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Altered thalamic functional connectivity in multiple sclerosis

European Journal of Radiology, In Press, Accepted Manuscript, Available online 19 January 2015



  • We demonstrated decreased connectivity between thalamus and cortical regions in MS.
  • Increased intra- and inter-thalamic connectivity was also observed in MS.
  • The increased functional connectivity is attenuated by increasing disease duration.



To compare thalamic functional connectivity (FC) in patients with multiple sclerosis (MS) and healthy controls (HC), and correlate these connectivity measures with other MRI and clinical variables.


We employed resting-state functional MRI (fMRI) to examine changes in thalamic connectivity by comparing thirty-five patients with MS and 35 age- and sex-matched HC. Thalamic FC was investigated by correlating low frequency fMRI signal fluctuations in thalamic voxels with voxels in all other brain regions. Additionally thalamic volume fraction (TF), T2 lesion volume (T2LV), EDSS and disease duration were recorded and correlated with the FC changes.


MS patients were found to have a significantly lower TF than HC in bilateral thalami. Compared to HC, the MS group showed significantly decreased FC between thalamus and several brain regions including right middle frontal and parahippocampal gyri, and the left inferior parietal lobule. Increased intra- and inter-thalamic FC was observed in the MS group compared to HC. These FC alterations were not correlated with T2LV, thalamic volume or lesions. In the MS group, however, there was a negative correlation between disease duration and inter-thalamic connectivity (r = −0.59,p < 0.001).


We demonstrated decreased FC between thalamus and several cortical regions, while increased intra- and inter-thalamic connectivity in MS patients. These complex functional changes reflect impairments and/or adaptations that are independent of T2LV, thalamic volume or presence of thalamic lesions. The negative correlation between disease duration and inter-thalamic connectivity could indicate an adaptive role of thalamus that is gradually lost with increasing disease duration.

Keywords: Multiple sclerosis, Resting-state functional MRI, Functional connectivity, Thalamus, Functional impairment, Brain plasticity.

1. Introduction

Multiple sclerosis (MS) is an inflammatory demyelinating disorder of the central nervous system (CNS). It has been traditionally recognized as predominantly involving the white matter (WM). However, gray matter, including deep gray matter, damage is now known to be prevalent in MS. This gray matter injury, caused by widespread axonal and neuronal degeneration, is thought to contribute substantially to MS disability progression[1], [2], and [3].

The thalamus, a key part of the deep gray matter, has extensive afferent and efferent connections with spinal afferents, the midbrain and the cerebral cortex. It is involved in motor planning, sensory information processing and many cognitive functions[4] and [5]. Numerous previous studies have demonstrated damage to the thalamus in MS, such as decreased neuronal integrity, loss of neurons and macroscopic atrophy [6] , and MRI abnormalities including T2 hypointensity [7] , hypometabolism [8] , decreased NAA [9] and increased diffusivity [10] . Furthermore, in several task-specific functional MRI studies, abnormal activation of the thalamus had been widely reported in patients with CIS and MS[11], [12], [13], [14], and [15].

Resting-state fMRI, as a relatively new branch of functional imaging, reflects baseline neural network connectivity in the “unattended” state, and assesses between-voxel correlations in spontaneous blood oxygen level dependent (BOLD) fluctuations. Functional connectivity (FC) changes in MS have been observed by resting-state fMRI in brain network or regions such as brain default mode network (DMN), the motor network, the hippocampus and the thalamus[16], [17], [18], and [19]. For thalamic-cortical connectivity, discordant results were reported in different studies[18] and [19]. In this study, we investigated thalamic FC between thalamus and other brain regions by resting-state fMRI in patients with MS and healthy controls (HC), and correlated the FC changes with other MRI and clinical variables.

2. Materials and methods

2.1. Participants

We studied thirty-five patients with relapsing-remitting multiple sclerosis[20] and [21](11 males, 24 females; mean age 38.1, SD 11.9). All subjects were assessed clinically by a single experienced neurologist (J.Y), who was unaware of the MRI results. The main demographic and clinical characteristics of the patients studied are reported in Table 1 . None of the participating patients had been treated with MS-specific medications (e.g., interferon-beta or immunosuppressive therapies) within three months of the MR images being obtained. We choose 35 age- and sex-matched HC (mean age 35.6, SD 10.5) with no previous history of neurological disease and with normal findings on neurological examination. The subjects were all right-handed as measured by the Edinburgh Inventory [22] . The institutional review board of Xuanwu Hospital approved the study, and written informed consent was obtained from each participant.

Table 1 Descriptive data of the study groups.

Number of subjects 35 35
Mean age (range) [years] 38.1 (18–58) 35.6 (18–54)
Sex (M/F) 11/24 11/24
Median EDSS (range) 2.5 (1.0–6.0)
Median disease duration (range) [months] 43.7 (6–204)
Median T2 lesion load (range) [mm3] 6225 (78–25481)

RRMS: relapsing–remitting multiple sclerosis; HC: healthy controls; EDSS: expanded disability status scale. See text for further details.

2.2. MRI acquisition

Imaging was performed on a 1.5 T Siemens Sonata scanner in the Radiology Department, Xuanwu Hospital, Capital Medical University. A standard head coil with foam padding was used to restrict head motion. All the routine axial slices were positioned parallel to a line that joins the most inferoanterior and inferoposterior parts of the corpus callosum, with an identical field of view (240 mm × 210 mm), matrix size (256 × 224), number of sections (30), section thickness (4 mm), and intersection gap (0.4 mm): (a) T2-weighted turbo spin echo (repetition time [TR] = 5500 ms, echo time [TE] = 94 ms, number of signals acquired = 3, echo train length = 11), (b) T1-weighted spin echo (TR/TE = 650/6, number of signals acquired = 3), (c) fluid-attenuated inversion recovery (FLAIR) (TR/TE = 8500/150, inversion time [TI] = 2200 ms, number of signals acquired = 3, echo train length = 8). Sagittal three-dimensional (3D) Volumetric T1-weighted magnetization-prepared rapid acquisition gradient echo (MPRAGE) (TR/TE = 1970/3.9 ms, TI = 1100 ms, flip angle = 15, FOV = 219 mm × 250 mm, matrix size = 256 × 256, slice thickness = 1.7 mm, voxel dimensions = 0.5 mm × 0.5 mm × 1.7 mm) images were also obtained. During resting-state fMRI, subjects were instructed to keep their eyes closed, to remain motionless, and to not to think of anything in particular. We used a gradient-echo echo-planar sequence sensitive to BOLD (Blood Oxygen Level Dependent) contrast to acquire functional images (TR = 2000 ms, TE = 60 ms, flip angle = 90°). Twenty axial slices were collected with 5 mm thickness, and a 2 mm gap. Resolution was 1.875 mm × 1.875 mm in-plane.

2.3. Thalamic and white matter lesion volume measurement

All visible lesions were identified from FLAIR and T2 images and manually extracted from T2-weighted scans using MRIcro software ( ) including lesions in the thalamus. Next, the T2 lesion volume of each patient was calculated (shown in Table 1 ).

The whole thalamus was traced and saved as a mask from the coronal three-dimensional MPRAGE images by an experienced radiologist (Y.D, with 8 years experience), blinded to clinical information. The thalamic boundaries were determined manually using MRIcro, and left and right thalamus was saved as masks for further FC analyses. Raw thalamic volumes were normalized within each subject as a ratio to the intracranial volume. The resulting normalized thalamic volume was referred to as the thalamic fraction (TF). To test the reproducibility of our findings, twenty randomly chosen subjects (10 patients with MS and 10 HC) had thalamic segmentation repeated by the same observer (Y.D) one month later and by another experienced observer (Y.L, with 7 years experience in neuroradiology) to determine intra- and inter-rater reliability.

2.4. Resting-state functional MRI data analysis

2.4.1. Image preprocessing

All analyses were conducted using a statistical parametric mapping software package (SPM5, ). The first 10 volumes of the functional images were discarded to reach signal equilibrium and allow participants adaptation to the scanning noise. The remaining 229 fMRI images were first corrected for within-scan acquisition time differences between slices and then realigned to the first volume to correct for interscan head motions. No participant had head motion of more than 1.5 mm maximum displacement in any of thex,y, orzdirections, or 1.5° of any angular motion throughout the course of scan. Next, we spatially normalized the realigned images to the standard echo-planar imaging template and resampled them to 3 mm × 3 mm × 3 mm. Subsequently, the functional images were spatially smoothed with a Gaussian kernel of 4 mm × 4 mm × 4 mm FWHW to decrease spatial noise. Following this, temporal filtering (0.01 Hz <f < 0.08 Hz) was applied to the time series of each voxel to reduce the effect of low-frequency drifts and high-frequency noise by using Resting-state fMRI Data Analysis Toolkit ( ). To further reduce the effects of confounding factors, we also used a linear regression process to further remove the effects of head motion and other possible sources of artifacts: (1) six motion parameters, (2) whole-brain signal averaged over the entire brain, (3) linear drift.

2.4.2. FC analysis

The left and right thalamic masks were co-registered to the functional MR images as the seed regions by using SPM5. For each subject and each seed region, we produced a correlation map by computing the correlation coefficients between the reference time series (computed from all the voxels within each ROI) and the time series from all other brain voxels. Correlation coefficients were normally transformed tozvalues using Fisher'sr-to-ztransform [23] .

2.5. Statistical analysis

The individualzvalue was entered into a random effect one-samplet-test in a voxel-wise manner to determine brain regions showing significant connectivity to the left and right thalamus within each group. Thezvalues were also entered into a random effect two-samplet-test to identify the regions showing significant differences in connectivity to the bilateral thalami between MS patients and HC. Within and between groups voxel-level intensity thresholds were set at false discovery rate (FDR) correctedp < 0.05. Correlations between thalamic connectivity changes and clinical variables (EDSS, disease duration) and MRI variables (T2LV and TF) were modeled by linear regression, with age and sex as covariates.

3. Results

3.1. Thalamic volume measurement

The MS patients were found to have a significantly lower TF than HC in both right (MS (mean ± SD): (4.86 ± 1.09) × 10−3; HC (mean ± SD): (6.21 ± 0.57) × 10−3;p = 2.1 × 10−7) and left (MS (mean ± SD): (5.02 ± 1.29) × 10−3; HC (mean ± SD): (6.16 ± 0.49) × 10−3;p = 4.6 × 10−6) thalami. The intra-rater coefficient of variation (COV) was 6.1% in the patients and 4.1% in HC, while the COV of the inter-rater reliability was 8.3% in the patients and 5.5% in the HC.

3.2. Thalamus connectivity: within-group analyses

Both in HC and MS patients, thalami showed strong connectivity to a number of brain regions. These regions included the precuneus, anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal lobe (IPL) and parahippocampus (PHG), and deep gray matter regions such as the contra-lateral thalamus, caudate and putamen ( Fig. 1 ).


Fig. 1 Brain regions with significant connectivity to bilateral thalami in MS patients and well-matched controls group withp < 0.05 (corrected). (a) HC left thalamus; (b) HC right thalamus; (c) MS left thalamus; and (d) MS right thalamus. Left in picture is left in the brain. The color scale representstvalues.

3.3. Thalamus connectivity: between-group analyses

Compared to HC, the MS group showed significantly decreased FC between thalamus and several other brain regions. Decreased connectivity of the left thalamus was detected with right middle frontal gyrus (for details see Table 2 and Fig. 2 ), and decreased connectivity of the right thalamus was detected with the right parahippocampal gyrus and the left inferior parietal lobule (for details see Table 3 and Fig. 3 ). Interestingly, we found a large increase in resting-state FC within each thalamus and between left and right thalami in the MS group compared to HC (for details seeTable 2 and Table 3andFig 2 and Fig 3). In order to negate the potential effect of thalamic T2 lesions, we identified and excluded the 6 (of 35) patients with thalamic lesions, but this did not alter the FC results.

Table 2 Regions showing decreased and increased connectivity to left thalamus in MS patients (p < 0.05 corrected).

Brain regions TAL coordinates BA Cluster size T-score
  x y z      
HC vs. MS
Rt. middle frontal gyrus 6 5 49 6 28 3.17
MS vs. HC
Lt. thalamus −6 −14 12   113 3.63
−18 −14 12     3.49
−12 −9 3     3.28
Rt. thalamus 6 −11 12   86 3.72
12 −8 6     3.03
6 −20 7     2.73

Fig. 2 Brain regions showing connectivity changes with the left thalamus in MS patients. (a) Decreased connectivity in right middle frontal gyrus; and (b) increased connectivity in bilateral thalami. The color scale representstvalues.

Table 3 Regions showing decreased and increased connectivity to right thalamus in MS patients (p < 0.05 corrected).

Brain regions TAL Coordinates BA Cluster size T-score
  x y z      
HC vs. MS
Rt. parahippocampal gyrus 24 −21 −15 35 33 3.55
24 −9 −19     2.92
18 −6 −7     1.83
Lt. inferior parietal lobule −59 −28 26 40 28 3.02
−56 −21 40     2.84
MS vs. HC
Lt. thalamus −12 −8 6   124 3.27
−18 −17 4     3.11
−9 −29 10     2.78
Rt. thalamus 12 −32 7   63 3.88
3 −5 9     3.60
6 −11 3     2.80

Fig. 3 Brain regions showing connectivity changes with the right thalamus in MS patients. (a) Decreased connectivity in right parahippocampal gyrus, left inferior parietal lobule; and (b) increased connectivity in bilateral thalami. The color scale representstvalues.

3.4. The correlation between FC and clinical and MRI variables

In the MS group, we examined the correlations between EDSS, disease duration, whole brain T2LV, thalamic fraction and the extent of altered thalamic FC. Interestingly, there was a negative correlation between disease duration and inter-thalamic FC (r = −0.59,p < 0.001) ( Fig. 4 ). None of the other correlations were found to be significant (p > 0.11).


Fig. 4 The negative correlation between disease duration and inter-thalamic functional connectivity (r = −0.59,p < 0.001).

4. Discussion

The thalamus is an area of great current interest in MS research [24] , as both structural and functional thalamic alterations are reported by numerous pathological and imaging studies[6], [7], [8], [9], [10], and [11]. However to our knowledge, very few previous studies systematically examined thalamic FC in MS by resting-state fMRI together with thalamic volume measurement [18] . We confirmed MS associated thalamic atrophy and demonstrated decreased thalamic FC with several cortical regions. Furthermore, a marked increase in both intra and inter-thalamic connectivity was identified in MS. These FC changes in thalamus were not associated with thalamic volume loss, presence of thalamic lesions, or cerebral T2 lesion volume.

The IPL is a key hub among the default mode network (DMN), a network consisting of functionally linked PCC/precuneus, and medial frontal regions[25] and [26]. Connectivity of the DMN plays an important role in human cognition, including the integration of cognitive and emotional processing, and monitoring the world around us [27] . The parahippocampal gyrus is important for memory encoding and retrieval, an essential part of cognition, while the middle frontal gyrus has been widely reported to be involved in the processing of higher information, mental set maintenance and response to task difficulty [28] . In the current study, the decreased connectivity between and the IPL, parahippocampal, middle frontal gyri and the thalamus in MS could be a substrate for early cognitive change in MS patients, and this hypothesis could warrant further examination with correlative and serial studies combining resting-state connectivity measures with formal cognitive testing. A previous study [18] demonstrated increased thalamic-cortical connectivity in hippocampal and dorsal–frontal components of the network and decreased connectivity in the cerebellum, cingulum and prefrontal cortex components in MS. The discordance of the findings with our current study may due to different clinical characteristics of the patients (such as EDSS and disease duration) or different analysis methods. A multi-center study with large sample and standardized MRI analysis method is warranted to confirm the results. The structural basis of the decreased FC between thalamus and several cortical regions is probably demyelination and neurodegeneration (structural disconnection) reported by a MRI-pathology study [29] .

Stein and colleagues [4] assessed FC of the thalamus in six healthy volunteers and found evidence of intra and inter-thalamic connectivity. In our study, evidence of intra- and inter-thalamic FC was also present in HC, and this was markedly increased in MS patients, which is consistent with a previous study using a resting-state functional homotopic method and showing increasing FC between bilateral thalami [19] . The thalamus, a central hub in the brain, functions as a relay of sensory and motor information to and from the cerebral cortex, and has documented key roles in general arousal states including the regulation of consciousness, sleep and attention. Increased activation of the thalamus had been widely reported in patients with CIS and MS in a whole range of task-specific functional MRI studies during simple motor tasks [30] , visual tasks [31] attention and working memory tasks and planning tasks [13] . This increased activation in multiple task-related fMRI studies, the previously reported increase in overall thalamic resting-state activity [32] and our demonstration of enhanced resting-state intra- and inter-thalamic connectivity, could all reflect the same disease-associated functional change. One possibility is that this phenomenon reflects a role for the thalamus as a coordinator or circuit element for brain plasticity and functional remapping in MS, compensating for the relative loss of cortex and gradual loss of afferent and efferent cortical connections. An alternate explanation for our finding of increased resting-state thalamic connectivity is that this is a functionally irrelevant epiphenomenon, perhaps secondary to functional disconnection of the thalami from afferents or efferents (the latter also demonstrated in our study).

The negative correlation we identified between disease duration and inter-thalamic connectivity would support an adaptive or compensatory function (itself gradually lost with increasing MS duration), as one would expect an epiphenomenon or directly pathologically driven change in MS to worsen and not reverse with increasing damage over time. A longitudinal study is warrant to corroborate these findings. The absence of correlation between thalamic volume loss and FC changes in the current study could be viewed as contradicting a recent study [33] showing that lower alpha2 band resting-state FC using magnetoencephalography (MEG) in the visual network was correlated with thalamic atrophy. However, MEG resting-state only acquires data from cortical regions, so that thalamic resting-state connectivity measures cannot be acquired. Our results indicate that the FC changes directly involving the thalami are independent of thalamic volume changes, thalamic lesion presence or cerebral T2LV, supporting the hypothesis that the connectivity alterations within the thalami are not markers of increasing disease burden, and more likely represent an active adaptive phenomenon.

There are several limitations in our study. First, formal neuropsychological tests were not performed in this study, which prevented us from correlating cognitive test scores with our fMRI results. Most importantly, prospective studies would be required to understand the functional significance of altered thalamic connectivity in early relapsing–remitting MS. Secondly, the current study is a cross-sectional study based on 1.5 T MRI data, and 3 T resting-state fMRI acquisition would provide a higher signal-to-noise ratio, but would be unlikely to alter our significant results. A longitudinal study with 3 T MRI data is warranted to confirm the current findings and investigate the dynamic changes of the thalamic-cortical FC in MS. Thirdly, at the technical level, resting-state fMRI is prone to respiratory and cardiac cycle artifacts, enhanced by slow sampling rates. (e.g., TR = 2 s in this study). However, these effects would have to apply differentially to the MS patients and healthy controls to affect the results, which is unlikely [34] .

5. Conclusion

In patients with relapsing–remitting MS, we demonstrate decreased resting-state functional FC between the thalamus and several cortical regions, and increased intra- and inter-thalamic connectivity. This increased resting-state FC between thalami is independent of T2LV, thalamic volume or thalamic lesions, but is attenuated by increasing disease duration, suggesting an adaptive role of the thalamus that is gradually lost as disease progresses.

Conflict of interest


Author's contribution

Kuncheng Li, Yaou Liu: guarantor of integrity of the entire study

Yaou Liu, Peipeng Liang: study concepts

Yaou Liu, Peipeng Liang: study design

Yaou Liu, Peipeng Liang;Yunyun Duan: definition of intellectual content

Yaou Liu, Peipeng Liang: literature research

Huiqing Dong, Jing Ye, Yunyun Duan: clinical studies

Jing Ye, Yaou Liu, Yunyun Duan: experimental studies

Yunyun Duan, Yaou Liu, Jing Huang, Zhuoqiong Ren: data acquisition

Peipeng Liang, Xiuqin Jia, Yunyun Duan: data analysis

Peipeng Liang, Yaou Liu: statistical analysis

Yaou Liu, Peipeng Liang: manuscript preparation

Yaou Liu, Helmut Butzkueven, Fu-dong Shi: manuscript editing

Fu-dong Shi, Helmut Butzkueven, Kuncheng Li: manuscript review


Dr Yaou Liu, Dr Peipeng Liang, Dr Yunyun Duan, Dr Jing Huang, Dr Zhuoqiong Ren, Dr Huiqing Dong, Dr Jing Ye, Professor Fu-dong Shi, Dr Helmut Butzkueven, and Professor Kuncheng Li report no disclosures.


This work was supported by the National Natural Science Foundation of China (Nos. 81101038, 30930029), Beijing Natural Science Foundation (No. 7133244) and Beijing Nova Program (No. xx2013045).


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a Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China

b Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China

c Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052, PR China

d Department of Medicine, University of Melbourne, Parkville 3010, Australia

lowast Corresponding author. Tel.: +86 13911099059; fax: +86 10 83198376.