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October 02, 2012; 79 (14) Articles

Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis

Maria A. Rocca, Paola Valsasina, Vittorio Martinelli, Paolo Misci, Andrea Falini, Giancarlo Comi, Massimo Filippi
First published September 5, 2012, DOI: https://doi.org/10.1212/WNL.0b013e31826d5f10
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Abstract

Objectives: Given that multiple sclerosis (MS) hits diffusely the brain hemispheres, we hypothesized that this should result in a distributed pattern of functional connectivity (FC) abnormalities. To this aim, we assessed, using resting-state (RS) fMRI, intrinsic FC and functional network connectivity (FNC) of brain large-scale neuronal networks from 85 patients with relapsing-remitting MS (RRMS) and 40 matched controls.

Methods: Independent component analysis was used to analyze RS fMRI data. Intrinsic FC of each cluster of each RS network (RSN) was compared between controls and patients (analysis of variance adjusted for age, gender, and gray matter volume). The FNC toolbox was used to assess interactions among RSNs.

Results: Compared to controls, patients with RRMS experienced a decreased RS FC in regions of the salience (SN), executive control (ECN), working memory (WMN), default mode (DMN), sensorimotor, and visual networks. They also had an increased RS FC in regions of the ECN and auditory RSN. Decreased RS FC was significantly correlated with disability and T2 lesion volumes. In patients with RRMS, when compared to controls, FNC analysis showed that the ECN had an increased connectivity with the SN and a decreased connectivity with the DMN. An abnormal connectivity between the WMNs and sensory networks was also found.

Conclusions: Functional abnormalities within and between large-scale neuronal networks occur in patients with RRMS and are related to the extent of T2 lesions and the severity of disability. Longitudinal studies should ascertain whether such functional abnormalities confer a systematic vulnerability to disease progression or, conversely, protect against the onset of clinical deficits.

GLOSSARY

ANOVA=
analysis of variance;
CIS=
clinically isolated syndrome;
DMN=
default mode network;
ECN=
executive control network;
EDSS=
Expanded Disability Status Scale;
FC=
functional connectivity;
FNC=
functional network connectivity;
GM=
gray matter;
GMV=
gray matter volume;
ICA=
independent component analysis;
IFG=
inferior frontal gyrus;
ITG=
inferior temporal gyrus;
LV=
lesion volume;
MS=
multiple sclerosis;
RRMS=
relapsing-remitting multiple sclerosis;
RS=
resting-state;
RSN=
resting-state network;
SN=
salience network;
WMN=
working memory network

Resting-state (RS) fMRI allows us to map function of brain large-scale neuronal networks, thus contributing to clarify pathophysiologic aspects of neurologic and psychiatric conditions.1,–,6 Compared to active fMRI, this task-free approach allows inferences related to differences between healthy and diseased subjects, which are not influenced by task performance.

Only a few studies have explored RS functional connectivity (FC) in patients with multiple sclerosis (MS).4,–,6 However, the generalizability of these results to the overall MS population is limited by the small numbers of patients enrolled and the a priori selection of a network of interest (e.g., the default mode network [DMN]).7 Additionally, previous studies did not account for the regional distribution of gray matter (GM) volume, which is known to differ significantly between patients with MS and healthy controls.8,9

Against this background, we wished to explore the integrity of the principal brain RS networks (RSNs) in a relatively large sample of right-handed patients with relapsing-remitting MS (RRMS). In addition to a “classic” analysis of RS data, we also performed a functional network connectivity (FNC) analysis to investigate functional interactions among the RSNs studied. Finally, to provide some clues about the nature and role of the detected abnormalities, we also investigated whether and at which magnitude they were correlated with clinical and MRI measures of disease severity, as well as with disease duration. We hypothesized that, since MS is a multisystem disconnection syndrome, RSN abnormalities should not be restricted to the DMN, but, conversely, involve at least many of the principal large-scale neuronal networks of the brain.

METHODS

Subjects.

We studied 85 consecutive right-handed patients with RRMS10 and 40 age- and gender-matched healthy controls with no previous history of neurologic, psychiatric, or cardiovascular disorders and a normal neurologic examination. To be included, patients had to be relapse- and steroid-free for at least 3 months. All patients underwent a neurologic examination, with Expanded Disability Status Scale (EDSS) score rating.11 None of the patients had a benign form of MS (EDSS score ≤3.0 and disease duration ≥15 years). Exclusion criteria were concomitant therapy with antidepressants, psychoactive drugs, and a history of major medical, neurologic, or psychiatric disorders as well as drug and alcohol abuse. The main demographic and clinical characteristics of the study groups are shown in table 1.

View this table:
Table 1

Main demographic, clinical, and conventional MRI characteristics of healthy subjects and patients with relapsing-remitting multiple sclerosis

Ethics committee approval.

Approval was received from a local ethical standards committee on human experimentation and written informed consent was obtained from all subjects participating in the study.

MRI acquisition.

A detailed description of MRI acquisition and analysis is provided in appendix e-1 on the Neurology® Web site at www.neurology.org. Using a 3.0-T scanner, the following sequences of the brain were acquired: dual-echo turbo spin echo, 3D T1-weighted fast field echo, and T2*-weighted single-shot echoplanar imaging. RS fMRI acquisition time was about 10 minutes.

Analysis of intrinsic brain FC in RSNs.

RS fMRI analysis is described in appendix e-1. After preprocessing, RS FC was assessed using an independent component analysis (ICA) with the GIFT software.12 RSNs of interest were selected using spatial correlation against a set of a priori defined network templates. The DMN was identified through a voxel-wise correlation with the DMN template supplied in GIFT.13 The remaining network templates were generated using the WFU Pickatlas14 on the basis of the Brodmann areas and cluster peaks reported in the literature (salience network [SN]2; frontoparietal working memory networks [WMNs], lateralized to the left and right hemisphere15; executive, sensorimotor, primary visual, secondary visual, and auditory networks16). The independent components with the highest square correlation coefficients (R2, as implemented in GIFT software) with each of these templates were selected.

Analysis of intrinsic brain connectivity among RSNs.

Temporal correlations among the RSN time series were assessed using the FNC toolbox (http://mialab.mrn.org), and computing a constrained maximal lagged correlation between component time courses,17 as described in appendix e-1.

Structural MRI analysis.

Dual-echo lesion volumes (LVs) were measured (Jim 5.0, Xinapse System, www.xinapse.com). On 3D fast-field echo images, normalized brain volumes, gray matter (GM) volumes, and white matter volumes were calculated. To exclude possible influence of GM atrophy on the comparisons of RS activity between controls and patients with RRMS, the average Z scores of regional GM volumes (GMVs) were extracted from each cluster of each RSN of interest and used as a confounding covariate in the statistical analysis,18 as described in appendix e-1.

Statistical analysis.

A 2-sample t test was used to assess between-group differences in structural MRI measures using the SPSS software. Individual subjects' data from all RSNs of interest were entered into the SPM8 random-effect analysis, to assess the spatial extent of within-group FC (one-sample t test masked with a GM probability map thresholded to retain only pixels having a probability >50% of belonging to GM, p < 0.05; family-wise error corrected; cluster extent = 50 voxels). Then, the average Z scores of RS FC and the corresponding average Z scores of regional GMV were extracted from each significant SPM cluster of each network using the Marsbar toolbox.19 The mean RS FC within each RSN was computed by averaging the Z scores of a given RSN for each study subject. The intrinsic FC within each cluster of each RSN was also calculated. Average FC values of each RSN were compared between controls and patients with RRMS using the SPSS software and analysis of variance (ANOVA) models adjusted for age, gender, and normalized GMV, whereas regional FC values were compared between groups using ANOVA models adjusted for age, gender, and the corresponding regional Z score of GMV (p values <0.05, Bonferroni-corrected for multiple comparisons). Correlations between RS abnormalities (i.e., Z scores of the clusters showing significant differences between groups) vs disease duration, EDSS, and T2 LV were assessed using multiple regression models adjusted for age, gender, and the regional Z scores of GMV. The whole analysis was also repeated without GMV as a covariate.

To compare FNC between controls and patients with RRMS, correlations and lag values obtained for each subject with the FNC toolbox were averaged for the 2 groups. Using one-sample t tests, statistically significant pairwise correlations between networks were extracted for each group, separately, to define within-group correlations among RSNs. Between-group differences of FNC were then assessed using a 2-sample t test, both at an uncorrected and at a false discovery rate threshold, as implemented in the FNC toolbox.17

RESULTS

Structural MRI.

Table 1 reports the main structural MRI findings from controls and patients with RRMS. Compared to controls, patients with RRMS had significantly lower normalized brain volumes and normalized GMV.

Intrinsic brain FC in RSNs.

The analysis of RS fMRI detected 10 spatial maps of potentially relevant RSNs, including those related to SN (R2 with the SN template = 0.22), executive control network (ECN) (R2 with the ECN template = 0.31), 2 WMNs (R2 with the WMN templates = 0.26 and 0.25, respectively), DMN (R2 with the DMN template = 0.50), sensorimotor areas (sensorimotor RSN I and II, R2 with the sensorimotor network template = 0.32 and 0.21, respectively), primary and secondary visual cortical areas (R2 with the templates of the primary and secondary visual networks = 0.55 and 0.32, respectively), and primary and secondary auditory areas (R2 with the auditory network template = 0.61) (figure 1). All these components were stable across multiple runs of independent component decomposition (stability index assessed by ICASSO ranged from 0.93 to 0.98).

Figure 1
Figure 1 Spatial maps of potentially functional relevant resting-state networks from healthy subjects and patients with relapsing-remitting multiple sclerosis (effects of interest masked with a >50% gray matter probability map; 2-sample t test)

(A) Salience network; (B) executive control network; (C) right frontoparietal working memory network; (D) left frontoparietal working memory network; (E) default mode network; (F, G) sensorimotor networks; (H) primary visual network; (I) secondary visual network; (J) auditory network. Images are in neurologic convention.

Table e-1 shows the mean FC values (expressed as Z scores) of each RSN in controls and patients with RRMS. Compared to controls, patients experienced a decreased mean RS FC of the SN and secondary visual networks. The results did not change when performing the analysis without correction for GMV (table e-1).

Table e-2 summarizes the regional differences between controls and patients with RRMS in the previous RSNs. When correcting for GMV, apart from the sensorimotor RSN II, all the remaining networks differed significantly between groups. In particular, compared to controls, patients with RRMS had a decreased RS FC in regions of the SN (right insula), ECN (bilateral inferior frontal gyrus [IFG]), left WMN (right cerebellum and left thalamus) and right WMN (left cerebellum, right supramarginal gyrus, and left inferior parietal lobule), DMN (right IFG and left inferior temporal gyrus [ITG]), sensorimotor RSN I (left precentral gyrus, right cerebellum, and right cuneus), primary (left ITG) and secondary (left superior parietal lobule) visual RSNs.

Patients with RRMS also had an increased RS FC in regions of the ECN (right anterior cingulate cortex) and auditory RSN (right thalamus).

Intrinsic brain connectivity among RSNs.

Table 2 reports the significant correlations found among RSNs from controls and patients with RRMS, separately, and the significant correlation differences between groups. Compared to controls, patients with RRMS had increased connectivity between the SN vs the ECN (p = 0.03), and the right WMN vs the primary visual RSN (p = 0.02). They also showed decreased connectivity between the ECN vs the DMN (p = 0.03), and the left WMN vs the sensorimotor network I (p < 0.001) (figure 2).

View this table:
Table 2

Within-group correlation coefficients (R) among resting-state networks with potential functional relevance in healthy controls and patients with relapsing-remitting multiple sclerosis assessed with functional network connectivity analysis

Figure 2
Figure 2 Diagram showing resting-state networks with a significantly different pairwise connectivity between healthy controls and patients with relapsing-remitting multiple sclerosis (RRMS), as assessed using a functional network connectivity analysis

Solid arrows indicate increased network connectivity in patients with RRMS vs controls; dotted arrows indicate decreased network connectivity in patients with RRMS vs controls. DMN = default mode network; ECN = executive control network; SN = salience network; WMN = working memory network.

The results of the one-sample t tests of the FNC coefficients in controls and patients with RRMS, separately, showed that the significant connections found in controls between the sensorimotor, the secondary visual, and the auditory RSNs vs the WMNs were not seen in patients with RRMS. Conversely, in patients with RRMS, the SN was correlated with the sensorimotor I network and the primary visual network, the ECN with the sensorimotor II network, the right WMN with the DMN, and the left WMN with the primary visual RSN.

Analysis of correlations.

Correlations between RSN abnormalities and clinical and conventional MRI findings are summarized in table 3. In patients with RRMS, significant correlations were found between 1) EDSS score and decreased RS FC in regions of the ECN, DMN, and WMNs (figure e-1); and 2) T2 LV and decreased RS FC in regions of the ECN, DMN, SN, and WMNs (figure e-2).

View this table:
Table 3

Correlations between measures of resting-state activity of the main brain networks vs clinical and structural MRI findings in patients with relapsing-remitting multiple sclerosis

No correlation was found between RSN abnormalities and disease duration.

DISCUSSION

We applied a whole-brain, unbiased analysis to investigate abnormalities of RSN FC and FNC in a large sample of patients with RRMS, spanning a wide range of disabilities and disease durations. Given the complexity and heterogeneity of MS in terms of clinical manifestations, clinical course, and structural abnormalities, we decided to focus our analysis on a single MS clinical phenotype (RRMS) to provide a general characterization of FC abnormalities at this stage of the disease.

Using ICA, we identified 10 potentially relevant RSNs both in controls and patients. With the exception of one (the sensorimotor II), for all these networks significant differences of RS FC were found between controls and patients with RRMS. In particular, compared to controls, patients with RRMS had a decreased RS FC in sensory- and cognitive-related RSNs. For the ECN and auditory network, regions of increased RS FC in patients with RRMS vs controls were also found. The notion that the FC of the principal brain RSN is altered in the presence of CNS damage is in line with previous findings in neurodegenerative1,2 and psychiatric3 conditions. At present, only a few studies have investigated RS FC abnormalities in patients with RRMS.4,5 One study,4 which restricted the analysis to the DMN, found a reduced FC of the anterior and posterior cingulate cortex in patients with RRMS with and without cognitive impairment vs controls, as well as an increased FC in the peripheral portions of the posterior cingulate cortex and the parietal cortex. In contrast, another study5 found no RS FC abnormalities in 31 patients with RRMS vs controls, but it detected such abnormalities in patients with clinically isolated syndromes (CIS).

Several factors likely explain the discrepancies between the current and previous studies, including the use of different scanner field strengths (1.5 T in one study5), the a priori selection of a few ICA components (DMN in one of the studies4 and extraction of only 21 ICA in another one5), the different numbers of subjects enrolled (much higher in our case), different clinical characteristics of the patients studied (in terms of EDSS, neuropsychological features, and disease duration), and the absence of a proper correction of RS fMRI results for regional variations of GMV in all previous investigations.

Once established that RS FC abnormalities occur in patients with RRMS, several other issues need to be addressed, including their pathogenetic role and their association to the clinical manifestations of the disease. The demonstration of a distributed reduction of RS FC in patients with MS is in line with PET and MRI perfusion studies which have shown a diffuse brain hypometabolism20,21 and hypoperfusion22 in this condition, likely due to the progressive accumulation of focal lesions and normal-appearing white matter damage. Such a notion is also supported by the finding, in the current study, of a significant correlation between T2 LV and the reduction of RS FC in the majority of the networks investigated. This, combined with the EDSS correlation analysis, suggests that the accumulation of white matter lesions might result in distributed abnormalities of network efficiency in patients with MS, which, in turn, might contribute to the severity of clinical disability. The recent demonstration in patients with the progressive phenotypes of MS6 of a correlation between the reduced function in the anterior portions of the DMN and the severity of cognitive deficits further strengthens this hypothesis. Clearly, we cannot rule out that additional factors, which have been shown consistently to play a role in this disease (such as GM lesions and diffuse damage to the normal-appearing white matter and GM),23 might contribute to the balance between brain functional network rewiring and clinical dysfunction. It is worth noting that no correlation was found between EDSS and RS FC abnormalities within the sensorimotor network. Such a finding is likely due to the relatively low level of disability of our patients and supports the use of different clinical scales, more sensitive to impairment of specific functional domains, for the assessment of disability in these patients.

The interpretation of the increased RS FC observed in patients with RRMS is more critical, particularly when considering that such an abnormality was related neither to T2 LV nor to disability. An increased activation of selected CNS regions during the performance of active fMRI paradigms in clinically preserved patients with MS has been interpreted by several authors as an adaptive compensatory mechanism contributing to limit the clinical consequences of disease-related tissue damage23; with increasing cognitive load, this mechanism might reach a threshold and contribute to the presence of cognitive deficits.24,25 A distributed increase of RS FC of several networks has been found in cognitively preserved patients with CIS suggestive of MS5 in the absence of atrophy or diffusivity abnormalities in the normal-appearing white matter, but not in patients with RRMS with cognitive deficits and diffuse brain structural abnormalities. As a consequence, it has been supposed that an increased synchronization within the RSNs might represent an early and finite process. Although this deserves further investigation (particularly in patients with progressive MS), ours and previous results4,26 indicate that such a mechanism is present also in patients with established MS (not only CIS), in whom diffuse damage to the normal-appearing white matter and GM is known to occur.23 Notably, in young healthy adults an increased RS FC in the DMN has been demonstrated in APOE ϵ4 carriers relative to noncarriers,27 and it has been interpreted as a reflection of neuronal mechanisms with the potential to compensate for reduced synaptic plasticity, reduced neuronal growth, and altered long-term potentiation known to occur in these conditions. Whether such an increased RS FC in a given brain network in patients with MS might protect against the onset of clinical deficits or, conversely, confer a systematic vulnerability to such a network can only be addressed by longitudinal studies.

The interaction between brain networks also deserves to be considered when interpreting abnormalities of RS connectivity of the different brain functional networks, as emphasized by recent neurobiological theories.28 Using FNC analysis, which allows us to estimate the temporal relationships among ICA component time courses,17 we found that in patients with RRMS, when compared with controls, the ECN had an increased connectivity with the SN and a decreased connectivity with the DMN. Patients also experienced an abnormal connectivity between the WMNs and the sensorimotor I and primary visual networks.

The ECN encompasses a series of brain regions that have been demonstrated to be strongly activated during several cognitive paradigms, as well as action-inhibition tasks and emotion processing.15,16 The SN, which includes the fronto-insular circuit and the anterior cingulate cortex, is supposed to contribute to the integration of highly processed sensory with visceral and autonomic information to guide subjects' behavior,2 which in turn initiates the control of signals that activate the ECN. In healthy individuals, the SN plays a critical role in switching between the ECN and the DMN across task paradigms.29 Abnormal switching between the ECN, the DMN, and the SN has been considered to explain working memory abnormalities in patients with posttraumatic stress disorder,30 whereas a selective vulnerability of these networks has been found in patients with frontotemporal dementia vs those with Alzheimer disease.1 As a consequence, it is tempting to speculate that in patients with RRMS the SN, in isolation or through its connection with the ECN, might have an important role in modulating subjects' behavior. This hypothesis is supported by the increased connectivity we detected between the SN and the sensorimotor and visual networks as well as between the ECN and the sensorimotor network. Remarkably, the FNC analysis also disclosed the presence of connectivity abnormalities between the WMNs and the sensory networks, suggesting that, different from what has been suggested in neurodegenerative diseases in which a selective functional, regional, and neuronal vulnerability of large-scale human brain systems has been hypothesized,31 MS alters CNS functioning in a more distributed manner. This is not unexpected, considering that patients with MS harbor focal lesions, randomly and unevenly distributed across the brain hemispheres, in combination with different degrees of “occult” white matter and GM structural abnormalities. In this perspective, the demonstration of a diffuse abnormality of brain functional connectivity in patients with RRMS is in line with the results of recent studies that have shown aberrant cortical morphology among widely distributed brain regions in these patients.32,33

AUTHOR CONTRIBUTIONS

Maria A. Rocca contributed to drafting/revising the manuscript and statistical analysis. Paola Valsasina contributed to MRI data postprocessing and statistical analysis. Vittorio Martinelli contributed to patients' enrollment and analysis of the data. Paolo Misci contributed to the analysis of MRI data. Andrea Falini contributed to MRI acquisitions and analysis of the data. Giancarlo Comi contributed to study concept. Massimo Filippi contributed to drafting/revising the manuscript, study concept, and analysis and interpretation of the data. He also acted as study supervisor.

DISCLOSURE

M.A. Rocca serves as consultant to Bayer Schering Pharma, received speakers' bureaus for Biogen-Dompé, and receives research support from Italian Ministry of Health. P. Valsasina reports no disclosures. V. Martinelli has received funding for travel and speaker honoraria from Biogen Idec, Merck Serono, Bayer Schering Pharma, Novartis, and sanofi-aventis and has served as a consultant to Bayer Schering Pharma, sanofi-aventis, and Teva Pharmaceutical Industries Ltd. P. Misci and A. Falini report no disclosures. G. Comi has received personal compensation for activities with Teva Neuroscience, Merck Serono, Bayer-Schering, Novartis, Sanofi-Aventis Pharmaceuticals, and Biogen-Dompè as a consultant, speaker, or scientific advisory board member. M. Filippi serves on scientific advisory boards for Teva Pharmaceutical Industries Ltd. and Genmab A/S; has received funding for travel from Bayer Schering Pharma, Biogen-Dompè, Genmab A/S, Merck Serono, and Teva Pharmaceutical Industries Ltd.; serves as a consultant to Bayer Schering Pharma, Biogen-Dompè, Genmab A/S, Merck Serono, and Teva Pharmaceutical Industries Ltd.; serves on speakers' bureaus for Bayer Schering Pharma, Biogen-Dompè, Genmab A/S, Merck Serono, and Teva Pharmaceutical Industries Ltd.; and receives research support from Bayer Schering Pharma, Biogen-Dompè, Genmab A/S, Merck Serono, Teva Pharmaceutical Industries Ltd., and Fondazione Italiana Sclerosi Multipla. Go to Neurology.org for full disclosures.

Footnotes

  • Study funding: Supported by a grant from FISM/2008/R/13.

  • Editorial, page 1416

  • Supplemental data at www.neurology.org

  • Received December 19, 2011.
  • Accepted April 6, 2012.

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