0.8 Hz for controls; mean = 3.6 Hz, SD = 0.7 Hz for patients).
Experimental design.
Using a block design (ABAB) where six periods of activation were alternated with six periods of rest (each period of rest and activity consisting of five measurements), the subjects were scanned while performing a simple motor task consisting of repetitive flexion-extension of the last four fingers of the dominant right hand moving together. A standardized frame was used to restrict the amplitude of the extension to approximately 3 cm from the base. The movements were paced by a metronome at a 1-Hz frequency. Patients were trained before performing the study. The subjects were instructed to keep their eyes closed during fMRI acquisition and were monitored visually during scanning to ensure accurate task performance and to check for additional movements (e.g., mirror movements).
fMRI acquisition.
Brain MRI scans were obtained using a 3.0-T Philips Intera scanner (Philips Medical Systems, Best, the Netherlands) with a gradient strength of 40 mT/m and an eight-channel head coil. Functional MR images were acquired using a T2*-weighted single-shot echo-planar imaging sequence (echo time [TE] = 35 milliseconds, flip angle = 85 degrees, matrix size = 128 × 128, field of view [FOV] = 240 mm2, repetition time [TR] = 3.7 seconds). Thirty interleaved 4-mm-thick axial slices, covering the whole brain, were acquired during each measurement. All slices were positioned to run parallel to a line that joins the most inferoanterior and inferoposterior parts of the corpus callosum.11 Shimming was performed for the entire brain using an autoshim routine, which yielded satisfactory magnetic field homogeneity.
Structural MRI acquisition.
On the same occasion and using the same magnet, the following images of the brain were also obtained from all subjects: 1) dual-echo turbo spin echo sequence (TR/TE = 3000/45 to 120 milliseconds; echo train length = 5; flip angle = 90 degrees; matrix size = 256 × 256; FOV = 230 × 230 mm2; 30 contiguous, 4-mm-thick, axial sections); 2) three-dimensional T1-weighted fast field echo (TR/TE = 25/4.6 milliseconds; flip angle = 30 degrees; matrix size = 256 × 256; FOV = 230 × 230 mm2; 220 contiguous, axial slices with voxel size = 0.89 × 0.89 × 1 mm); 3) pulsed-gradient SE echo planar with SENSE (acceleration factor = 2.5; TE/TR = 80/8283.2 milliseconds; acquisition matrix size = 96 × 96; FOV = 240 × 240 mm2; 55 contiguous, 2.5-mm-thick axial slices; after SENSE reconstruction, the matrix dimension of each slice was 256 × 256, and in-plane pixel size was 0.94 × 0.94 mm) and diffusion gradients applied in 32 noncollinear directions, using a gradient scheme that is standard on this system (gradient overplus) and optimized to reduce echo time as much as possible. Two optimized b factors were used for acquiring diffusion-weighted images12 (b1 = 0, b2 = 1000 s/mm2). Fat saturation was performed to avoid chemical shift artifacts. Slices were positioned as those of the fMRI data set.
fMRI analysis.
All image postprocessing was performed on an independent computer workstation (Sun Sparcstation, Sun Microsystems, Mountain View, CA). FMRI data were analyzed using the statistical parametric mapping (SPM2) software developed by Friston et al.13 Before statistical analysis, all images were realigned to the first one to correct for subject motion, spatially normalized into the standard space of SPM, and smoothed with a 10-mm, three-dimensional Gaussian filter. Subjects were included in the subsequent statistical analysis if they had a maximum translation/rotation lower than 3.0 mm in the x, y, and z planes.
Changes in blood oxygenation level–dependent contrast associated with the performance of the motor task were assessed on a pixel-by-pixel basis, using the general linear model and the theory of Gaussian fields.13 Specific effects were tested by applying appropriate linear contrasts. Significant hemodynamic changes were assessed using t statistical parametric maps. The intragroup activations and comparisons between groups were investigated using a random-effect analysis,14 with one-sample or two-sample t tests, as appropriate. We report activations below a threshold of p < 0.05, corrected for multiple comparisons (family-wise error). To achieve a more precise definition of the anatomic locations of activated regions in each individual, three-dimensional fast field echo images from each subject were coregistered with the corresponding fMRI data sets and normalized into the same standard space. Then, fMRI results were superimposed onto these high-resolution images and, using cluster analysis on a patient-by-patient basis, we evaluated the coordinates of the centers of the activations in those areas with significantly different relative activations at group analysis.
Analysis of functional connectivity.
The interactions between different regions involved in the motor task were calculated using a dynamic causal model (DCM) approach.15 Treating the brain as an input-state-output system, DCM estimates how hemodynamic activity in a given brain region (output) depends on its interconnectivity with other brain regions whose activity correlates with the task. Definition of brain regions included in the DCM relied on activation clusters obtained from the analysis of fMRI data from the entire study group (patients vs healthy controls). Time series, which were adjusted for the effect of interest, were extracted from a spherical volume (5-mm radius) centered at the most significant voxel within each cluster in the t statistical parametric map displaying brain areas activated during the motor task at a p < 0.001, uncorrected, in each subject. Volumes of interest were extracted from the primary sensorimotor cortex (SMC), bilaterally, the right cerebellum, and the left supplementary motor area (SMA). We assumed that the effect of the task entered the network via the activation cluster within the left (L) primary SMC. This assumption was based on conventional wisdom and was confirmed by the results obtained from the within- and between-group analyses of task-related activations seen in this study.
For each subject, the DCM was used to investigate the intrinsic connectivity pattern between all regions of interest previously extracted, which were involved in the motor task. To this end, a DCM was constructed in which all regions were assumed to be connected bidirectionally with each other. The intrinsic connectivity strength coefficients were then estimated using a Bayesian approach.15 A one-sample t test was used to assess the within-group strength of the connectivity coefficients. A Mann–Whitney two-independent-samples test was used to compare the strength of the connectivity coefficients previously estimated between controls and MS patients.
Structural MRI postprocessing.
All structural MRI analysis was performed by a single, experienced observer, unaware of who the scans belonged to and blinded to the fMRI results. Brain lesions were identified on the proton density-weighted scans, and lesion loads (LL) were measured using a local thresholding segmentation technique.16 The corresponding T2-weighted images were always used to increase confidence in lesion identification.
For DT MRI images, the DT was estimated using a nonlinear regression (Marquardt–Levenberg method), assuming a monoexponential relationship between signal intensity and the b-matrix components.17 After diagonalization of the estimated tensor matrix, the two scalar invariants of the tensor, mean diffusivity (MD) and fractional anisotropy (FA), were derived for every pixel. Then, using the VTK CISG Registration Toolkit,18 the rigid transformation needed to correct for position between the b = 0 images (T2 weighted, but not diffusion weighted) and T2-weighted images was calculated. Normalized mutual information19 was the similarity measure chosen for the matching. The same transformation parameters were then used to coregister the MD and FA images to the T2-weighted images.
A tractography algorithm was used to reconstruct the anatomy of brain white matter (WM) fiber bundles, by means of DT-derived indexes calculated inside the tracts. Because the direct application of tractography in patients is hampered by the presence of regions with decreased FA (indicating an increased uncertainty of the principal diffusion direction), a tractography algorithm was used to construct the probability maps for each WM fiber bundle analyzed, using data from healthy subjects20; these probability maps were then applied to patients' data to derive diffusivity measures inside the WM fiber bundle of interest. In details, we used the following procedure. First, an FA atlas was created using data from healthy volunteers. To this aim, a registration algorithm17 was used to register controls' T2-weighted images to the Montreal Neurological Institute atlas21 with an affine transformation. Then, the transformation was applied to FA maps, already registered into the T2-weighted space, before their averaging to obtain the atlas. Then, using the same data, the probability maps for the corpus callosum (CC), the corticospinal tract (CST), the optic radiation (OR), the superior fronto-occipital fasciculus (SFOF), the uncinate fasciculus, the dentatorubrothalamic (DRT) tract, the cingulum, and the superior longitudinal fasciculus (SLF) were created. The trajectories of the different tracts were obtained by placing regions of interest (ROIs) believed to contain a section of the desired WM fiber bundles on both FA and color-coded maps for the principal diffusion directions,22 by cross-referencing neuroanatomic23,24 and previous tractography20,25–27 studies. A two-ROI approach was used to define the CST, the OR, the SFOF, the uncinate fasciculus, and the DRT. Trajectories were checked by qualitative visual assessment after reconstructing them in three dimensions. Figure 1 shows an example of the WM fiber bundle reconstructions in an individual subject before constructing the probability map. Once trajectories were obtained, these were transformed using the same change calculated at the previous step, before their averaging to produce tract probability maps (a threshold of 0.4 was applied).28 Because of the possible presence of atrophy, a nonlinear deformation algorithm29 was used to transform patients' FA maps into the atlas space. Then, WM fiber bundle probability maps were superimposed onto the transformed MD and FA maps. In this way, the contribution of each pixel to the mean value was weighted by its value in the probability map. For each WM fiber bundle, average MD and FA were obtained. With the exception of the CST, for WM fiber bundles with a bilateral location in the brain, the averages of the MD and FA values measured in the R and the L fiber bundles entered the analysis.
Figure 1 Behavior of the main white matter fiber bundles of the study in a single subject, before spatial normalization
(A) corticospinal tract, (B) corpus callosum, (C) optic radiation, (D) superior fronto-occipital fasciculus, (E) uncinate fasciculus, (F) dentatorubrothalamic tract, (G) cingulum, and (H) superior longitudinal fasciculus.
Volumes of T2-visible lesion in the different WM fiber bundles were derived by applying fiber bundles probability maps obtained using DT tractography and calculating the volume of lesions inside the fiber bundles. The method is described in detail elsewhere.20
Statistical analysis.
Differences of structural MR-derived metrics between patients and controls were assessed using a nonparametric Mann–Whitney test for two independent samples. Univariate correlations between functional and structural changes were assessed using the Spearman correlation coefficient. All statistical analysis was performed using SPSS for Windows (version 13.0).
RESULTS
Structural MRI.
All healthy volunteers had normal brain MRI scans. In MS patients, the median T2 LL was 11.4 mL (range = 1.0–24.3 mL). In table 1, the median values of T2-visible lesions burdens of each of the different WM fiber bundles of interest are reported. No significant difference was found for LL along the right (R) and L CST (mean R CST LL = 0.24, SD = 0.64; mean L CST LL = 0.32 mL, SD = 0.8; p = 0.8). In table 1, the average MD and FA values of the different WM fiber bundles analyzed in healthy controls and MS patients are reported. Significant differences were found in average MD and FA values of all the WM fiber bundles analyzed between MS patients and healthy controls. In MS patients, average MD was significantly higher in the L CST compared with the R CST (p = 0.001). In table 2, the correlations found in patients with MS, between MD and FA average values of the different WM fiber bundles analyzed, and regional and total LLs are reported. No correlation was found between structural MRI metrics and clinical findings.
Table 1 T2 lesion loads, mean diffusivity, and fractional anisotropy values of several white matter fiber bundles from healthy volunteers and multiple sclerosis patients
Table 2 Correlations between MD and FA average values of the different white matter fiber bundles analyzed, and regional and total T2 lesion loads
Functional MRI: Between-group analysis.
All subjects performed the tasks correctly, and no additional movements were observed during fMRI acquisition. In figure 2, the brain areas with significant activations detected while performing the task in healthy subjects and MS patients are shown. Compared with healthy volunteers, MS patients had more significant activations of the contralateral (L) SMA (SPM space coordinates: −10, −18, 64), the contralateral (L) primary SMC (SPM space coordinates: −26, −16, 48), and the ipsilateral (R) anterior lobe of the cerebellum (SPM space coordinates: 16, −34, −22) (figure 2). No significant differences were found in coordinates of centers of activations of the above-reported areas between patients and controls.
Figure 2 Cortical activations on a rendered brain from right-handed healthy subjects and patients with relapsing-remitting multiple sclerosis (MS) during performance of a simple motor task
Cortical activations on a rendered brain from right-handed healthy subjects (top row) and patients with relapsing-remitting MS (middle row) during performance of a simple motor task with their clinically unimpaired and fully normal functioning right hands (within-group analysis, one-sample t tests, p < 0.05, corrected for multiple comparisons). The between-groups differences (areas with more significant activations in MS patients than controls) are shown in the bottom row (two-sample t test, p < 0.05, corrected for multiple comparisons). Images are in neurologic convention. See text for further details.
Functional connectivity analysis.
The results of the analysis of within-group individual correlations between activity changes in anatomically connected regions are reported in table 3. Compared with controls, MS patients had increased functional connectivity between the R primary SMC and the R cerebellum (p = 0.01) and between the L SMA and the L primary SMC (p = 0.04) (figure 3).
Table 3 Significant paths coefficients (mean values) between brain regions for patients and controls (one-sample t test in each group)
Figure 3 Differences of functional connectivity in the motor network during performance of a simple motor task with their clinically unimpaired and fully normal functioning right hands in patients with relapsing-remitting multiple sclerosis (RRMS) and healthy subjects
The light blue arrows identify the connections with similar path coefficients in healthy subjects and in RRMS patients. The red arrows identify the connections with higher path coefficients in RRMS patients than in healthy subjects. SMA = supplementary motor area; SMC = sensorimotor cortex.
Correlations between measures of functional and structural connectivities.
In patients with MS, significant correlations were found between a) coefficients of connectivity between the R primary SMC and the R cerebellum and average FA (r = −0.73, p = 0.02) and average MD (r = 0.85, p = 0.004) of the DRT tracts, and b) coefficients of connectivity between the L SMA and the L primary SMC and CST LL (r = 0.64, p = 0.04). This correlation remained significant when considering the LL along the L CST separately (r = 0.66, p = 0.03).
DISCUSSION
In this study, we combined measures of functional and structural connectivity to improve the knowledge of the mechanisms related to fMRI changes typically seen in MS patients. Our main hypothesis was that the functional cortical changes described by several authors in these patients3 might be the consequence of an abnormal connectivity between specific brain regions, and that the presence and extent of damage to the WM fiber bundles connecting these regions might be one of the factors associated to such functional changes. On the basis of previous findings that have reported a different movement-associated brain pattern of cortical activations in patients with MS, which varied according to the level of disability,30,31 the disease phenotype,32 and the extent of macro- and microscopic tissue involvement,3 we decided to limit the study to patients with RRMS, mild clinical disability, and no upper-limb motor impairment, to minimize potential biasing factors associated with task performance on fMRI results.
Although the sample size studied was relatively small, our results are likely to be robust, given that cortical recruitment associated with the performance of the simple motor task was in line with the findings of previous studies performed in patients with similar clinical characteristics.33 The analysis of simple movement execution with the right dominant hand indeed showed an increased activation of the primary SMC and the SMA in the contralateral hemisphere of MS patients when compared with healthy controls. Several explanations have been proposed for these functional cortical changes in patients with MS, including the “unmasking” or disinhibition of preexisting latent pathways5 and the formation of new synapses and axonal sprouting.34 We also found that MS patients had an increased activation of the anterior lobe of the ipsilateral cerebellum. Considering the key role of this structure in the motor system,35 this latter finding is not unexpected. However, it is worth noting that this was not reported by previous studies of RRMS.3,32,33 Only two studies of primary progressive MS have described a reduced cerebellar activation in these patients when contrasted to controls.31,36 Differences in the statistical approach used (regional vs whole-brain analysis of activations) and in the scanner magnetic field strength (1.5 vs 3 T, with enhanced blood oxygenation level–dependent effect and the higher SNR at 3 T) might, at least partially, account for the discrepancy between the present and previous studies.
Using different methods of analysis, an abnormal connectivity inside the cognitive network of patients with MS has been described.4,5 In this study, we have shown that in patients with MS, abnormal functional connectivity does occur inside the motor network as well. In particular, an increased connectivity was detected between the L SMA and the L primary SMC and between the R SMC and the R cerebellum. Therefore, movement-associated functional cortical changes in MS are not only attributable to an altered activity of “focal” regions, but also to an abnormal interaction between regions in the motor network.
The analysis of activations demonstrated exclusively an increased recruitment of several brain areas in MS patients when compared with healthy controls. The analysis of interaction showed that the increased activity of these areas was related to an increase of the strength of their connections with other areas of the motor network, which are known to be functionally and anatomically connected to them. In addition, we also demonstrated an abnormal connectivity between areas that had a normal function at the analysis of the activations, including the R primary SMC. This latter region was included in our DCM, given its role in simple movement execution and the results of previous fMRI studies of the motor system, showing its abnormal recruitment in patients with different neurologic conditions, including MS.3,30,37 All of this prompted us to apply both measures to achieve a more complete picture of the effective changes of function in MS. Interestingly, the analysis of functional connectivity highlighted the role of the cerebellum in motor act performance in our sample of patients. The cerebellum has been implicated not only in fine fingers movement, but also in motor learning,38 as suggested by the following observations: 1) data from patients with focal brain lesions in the cerebellum have shown impairment in learning new motor skills,39–41 2) imaging studies of motor learning have shown prominent cerebellar activation in healthy individuals,42,43 and 3) imaging studies have demonstrated an association between increased cerebellar activation and a favorable clinical outcome in patients with stroke.44,45 The cerebellum has also been involved in the “automatization” (improvement of motor performance) of learned skills, the establishment of movement strategies, and the consolidation of such motor knowledge.40,46,47 The notion that the modulation of the connections between the cerebellum and cortical motor areas might have a role in limiting disease-related manifestations in MS has been already suggested by a preliminary study, where an abnormal correlation between signal intensity changes in the cerebellum and the premotor and motor cortices has been detected, bilaterally.48 The absence of consistent, task-related activations in the R cerebellum in the subjects of our study did not allow us to investigate its connectivity strengths with the remaining regions of our model.
The second main aspect of the present study pertains to the assessment of intrinsic damage of the main brain WM fiber bundles in patients with MS. Because the direct application of tractography to MR data from MS patients suffers from the fact that the disease causes both focal and diffuse alteration of tissue organization, resulting in a decreased anisotropy of the underlying barriers and a consequent increase in uncertainty of the principal diffusion direction, probability maps of the WM fiber bundles of interest were constructed from data of healthy controls and then applied to those of patients. This method has been already validated in patients with clinically isolated syndromes suggestive of MS, and it has been shown to be able to ameliorate the relationship between MR-derived metrics and the clinical manifestations of the disease.20 By using this approach, we found widespread tissue damage in patients with RRMS, as reflected by an increase of the MD and a decrease of the FA values of all the WM fiber bundles studied. These results are in agreement with previous ROI-based and histogram-based studies showing that MS-related damage is not confined to specific brain structures but that it involves diffusely the brain tissues that appear normal on conventional imaging.2,49 Such DT MRI changes might reflect both the presence of microscopic lesions or, but not mutually exclusive of, the presence of Wallerian degeneration, as a result of fiber transection when passing through lesions anywhere along the axonal path. This second hypothesis is supported by the correlations found in the present and in previous studies20,50–52 between lesion volumes of specific regions/bundles and corresponding abnormalities detected using nonconventional MR measures (including MR spectroscopy and DT MRI). The analysis of asymmetry of CST involvement showed more pronounced abnormalities in the L CST than in the R CST. The lack of a correlation between T2 and LL along the CST- and DT MRI-derived metrics supports the notion that microscopic changes beyond the resolution of conventional MRI might contribute significantly to overall CST damage in MS.
This study also shows that several strong correlations do exist, not only between measures of functional and structural connectivity, but also between measures of functional connectivity and those of regional damage in terms of T2-visible lesions, suggesting an adaptive role of functional connectivity in limiting the clinical consequences of structural brain damage. In detail, a correlation was found between measures of damage of the DRT tract and of increased functional connectivity of the R primary SMC and the R cerebellum. A correlation was also found between increased functional connectivity between the L SMA and the L primary SMC and T2-visible lesions of the CST tract. The notion that the presence of macroscopic T2-visible lesions, rather than that of microscopic tissue abnormalities in the CST, has an influence on the observed movement-associated brain pattern of cortical activation in MS patients, agrees with the results of a study conducted on a sample of 76 MS patients53 and with the observation that, in patients with MS, disease-related microscopic damage to the internal capsule is not related to the severity of motor deficits.54 It is worth noting that, despite the high number of WM fiber bundles studied, these were the only structures whose involvement was related to abnormalities of function. Both the DRT and the CST tracts represent important critical nodes of the motor network. In particular, the CST is the main efferent pathway of the motor system, whereas the DRT tract is the motor output that projects back from the cerebellum to the primary motor cortex to influence motor control.35 Disappointingly, no correlations were found between measures of abnormal functional connectivity and structural MRI measures of CC damage. Several explanations are readily available for this latter finding. First, we investigated the performance of a simple motor task, whereas several studies suggest that the role of interhemispheric CC fibers might be more important in complex bimanual function.55 Secondly, the results of fMRI analysis suggest that the role of interhemispheric connectivity in our sample of patients is likely to be minimal, as indicated by the fact that the activation of the primary SMC in the right hemisphere and the coefficients of connectivity between the primary SMC of the two hemispheres were similar to those observed in healthy subjects. The lack of a correlation between measures of functional connectivity and tissue damage to WM fiber bundles, which are not part of the motor network, such as the OR, is another piece of evidence for the robustness of our findings.
Footnotes
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Supported by a grant from FISM (2004/R/7).
Disclosure: The authors report no conflicts of interest.
Received April 17, 2007. Accepted in final form June 1, 2007.
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