Characterization of White Matter Fiber Bundles With T*2Relaxometry and Diffusion Tensor Imaging

Andrea Cherubini,1,2 Patrice Peran,2,3 Gisela Elisabeth Hagberg,4 Ambra Erika Varsi,1

Giacomo Luccichenti,2 Carlo Caltagirone,1,5 Umberto Sabatini,2 andGianfranco Spalletta1

In this study, diffusion tensor imaging (DTI) and T*2 multiechorelaxometry were combined in 30 healthy subjects at 3T, withthe aim of characterizing the spatial distribution of relaxationrates in white matter (WM). Region of interest (ROI) analysiswas performed in 23 different fiber tracts automatically definedin standard space. Spearman rank analysis was performed onregional values of T*2, fractional anisotropy (FA), and radial dif-fusivity (RD). A strong relationship was observed between thelocation and direction of fiber bundles and relaxation rates, andadjacent fiber bundles with similar orientation showed verydifferent relaxation rates. Moreover, while relaxation rates var-ied largely between different fiber tracts, variation of the sameparameter within the same anatomical fiber bundle across in-dividuals was remarkably limited. The rich variability of relax-ation rates in WM and their complex relationship with DTI datasuggested that the two techniques might be sensitive to com-plementary characteristics of myelin structure. This has tre-mendous potential to allow for a more detailed understandingof brain development and pathology, in particular in the contextof age-related cognitive decline. Magn Reson Med 61:1066–1072, 2009. © 2009 Wiley-Liss, Inc.

Key words: DTI; FA; iron; susceptibility; orientation

Many developmental, aging, and pathologic processes ofthe brain affect the microstructural composition and archi-tecture of cerebral white matter (WM). Characterizingthese structure variations in vivo is essential to our under-standing of brain function across the lifespan.

Magnetic resonance imaging (MRI) provides methods toinvestigate in vivo WM microstructure variations inducedby pathology or aging (1). MRI techniques, such as diffu-sion tensor imaging (DTI) (2,3), magnetization transfer im-aging (4), magnetic resonance spectroscopy (5), and T2-relaxometry (6,7), can explore the characteristics that dif-ferentiate WM in various regions of the brain, or highlightthe effects of pathological processes affecting WM duringthe earlier stages of disease.

In particular, in recent years DTI has rapidly imposeditself as a highly sensitive method able to detect subtlestructural variations in WM. In this context the mostwidely used scalar parameter derived from DTI is repre-sented by fractional anisotropy (FA), as originally definedby Pierpaoli and Basser (8). However, since it is becomingincreasingly clear that DTI is sensitive to multiple factorsrelated to axonal integrity and to the organization andalignment of groups of fibers in WM (2,3), several recentstudies have suggested that a combination of the diffusiontensor eigenvalues, such as radial diffusivity (RD), couldhave a more direct relationship to myelin structure or tofiber density (2,3,9).

The limited specificity of current methods in character-izing WM microstructure can prevent the formation of anyconclusions about underlying biological events, especiallywhen a single contrast mechanism is available. This isparticularly true when MRI is used to detect structuralalterations in WM induced by neurodegenerative patholo-gies such as Parkinson’s or Alzheimer’s disease.

Improvements in MRI technology and image analysismethods have brought about novel interest in T*2-weightedimaging, a technique often troubled by artifacts and tech-nical limitations. T*2-weighted imaging has recently beenused for brain venography (10,11) and for mapping areas ofthe brain with high iron content (12–14). Most recently, Liet al. (15) have applied this methodology to the investiga-tion of WM, observing a large degree of heterogeneity insignal intensity of T*2 images at 7T. Even if the exact originof this heterogeneity remains uncertain, the contrast in MRsignal observed by these authors could be exploited toprecisely characterize structural differences between fiberbundles, in normal and pathological brains.

The relationship between T*2 and underlying biologicalstructure could potentially be clarified by using a secondmethod to investigate WM, combining the sensitivity ofMRI techniques to different physical or chemical proper-ties of tissue in order to enhance our understanding of therelationship between MR images and WM characteristics.

The main aim of this study was to investigate the poten-tial spatial variations in T*2 signal in cerebral WM. To thisaim, we combined DTI and T*2 relaxometry in healthysubjects. We used DTI for two purposes. First, DTI scalarparameters (FA and RD) were derived to define the localmicrostructure of WM anatomy. Second, the orientationinformation was used to define various intra-WM struc-tures. An automated region of interest (ROI) approach wasused to characterize the spatial distribution of relaxationrates in different fiber bundles.

1Department of Clinical and Behavioral Neurology, Santa Lucia Foundation,Rome, Italy.2Department of Radiology, Santa Lucia Foundation, Rome, Italy.3Institut National de la Sante et de la Recherche Medicale (INSERM) U825,Toulouse, France.4Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy.5Department of Neuroscience, University of Tor Vergata, Rome, Italy.*Correspondence to: Andrea Cherubini, Department of Radiology, Santa Lu-cia Foundation, Via Ardeatina, 306, 00179, Rome, Italy. E-mail:a.cherubini@hsantalucia.itReceived 19 September 2008; revised 11 December 2008; accepted 6 Jan-uary 2009.DOI 10.1002/mrm.21978Published online in Wiley InterScience (www.interscience.wiley.com).

Magnetic Resonance in Medicine 61:1066–1072 (2009)

© 2009 Wiley-Liss, Inc. 1066

MATERIALS AND METHODS

Thirty healthy subjects (17 women, 13 men; mean age �standard deviation [SD] � 37.4 � 14.0 years) providedinformed written consent and participated in this study,which was approved by the local ethics committee. Allsubjects described themselves as right-handed. None ofthe subjects had a history of head injury or stroke, nor ofany neurological or psychiatric disease. An expert radiol-ogist examined all MRIs to exclude potential brain abnor-malities. These volunteers were examined using a 3T Al-legra MR Imager (Siemens Medical Solutions, Erlangen,Germany) with a standard quadrature head coil.

Acquisition

Participants underwent the same MRI protocol, includingwhole-brain T*2-weighted, T1-weighted, and DTI scanning.All planar sequence acquisitions were obtained along aplane running through the anterior and posterior conni-sures. Particular care was taken to center the subject in thehead coil and to restrain the subject’s movements withcushions and adhesive medical tape. Six consecutive T*2-weighted gradient-echo whole-brain volumes were ac-quired using a segmented echo-planar imaging sequence atdifferent TEs: 6, 12, 20, 30, 45, and 60 ms (TR � 5000,bandwidth � 1116 Hz/voxel, matrix size � 128 � 128;axial slices � 80; flip angle � 90°; voxel size � 1.8 � 1.8 �1.8 mm3). Diffusion-weighted volumes were acquired us-ing spin-echo echo-planar imaging (TE/TR � 89 ms/8500 ms, bandwidth � 2126 Hz/voxel, matrix size � 128 �128, axial slices � 80, voxel size � 1.8 � 1.8 � 1.8 mm3)with 30 isotropically distributed orientations for the dif-fusion-sensitizing gradients at a b-value of 1000 s�mm2 andsix b � 0 images. To increase the signal-to-noise ratio,scanning was repeated three times. Since DTI and T*2 vol-umes both consisted of 128 � 128 � 80 identical isotropicvoxels, the slice positioning and orientation of diffusion-weighted volumes were set to be identical with the T*2volumes in order to improve subsequent coregistration.Finally, whole-brain T1-weighted images were obtained inthe sagittal plane using a modified driven equilibriumFourier transform (MDEFT) (16) sequence (TE/TR �2.4 ms/7.92 ms, flip angle � 15°, voxel size � 1 � 1 �1 mm3).

Postprocessing

All image processing was performed using FSL 4.0 (17)and in-house developed software in Matlab (version 6.5;The MathWorks, Natick, MA, USA). Image distortions in-duced by eddy currents and head motion in the DTI datawere corrected applying a three-dimensional (3D) full af-fine (mutual information cost function) alignment of eachimage to the mean of the image with no diffusion weight-ing (i.e., the b0 image). After distortion corrections, the DTIdata were averaged and concatenated into 31 volumes (1 b0

and 30 b1000) volumes. A diffusion tensor model was fit ateach voxel, generating FA and RD maps. In this work, RDis defined as the average of the second and third eigenval-ues of the diffusion tensor (9).

The six T*2-weighted volumes were averaged and thecalculated mean T*2-weighted volume was realigned to the

mean b0 image. A full-affine 3D alignment was calculatedbetween each of the six T*2-weighted volumes and themean T*2-weighted volume. This latter transformation wascombined with the former and applied to each of the sixT*2-weighted volumes. As a result of this processing, T*2-weighted volumes were corrected for head movements,and shared an identical reference space with the DTI datasets.

For each subject we performed a voxel-by-voxel nonlin-ear least-squares fitting of the data acquired at the six TEsto obtain a monoexponential signal decay curve (S � S0e–

t/T2*). In order to facilitate analysis of relaxation results, weconsidered the inverse of relaxation times, i.e., relaxationrates R*2 � 1/T*2, in this work.

Voxel Classification and Selection

The FA was chosen as an index able to differentiate WMvoxels from non-WM tissue. In all the subsequent analyseswe considered the arbitrary, but conservative, value ofFA � 0.3 to identify the WM for each subject (3,18).

ROI selection was performed automatically using theWM labeling in standard space (JHU ICBM DTI 81 Atlas;http://www.mristudio.org) of Mori et al. (19). The JHUAtlas contains WM tract labels created by hand segmenta-tion of a standard-space average of diffusion MRI tensormaps from 81 subjects. In order to transfer the JHU Atlaslabeling to each subject’s reference space, the FA map ofeach subject was normalized using a full-affine transfor-mation. As a reference, we chose the JHU 81 Atlas meanFA volume contained in the FSL software distribution.The calculated transformation matrix was subsequentlyinverted in order to map the JHU Atlas to individual space.Finally, binary masks consisting of only those voxels withFA � 0.3 were created for each subject for each of theconsidered fiber tracts. (Fig. 1c shows examples of cross-sections of these binary masks). The normalization matrixwas also applied to individual R*2 maps, considering onlyvoxels with FA �0.3. These maps were subsequently av-eraged between subjects. In order to facilitate anatomicalidentification, the average normalized R*2 map was super-imposed to a Montreal Neurological Institute (MNI) stan-dard template.

Statistical Analysis

A two-dimensional (2D) distribution of R*2 vs. FA wascalculated considering only brain extracted tissue (BET)for each subject. Mean R*2 values across subjects werecalculated averaging only WM voxels.

For each subject and each ROI, we calculated the aver-age values of R*2, FA, and RD using the binary masksobtained as described above. Mean R*2 values were com-pared using a repeated measures multivariate analysis ofvariance (MANOVA), with a three-factor design that in-cluded two between-ROI factors—1) anatomical region(i.e., inferior corona radiata, superior longitudinal fascic-ulus, etc.) and 2) lateralization (left and right hemi-sphere)—and 3) a between subject factor (gender).

Finally, for each ROI and each subject we performed anonparametric correlation (Spearman rank correlation) be-tween R*2 and FA to test the dependence of these parame-

Characterization of WM With T*2 and DTI 1067

ters within the same tract. The same analysis was per-formed for R*2 vs. RD. A level of significance of P � 0.05was used throughout the study.

RESULTS

By combining DTI and T*2-relaxometry, we explored therelaxation properties of WM. Within each voxel for eachsubject, we evaluated the R*2 value and two scalar param-eters derived from DTI, namely FA and RD (the average ofthe second and third eigenvalues). First, we considered FAas an index able to differentiate WM from gray matter.Figure 2 shows a 2D histogram representing the total num-ber of voxels with a given FA and relaxation rate on theentire subjects’ sample.

Observing the distribution of relaxation rates for differ-ent FA values, the range of variation of this parameter indifferent tissues can be appreciated. Non-WM voxels pre-sented a large degree of variation in R*2, with over 90% ofthe values ranging from 10 s–1 to 24 s–1. This finding isconsistent with previous studies where whole brain R*2values were evaluated, with iron-containing tissues pre-senting very high relaxation rates (13).

However, even WM showed a substantial degree of vari-ation, with over 90% of WM voxels ranging from 15 s–1 to22 s–1. This large range of variation was unexpected for atissue such as healthy WM, which did not show a similarinternal signal variation for other conventional MR param-eters such as T1, T2, or proton density (Fig. 1a and d).

Next, we investigated the spatial distribution of R*2 val-ues within the WM. An evident pattern emerges from theaverage R*2 map. In fact, voxels with high relaxation rateWM do not appear spatially scattered throughout the

brain; instead, they concentrate within a limited numberof major fiber bundles, as is evident in Fig. 3.

In order to better characterize the heterogeneity of R*2 inWM, we combined the directional information extrapo-

FIG. 1. Different image contrasts depicted for the same slice in atypical subject: (a) R*2 map; (b) DTI colored image; (c) cross-sectionsof fiber tracts superimposed on FA map; and (d) T1-weighted image.The heterogeneity of relaxation rates in white matter can be appre-ciated by comparing the R*2 map with the relative homogeneity of thesame tissue in the T1-weighted image.

FIG. 2. 2D histogram of R*2 and FA voxels distribution on 30 healthysubjects. FA values are used to distinguish WM voxels from graymatter and cerebrospinal fluid; R*2 � 1/T*2 measure the relaxation rate inthe same voxels. The color is proportional to the fraction of voxel witha given FA and R*2. Considering the conservative threshold FA � 0.3 asWM, 90% of voxels have an R*2 value in the range between 15 s–1 and22 s–1, with a mean value of 19.3 s–1. The Spearman correlationcalculated between R*2 and FA in WM voxels was not significant.

FIG. 3. Average map of R*2 in WM superimposed on the MNI tem-plate; from the R*2 colorimetric scale, it can be appreciated how evenadjacent fiber bundles can have very different relaxation rates. Thecolor image was obtained by averaging R*2 values corresponding toFA � 0.3 on single subjects.

1068 Cherubini et al.

lated from the diffusion tensor with the relaxation rates ofR*2 maps, and further, we used a ROI approach on individ-ual brains.

We chose to graphically visualize the relationship be-tween relaxation rates and directional information with acolored scatter plot (Fig. 4) of WM voxels. The local fiberdirection can be visualized using the red-green-blue colorscheme conventionally used for DTI images, where redrepresents the right–left, green the anterior–posterior, andblue the superior–inferior orientations.

Observing Fig. 4, a remarkable clustering of differentrelaxation rates related to the local fiber directionality isevidently present. In particular, it appears that many fiberbundles with higher relaxation rates run along the anteri-or-posterior direction, while tracts with lower relaxationrates run along the superior-inferior direction.

To further explore this phenomenon and its relationshipwith the relaxation rate in different tracts, we employed aROI-based approach, by segmenting the regions corre-sponding to major fiber bundles as defined in the JHUAtlas (19) for each subject (Fig. 1c). We chose to excludethe tracts that could be affected by susceptibility artifacts(located in the brainstem) or by partial volume effects(tracts with smaller size) from this analysis. For each sub-ject and each ROI, we extracted the mean values for R*2 andtwo DTI-derived scalar parameters: FA and RD.

The MANOVA analysis showed a significant effect ofanatomical regions for R*2 mean values, demonstrating theheterogeneity of relaxation rates in different fibers. Thisvariation of relaxation rates can be appreciated observing

data reported in Table 1. We observed no significant effectsof gender on R*2, while a significant lateralization waspresent, with higher relaxation rates observed in fiber bun-dles in the right hemisphere.

In Table 1 we denoted with Subj-SD the SD of meanvalues for a given ROI (i.e., std[mean_subj1, mean_subj2,. . .]), and with ROI-SD the mean value of SDs for a givenROI (i.e., mean[std_subj1, std_subj2, . . .]). Subj-SD shouldbe indicative of interindividual variability, while ROI-SDshould be proportional to the variability of the parameterwithin the ROI considered. Although the method used forthe ROI definition could be prone to partial volume errors,the interindividual variability of relaxation rates, whichone would expect to be inflated if partial volume artifactswere driving this effect, appears remarkably limited, espe-cially if compared to the large range of variation of R*2 indifferent tracts.

We proceeded to characterize the correlation betweenrelaxation rates and DTI parameters (FA and RD) for eachof the different ROIs in each subject. In Table 1 we reportthe average value of the Spearman Rho-value for a giventract across subjects, and the percentage of subjects thatreached significance (P � 0.05) in the same tract.

Spearman correlation analysis including all WM voxelsin the entire subjects’ sample showed no significant corre-lation between R*2 and FA, nor between R*2 and RD. On theother hand, considering individual tracts, we observed aconsistent trend represented by a positive correlation be-tween R*2 and FA, accompanied by a negative correlationbetween R*2 and RD in many tracts.

FIG. 4. Scatter plot of R*2 vs. FA for WM voxels in 30 healthy subjects, with each point colored according to the standard DTI direction/colorconvention. Subplots (a–c) show the same data divided according to principal direction. It is evident that many voxels containing fiber withanteroposterior direction (green) are clustered in the region with high relaxation rates (R*2 � 19.3 s–1), while fiber with inferior-superiordirection (blue) are clustered in the region of the graph with low relaxation rates.

Characterization of WM With T*2 and DTI 1069

DISCUSSION

The novel combination of DTI and T*2 relaxometry in WMshowed that relaxation rates have a large range of variationand a well-defined spatial pattern in healthy subjects. Inparticular, we observed that a striking relationship existsbetween the location and direction of fiber bundles andrelaxation rates, and that even adjacent fiber bundles canhave very different relaxation rates. Moreover, while re-laxation rates vary largely between different fiber tracts,variation of the same parameter within the same anatom-ical fiber bundle across individuals is remarkably limited.

In this study, we used DTI for two purposes. First, DTIscalar parameters (FA and RD) were derived to define localmicrostructure of WM anatomy. Second, the orientationinformation was used to define various intra-WM struc-tures and to examine the potential relationship betweenrelaxation rates and the orientation of WM fibers.

Relaxation Rates and Orientation of Fibers

The combination of directional information derived fromDTI data with relaxation rates (Fig. 4) seems to suggest thatthe local orientation of fiber is linked to the R*2 value. Thisresult could be due to a structural characteristic shared byfibers with identical orientation, or could be an effect ofthe fiber orientation relative to the main magnetic field ofthe scanner. In fact, R*2 relaxation rates are influenced bythe effective local magnetic field. which, in heterogeneousand anisotropic structures such as those present in livingsystems, can depend on the orientation with respect to themain field of the MRI scanner (20–22). According to thishypothesis, fibers that are aligned perpendicular to themain field would generate a larger magnetic field gradientif compared to fibers parallel to it, which in turn wouldresult in larger relaxation rates. This would explain why

Table 1Regional Mean Values of R*2, FA, and RD in the 23 Fiber Tracts Considered in this Work‡

FascicleR*2 [s–1] FA RD [10–3 mm2 s–1] R*2 vs. FA R*2 vs. RD

Mean Subj-SD ROI-SD Mean Subj-SD ROI-SD Mean Subj-SD ROI-SD Rho Fraction Rho Fraction

Projection fibersAnterior corona radiata L 19.04 0.95 1.87 0.47 0.04 0.10 0.49 0.05 0.08 0.15 0.73 –0.20 0.70Anterior corona radiata R 19.13 0.85 1.86 0.46 0.04 0.09 0.51 0.05 0.07 0.14 0.63 –0.16 0.70Superior corona radiata L 17.48 0.79 1.33 0.46 0.02 0.09 0.49 0.02 0.06 –0.18 0.57 0.07 0.63Superior corona radiata R 17.55 0.96 1.32 0.46 0.02 0.09 0.49 0.02 0.06 –0.20 0.60 0.03 0.60Posterior corona radiata L 17.67 1.16 1.45 0.46 0.03 0.10 0.53 0.04 0.08 0.12 0.57 –0.14 0.67Posterior corona radiata R 17.93 1.13 1.54 0.48 0.05 0.11 0.51 0.06 0.10 0.20 0.60 –0.21 0.63Anterior limb of internal

capsule L 20.37 0.57 1.84 0.59 0.02 0.13 0.41 0.02 0.10 –0.07 0.57 0.08 0.53Anterior limb of internal

capsule R 20.24 0.66 1.86 0.59 0.02 0.13 0.41 0.02 0.10 –0.14 0.60 0.15 0.63Posterior limb of internal

capsule L 18.72 0.90 2.01 0.60 0.03 0.12 0.41 0.03 0.08 –0.18 0.77 0.13 0.73Posterior limb of internal

capsule R 18.75 0.86 1.94 0.60 0.03 0.12 0.42 0.02 0.09 –0.21 0.77 0.19 0.80†Retrolenticular part of

internal capsule L 20.18 0.80 1.78 0.55 0.03 0.09 0.49 0.03 0.08 0.14 0.60 –0.13 0.60Retrolenticular part of

internal capsule R 20.50 0.82 1.74 0.54 0.05 0.10 0.49 0.05 0.10 0.13 0.67 –0.12 0.60Posterior thalamic

radiation L 19.60 1.00 1.86 0.55 0.04 0.11 0.50 0.05 0.12 0.37 0.93† –0.31 0.90†Posterior thalamic

radiation R 19.59 1.11 1.87 0.56 0.05 0.12 0.48 0.06 0.13 0.30 0.87† –0.26 0.90†Association fibers

Superior longitudinalfasciculus L 19.28 0.63 1.88 0.48 0.03 0.10 0.49 0.03 0.07 0.06 0.50 –0.04 0.50

Superior longitudinalfasciculus R 19.45 0.66 1.93 0.50 0.03 0.11 0.48 0.04 0.09 0.02 0.57 –0.07 0.57

Sagittal stratum L 20.44 1.01 1.73 0.53 0.04 0.10 0.53 0.05 0.11 0.21 0.90† –0.25 0.83†Sagittal stratum R 20.35 1.01 1.79 0.53 0.03 0.11 0.52 0.05 0.11 0.27 0.87† –0.25 0.80†Cingulum gyrus L 19.14 0.88 1.76 0.57 0.04 0.14 0.45 0.04 0.14 0.40 0.90† –0.46 0.90†Cingulum gyrus R 19.38 0.75 1.74 0.55 0.05 0.14 0.45 0.05 0.14 0.35 0.90† –0.38 0.87†

Commissural fibersGenu of corpus callosum 19.36 0.93 1.42 0.64 0.03 0.18 0.42 0.04 0.21 0.14 0.83† –0.12 0.90†Body of corpus callosum 18.07 0.86 1.72 0.64 0.04 0.17 0.43 0.06 0.22 0.19 0.93† –0.19 0.97†Splenium of corpus

callosum 19.51 1.08 1.85 0.71 0.05 0.19 0.36 0.06 0.24 0.28 0.90† –0.28 0.93†

‡For any given tract, the standard deviation (SD) of mean values is denoted Subj-SD, and the mean value of SDs is denoted ROI-SD, whichcan be considered as the estimate of interindividual variability and intratract variability, respectively. The four rightmost columns show theresults of Spearman correlation analysis. For each tract, we report the average Rho value and the fraction of subjects that reachedsignificance (P � 0.05) in the same tract († indicates significance for at least 80% of subjects).

1070 Cherubini et al.

“blue” fibers in Fig. 4 have on average smaller R*2 values ifcompared to “red” and “green” fibers.

However, it is evident from Fig. 3 that fibers in spatialproximity and sharing identical orientation such as thosein the corpus callosum also show a wide range of relax-ation rates. Moreover, fibers that appear to have similarrelaxation rates can be aligned along different orientations.Thus, it is possible that this “angle effect” represents justone of many mechanisms that contribute to the determi-nation of the local relaxation rate in WM tracts. Since thisorientation effect is expected to increase with the magneticfield, it is possible that its relative contribution to thecontrast may be more relevant at higher fields.

Correlation Between R*2 and DTI Scalars

Spearman rank analyses performed on global WM in theentire sample showed no significant correlation betweenR*2 and DTI scalar parameters. On the other hand, in se-lected tracts we observed a consistent trend of correlation,although with small Spearman Rho values. In the posteriorthalamic radiation, sagittal stratum, cingulum gyrus, andcorpus callosum we observed a positive correlation be-tween R*2 and FA accompanied by a negative correlationbetween R*2 and RD. This result seems to suggest that evenif R*2 and DTI methodologies are generally sensitive todifferent structural characteristics of WM tissue, selectedregions exist where R*2 is influenced either directly orindirectly by some of the physical properties usually as-sociated with DTI scalars. Previous works (23,24) correlat-ing FA with T2 values in specific WM tracts have high-lighted characteristic signatures of individual tracts. Inparticular, it has been shown that the corticospinal tracthas relatively long T2 values, while the splenium of thecorpus callosum is characterized by high T2 and FA val-ues. These observations are in agreement with our T*2/DTImeasurements (Table 1).

R*2 is potentially sensitive to many factors that introducelocal inhomogeneity of the magnetic field. In addition tothe “angle effect” mentioned above, other potential mech-anisms include iron content (12), myelin content andstructure (7), and density and orientation of WM vascula-ture (10). In particular, previous studies observed that ahigh local myelin content can produce a signal drop in T2

and T*2 relaxometry (25,26), in particular in heavily my-elinated fibers, such as cingulum, splenium, and opticalradiations. This pattern of differential myelination wouldpartially fit with our observed distribution of high relax-ation rates.

The difference in relaxation rates that we observed be-tween splenium and body in the corpus callosum couldsuggest that the caliber of fibers represents another possi-ble factor influencing R*2, with high relaxation rates corre-sponding to regions with smaller calibers (27). In thishypothesis, R*2 and DTI scalars could possibly be corre-lated because complementary characteristics of myelinstructure in these regions influence the diffusivity andrelaxation rates of water molecules. In fact, it has beensuggested that RD represents an index of local fiber density(2,3,9), and it can be expected to be large both in heavilymyelinated fibers and in densely packed, small caliberfibers.

It is probable that R*2 in WM is determined by a combi-nation of the mechanisms mentioned above; on the otherhand, additional mechanisms such as iron concentration(14) or microvasculature orientation could be involved.Thus, further experiments are needed to disentangle thedifferent contributions to relaxation rates.

Technical Limitations

Previous studies have showed that quantitative measure-ments of T*2 can be troubled by a number of technicalproblems (12,28). In this study, we chose a simple ap-proach where the T*2 dataset was fitted with an exponentialdecay, without further corrections. Although the relax-ation rates obtained without corrections can be affected bylarge-scale field inhomogeneities, we have previouslyshown (13) that the intrasubject variability of R*2 values forscan-rescan measurements is below 5% in our setting.Consequently, we assume this latter value as an acceptableestimate of the error in our measurements of relaxationrates without any correction for large-scale field inhomo-geneities.

It has been shown (29) that the complex structure ofmyelin gives rise to multiexponential decays in relaxationstudies. For example, the myelin-associated water repre-sents a separate relaxation compartment. However, previ-ous studies have shown that these additional compart-ments are characterized by short relaxation times (30).Thus, although the contribution of myelin water is cer-tainly present in our data, it is unlikely that its contribu-tion is predominant with the range of echo times used inthis study.

In order to further characterize the anatomical distribu-tion pattern of relaxation rates, we performed an ROI anal-ysis that mapped individual fiber bundles on each subject.To our knowledge, this is the first study that performedsuch a detailed characterization of relaxometry in differentfiber bundles. A strong limitation of our template-drivenapproach is represented by the erroneous identification ofthe same fiber tract on different subjects during the auto-matic normalization step. In fact, although rapid and com-pletely automatic, this approach can cause partial volumeeffects in the selected ROIs. In order to minimize theseerrors, images with the fiber bundles outlined for eachsubject were visually examined by an expert radiologist toexclude gross misregistrations (Fig. 1c). Moreover, the bi-nary masks in each subject comprised only voxels whereFA � 0.3, with high probability of excluding non-WMtissues. The low interindividual variability of relaxationrates (Table 1), which one would expect to be inflated ifpartial volume artifacts were frequent, can at least in partbe considered a result of the accuracy in the segmentationof fiber tracts.

CONCLUSIONS

The combination of DTI and T*2 Relaxometry showed thatrelaxation rates are influenced by the location and spatialorientation of fiber bundles. The rich variability of relax-ation rates in WM and their complex relationship with DTIdata suggest that the two techniques might be sensitive tocomplementary characteristics of myelin structure. The

Characterization of WM With T*2 and DTI 1071

combined use of these measurement parameters may havetremendous potential to allow for a more detailed under-standing of brain development and pathology, in particu-lar in the context of age-related cognitive decline (6). Fur-ther experiments are needed to establish to what extent theheterogeneity of relaxation rates originate from myelincharacteristics and local orientation of fiber tracts.

REFERENCES

1. Wozniak J, Lim K. Advances in white matter imaging: a review of invivo magnetic resonance methodologies and their applicability to thestudy of development and aging. Neurosc Biobehavior Rev 2006;30:762–774.

2. Mori S, Zhang J. Principles of diffusion tensor imaging and its appli-cations to basic neuroscience research. Neuron 2006;51:527–539.

3. Alexander A, Lee J, Lazar M, Field A. Diffusion tensor imaging of thebrain. Neurotherapeutics 2007;4:316–329.

4. Wolff S, Balaban R. Magnetization transfer imaging: practical aspectsand clinical applications. Radiology 1994;192:593–599.

5. Kuker W, Ruff J, Gaertner S, Mehnert F, Mader I, Nagele T. Modern MRItools for the characterization of acute demyelinating lesions: value ofchemical shift and diffusion-weighted imaging. Neuroradiology 2004;46:421–426.

6. Bartzokis G, Sultzer D, Lu P, Nuechterlein K, Mintz J, CummingsJ. Heterogeneous age-related breakdown of white matter structuralintegrity: implications for cortical “disconnection” in aging and Alz-heimer’s disease. Neurobiol Aging 2004;25:843–851.

7. MacKay A, Laule C, Vavasour I, Bjarnason T, Kolind S, Madler B.Insights into brain microstructure from the T2 distribution. Magn ResonImag 2006;24:515–525.

8. Pierpaoli C, Basser P. Toward a quantitative assessment of diffusionanisotropy. Magn Reson Med 1996;36:893–906.

9. Song S-K, Sun S-W, Ju W-K, Lin S-J, Cross A, Neufeld A. Diffusiontensor imaging detects and differentiates axon and myelin degenerationin mouse optic nerve after retinal ischemia. Neuroimage 2003;20:1714–1722.

10. Reichenbach J, Venkatesan R, Schillinger D, Kido D, Haacke E. Smallvessels in the human brain: MR venography with deoxyhemoglobin asan intrinsic contrast agent. Radiology 1997;204:272–277.

11. Brainovich V, Sabatini U, Hagberg G. Advantages of using multiple-echo image combination and asymmetric triangular phase masking inmagnetic resonance venography at 3T. Magn Reson Imaging 2009;27:23–37.

12. Haacke E, Cheng N, House M, Liu Q, Neelavalli J, Ogg R, Khan A, AyazM, Kirsch W, Obenaus A. Imaging iron stores in the brain using mag-netic resonance imaging. Magn Reson Imaging 2005;23:1–25.

13. Peran P, Hagberg G, Luccichenti G, Cherubini A, Brainovich V, CelsisP, Caltagirone C, Sabatini U. Voxel-based analysis of R*2 maps in thehealthy human brain. J Magn Reson Imaging 2007;26:1413–1420.

14. Bartzokis G, Tishler T, Lu P, Villablanca P, Altshuler L, Carter M,Huang D, Edwards N, Mintz J. Brain ferritin iron may influence age-and gender-related risks of neurodegeneration. Neurobiol Aging 2007;28:414–423.

15. Li T-Q, van Gelderen P, Merkle H, Talagala L, Koretsky A, Duyn J.Extensive heterogeneity in white matter intensity in high-resolutionT*2-weighted MRI of the human brain at 7.0 T. Neuroimage 2006;32:1032–1040.

16. Deichmann R, Schwarzbauerb C, Turner R. Optimisation of the 3DMDEFT sequence for anatomical brain imaging: technical implicationsat 1.5 and 3 T. Neuroimage 2004;21:757–767.

17. Smith S, Jenkinson M, Woolrich M, Beckmann C, Behrens T, Johansen-Berg H, Bannister P, De Luca M, Drobnjak I, Flitney D, Niazy R,Saunders J, Vickers J, Zhang Y, De Stefano N, Brady J, Matthews P.Advances in functional and structural MR image analysis and imple-mentation as FSL. Neuroimage 2004;23:S208–S219.

18. Liu T, Li H, Wong K, Tarokh A, Guo L, Wong S. Brain tissue segmen-tation based on DTI data. Neuroimage 2007;38:114–123.

19. Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, Hua K, Faria A,Mahmood A, Woods R, Toga A, Pike G, Neto P, Evans A, Zhang J,Huang H, Miller M, van Zijl P, Mazziotta J. Stereotaxic white matteratlas based on diffusion tensor imaging in an ICBM template. Neuro-image 2008;40:570–582.

20. Henkelman R, Stanisz G, Kim J, Bronskill M. Anisotropy of NMRproperties of tissues. Magn Reson Med 1994;32:592–601.

21. Chappell K, Robson M, Stonebridge-Foster A, Glover A, Allsop J, Wil-liams A, Herlihy A, Moss J, Gishen P, Bydder G. Magic angle effects inMR neurography. AJNR Am J Neuroradiol 2004;25:431–440.

22. Wolber J, Cherubini A, Leach M, Bifone A. Hyperpolarized 129Xe NMRas a probe for blood oxygenation. Magn Res Med 2000;43:491–496.

23. Stieltjes B, Kaufmann W, van Zijl P, Fredericksen K, Pearlson G, So-laiyappan M, Mori S. Diffusion tensor imaging and axonal tracking inthe human brainstem. Neuroimage 2001;14:723–735.

24. Wakana S, Caprihan A, Panzenboeck M, Fallon J, Perry M, Gollub R,Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S. Repro-ducibility of quantitative tractography methods applied to cerebralwhite matter. Neuroimage 2007;36:630–644.

25. Curnes J, Burger P, Djang W, Boyko O. MR imaging of compact whitematter pathways. AJNR Am J Neuroradiol 1988;9:1061–1068.

26. Hajnal J, De Coene B, Lewis P, Baudouin C, Cowan F, Pennock J, YoungI, Bydder G. High signal regions in normal white matter shown byheavily T2-weighted CSF nulled IR sequences. J Comput Assist Tomogr1992;16:506–513.

27. Aboitiz F, Scheibel A, Fisher R, Zaidel E. Fiber composition of thehuman corpus callosum. Brain Res 1992;598:143–153.

28. Dahnke H, Schaeffter T. Limits of detection of SPIO at 3.0 T using T2

relaxometry. Magn Reson Med 2005;53:1202–1206.29. Whittall K, MacKay A, Graeb D, Nugent R, Li D, Paty D. In vivo

measurements of T2 distributions and water contents in normal humanbrain. Magn Reson Med 1997;37:34–43.

30. Andrews T, Lancaster J, Dodd S, Contreras-Sesvold C, Fox P. Testingthe three-pool white matter model adapted for use with T2 relaxometry.Magn Reson Med 2005;54:449–454.

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