Journal of the Neurological Sciences 282 (2009) 61–66

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Journal of the Neurological Sciences

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Connecting white matter injury and thalamic atrophy in clinically isolated syndromes

Roland G. Henry a,c,⁎, Mason Shieh a, Bagrat Amirbekian a, SungWon Chung a,c,Darin T. Okuda b, Daniel Pelletier b

a Department of Radiology and Biomedical Imaging, University of California at San Francisco, USAb Department of Neurology, University of California at San Francisco, USAc Graduate Group in Bioengineering, University of California at Berkeley and San Francisco, USA

⁎ Corresponding author. Center for Molecular and FStreet, Suite 350, San Francisco, CA 94107, United States.415 353 9425.

E-mail address: Roland.Henry@radiology.ucsf.edu (R

0022-510X/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.jns.2009.02.379

a b s t r a c t

a r t i c l e i n f o

Article history:Received 26 November 2008Received in revised form 24 February 2009Accepted 28 February 2009Available online 23 April 2009

Keywords:ThalamusAtrophyGrey matterCISVBMMultiple sclerosisDTITractography

Previous studies suggest that thalamic degeneration is prominent in multiple sclerosis (MS) and even in pre-MS patients presenting with a clinically isolated syndrome (CIS). However, the relationships between whitematter lesions and deep grey matter loss are not well understood.We analyzed the association between white matter lesions and the thalami in CIS patients to determine ifconnectivity is an important determinant. We studied 24 CIS patients and 18 normal controls withanatomical and diffusion tensor (DTI) MRI images. DTI fiber tracking was used to create probabilistictemplates of the thalamocortical white matter and to define white matter connecting lesions and thalami.DTI metrics in the lesions and normal-appearing white matter (NAWM) regions were compared between CISand controls, and correlated with thalamic volume changes estimated by voxel-based morphometry.There was 10 times higher density of lesions in thalamocortical compared to other brain white matter.Increased diffusivities and decreased fractional anisotropies were measured in the thalamocortical NAWM ofCIS patients compared to controls. A step-wise regression analysis demonstrated that thalamocortical lesionvolume and the mean diffusivity in track regions connecting lesion and thalami were significantly correlatedwith thalamic volumes in patients (Rsq=0.66, pb0.001), a finding not observed in regions outside thethalamocortical white matter. These results provide compelling evidence for a direct relationship betweenwhite matter lesions and thalamic atrophy in CIS patients.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction

While white matter lesions are the most important MRI metriccurrently used for the diagnosis of multiple sclerosis (MS), thedetection of decreased grey matter volumes suggests the presence ofirreversible neuronal degeneration [1,2]. The cause of grey mattervolume loss is poorly understood however and is receiving well-deserved attention recently [3–5].

Reduced regional volume in CIS patients at presentation usingvoxel-based morphometry (VBM) was previously demonstrated [6].In this group of CIS patients, a wide variation in global grey matteratrophywas observed; however, this total greymatter volumewas notsignificantly different between CIS and controls. On the other hand,the thalamus exhibited consistent reduced volume across CIS patients.Functionally, the human thalamus is a complex brain relay centerresponsible for both sensory and motor functions, along withawareness, attention, memory and language [7]. Up to 50–60 thalamicnuclei project individual afferent neurons to several well-defined

unctional Imaging, 185 BerryTel.: +1415 353 9406; fax: +1

.G. Henry).

ll rights reserved.

cortical areas, which in return send information through efferentneurons back to the thalamus [8].

The reason for thalamic atrophy at this earliest stage of CIS patientssuggestive of MS is unclear but is supported by previous observationsof thalamic degeneration in early relapsing–remitting MS, includinglesions in this grey matter region [9,10]. The relationship betweenlesion volume and thalamic atrophy is also unclear and may representconcurrent injury due to a mutual correlation of white and greymatter injury to a common disease process. On the other hand, theremay be a direct relationship between the white and grey matterpathology due to injury in lesions in the white or grey matter, andsubsequent degeneration of axonal projections connected to thalamicnuclei.

In order to further establish the relationship between thalamicatrophy and white matter lesions, we have utilized diffusion tensorMRI (DTI) to define the structural connectivity between white matterlesions and the thalami in untreated CIS patients at first clinicalpresentation. Furthermore, we propose to determine the distributionof white matter lesions relative to thalamocortical (TC) white matterprojections, and investigate a structural rationale for connecting thewhite matter lesions and thalamic volume loss. Finally, we correlatethe DTI metrics in lesional and normal-appearing white matter(NAWM) TC tracts with thalamic volumes from a VBM study.

62 R.G. Henry et al. / Journal of the Neurological Sciences 282 (2009) 61–66

2. Methods

The study cohort consisted of twenty-four untreated CIS patients(M/F=7/17, age range 21–56) and eighteen control subjects (M/F=8/10, age range 23–46) who were evaluated at the University ofCalifornia, San Francisco Multiple Sclerosis Center. The subjectspresenting with their first well-defined, neurological event persistingfor more than 48 h involving the optic nerve, brainstem, cerebellum orspinal cord were included in the classification as patients with CIS andinvited for participation. All forty-two subjects were scanned withhigh-resolution T1-weighted SPGR volumes (1×1×1.5 mm resolution;flip angle 40°; 27/6 ms TR/TE; field of view, 180×240×186 mm3;matrix, 192×256×124) and DTI (1.7×1.7×2.1 mm resolution, 9averages, TR/TE=7 s/100 ms; b value=2000 s/mm2), and axial dualspin-echo images (TE1/TE2/TR=20/80/2500 ms, 192×256 matrix,180×240 mm FOV, 3 mm thick slices, 50 slices) on a 1.5 T GeneralElectric Medical Systems Signa scanner (General Electric, Milwaukee,WI) with 4 G/cm gradients and a standard quadrature head coil. All CISpatients demonstrated at least two abnormal foci on brain MR imagingmeasuring≥3mm2. None of the CIS patientswas treatedwith approveddisease-modifying therapy prior to their MRI scan and no steroidtherapywasusedwithin fourweeks of theMRI scan. Thiswas a subset ofthe cohort previously reported inHenry et al. [6], forwhomDTI datawasacquired. The research investigation was performed with the approvalfrom the University of California, San Francisco Committee on HumanResearch. Written informed consent was obtained from all studyparticipants.

Two distinct approaches to understanding the relationshipbetween white matter lesions and thalamic atrophy were developed.First, in order to compare between CIS and controls, a probabilistictemplate was created to define the TC projections based on diffusionMRI fiber tracking. This approach also allowed us to determine thelesion distribution relative to TC white matter. The second approachutilized diffusion MRI fiber tracking to specifically define only thosewhite matter regions that connect lesions to the thalami. Thisapproach enabled a more specific determination of white matterabnormalities associated with both lesions and the thalami. Thesemetrics were then used in a correlation with thalamic volumes usingprevious VBM results [6].

For clarity, we have chosen to use “tracts”when referring to axonalbundles and “tracks” when referring to the synthetic streamlinesdetermined from diffusion MRI fiber tracking that map out thesepathways.

Fig. 1. Flow chart of processing methods for creation and ap

2.1. Probabilistic thalamocortical white matter templates

Eighteen control subjects were used to create templates of thewhite matter connecting the major cortical lobes to the thalamus(Fig. 1). All DTI processing and fiber tracking was done using softwaredeveloped in our laboratory. Using whole brain seeding, the stream-line FACT algorithm [11] was used to create DTI tracks in the nativespace for each control. These tracks were then used to define 8 TCwhite matter regions of interest (ROI) by pair-wise targeting thethalamus and each of the left and right temporal, frontal, parietal, andoccipital lobes. The thalamic ROI were drawn manually on eachsubject. In the subject's native space, the cortical lobar ROI weredetermined by non-linear registration of regions defined in MontrealNeurological Institute (MNI) space from modified standard templates[12]. These lobar regions were further masked with the subject's greyand white matter masks obtained using the SIENAX algorithm [13]from the FSL library (http://www.fmrib.ox.ac.uk/analysis/research/siena); neighboring white matter was included in order to providevoxels that were at the fiber tracking anisotropy threshold. The DTIfiber tracks were then converted to TC white matter masks for eachsubject, and then registered to MNI space by a series of linear (FLIRT)and non-linear transformations [14]. The registration process includedtransformations between the T2-weighted echo-planar images fromthe DTI data and the subjects' T1-weighted volume via affine and non-linear transformations (Fig. 2). The subjects' T1-weighted volume andthe MNI T1-weighted template were also registered using affine andnon-linear transformations.

In MNI space, the TC white matter masks were then combined andeach voxel was assigned a value reflecting the fraction of 18 subjectsfor which that voxel was found to be part of the corresponding TCwhite matter. This resulted in 8 TC white matter templates in MNIspace. The volume fraction of the TC white matter to the total whitematter was determined in MNI space using only those voxels withvalues of 50% (9 of 18 subjects) to assure reliability in the resultsconsidering individual variability and partial volume effects due to theregistration to MNI space.

2.2. Lesion volume calculations

For the CIS patients, white matter lesions were manually drawn byexperienced MS neurologists (DP and DO) on T1-weighted 3D spoiledgradient echo volumes, yielding estimates of T1 lesion volumes(T1LV). The lesion volumes in each of the TC and non-TC white matter

plication of the thalamocortical white matter template.

Fig. 2. Example of the registration of T1-weighted volumes to the echo-planar T2-weighted image from the DTI dataset. (a) The original T1-weighted image, (b) the minimallydiffusion weighted echo-planar image, and (c) the registered T1-weighted image are shown.

63R.G. Henry et al. / Journal of the Neurological Sciences 282 (2009) 61–66

regions were calculated by masking with the TC white mattertemplates. In order to assess the possible impact of lesions on aparticular region, the T1LV density (volume fraction of the regionoccupied by lesions) was also calculated (ROI lesion density=“ROIT1LV”/“ROI Volume”). In order to verify the sensitivity of identifyinglesions on the T1-weighted gradient echo volumes, we then comparedthe T1 lesion regions with lesions identified on the T2-weightedimages. This analysis demonstrated that 93% of the lesions identifiedwith the T2-weighted images were initially identified on the T1spoiled gradient echo volumes. Furthermore, for each T2-weightedlesion, a corresponding T1-weighted lesion was retrospectivelyidentified (100% concordance).

2.3. Quantification of DTI metrics

The TCwhitematter templateswere registered to each subject (CISand controls) using linear and non-linear transformations [14]. In thesubjects' space, the template was threshold at 50% (i.e., only thosevoxels found for least half of the control subjects) and applied as anROI. The TC and non-TC white matter regions were further masked toremove lesions and therefore determine values only in normal-appearingwhite matter (TC and non-TC NAWM). Themean for each ofthe DTImetrics (mean diffusivity: MD, fractional anisotropy: FA, majoreigenvalue: λ1, and average of the minor eigenvalues: λT) wascalculated (as described elsewhere [15,16]) in each of these 8 TC ROI(separately in the NAWM and lesions). The metrics in the left and

Fig. 3. Schematic representation of afferent and efferent thalamocortical projectionsand regions of interest generated by the DTI fiber tracking. Of note, temporal lobeprojections are not shown. NAWM = normal-appearing white matter; Th = thalamus;TC NAWM= thalamocortical NAWM; TC T1L= thalamocortical T1 whitematter lesion;L-TC NAWM = lesional-thalamocortical NAWM.

right regions were not significantly different and therefore wereaveraged for further analyses. General linear models (GLM) wereperformed to compare CIS and control DTI metrics with covariance forage and total intracranial volume. Although there were no significantdifferences between the ages, sex, or intracranial volumes betweenCIS and control cohorts, we kept them in the GLM models to accountfor dependencies on the DTI metrics.

2.4. Lesion fiber tracking

In the subjects' space, lesion regions were dilated by 1 voxel usingthe 6-center neighbors as the kernel. These dilated regions were thenseeded to produce fiber tracks with the FACT algorithm. The trackswere then targeted to the thalamic and cortical lobar regions. The DTImetrics were quantified in the NAWM connecting lesions andthalamus, and in the TC and the total lesion region separately. Fig. 3provides a schematic overview of all studied regions of interest (seealso Table 1).

2.5. Correlations with thalamic volumes

The VBM analyses were previously reported [6] but brieflyrepeated here. A local template was created using the subjectsincluded in this study, as well as segmented greymatter, white matter,and CSF, and modulated gray matter images. The procedure was theoptimized VBM method [17] and included the masking of lesionsduring normalization to prevent large distortions due to the costfunction [6]. A correlation analysis was conducted using each patient's

Table 1Regions of interest defined in this study.

Label Definition Method

TC WM Thalamocortical whitematter

Probabilistic template fromthalamocortical tractographyin controls

TC T1LV T1LV in thalamocorticalwhite matter

Lesion volume in thethalamocortical white mattermask

TC NAWM Thalamocortical NAWM Thalamocortical template withlesions excluded

Non-TC NAWM Non-thalamocortical NAWM NAWM template outside ofthalamocortical white matterregions

L-TC NAWM Thalamocortical NAWMconnected to thalamocorticalWM lesions

Tractography in patient space,seeded in lesions & targeted in thalami and cortices

WM: white matter; T1LV: T1-weighted lesion volume; TC: thalamocortical; NAWM:normal-appearing white matter.

Fig. 4. (a) Example of a probabilistic thalamo-parietal map (thresholded at 50%) used in this study. TheMNI transformedmap is registered onto the patient's space; thalamo-parietalROI in orange and the thalamus ROI in green. (b) Example of two thalamocortical fiber tracks (red) seeded through white matter lesions (blue) connecting the thalamus (green) andthe cortex (not shown). The colors on the fiber tracks represent the mean diffusivity values increasing from red to yellow.

Table 2Cross-sectional comparisons of DTI metrics between CIS (n=24) and controls (n=18)using a general linear model with age and intracranial volume as covariates.

FA MD λ1 λT

TC NAWM p=0.002 p=0.001 p=0.034 pb0.001CIS 0.293 741 961 632Controls 0.308 714 939 602

Non-TC NAWM p=0.718 p=0.719 p=0.605 p=0.790CIS 0.270 660 847 566Controls 0.270 655 840 562

Cells have p-values, and mean values for CIS and control subjects. Diffusivities are inunits of 10−6 mm2/s. FA is a dimensionless quantity defined between 0 and 1.

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T1LV values with age and total intracranial volume as covariates todetermine grey matter volumes correlations with T1LV, as T1LV waspreviously found to correlate with thalamic volume [6].

The primary aim of the correlation with thalamic volumes was todetermine which white matter compartments best explained thevariance observed in CIS thalamic atrophy. We hypothesized thatlesions directly connected to the thalami and the NAWM connectinglesions and thalami would best co-vary with thalamic volumes. Thenon-TC white matter was not expected to correlate with thalamicatrophy but may be affected by lesions in TC WM due to otherpathways not connecting to the thalami. Given the strong correlationsamong these variables, we used a step-wise regression model toinvestigate the variables that best and significantly contributed to theobserved variance in thalamic volumes in CIS patients. The responsevariable in this regression was relative thalamic volumes and themodel effects included age, sex, total intracranial volumes, DTI metricsand T1LV in TC NAWM and in non-TC NAWM, as well as DTI metrics inall lesions and TC lesions. For the DTI metrics, separate models wereinvestigated using either FA and MD, or major and minor diffusioneigenvalues. The step-wise regression used 0.1 as the entry and exitthresholds. The results of the step-wise regression were analyzed in ageneral linear model where age and total intracranial volume wereincluded as covariates. Sex was not included in the GLM because it wasnot significantly correlated with the normalized volumes or othermetrics beyond the correlations explained by age and total intracra-nial volume.

In order to evaluate the possible influence of thalamic degenera-tion and lesions on TC NAWM, we performed a step-wise regression toexplain the TC NAWM DTI metrics by thalamic volumes, TC T1LV, andDTI metrics in TC white matter lesions.

3. Results

Examples of the thalamocortical white matter template in a CISpatient, and the individual tracking between lesions and thalami areshown in Fig. 4. The thalamocortical white matter is connected to theparietal lobe (Fig. 4a). The tracks are colored from red to yellowrepresenting increasing mean diffusivity (Fig. 4b).

3.1. Connectivity of WM lesions and thalami

The mean T1LV was 1.44 cm3 in TC white matter and 1.85 cm3 inwhole brain white matter. Therefore 78% of the total T1LV in the CISpatients was within the TC white matter, while the TC white matterwas less than 28% of the total white matter. The fraction of non-TCwhite matter occupied by lesions was 0.15% compared to 1.53% for TCwhite matter. These striking results suggest a 10 to 1 increase in the

density of whitematter lesions in TC projections compared to the non-TC white matter in CIS patients.

3.2. CIS versus controls

Cross-sectional studies were performed to determine if TC NAWMor non-TC NAWM were abnormal in this CIS cohort. The cross-sectional comparison between CIS and controls of DTI metrics areshown in Table 2. CIS patients had increased diffusivities (p=0.001)and decreased FA (p=0.002) compared to controls in TC NAWM. Incontrast, no differences were observed between DTI metrics in non-TCNAWM between CIS and controls.

3.3. Correlations with thalamic volumes

The aim of this section was to validate the hypothesis that whitematter lesions that are structurally connected to the thalami will bethe best predictors of thalamic atrophy. The response variable in thisregression was relative thalamic volumes and the model effectsincluded age, sex, total intracranial volumes, DTI metrics and T1LV inTC NAWM and in non-TC NAWM, as well as DTI metrics in all lesionsand TC lesions. Alone, TC T1LV was responsible for 47% of the thalamicvolume variance in CIS patients (pb0.001). Among all the othervariables only the MD in the NAWM connecting lesions and thalamicontributed further to thalamic volume variance (Rsq=0.66,pb0.001; Fig. 5). Of note, no metric (DTI or lesion volume) outsideof the TC regionwas significantly correlatedwith thalamic volume andtherefore did not contribute to additional variance in explainingthalamic atrophy.

3.4. Correlations with TC NAWM

Using a step-wise regression, we investigated the contributions ofthalamic volume, TC T1LV, and TC lesions DTI metrics on the TC

Fig. 5. Actual versus predicted relative thalamic volumes modeled by thalamocorticalT1LV andMD in thalamocortical NAWM connected to lesions, corrected for age and totalintracranial volume. The blue and red lines represent the mean value and (5%, 95%)confidence intervals from the fit, respectively. (For interpretation of the references tocolor in this figure legend, the reader is referred to the web version of this article.)

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NAWMDTI metrics (Table 3). While TC T1LV only was correlated withMD and λT, thalamic volumes only correlated with λ1. The TC NAWMλ1 versus relative thalamic volume is shown in Fig. 6.

4. Discussion

Multiple sclerosis injury is present in both grey and white mattersin the central nervous system. Tissue loss and foci of demyelination,with varying degrees of inflammation, have been described in allneocortical layers, deep grey matter nuclei and throughout the whitematter. Understanding the relation between focal lesions and tissuedegeneration is crucial to demystify the underlying mechanismsleading to disease progression and to prevent clinical deteriorationusing the correct treatment strategies.

In this study, we have found strong evidence for a directconnection between thalamic atrophy and white matter lesions inCIS patients at presentation. In particular we have determined that78% of the lesions are within the TC white matter, which is a 10 to 1bias in density of lesions occupying TC white matter. Furthermore,diffusion metrics in this normal-appearing TC white matter werefound to be abnormal compared to controls, and to correlatewith bothT1 lesion volumes and thalamic volumes. On the other hand there isno difference in non-TC NAWM between CIS and control subjects.Lastly the thalamic volumes were strongly correlated to TC lesionvolume and the mean diffusivity in the normal-appearing whitematter connecting TC lesions. These results suggest that the thalamicatrophy found early in the disease is mechanistically related to thewhite matter lesions.

Table 3Modeling DTI metrics in TC NAWM with thalamic volume, thalamocortical T1LV, andDTI metrics in TC Lesions.

Rsq TC T1LVp-values

TC lesions DTIp-values

Thalamic volumep-values

TC NAWM FA 0.14 0.515 0.080 0.669TC NAWM MD 0.40 0.001, R=0.63 0.555 0.212TC NAWM λ1 0.40 0.143 0.307 0.001, R=−0.63TC NAWM λT 0.37 0.002, R=0.62 0.506 0.340

Significant correlations (pb0.05) are in bold and correlation coefficients are shown.

Possible contributors to thalamic atrophy are grey matter lesions,secondary Wallerian degeneration fromwhite matter lesions, and on-going degeneration coming from injured spinal cord ascendingpathways, especially the posterior column-medial lemniscus system.As such, a limitation of our study is that we did not acquire spinal cordanatomical images and DTI datasets in these patients, a challengingtask anyway with current in vivo technology. Later in the disease, i.e.secondary progressive MS, grey matter loss may be driven or causedby other pathological processes as the number of grey matter lesionsseems to increase over time [18] as evidenced by Fisher et al. [1].Though the notion that thalamic pathology causing white matterlesions has not be previously entertained, this possibility cannot beruled out entirely. For example, it is possible that thalamocorticalprojections may have a multifaceted risk profile including bothfunctional and structural compromise due to thalamic pathologicalchanges in addition to proximity to areas already susceptible toinflammatory lesions.

The relative thalamic volumes used in this study were taken from avoxel-based morphometry study comparing CIS and control subjects.The reduced volumes in the CIS patients compared to control subjectswere interpreted as atrophy; however due to the VBM methodology,the apparent reduced volumes may also arise from differences incontrast that lead to systematic underestimation of the thalamusduring segmentation. That said, other evidence for thalamic degen-eration early in MS [9,19–22], our results demonstrate abnormalitiesonly in the thalamocortical white matter, and strong correlations ofthese abnormalities with thalamic volumes, increase confidence in theinterpretation of thalamic injury.

Conversely, primary thalamic injury itself can potentially lead tonormal-appearing white matter injury. We investigated this questionby performing a step-wise regression analysis to compare the relativecorrelations of lesion and thalami to explain the TC NAWM, and foundthat thalamic volumes and not lesions correlated with λ1 (R2=0.40,p=0.001) in TC NAWM with no effect of thalamic volumes on non-thalamocortical NAWM DTI metrics. While TC lesions and the NAWMconnecting lesions and thalami contribute strongly to thalamicatrophy, the normal-appearing white matter injury appears to relateto bothwhitematter lesions and greymatter damage. The informationavailable on the interpretation of directional diffusion appearssomewhat contradictory with respect to axial diffusivity, but withtrends towards a difference in animal versus human studies, and earlyversus late stage degeneration. In animal studies decreased axialdiffusion has been associated with axonal damage and loss [23–30],while in human studies [31–34] both decreased and increased axialdiffusion has been noted. In particular, in clear cases of tissue loss inhuman studies, axial diffusion has been reportedly decreased[31,33,34]. In the present study we have identified that thalamic

Fig. 6. Correlation of TC NAWM λ1 by relative thalamic volume (R2=0.40, p=0.001).

66 R.G. Henry et al. / Journal of the Neurological Sciences 282 (2009) 61–66

atrophy is correlated with increased axial diffusivity in the connectingnormal-appearing white matter, which is consistent with otherstudies looking at atrophy correlates in humans (references availableor not?).

The accuracy of the probabilistic thalamocortical WM template isnot known and threshold at 50% may represent a conservativedefinition of this whitematter region. This threshold valuewas chosenbased on the fact that even if all of the subjects had the same regionsegmented, resampling after registration will lead to voxels havingpartial volume of thalamocortical white matter. At 50% threshold thethalamocortical white matter volume is not allowed to vary due to thepartial volume effects. If 30% was instead chosen for the threshold,then the thalamocortical white matter would grow from 28% to 45% ofthe total white matter but would include 92% of the white matterlesions; this would represent a 25:1 bias in the density of lesions in thethalamocortical versus non-thalamocortical white matter. Anotherlimitation associated with this process is that DTI deterministicalgorithms are not able to track across complex fiber areas andtherefore, some thalamocortical tracts may be systematically omittedfrom the template. Nonetheless, DTI fiber tracking remains the onlymethod of in vivo delineation of the thalamocortical white matter.

While this data does not provide evidence regarding the directionof a causal relationship between white matter lesions and thalamicatrophy, the results of this study can reasonably be interpreted assuggesting the existence of a causal relationship. It is unlikely that theonly two clear pathologies in the brain of CIS patients (thalamicatrophy and white matter lesions) are coincidentally present givenour evidence that these pathologies are structurally connected. Ifthere were no causal relationships between thalamic atrophy andwhite matter lesions, we would expect that the lesions would occupyregions that were structurally uncorrelatedwith the thalamus. Insteadwe find a very strong bias towards white matter lesions occupying thethalamocortical projections.

In conclusion, our study demonstrated that thalamocortical lesionvolumes, diffusion MRI changes in thalamocortical white matterlesion and in the NAWM connecting lesions and thalami weresignificantly correlated with thalamic volumes measured by VBM,but not for any regions outside the thalamocortical white matter.These results suggest a direct causal connection betweenwhite materlesions and thalamic atrophy in CIS patients but further studies arerequired to establish the direction of this causal relationship.

Acknowledgements

We would like to acknowledge the funding of this work by theNational Multiple Sclerosis Society research grant RG3240A1. Dr.Pelletier is a Harry Weaver Neuroscientist Scholar of the U.S. NationalMultiple Sclerosis Society.

References

[1] Fisher E, Lee JC, Nakamura K, Rudick RA. Gray matter atrophy in multiple sclerosis:a longitudinal study. Ann Neurol 2008;64:255–65.

[2] Charil A, Dagher A, Lerch JP, Zijdenbos AP, Worsley KJ, Evans AC. Focal corticalatrophy in multiple sclerosis: relation to lesion load and disability. Neuroimage2007;34:509–17.

[3] Brink BP, Veerhuis R, Breij EC, van der Valk P, Dijkstra CD, Bo L. The pathology ofmultiple sclerosis is location-dependent: no significant complement activation isdetected in purely cortical lesions. J Neuropathol Exp Neurol 2005;64:147–55.

[4] Peterson JW, Bo L,Mork S, ChangA, Trapp BD. Transectedneurites, apoptotic neurons,and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol2001;50:389–400.

[5] Sharma R, Narayana PA,Wolinsky JS. Greymatter abnormalities inmultiple sclerosis:proton magnetic resonance spectroscopic imaging. Mult Scler 2001;7:221–6.

[6] Henry RG, Shieh M, Okuda DT, Evangelista A, Gorno-Tempini ML, Pelletier D.Regional grey matter atrophy in clinically isolated syndromes at presentation. JNeurol Neurosurg Psychiatry 2008;79:1236–44.

[7] Johnson MD, Ojemann GA. The role of the human thalamus in language andmemory: evidence from electrophysiological studies. Brain Cogn 2000;42:218–30.

[8] Percheron G, Francois C, Talbi B, Yelnik J, Fenelon G. The primate motor thalamus.Brain Res Brain Res Rev 1996;22:93–181.

[9] Cifelli A, Arridge M, Jezzard P, Esiri MM, Palace J, Matthews PM. Thalamicneurodegeneration in multiple sclerosis. Ann Neurol 2002;52:650–3.

[10] Huitinga I, De Groot CJ, Van der Valk P, Kamphorst W, Tilders FJ, Swaab DF.Hypothalamic lesions inmultiple sclerosis. J Neuropathol ExpNeurol 2001;60:1208–18.

[11] Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonalprojections in the brain bymagnetic resonance imaging. Ann Neurol 1999;45:265–9.

[12] Tzourio-MazoyerN, Landeau B, PapathanassiouD, Crivello F, EtardO,DelcroixN, et al.Automated anatomical labeling of activations in SPMusing amacroscopic anatomicalparcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273–89.

[13] Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, et al. Accurate,robust, and automated longitudinal and cross-sectional brain change analysis.Neuroimage 2002;17:479–89.

[14] Sdika M. A fast nonrigid image registration with constraints on the Jacobian usinglarge scale constrained optimization. IEEE Trans Med Imaging 2008;27:271–81.

[15] Henry RG, Oh J, Nelson SJ, Pelletier D. Directional diffusion in relapsing–remittingmultiple sclerosis: a possible in vivo signature of Wallerian degeneration. J MagnReson Imaging 2003;18:420–6.

[16] ChungS, PelletierD, SdikaM, LuY, Berman JI,HenryRG.Wholebrainvoxel-wiseanalysisof single-subject serial DTI by permutation testing. Neuroimage 2008;39:1693–705.

[17] Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-basedmorphometric study of ageing in 465 normal adult human brains. Neuroimage2001;14:21–36.

[18] Kutzelnigg A, Lassmann H. Cortical lesions and brain atrophy in MS. J Neurol Sci2005;233:55–9.

[19] Dalton CM, Chard DT, Davies GR, Miszkiel KA, Altmann DR, Fernando K, et al. Earlydevelopment of multiple sclerosis is associated with progressive grey matteratrophy in patients presenting with clinically isolated syndromes. Brain2004;127:1101–7.

[20] Wylezinska M, Cifelli A, Jezzard P, Palace J, Alecci M, Matthews PM. Thalamicneurodegeneration in relapsing–remitting multiple sclerosis. Neurology2003;60:1949–54.

[21] Brass SD, Benedict RH, Weinstock-Guttman B, Munschauer F, Bakshi R. Cognitiveimpairment is associated with subcortical magnetic resonance imaging greymatter T2 hypointensity in multiple sclerosis. Mult Scler 2006;12:437–44.

[22] Houtchens MK, Benedict RH, Killiany R, Sharma J, Jaisani Z, Singh B, et al. Thalamicatrophy and cognition in multiple sclerosis. Neurology 2007;69:1213–23.

[23] Budde MD, Kim JH, Liang HF, Russell JH, Cross AH, Song SK. Axonal injury detectedby in vivo diffusion tensor imaging correlates with neurological disability in amouse model of multiple sclerosis. NMR Biomed 2008;21:589–97.

[24] Kim JH, Budde MD, Liang HF, Klein RS, Russell JH, Cross AH, et al. Detecting axondamage in spinal cord from a mouse model of multiple sclerosis. Neurobiol Dis2006;21:626–32.

[25] Sun SW, Liang HF, Cross AH, Song SK. Evolving Wallerian degeneration aftertransient retinal ischemia in mice characterized by diffusion tensor imaging.Neuroimage 2008;40:1–10.

[26] Budde MD, Kim JH, Liang HF, Schmidt RE, Russell JH, Cross AH, et al. Towardaccurate diagnosis of white matter pathology using diffusion tensor imaging.Magn Reson Med 2007;57:688–95.

[27] DeBoy CA, Zhang J, Dike S, Shats I, JonesM, Reich DS, et al. High resolution diffusiontensor imaging of axonal damage in focal inflammatory and demyelinating lesionsin rat spinal cord. Brain 2007;130:2199–210.

[28] Wu Q, Butzkueven H, Gresle M, Kirchhoff F, Friedhuber A, Yang Q, et al. MRdiffusion changes correlate with ultra-structurally defined axonal degeneration inmurine optic nerve. Neuroimage 2007;37:1138–47.

[29] Xu J, Sun SW, Naismith RT, Snyder AZ, Cross AH, Song SK. Assessing optic nervepathologywithdiffusionMRI: frommouse to human.NMRBiomed 2008;21:928–40.

[30] Roosendaal SD, Geurts JJ, Vrenken H, Hulst HE, Cover KS, Castelijns JA, et al. RegionalDTI differences in multiple sclerosis patients. Neuroimage 2009;44:1397–403.

[31] Kolbe S, Chapman C, Nguyen T, Bajraszewski C, Johnston L, Kean M, et al. Opticnerve diffusion changes and atrophy jointly predict visual dysfunction after opticneuritis. Neuroimage 2009.

[32] Bastin ME, Clayden JD, Pattie A, Gerrish IF, Wardlaw JM, Deary IJ. Diffusion tensorand magnetization transfer MRI measurements of periventricular white matterhyperintensities in old age. Neurobiol Aging 2009;30:125–36.

[33] Vernooij MW, de Groot M, van der Lugt A, Ikram MA, Krestin GP, Hofman A, et al.White matter atrophy and lesion formation explain the loss of structural integrityof white matter in aging. Neuroimage 2008;43:470–7.

[34] Fjell AM, Westlye LT, Greve DN, Fischl B, Benner T, van der Kouwe AJ, et al. Therelationship between diffusion tensor imaging and volumetry as measures of whitematter properties. Neuroimage 2008;42:1654–68.