Imaging Cortical Association Tracts in the Human BrainUsing Diffusion-Tensor-Based Axonal TrackingSusumu Mori,1,6,7* Walter E. Kaufmann,2–6 Christos Davatzikos,1 Bram Stieltjes,5,7

Laura Amodei,5 Kim Fredericksen,1 Godfrey D. Pearlson,5 Elias R. Melhem,1

Meiyappan Solaiyappan,1 Gerald V. Raymond,3,6 Hugo W. Moser,3,4,6 andPeter C.M. van Zijl1,7

Diffusion-tensor fiber tracking was used to identify the cores ofseveral long-association fibers, including the anterior (ATR) andposterior (PTR) thalamic radiations, and the uncinate (UNC), su-perior longitudinal (SLF), inferior longitudinal (ILF), and inferiorfronto-occipital (IFO) fasciculi. Tracking results were compared toexisting anatomical knowledge, and showed good qualitativeagreement. Guidelines were developed to reproducibly trackthese fibers in vivo. The interindividual variability of these recon-structions was assessed in a common spatial reference frame(Talairach space) using probabilistic mapping. As a first illustrationof this technical capability, a reduction in brain connectivity in apatient with a childhood neurodegenerative disease (X-linked ad-renoleukodystrophy) was demonstrated. Magn Reson Med 47:215–223, 2002. © 2002 Wiley-Liss, Inc.

Key words: MRI; diffusion; tensor; tracts; association; axonal

Complex cognitive and behavioral processes that involvedifferent functional areas of the brain are mediated byneural networks. The ability to identify and characterizethe axonal fiber bundles comprising these networks isimportant for understanding normal as well as pathologi-cal processes affecting higher cerebral functions. Moststudies of axonal fiber tracts to date have relied on invasivein vivo techniques in animals or ex vivo postmortem hu-man tissue analyses (1,2). In addition, noninvasive radio-logical techniques, such as conventional MRI, have beenable to parcellate only small portions of the white matterinto specific tracts in restricted brain regions (3,4). Conse-quently, knowledge concerning these pathways in vivo isbased mainly on primate–human extrapolations (2). In thelast decade, diffusion imaging has been shown to be direc-

tionally dependent in the white matter (anisotropic diffu-sion) (5–7), which has been attributed to the organizationof axonal fibers and their myelin sheaths. The magnitudeand orientation of this anisotropy can be assessed usingdiffusion-tensor imaging (DTI) (8–11), which has shownthat brain regions with a high density of axonal fibers (e.g.,deep white matter) have a high anisotropy. Using DTI andnewly developed data processing (tract-tracing) tech-niques, we have recently shown the capability to performdelineation and 3D reconstruction of the cores of someaxonal fiber bundles in animals (12,13), resulting in MRimages that agree with standard anatomical data. Furtherapplication of this methodology to the study of healthyvolunteers has confirmed the feasibility of this emergingtechnique for human studies (14–19). However, severalcrucial steps are still necessary to allow a comprehensiveuse of this technique for the assessment of the anatomy ofhuman connectivity. These steps include the establish-ment of procedures to reconstruct reproducibly the fibertrajectories in different individuals, and the validation oftheir origin through comparison with existing neuroana-tomical knowledge. Although the latter validation canonly be qualitative for human studies, it remains essential.This work describes our first efforts towards this goal, inwhich we illustrate the capability of the 3D tracking tech-nique to identify the cores of several long-association fi-bers. The interindividual variability of these reconstruc-tions is subsequently assessed in a common spatial cere-bral reference frame (Talairach space) using probabilisticmapping. Fiber assignment in such a generalized referenceframe is a prerequisite for developing tracking protocols aswell as for establishing normative databases that can assistin the interpretation of pathological changes.

METHODS AND MATERIALS

MRI Data Acquisition

All studies were performed using a 1.5 T Philips GyroscanNT system. Diffusion-weighted imaging was accomplishedusing multislice, segmented echo-planar imaging (EPI)with cardiac triggering and navigator echo phase correc-tion (motion correction). A data matrix of 128 � 95 over afield of view (FOV) of 230 � 173 mm was obtained usingan acquisition of 15 echoes per excitation. Slice thicknesswas 3 mm (40–60 slices), with a TE of 92 ms and TR of fiveheartbeats. Diffusion weighting was performed along sixindependent axes, using a b-value of 600 s/mm2 at themaximum gradient strength of 21 mT/m. A reference im-

1Department of Radiology and Radiological Science, Johns Hopkins Univer-sity School of Medicine, Baltimore, Maryland.2Department of Pathology, Johns Hopkins University School of Medicine,Baltimore, Maryland.3Department of Neurology, Johns Hopkins University School of Medicine,Baltimore, Maryland.4Department of Pediatrics, Johns Hopkins University School of Medicine,Baltimore, Maryland.5Department of Psychiatry and Behavioral Sciences, Johns Hopkins Univer-sity School of Medicine, Baltimore, Maryland.6Kennedy Krieger Institute, Baltimore, Maryland.7F.M. Kirby Research Center for Functional Brain Imaging, Baltimore, Maryland.Grant sponsor: NIH; Grant numbers: AG016028; HD37931; HD24061;MH52886; Grant sponsor: NIH/NCRR; Grant number: RR15241; Grant spon-sors: Shapiro Foundation; Whitaker Foundation.*Correspondence to: Susumu Mori, Ph.D., Dept. of Radiology, Johns HopkinsUniversity School of Medicine, 217 Traylor Bldg., 720 Rutland Ave., Baltimore,MD 21205. E-mail: susumu@mri.jhu.eduReceived 2 August 2001; revised 12 October 2001; accepted 17 October 2001.

215© 2002 Wiley-Liss, Inc.DOI 10.1002/mrm.10074

Magnetic Resonance in Medicine 47:215–223 (2002)COMMUNICATIONS

age with low-diffusion weighting (b � 0.33 � 105 s/cm2)was also recorded. A single set of these seven measure-ment took 5–8 min. These measurements were repeatedsix times to increase the signal-to-noise ratio (SNR). Dou-ble-echo T2-weighted (TEs of 22 and 100 ms; image reso-lution equal to diffusion-tensor imaging (DTI)) and T1-

weighted imaging were also performed for anatomicalguidance, and the entire exam was completed within 1 hr.Recent studies have shown biexponential behavior of thesignal decay in the central nervous system (CNS) tissue,and the b-value of 600 s/mm2 used in this study predom-inantly samples the fast-diffusing component (20).

FIG. 1.

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Data ProcessingImages were first realigned using the Automated ImageRegistration (AIR) program to remove any potential smallbulk motions that occurred during the scans. Subse-

quently, all individual images were visually inspected todiscard slices with motion artifacts, after which the re-maining images were added for each slice. The pixel in-tensities of the multiple diffusion-weighted images were

FIG. 1. Continued.

Imaging Cortical Association Tracts in Brain 217

then fitted to obtain the six elements of the symmetricdiffusion tensor. The diffusion tensors at each pixel werediagonalized to obtain pixel eigenvalues and eigenvectors.An eigenvector (v1) associated with the largest eigenvalue(�1) was assumed to represent the local fiber direction. Foranisotropy mapping, the orientation-independent frac-tional anisotropy (FA) (21) was used.

Fiber Tracking

Tract tracking was performed using our previously-describedfiber assignment by continuous tracking (FACT) (12) method.Briefly, tracing was initiated from a seed pixel from which aline was propagated in both retrograde and orthograde direc-tions according to v1 at each pixel. The tracking was termi-nated when it reached a pixel with FA lower than a thresholdof 0.25, or when the inner product between the two eigen-vectors to be connected by the tracking was smaller than0.75. In order to reconstruct branching patterns, the trackingwas performed from every pixel inside the brain, but onlyfibers that penetrated regions of interest (ROIs) defined fromanatomical landmarks were retained (14). One or two ROIswere drawn for each tract of interest at anatomical landmarkswhere the tracts were discretely identified.

Statistical Maps

T1 images were first resliced parallel to the anterior commis-sure-posterior commissure (AC-PC) plane, in accordancewith the Talairach atlas. The same reslicing parameters weresubsequently applied to all the coregistered fiber images. The

T1 volumetric image of each subject was then elasticallytransformed according to the method described by Davatzi-kos (22), and was brought into registration with the Talairachatlas. The elastic warping method is based on mapping theouter (cortical) and inner (ventricular) brain boundaries of asubject to their counterparts in the atlas, and then interpolat-ing the transformation everywhere else by solving the elasticequations. The same elastic transformation was then appliedto the coregistered fiber images, placing them into the Ta-lairach space. We determined this transformation from the T1

images, since the fiber images do not have the appropriateanatomical detail. Images were obtained from 10 healthyvolunteers (five males and five females, 20–30 years old).Informed consent from all subjects was obtained in accor-dance with institutional guidelines.

RESULTS AND DISCUSSION

White Matter Tract Reconstruction and Comparison WithNeuroanatomy

Existing neuroanatomical knowledge (1,2,23) of the ap-proximate location of the association pathways was usedto define reference ROIs through which the tracts mustpass. The protocols we developed for reconstructing thecortical association fibers are summarized in Fig. 1. Be-cause these tracts do not always have clear anatomicallandmarks to discretely identify them, the success andgeneral applicability of the approach was optimized bychoosing reference ROIs that include large discernableanatomical units, such as the frontal, temporal, and occip-ital lobes. These anatomical landmarks and ROIs werelocated using T1-weighted images, except for the superiorlongitudinal fasciculus (SLF) (Fig. 1d), which could onlybe defined using a color-coded map. One drawback of thisanatomy-based, double-ROI reference approach is its in-ability to reconstruct branching between chosen pairs ofROIs; as a consequence, it is mainly suitable for tracts thatconnect two distant regions and do not branch out be-tween the two target areas. Therefore, only one ROI wasused for SLF, which is known to have extensive branchingin the frontal, parietal, and temporal lobes. A 3D represen-tation of the fibers that can be tracked is given in Fig. 2.

A qualitative comparison between the reconstruction re-sults of several major fiber bundles and a “white matter”anatomical preparation in Fig. 3a–c shows good spatialagreement. For more anatomical detail, slice-by-slice loca-tions of the tracking results are shown in Fig. 3d. It can beseen that cores of the reconstructed tracts penetrate knownanatomical landmarks. Both the reconstructed image andhistological preparation shown in Fig. 3b reveal a compre-hensive view of the superior longitudinal fasciculus (SLF(pink)), including extensive branching into the parietal lobes,C-shape projections around the Sylvian fissure, and projec-tions into the temporal lobe. The 2D slices in Fig. 3d alsoshow prominent projections to Broca’s area in the frontallobe. The inferior longitudinal fasciculus (ILF (blue)) behindthe SLF can also be appreciated. Figure 3c shows that thereconstructed inferior fronto-occipital fasciculus (IFO) anduncinate fasciculus (UNC) are in agreement with the post-mortem picture. For the identification of the IFO, the tworeference ROIs used included a large part of the entire frontaland occipital lobes. As can be seen in Fig. 1e and f, and also

FIG. 1. Guidelines for tracking the cortical association fibers. a: Thelocations of reference ROIs necessary to track particular fibers onfour coronal slices (A1–A4). Location A1 is at the slice level wherethe frontal and temporal lobes are separated. Location A2 is wherethe anterior part of substantia nigra first appears. Location A3 is atthe posterior tip of the putamen. Location A4 is at the parieto-occipital sulcus, and is identified at the middle of the coronal slicealong the superior-inferior axis. b–f: Locations of the reference ROIsfor each tract are shown together with reconstruction results. Thefirst and second columns show the coronal ROI locations. The thirdand fourth columns show the ROI locations and reconstructionresults projected onto the 3D axial (both superior and inferior views)and sagittal planes, respectively. b: For anterior thalamic radiation(ATR), which connects the frontal lobe and thalamus, one ROIdefines the frontal lobe (slice level A1) and the other the anterior partof thalamus at slice level A2. c: For the posterior thalamic radiation,which connects the occipital lobe and thalamus (includes opticradiation), one ROI defines the occipital lobe (slice level A3) and theother the posterior part of thalamus at one slice posterior to slicelevel A3. d: To visualize the superior longitudinal fascicules (SLF),one reference ROI was used in slice level A3, which can be dis-cretely identified using a DTI-based color map. e: For the inferiorlongitudinal fasciculus (ILF), which is known to connect the temporallobe and occipital lobe, one ROI defines the occipital lobe (slice levelA4) and the other the mid-temporal lobe (slice level A3). This two-ROI combination tracks both ILF and inferior fronto-occipital fascic-uli (IFO). The tracking results that fulfill criteria (ROIs) for the IFO arelater removed. f: For the IFO, which is known to connect the frontaland occipital lobes, one ROI defines the frontal lobe (slice level A1)and the other the occipital lobe (slice level A4). g: To define theuncinate fascicules (UNC), which is known to connect the frontallobe and temporal pole, two ROIs are necessary in slice level A1.

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in Fig. 3d, the ILF and IFO share many picture elements(pixels) in the occipital lobe. In the mid-temporal lobe, theIFO occupies the medial part of the ILF. Because of thisproximity of the two tracts, the two reference ROI sets usedfor the ILF lead to partial inclusion of the IFO, which couldonly be removed by rejecting tracking results that fulfill thecriteria of the IFO. Based on neuroanatomical literature(1,23,24) we expected the identification of two major fascic-uli, namely the inferior and superior fronto-occipital fascic-uli (SFO), which connect the two large ROIs specified in Fig.

1f. However, the SFO could not be identified in any of the10 volunteers. Thus, while some previous reports indicatedthat a SFO should be located at the lateral horn of the caudatenucleus, and despite the fact that a discrete tract can indeedbe identified in slices in the 2D DTI color maps at the corre-sponding area (see Fig. 3d, indicated by an asterisk), the 3Dconnection analysis could not identify the fronto-occipitalcortico-cortical connection in this region. These findingsseem to support recent reports on the SFO, suggesting that itmay not exist as a long-association tract (23,25).

Reconstruction in a Common Reference Frame

An important prerequisite for the clinical application oftract-tracking is the ability to obtain anatomical maps ofnormal white matter fibers in a general reference frame, andto quantify their anatomical and functional properties. Oneapproach to analyze the reliability of a tracking method, andto determine normal and pathological variations in patternsof these reconstructed axonal tracts and their associationwith different cortical areas, is the generation of statisticalmaps for each tract system. To achieve this, we standardizedindividual brains into the Talairach coordinate brain refer-ence frame using elastic warping (22), and then calculatedthe probability of having a particular tract system delineatedas explained above for each pixel for a population of10 healthy volunteers (Fig. 4a). The results of this approachare shown for the SLF, ILF, and IFO in Fig. 4b. In theseprobabilistic maps, each pixel reflects the percentage of nor-mal subjects that contained a particular tract. This point canbe seen more clearly in Fig. 4c, in which representative 2Dslices of the probabilistic map are shown. In this figure, theprobability of having the SLF in a certain pixel, as based on

FIG. 2. 3D reconstruction of the major cortical association fibers. a:Six association fibers are simultaneously presented in a tri-planerview to illustrate spatial relationships between the tracts. b: SLF andILF are removed to allow a more medial view of the tracts. Recon-structed tracts: anterior thalamic radiation (ATR, red); posterior tha-lamic radiation (including the optic radiation) (PTR, light blue); su-perior longitudinal fasciculus (SLF, pink); inferior longitudinal fascic-ulus (IFL, blue); inferior fronto-occipital fasciculus (IFO, green); anduncinate fasciculus (UNC, yellow).

FIG. 3. Qualitative neuroanatomical validationof fiber tracking using (a–c) postmortem data(35) and (d) comparison with white matter ana-tomical images. The anatomical preparation ina reveals the anterior and posterior thalamicradiations, and our tracking results of thesetracts are superimposed on a sagittal image atthe similar slice level. Similarly, b shows SLFand ILF and c shows IFO and UNC. Small ar-rowheads indicate approximate locations oftracts of interest in the anatomical prepara-tions. d: DTI-based color map (left column) andresults of 3D fiber tracking are shown in 2Drepresentative slices (right column). For ana-tomical guidance, the tracking results are su-perimposed on the left cerebral hemisphere ofanatomical images. In the right hemisphere, ap-proximate locations of tracts of interest are in-dicated according to anatomical knowledge.Asterisks indicate the location of the superiorfronto-occipital fasciculus postulated previ-ously.

Imaging Cortical Association Tracts in Brain 219

the normative data, is presented using a color shading scale.It can be seen that the core as well as some of the branches ofthe SLF in the frontal, parietal, and temporal lobes are welldefined, with high probability, indicating a high consistencyof the tracking results in these regions among normal sub-jects. Advances in warping techniques should improve thequality of the normalization process and may lead to proba-bilistic maps showing less variability for specific white mat-ter tracts in normal controls.

As an example of how new insight into the neuropathol-ogy of patients with white matter diseases could be obtained

using the probabilistic map approach described above, wecompared a control data set of 10 subjects to a patient (17-year-old male) with X-linked adrenoleukodystrophy. Thiswhite matter disease is caused by a defect in peroxisomaldegradation of very long-chain fatty acids, and is character-ized neuropathologically by demyelination and variable in-flammation and gliosis. Although white matter involvementin X-ALD is rather diffuse, distinctive topographic patterns ofdemyelination are generally recognized. In this particularpatient, as shown in Fig. 5, conventional (Fig. 5a and b) aswell as diffusion anisotropy (Fig. 5c) images show that the

FIG. 3. Continued.

220 Mori et al.

genu of corpus callosum is severely damaged. The results inFig. 5 indicate that 3D fiber tracking can provide a newdimension into the interpretation of this neurological condi-tion. Figure 5d shows the normal probabilistic map(10 volunteers) for all tracts that reach the left frontal lobethrough the reference ROI (indicated by the white box).These normal-control results identify three major pathwaysinto this lobe. One connects homologous regions of eachhemisphere through the genu of the corpus callosum (CC),the second leads to the thalamus via the anterior thalamicradiation (ATR), and the third is the inferior frontoccipitalfasciculus (IFO). The same tracking approach in the patientindicates that the frontal-frontal connections via the corpuscallosum are selectively affected, while the other two path-ways are relatively preserved (Fig. 5e). In agreement withthese neuroanatomical findings, the neurological and neuro-psychological examinations of this patient were indeedfound to deviate from normal for tasks involving specificfrontal lobe functions. The latter included deficits in seman-tic fluency (1.16 SD below mean for age), phonemic fluency(2.08 SD below normal), and verbal encoding strategies(3.00 SD below normal mean). Other brain functions werefound to be within normal limits, as reflected by a highaverage full-scale IQ of 111.

Analysis of MRI Properties of Individual White MatterTracts (Tract-Specific MRI)

Once a white matter tract of interest is reconstructed foran individual, it becomes possible to perform tract-spe-cific studies of diffusion parameters, such as anisotropy.Other MR indices, such as relaxation times and magne-tization transfer properties (26), can in principle also beobtained through coregistration with the diffusion im-ages and superimposition of the tracking coordinates.As an example, we show an anisotropy comparison be-tween healthy individuals and the X-ALD patient for thecorpus callosum and anterior thalamic radiation. In Fig.5g, the anisotropy profiles for the normal group and theX-ALD patient are plotted along the anterior-posterioraxis, as a function of distance with respect to the ante-rior commissure. It can be seen that the anisotropy of theshort tracts of the patient is comparable to that of thecallosal fibers in the healthy volunteers between coor-dinates �67 mm (near frontal pole) and �42 mm. How-

ever, the anisotropy decreases between �42 mm and�39 mm, and becomes virtually undetectable (� 0.3)near the genu (�21 to �27 mm). No changes in anisot-ropy were apparent along the ATR. This result demon-strates the potential to obtain valuable informationabout the evolution of white matter pathology. In thecase of X-ALD, the use of more sensitive and specificimaging techniques could be important for the earlydetection of abnormalities in at-risk populations.

Technical Limitations

Although the 3D fiber tracking technique provides us anexciting opportunity to visualize association pathways invivo, its inherent limitations should also be realized. As with

FIG. 4. Probabilistic maps of the long-association fiber systems inTalairach coordinates. The 3D reconstruction results for the superiorlongitudinal fasciculus (SLF, pink) of 10 healthy volunteers (three rep-resentative cases are shown in a) were standardized using elasticwarping (22), and probabilistic maps were created by superimposingthe data onto the standard coordinate. Consequently, the intensity ofeach pixel contains the information on the intersubject agreement(probability) in the spatial configuration of a particular fiber bundle.Warping was also applied to the inferior fronto-occipital fasciculus(IFO, blue) and the inferior longitudinal fasciculus (ILF, yellow) as shownin b. Actual probability can be more easily appreciated in c, whererepresentative slices of the statistical map of the SLF are shown, asobtained from 10 healthy volunteers using the same tracking criteria(ROI, anisotropy, and vector product, as indicated in Fig. 1 and theMethods and Materials section). Intensity of the colored pixels repre-sents the probability of the location of the tract (100 means 100%matching among the 10 subjects).

Imaging Cortical Association Tracts in Brain 221

any other MRI technique, DTI can only provide anatomicalinformation about the brain at a macroscopic level. Specifi-cally, fiber orientation revealed by DTI reflects the averageorientation of axonal fibers for each pixel, and is susceptibleto tissue heterogeneity. Within a pixel, numerous fibers maybe crossing, or there may be a small portion of fibers that have

different orientations from dominant fibers (27). Therefore,DTI fiber tracking is presently limited to visualization of invivo gross anatomy of white matter tracts connecting func-tional brain regions. However, several new techniques toimprove DTI data acquisition and tract tracking are nowbecoming available. These include high angular sampling of

FIG. 5. Example of the clinical application of fibertracking to an X-ALD patient. (a) T1- and (b) T2-weighted images, and (c) an FA map of an X-ALDpatient. The genu of the corpus callosum is severelyaffected in this patient (indicated by arrows). Probabi-listic maps of tracts that are connected to the leftfrontal lobe in normal controls (N � 10) and the X-ALDpatient are shown in d and e, respectively. The inten-sity level of the green (normal) color represents theprobability. White boxes indicate the location fromwhich the reference ROI was drawn for the tracking.Abbreviations: CC � corpus callosum, ATR � anteriorthalamic radiation, ILF � inferior longitudinal fascicu-lus. f: Comparison of tracking of the genu of the CCand the ATR in the frontal lobe of the X-ALD patientand the healthy volunteers. g: FA of the reconstructedprojections (circles � volunteers; squares � patient)plotted along the anterior-posterior axis as a functionof its distance from the anterior commissure (AC). f:The location of the AC (dotted lines) and a scale (solidline indicating the length of 2 cm) are shown for ana-tomical orientations.

222 Mori et al.

b-space (28–32), interpolation or regularization of the tensorfield (14,17,18), and global energy minimization for tracttracking (33,34). Many of these new developments are com-patible with the concepts introduced in this work, includingthe knowledge-based multiple-ROI approach and the stan-dardization technique for statistical analysis of tract coordi-nates.

CONCLUSIONS

Long-cortical-association fibers have been postulated to becritical for the functional integration of these neural net-works, and may play a specific role in various cognitive andbehavioral disorders such as Alzheimer’s disease, conduc-tion aphasia, dyslexia, and schizophrenia. Data were pre-sented showing that these major cortical association fiberscan be tracked noninvasively in humans using DTI, and thatthe tracking of the cores of these fibers is highly reproduciblebetween individuals. This capability is potentially useful fordiagnosing various white matter disorders, and improvingunderstanding of brain function by allowing correlation ofDTI findings and clinical symptoms. As a first illustration ofthis capability, a deficiency in the collosal fibers of a patientwith X-ALD was detected which correlated with functionaltesting. Future potential clinical applications include bettercharacterization of brain malformations, identification ofearly changes in neurodegenerative disorders that selectivelyaffect specific fibers, and evaluation of local and distant in-jury in patients with brain tumors, stroke, and trauma. Theincorporation of axonal mapping into the presurgical diag-nostic battery of patients with brain tumors or intractableepilepsy should also be feasible.

ACKNOWLEDGMENTS

This study was supported by the Whitaker Foundation, theNIH (grants AG016028 and HD37931 to S.M., HD24061 toW.E.K, and MH52886), and the Shapiro Foundation toG.D.P, and the NIH/NCRR (grant RR15241 to P.v.Z. andS.M.). We are grateful to Dr. Paul Folkers (Philips MedicalSystems, Best, The Netherlands) for helpful suggestions,Dr. Ryuta Ito for help in the patient study, and Dr. Chris-tine Cox (Kennedy Krieger Institute) for providing theneuropsychological data for the ALD patient.

REFERENCES1. Crosby E, Humphrey T, Lauer E. Correlative anatomy of the nervous

system. New York: MacMillan; 1962.2. Krieg W. Connections of the cerebral cortex. Evanston, IL: Brain Books; 1963.3. Paus T, Zijdenbos A, Worsley K, Collins LD, Blumenthal J, Giedd JN,

Rapoport JL, Evans AC. Structural maturation of neural pathways inchildren and adolescents: in vivo study. Science 1999;283:1908–1911.

4. Meyer JW, Makris N, Bates JF, Caviness VS, Kennedy DN. MRI-basedtopographic parcellation of human cerebral white matter. Neuroimage1999;9:1–17.

5. Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wend-land MF, Tsuruda J, Norman D. Diffusion-weighted MR imaging ofanisotropic water diffusion in cat central nervous system. Radiology1990;176:439–445.

6. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion innerves. Magn Reson Med 1994;31:394–400.

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

8. Basser PJ, Mattiello J, Le Bihan D. MR diffusion tensor spectroscopyand imaging. Biophys J 1994;66:259–267.

9. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensorMR imaging of human brain. Radiology 1996;201:637–648.

10. Makris N, Worth AJ, Sorensen AG, Papadimitriou GM, Reese TG,Wedeen VJ, Davis TL, Stakes JW, Caviness VS, Kaplan E, Rosen BR,Pandya DN, Kennedy DN. Morphometry of in vivo human white matterassociation pathways with diffusion weighted magnetic resonance im-aging. Ann Neurol 1997;42:951–962.

11. Ulug A, van Zijl PCM. Orientation-independent diffusion imaging withouttensor diagonalization: anisotropy definitions based on physical attributesof the diffusion ellipsoid. J Magn Reson Imaging 1999;9:804–813.

12. Mori S, Crain BJ, Chacko VP, van Zijl PCM. Three dimensional trackingof axonal projections in the brain by magnetic resonance imaging. AnnNeurol 1999;45:265–269.

13. Xue R, van Zijl PCM, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusiontensor imaging. Magn Reson Med 1999;42:1123–1127.

14. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS,McKinstry RC, Burton H, Raichle ME. Tracking neuronal fiber path-ways in the living human brain. Proc Natl Acad Sci USA 1999;96:10422–10427.

15. Mori S, Kaufmann WK, Pearlson GD, Crain BJ, Stieltjes B, SolaiyappanM, van Zijl PCM. In vivo visualization of human neural pathways byMRI. Ann Neurol 2000;47:412–414.

16. Jones DK, Simmons A, Williams SC, Horsfield MA. Non-invasive as-sessment of axonal fiber connectivity in the human brain via diffusiontensor MRI. Magn Reson Med 1999;42:37–41.

17. Poupon C, Clark CA, Frouin V, Regis J, Bloch L, Le Bihan D, Mangin JF.Regularization of diffusion-based direction maps for the tracking ofbrain white matter fascicules. NeuroImage 2000;12:184–195.

18. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vitro fibertractography using DT-MRI data. Magn Reson Med 2000;44:625–632.

19. Stieltjes B, Kaufmann WE, van Zijl PCM, Fredericksen K, Pearlson GD,Mori S. Diffusion tensor imaging and axonal tracking in the humanbrainstem. NeuroImage 2001;14:723–735.

20. Mulkern RV, Gudbjartsson H, Westin CF, Zengingonul HP, Gartner W,Guttmann CR, Robertson RL, Kyriakos W, Schwartz R, Holtzman D,Jolesz FA, Maier SE. Multi-component apparent diffusion coefficientsin human brain. NMR Biomed 1999;12:51–62.

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

22. Davatzikos C. Mapping of image data to stereotaxic spaces: applicationsto brain mapping. Hum Brain Mapp 1998;6:334–338.

23. Mettler F. Neuroanatomy. St. Louis, MO: C.V. Mosby Co.; 1948.24. Tusa R, Ungerleider L. The inferior longitudinal fasciculus: a reexam-

ination in humans and monkeys. Ann Neurol 1985;18:583–591.25. Ture U, Yasargil M, Pait T. Is there a superior occipitofrontal fasciculus? A

microsurgical anatomic study. Neurosurgery 1997;40:1226–1232.26. Henkelman R, Huang X, Xiang Q-S, Stanisz GJ, Swanson SD, Bronskill

MJ. Quantitative interpretation of magnetization transfer. Magn ResonMed 1993;29:759–766.

27. Wiegell M, Larsson H, Wedeen V. Fiber crossing in human brain depictedwith diffusion tensor MR imaging. Radiology 2000;217:897–903.

28. Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuringdiffusion in anisotropic systems by magnetic resonance imaging. MagnReson Med 1999;42:515–525.

29. Skare S, Hedehus M, Moseley ME, Li TQ. Condition number as ameasure of noise performance of diffusion tensor data acquisitionschemes with MRI. J Magn Reson 2000;147:340–352.

30. Wedeen V, Reese TG, Tuch DS, Weigel MR, Dou JG, Weiskoff RM,Chessler D. Mapping fiber orientation spectra in cerebral white matterwith Fourier-transform diffusion MRI. In: Proceedings of the 8th An-nual Meeting of ISMRM, Denver, 2000. p 82.

31. Elshafiey I, Narayana PA. Exploration of b-space for diffusion tensorimaging of human brain. In: Proceedings of the 9th Annual Meeting ofISMRM, Glasgow, Scotland, 2001. p 1537.

32. Lazar M, Alexander AL. Error analysis of white matter tracking algo-rithms (streamlines and tensorlines) for DT-MRI. In: Proceedings of the9th Annual Meeting of ISMRM, Glasgow, Scotland, 2001. p 506.

33. Tuch DS, Belliveau JW, Wedeen V. A path integral approach to whitematter tractography. In: Proceedings of the 8th Annual Meeting ofISMRM, Denver, 2000. p 791.

34. Parker GJ. Tracing fiber tracts using fast marching. In: Proceedings ofthe 8th Annual Meeting of ISMRM, Denver, 2000. p 85.

35. Williams TH, Gluhbegovic N, Jew JY. The human brain: dissections ofthe real brain. Virtual Hospital, University of Iowa, 1997. http://www.vh.org/Providers/Textbooks/BrainAnatomy, and http://www.brain-university.com.

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