Accepted Manuscript Title: The role of diffusion tensor imaging in brain tumor surgery: A review of the literature Author: Adriaan R.E. Potgieser Michiel Wagemakers Arjen L.J. van Hulzen Bauke M. de Jong Eelco W. Hoving Rob J.M. Groen PII: DOI: Reference:

S0303-8467(14)00213-3 http://dx.doi.org/doi:10.1016/j.clineuro.2014.06.009 CLINEU 3745

To appear in:

Clinical Neurology and Neurosurgery

Received date: Revised date: Accepted date:

25-2-2014 27-5-2014 8-6-2014

Please cite this article as: Potgieser Adriaan RE, Wagemakers Michiel, van Hulzen Arjen LJ, de Jong Bauke M, Hoving Eelco W, Groen Rob J.M.The role of diffusion tensor imaging in brain tumor surgery: A review of the literature.Clinical Neurology and Neurosurgery http://dx.doi.org/10.1016/j.clineuro.2014.06.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The role of diffusion tensor imaging in brain tumor surgery: a review of the literature. Adriaan R.E. Potgieser1, M.D., Michiel Wagemakers1, M.D., Arjen L.J. van Hulzen, M.Sc.1,

Department of Neurosurgery, University Medical Center Groningen, University of

cr

1

ip t

Bauke M. de Jong2, M.D. Ph.D., Eelco W. Hoving1, M.D. Ph.D., Rob J.M. Groen1, M.D. Ph.D.

2

us

Groningen, Groningen, The Netherlands.

Department of Neurology, University Medical Center Groningen, University of Groningen,

an

Groningen, The Netherlands.

M

Corresponding author: A.R.E. Potgieser

Ac ce pt e

Hanzeplein 1, P.O. Box 30.001

d

Department of Neurosurgery, University Medical Center Groningen

9700 RB Groningen, The Netherlands Telephone: +31 50 361 2400 Fax: +31 50 361 1707

Email: [email protected]

Financial disclosure: A.R.E. Potgieser received a Junior Scientific Masterclass grant of the University of Groningen.

Word count: 3282.

Page 1 of 23

Highlights

► We outline the pitfalls and benefits of DTI as a tool for neurosurgical planning. ► DTI is a promising technique for neuronavigation in tumor surgery. ► DTI has a role in

ip t

multimodal navigation during brain tumor surgery.

cr

Abstract

Diffusion tensor imaging (DTI) is a recent technique that utilizes diffusion of water molecules

us

to make assumptions about white matter tract architecture of the brain. Early on, neurosurgeons recognized its potential value in neurosurgical planning, as it is the only

an

technique that offers the possibility for in vivo visualization of white matter tracts. In this review we give an overview of the current advances made with this technique in

M

neurosurgical practice. The effect of brain shift and the limitations of the technique are highlighted, followed by a comprehensive discussion on its objective value. Although there are many limitations and pitfalls associated with this technique, DTI can provide valuable

d

additional diagnostic information to the neurosurgeon. We conclude that current evidence

Ac ce pt e

supports a role for DTI in the multimodal navigation during tumor surgery.

Key words: Diffusion tensor imaging, tractography, glioma, neuronavigation. Running title: DTI in brain tumor surgery.

INTRODUCTION The consequence of the extent of glioma resection regarding life expectancy is still debated due to lack of class I evidence, although current results seem to favor a more radical resection for both low grade and high grade gliomas [1]. The ultimate goal and major challenge in

Page 2 of 23

glioma surgery is to obtain maximal resection while minimizing loss of neurological function. DTI is a tool that contributes to achieve this goal by visualizing white matter tracts. From the early introduction of the fiber tracking technique neurosurgeons saw potential for noninvasive mapping of white matter tracts, especially in planning the optimal approach to a lesion. The first practical implementation was the integration of the pyramidal tracts, estimated with diffusion weighted imaging, in a neuronavigation system [2]. Meanwhile, a

ip t

large number of studies investigated the value of diffusion tensor tractography for neurosurgical planning, focusing mainly on the major white matter tracts as they are most

cr

easily visualized. Most studies involved benign or malignant lesions involving the corticospinal tract [3-35], optic tract [3,17,19,23,29,36-43], superior longitudinal fasciculus

us

[5,26,27,44] and arcuate fasciculus [3,19,29,45,46]. Nearly all authors report a positive experience with this relatively new technique. However, the beneficial effect is hard to

an

quantify. Many authors describe the use of tractography as ‘helpful’, ‘of great help’ or ‘beneficial’, without exactly determining what the value was. It may have influenced the choice of approach and intra-operative decision making, but whether it has affected the

M

quality and quantity of the resection remains difficult to ascertain. Few authors described in detail how tractography changed their surgical strategy, e.g. Chen et al. or Nimsky et al.

d

[22,34]. Until now, there are no controlled studies that relate clinical outcome to the

Ac ce pt e

integration of DTI in a neuronavigation system.

Following a general introduction to the method, we want to outline the pitfalls and potential benefits of DTI as a tool for pre- and intra-operative neurosurgical planning in tumor resection in this review.

PRINCIPLES OF DTI

In vivo quantification of diffusion of water molecules using magnetic resonance imaging (MRI) was first described in 1986 [47]. Diffusion can be described as random thermic motion, or Brownian motion [48]. The technique utilizes the fact that in tissue diffusion is not necessarily random due to barriers that limit diffusion in one or more directions (see Le Bihan and Johansen-Berg for an excellent introduction) [49]. Unhindered diffusion of water molecules is referred to as isotropic diffusion. Restriction of movement along only one axis is called anisotropic diffusion. Among others, the measured diffusion process depends on the applied magnetic gradients and the axis of myelinated white matter tracts [50-53]. The mechanism of this anisotropy along white matter tracts is not exactly known. From studies in developing brains it has become clear that the degree of myelinisation has a role, with more

Page 3 of 23

myelinisation causing more anisotropy [54-57]. However, this is only a partial explanation, because anisotropy is also apparent before the white matter is myelinated [58]. With DTI it is possible to use anisotropy to analyze axonal organization of brain. This is based on the concept of a diffusion tensor, which is a mathematical model that describes the threedimensional process of diffusion in different axes [59]. In theory, it is already possible to get an impression of the diffusion process if scanning is performed in six directions [60,61], but

ip t

generally many more directions are scanned to obtain a better recording. There are various ways to perform tractography based on the diffusion tensor. Fiber assignment by a continuous

cr

tracking (FACT) algorithm is frequently used [62]. This is a deterministic approach that uses the average axonal orientation within a voxel to estimate axonal projections, based on user-

us

defined variables such as the fractional anisotropy (FA) and the maximum tract angle. Later on, a probabilistic approach was introduced to be able to trace tracts up to and within the gray

an

matter without relying on the arbitrary anisotropy threshold [63]. This method is also more resistant to noise, because it is less vulnerable to halts in the tractography due to individual voxels with an ambiguous fiber direction.

M

Several research groups used DTI to create white matter tracts atlases [64-68]. These atlases

d

show good correspondence with atlases created with real-tissue dissections.

Ac ce pt e

TECHNICAL LIMITATIONS OF THE TECHNIQUE

Numerous technical considerations should be addressed in order to create a realistic concept of DTI when using this technique for neurosurgical planning. Current DTI techniques fall short on spatial resolution, signal to noise ratio (SNR) and susceptibility to a heterogenic magnetic field [69]. A first remark should be made about the resolution of diffusion-weighted imaging, which is in the range of millimeters. The diffusion process that is the source for computational modeling of white matter tracts takes place at molecular level [49]. Thus, DTI does not depict the actual diffusion process and derived actual tracts. For the intraoperative tractography the air-tissue boundary introduces additional susceptibility artifacts. Another issue is the user dependedness of the technique, which starts at the acquisition of the data. Differences may be introduced in for example the amount of scanning directions, diffusionweighting [70] and in magnetic field strength. It has been shown that the SNR increases when using a higher field strength [71-74]. This leads to enhanced visualization of fiber tracts. The SNR is a quantitative measure to compare the strength of the MRI signal with the noise. There are many different methods to calculate the SNR [75]. For example, the easiest method to get an impression of the SNR is to define the signal (S) as the mean signal intensity in a 2D

Page 4 of 23

region of interest of ten by ten voxels with maximum uniform signal intensity in every slice of the DTI scan. The noise (σ) is the standard deviation of the signal intensity in this region of interest. SNR is then calculated according to the following formula [75]: SNR = S / σ. Recently it has been stated that for analysis of major white matter tracts (such as the corpus callosum with an FA of 0.8) an SNR of 20 is a minimum requirement, being 40 for tracts with

ip t

lower FA values (0.45) [76]. The optimal FA threshold for integration of tractography in the neuronavigational device was suggested to be in the range of 0.15 to 0.2 [77], which needs an

cr

even higher SNR.

At lower SNR values the accuracy, precision and reproducibility of measured FA values is

us

negatively influenced [78]. More noise has the effect that it converts diffusion isotropy to anisotropy and it augments anisotropy by an underestimation of the smallest eigenvalue (λ3)

an

and an overestimation of the largest eigenvalue (λ1) [79,80]. Clearly, this has an effect on the tractography results. SNR appears to be underreported in the studies that investigated the value of DTI in neurosurgical planning. Several methods can be used to increase the SNR,

M

such as scanning with a higher magnetic field, increase the number of repetitions, scan a b0

Ac ce pt e

METHOD OF TRACKING

d

image for every eight diffusion weighted images and cardiac gating [81-85].

An important choice to be made is the method of tracking. Currently most studies perform tracking using a deterministic or probabilistic approach (for an example see Fig. 1). Most studies evaluating the use of DTI in neurosurgical planning use a deterministic approach, and only a few, such as in studies involving Meyer’s loop of the optic tract, use a probabilistic approach [36,40,41,43]. The deterministic method is based on the assumption that the orientation of fibers can be described by a single orientation. Clearly, this is an oversimplification as the millimeter-sized voxels contain thousands of axons, which can have different orientations. The probabilistic method can proceed in areas where the fiber direction is of lower certainty [86]. Due to the fact that this is a very time-consuming method it is currently not possible to intraoperatively integrate this method in the neuronavigation. Furthermore, this method creates a 3D map of possible connectivity, which is not necessarily based on actual anatomy. Newer methods that account for crossing or kissing fibers are being developed [37,87]. These methods take all the eigenvectors of a voxel into account and include high angular resolution diffusion imaging and diffusion spectrum imaging [88-91].

Page 5 of 23

USER-DEPENDENT FACTORS INFLUENCING THE TECHNIQUE

Jones and Cercignani nicely illustrate these relevant aspects and numerous pitfalls in DTI [92]. In addition to processing pitfalls, other factors should be considered as well. The choice of fiber tracking program may influence the tractography results [93]. A significant difference in anatomical accuracy was found when evaluating nine different programs [94]. After the choice of a program the tracking results may vary due to different choices of vector step

ip t

length, FA threshold and angular threshold [95]. There is also a registration error of less than 3 mm for the fiber tracts when integrating it in the neuronavigation [22]. Furthermore, the

cr

tracking depends on chosen location and size of the seed regions [95]. To reduce userdependedness, fMRI activations can serve as an objective method to derive seed regions,

us

although this cannot be updated intraoperatively [15,96,97]. On the other hand, fMRI also has

an

its flaws and pitfalls when it comes to identifying exact functional cortical regions.

User-dependedness is often quoted as one of the major drawbacks of the technique. However, for the experienced individual neurosurgeon the user-dependedness may be expected to add

M

value, because lessons can be learned from previous cases when using the same settings. It has been shown that experienced neurosurgeons have low intraobserver variability in

Ac ce pt e

process.

d

generating tracts [23]. As with other surgical capabilities this is part of a life long learning

EFFECT OF BRAIN SHIFT

A general problem of image-guided surgery with neuronavigation is that the preoperative coordinate system of the patient does not remain rigid. During the operation several factors may cause the brain to deform, such as patient positioning, the anesthesiology, the craniotomy, opening of the dura, resection of tissue, use of retractors, formation of edema, preoperative ischemic events, opening of tumor cysts or the ventricular system, and leakage of cerebrospinal fluid. As a consequence, the interpretation of preoperative data is affected. For example, at the cortical surface and near deep tumor margins brain shift can be up to 24 mm and 3 mm respectively [98]. Moreover, brain shift is unpredictable as it is a dynamic process that changes over time [99] and it does not necessarily move along the direction of gravity [100]. It therefore seems a logical step to correct for the brain shift to get an update of the real anatomy, because the preoperative tractography might lose its significance. For the specific situation of white matter tracts the brain shift has been well documented. Using intraoperative MRI Nimsky et al. [11] registered FA maps in the neuronavigation and showed that white

Page 6 of 23

matter tract shifting ranged from -8 to 15 mm, while it was unpredictable whether inward or outward shifting would occur. White matter tracts can also be localized by direct stimulation [101], bypassing the problem of brain shift. Without intraoperative updating, this resulted in a distance of +1.8 to +13.4 mm between the corticospinal tract assessed with DTI and the stimulated motor response point [28]. Furthermore, A combination of these techniques may

ip t

lead to a better starting point for stimulation [22].

The effect of brain shift does not withhold many neurosurgeons from using neuronavigation

cr

during the operation, keeping in mind that the interpretation of DTI should be put into this perspective. A mental update of the neuronavigation can be made using anatomical landmarks

us

and intraoperative neuromonitoring. DTI can also be of value in providing additional information. Intraoperative updating, whether real, mental or mathematical [102], seems to be

an

important. Intraoperative MRI is not widely available due to financial and practical limitations, although it seems to have benefits through a reduction in repeat surgery, length of stay in the hospital and costs [103]. A different option would be to use ultrasonography for

M

intraoperative updating, which has also been used for tractography-based neuronavigation

d

[104].

Ac ce pt e

WHITE MATTER TRACT ORGANIZATION

Several groups have tried to find changes in white matter tracts adjacent to a tumor using DTI metrics, mostly the FA. Field et al. [105] classified four patterns of tumor induced alteration of white matter tracts, namely displacement with a normal FA, normal tract locations and tensor directions with a decreased FA, decreased FA with abnormal tensor directions and near isotropy. Witwer et al. classified the tracts adjacent to tumor as displaced, disrupted, edematous or infiltrated [19]. Others invented a relative anisotropy index as a measure for white matter organization [106]. This index was significantly lower in areas of white matter adjacent to high-grade gliomas, but not with low-grade gliomas or metastases, which suggested white matter disruption in the high-grade gliomas. Several others tried to use DTI metrics to distinguish low-grade gliomas, high-grade gliomas and metastases or to distinguish related vasogenic edema from tumor infiltration, disruption or displacement [107-121]. Both tumor infiltration or disruption and vasogenic edema lead to a decreased FA. Compression of a tract leads to an increased FA [5]. In cats it was shown that a cortical cold lesion model inducing vasogenic edema enhances diffusion [122]. In order to demonstrate tumor infiltration in white matter tracts, Stadlbauer et al. correlated FA and fiber tracking results

Page 7 of 23

with histopathological findings from stereotactic biopsies, although not corrected for brain shift [77,123]. They suggested an FA threshold of 0.15 to 0.2 is most favorable to minimize faulty tracking, but to include infiltrated fibers [77]. This is an important comment, because earlier Kinoshita et al. showed that the size of a fiber tract was not accurately estimated and relying on it led to more neurological deficit in one patient [4]. As mentioned earlier, when using such a low FA it is a challenge to obtain an adequate SNR for bias-free FA

ip t

measurement, which stresses the importance of measurement of the SNR [76]. While DTI metrics such as the FA may help in the differentiation of normal, invaded and disrupted white

cr

matter tracts, the effects for tracking can be detrimental, because sometimes the reconstructed fibers cannot pass through an area of peritumoral edema, while the real fibers can

us

[17,44,124,125]. The interpretation of the significance of the tractography becomes even more complex because it has been shown that pathologically infiltrated white matter can have

an

preserved function [126,127]. Current DTI metrics are thus not capable of estimating exact white matter organization, nor white matter ‘integrity’ [69]. Figure 2 shows an example of a

DISCUSSION

M

case with normal, displaced and disrupted tracts due to a large tumor.

d

Currently, DTI is the only technique that creates in vivo depiction of white matter tracts.

Ac ce pt e

Although there are many limitations and pitfalls associated with this technique, it can provide the neurosurgeon with additional valuable morphological/anatomical information. As with many new techniques, its introduction gives rise to skepticism [128]. Although the results look very promising, the problem is that DTI-studies, by definition, cannot be blinded. The technique definitely needs further standardization, validation and technical improvement. Every increase in information about the tumor and the vital tracts will be of use to optimize decision-making before and during the operation. As suggested by Duffau, it seems that DTI is not reliable enough to base decisions on in the operating theater, given the current limitations of the technique [129]. However, we do not share the opinion that the technique is merely a research tool [130]. As with any diagnostic test, one needs to be able to interpret and to integrate the different findings. No specific test is infallible. DTI gives additional information that needs interpretation. Just like high signal intensity adjacent to tumor on a T2weighted images needs interpretation to determine whether it represents edema or tumor invasion that should be resected. Direct electrical stimulation, that could be considered the current gold standard, also has drawbacks, such as variation of the responses, due to anesthetic regimens and susceptibility to electrical excitability [131,132]. Furthermore, in our

Page 8 of 23

experience direct stimulation not always leads to unequivocal determination of functional areas to base the ongoing surgical strategy on. Ultimately, however, direct neurostimulation is the best we currently have. Unlike direct stimulation, DTI can provide additional preoperative information, which may help in determining the best surgical approach. Especially with noninvasive lesions, and in the absence of significant edema, others found that assessment of white matter tracts had influence on the initial surgical approach as it provided additional

ip t

information [31,34].

cr

As DTI is an anatomical (and not a functional) determinant, its combination with direct stimulation provides important functional validation of the anatomy involved. In fact, with

us

slow growing lesions, cortical reorganization may take place resulting in anatomically evident but functionally rudimentary tracts, which thus appeared to be resectable [133-135]. However,

an

this dichotomy between functional and anatomical tracts does not hold. With DTI a reorganization of white matter tracts, induced by training, has been depicted [136], which provided evidence for the plasticity of the neural circuits. The underlying mechanisms of

M

these changes are not clear. Also, one should bear in mind that damage to small blood vessels adjacent to a tract may result in ischemic injury, leading to a neurological deficit [137].

d

An additional advantage of DTI is that it facilitates the identification of tracts of interest,

Ac ce pt e

especially in combination with electrical stimulation, because one can have better expectations where to find the tract. In this respect, the combination of DTI with electrical stimulation appears to be synergistic [18]. As a result, mapping during awake surgery can be more effective and efficient, leading to less discomfort and less fatigue for the patient [27].

The aforementioned limitations of the technique, combined with the effect of brain shift and edema, brain displacement, disruption or tumor-invasion of tracts, may all lead to false negative or false positive estimation of tracts. The limitations and potential benefits of DTI are summarized in Table 1. This is why the DTI findings, especially in the clinical situation, should be interpreted with great caution [69]. In this light, the critical safety distance of 5 mm seems to be arbitrary [22,138]. It is wiser to apply the gold standard of direct electrical stimulation to assess the accuracy of the tractography [6,139,140]. There is a high concordance between DTI tractography and direct electrical stimulation (sensitivity of 92.6% and specificity of 93.2% in the study of Zhu et al.), which validates the tractography findings [6,20,27,141]. In another study it was found that the probability of eliciting a motor response during subcortical stimulation depends on the distance between the tractographic estimate of

Page 9 of 23

the pyramidal tract and the tumor [142]. If the distance of stimulation is closer to the tractography of the pyramidal tract, indicating close proximity of the pyramidal tract to the resection cavity, there seem to be more neurological deficits [143]. In fact, the largest study involving 238 patients reported less postoperative deficit, higher Karnofsky scores and a positive effect on the survival when surgery was guided by FA maps (not tractography)

ip t

during the operation [7].

CONCLUSION

cr

DTI has shown to be a promising technique in pre- and intraoperative navigation in tumor surgery. A thorough knowledge of its limitations and potential pitfalls is indispensable for the

us

interpretation and safe application of this promising new technique. For bias-free FA measurement, a sufficiently high SNR is of importance in the clinical use of DTI. As pointed

an

out, a thorough knowledge of the pitfalls of the tractography in the perioperative situation is of utmost importance. We believe that DTI has a role in multimodal navigation, especially in combination with intraoperative direct neurostimulation. The functional validation of DTI

M

tracking by intraoperative stimulation provides synergistic use of both techniques. It has the potential to aid in maximal tumor resection, without increasing morbidity. Intraoperative

Ac ce pt e

borders.

d

neurostimulation remains the gold standard to determine the anatomical and functional

DISCLOSURES:

The authors report no conflict of interest concerning the findings specified in this paper.

REFERENCES [1] Sanai N, Berger MS. Glioma extent of resection and its impact on patient outcome. Neurosurgery 2008 Apr;62(4):753-64; discussion 264-6. [2] Coenen VA, Krings T, Mayfrank L, Polin RS, Reinges MH, Thron A, et al. Three-dimensional visualization of the pyramidal tract in a neuronavigation system during brain tumor surgery: first experiences and technical note. Neurosurgery 2001 Jul;49(1):86-92; discussion 92-3.

Page 10 of 23

[3] Bagadia A, Purandare H, Misra BK, Gupta S. Application of magnetic resonance tractography in the perioperative planning of patients with eloquent region intra-axial brain lesions. J Clin Neurosci 2011 May;18(5):633-639. [4] Kinoshita M, Yamada K, Hashimoto N, Kato A, Izumoto S, Baba T, et al. Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 2005 Apr 1;25(2):424-429.

ip t

[5] Schonberg T, Pianka P, Hendler T, Pasternak O, Assaf Y. Characterization of displaced white matter by brain tumors using combined DTI and fMRI. Neuroimage 2006 May 1;30(4):1100-1111.

cr

[6] Zhu FP, Wu JS, Song YY, Yao CJ, Zhuang DX, Xu G, et al. Clinical application of motor pathway mapping using diffusion tensor imaging tractography and intraoperative direct subcortical stimulation in cerebral glioma surgery: a prospective cohort study. Neurosurgery 2012 Dec;71(6):1170-83; discussion 1183-4.

us

[7] Wu JS, Zhou LF, Tang WJ, Mao Y, Hu J, Song YY, et al. Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: a prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery 2007 Nov;61(5):935-48; discussion 948-9.

an

[8] Mikuni N, Okada T, Enatsu R, Miki Y, Hanakawa T, Urayama S, et al. Clinical impact of integrated functional neuronavigation and subcortical electrical stimulation to preserve motor function during resection of brain tumors. J Neurosurg 2007 Apr;106(4):593-598.

M

[9] Mikuni N, Okada T, Enatsu R, Miki Y, Urayama S, Takahashi JA, et al. Clinical significance of preoperative fibre-tracking to preserve the affected pyramidal tracts during resection of brain tumours in patients with preoperative motor weakness. J Neurol Neurosurg Psychiatry 2007 Jul;78(7):716-721.

d

[10] Parmar H, Sitoh YY, Yeo TT. Combined magnetic resonance tractography and functional magnetic resonance imaging in evaluation of brain tumors involving the motor system. J Comput Assist Tomogr 2004 JulAug;28(4):551-556.

Ac ce pt e

[11] Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, et al. Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 2005;56(1):130-7; discussion 138. [12] Maesawa S, Fujii M, Nakahara N, Watanabe T, Wakabayashi T, Yoshida J. Intraoperative tractography and motor evoked potential (MEP) monitoring in surgery for gliomas around the corticospinal tract. World Neurosurg 2010 Jul;74(1):153-161. [13] Kamada K, Todo T, Masutani Y, Aoki S, Ino K, Takano T, et al. Combined use of tractography-integrated functional neuronavigation and direct fiber stimulation. J Neurosurg 2005 Apr;102(4):664-672. [14] Okada T, Mikuni N, Miki Y, Kikuta K, Urayama S, Hanakawa T, et al. Corticospinal tract localization: integration of diffusion-tensor tractography at 3-T MR imaging with intraoperative white matter stimulation mapping--preliminary results. Radiology 2006 Sep;240(3):849-857. [15] Hendler T, Pianka P, Sigal M, Kafri M, Ben-Bashat D, Constantini S, et al. Delineating gray and white matter involvement in brain lesions: three-dimensional alignment of functional magnetic resonance and diffusion-tensor imaging. J Neurosurg 2003 Dec;99(6):1018-1027. [16] Laundre BJ, Jellison BJ, Badie B, Alexander AL, Field AS. Diffusion tensor imaging of the corticospinal tract before and after mass resection as correlated with clinical motor findings: preliminary data. AJNR Am J Neuroradiol 2005 Apr;26(4):791-796. [17] Yu CS, Li KC, Xuan Y, Ji XM, Qin W. Diffusion tensor tractography in patients with cerebral tumors: a helpful technique for neurosurgical planning and postoperative assessment. Eur J Radiol 2005 Nov;56(2):197204.

Page 11 of 23

[18] Ciccarelli O, Catani M, Johansen-Berg H, Clark C, Thompson A. Diffusion-based tractography in neurological disorders: concepts, applications, and future developments. Lancet Neurol 2008 Aug;7(8):715-727. [19] Witwer BP, Moftakhar R, Hasan KM, Deshmukh P, Haughton V, Field A, et al. Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 2002 Sep;97(3):568-575. [20] Berman JI, Berger MS, Mukherjee P, Henry RG. Diffusion-tensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. J Neurosurg 2004 Jul;101(1):66-72.

ip t

[21] Kamada K, Sawamura Y, Takeuchi F, Kawaguchi H, Kuriki S, Todo T, et al. Functional identification of the primary motor area by corticospinal tractography. Neurosurgery 2005 Jan;56(1 Suppl):98-109; discussion 98-109.

cr

[22] Nimsky C, Ganslandt O, Merhof D, Sorensen AG, Fahlbusch R. Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking. Neuroimage 2006 May 1;30(4):1219-1229.

us

[23] Nimsky C, Ganslandt O, Fahlbusch R. Implementation of fiber tract navigation. Neurosurgery 2006 Apr;58(4 Suppl 2):ONS-292-303; discussion ONS-303-4.

an

[24] Holodny AI, Schwartz TH, Ollenschleger M, Liu WC, Schulder M. Tumor involvement of the corticospinal tract: diffusion magnetic resonance tractography with intraoperative correlation. J Neurosurg 2001 Dec;95(6):1082.

M

[25] Coenen VA, Krings T, Axer H, Weidemann J, Kranzlein H, Hans FJ, et al. Intraoperative three-dimensional visualization of the pyramidal tract in a neuronavigation system (PTV) reliably predicts true position of principal motor pathways. Surg Neurol 2003 Nov;60(5):381-90; discussion 390.

d

[26] Bello L, Castellano A, Fava E, Casaceli G, Riva M, Scotti G, et al. Intraoperative use of diffusion tensor imaging fiber tractography and subcortical mapping for resection of gliomas: technical considerations. Neurosurg Focus 2010 Feb;28(2):E6.

Ac ce pt e

[27] Bello L, Gambini A, Castellano A, Carrabba G, Acerbi F, Fava E, et al. Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. Neuroimage 2008 Jan 1;39(1):369-382. [28] Gonzalez-Darder JM, Gonzalez-Lopez P, Talamantes F, Quilis V, Cortes V, Garcia-March G, et al. Multimodal navigation in the functional microsurgical resection of intrinsic brain tumors located in eloquent motor areas: role of tractography. Neurosurg Focus 2010 Feb;28(2):E5. [29] Romano A, D'Andrea G, Minniti G, Mastronardi L, Ferrante L, Fantozzi LM, et al. Pre-surgical planning and MR-tractography utility in brain tumour resection. Eur Radiol 2009 Dec;19(12):2798-2808. [30] Coenen VA, Krings T, Weidemann J, Hans FJ, Reinacher P, Gilsbach JM, et al. Sequential visualization of brain and fiber tract deformation during intracranial surgery with three-dimensional ultrasound: an approach to evaluate the effect of brain shift. Neurosurgery 2005 Jan;56(1 Suppl):133-41; discussion 133-41. [31] Niizuma K, Fujimura M, Kumabe T, Higano S, Tominaga T. Surgical treatment of paraventricular cavernous angioma: fibre tracking for visualizing the corticospinal tract and determining surgical approach. J Clin Neurosci 2006 Dec;13(10):1028-1032. [32] Buchmann N, Gempt J, Stoffel M, Foerschler A, Meyer B, Ringel F. Utility of diffusion tensor-imaged (DTI) motor fiber tracking for the resection of intracranial tumors near the corticospinal tract. Acta Neurochir (Wien) 2011 Jan;153(1):68-74; discussion 74.

Page 12 of 23

[33] Kovanlikaya I, Firat Z, Kovanlikaya A, Ulug AM, Cihangiroglu MM, John M, et al. Assessment of the corticospinal tract alterations before and after resection of brainstem lesions using Diffusion Tensor Imaging (DTI) and tractography at 3T. Eur J Radiol 2011 Mar;77(3):383-391. [34] Chen X, Weigel D, Ganslandt O, Fahlbusch R, Buchfelder M, Nimsky C. Diffusion tensor-based fiber tracking and intraoperative neuronavigation for the resection of a brainstem cavernous angioma. Surg Neurol 2007 Sep;68(3):285-91; discussion 291.

ip t

[35] Chen X, Weigel D, Ganslandt O, Buchfelder M, Nimsky C. Diffusion tensor imaging and white matter tractography in patients with brainstem lesions. Acta Neurochir (Wien) 2007 Nov;149(11):1117-31; discussion 1131.

cr

[36] Powell HW, Parker GJ, Alexander DC, Symms MR, Boulby PA, Wheeler-Kingshott CA, et al. MR tractography predicts visual field defects following temporal lobe resection. Neurology 2005 Aug 23;65(4):596599.

us

[37] Kuhnt D, Bauer MH, Sommer J, Merhof D, Nimsky C. Optic radiation fiber tractography in glioma patients based on high angular resolution diffusion imaging with compressed sensing compared with diffusion tensor imaging - initial experience. PLoS One 2013 Jul 26;8(7):e70973.

an

[38] Kikuta K, Takagi Y, Nozaki K, Hanakawa T, Okada T, Miki Y, et al. Early experience with 3-T magnetic resonance tractography in the surgery of cerebral arteriovenous malformations in and around the visual pathway. Neurosurgery 2006 Feb;58(2):331-7; discussion 331-7.

M

[39] Lober RM, Guzman R, Cheshier SH, Fredrick DR, Edwards MS, Yeom KW. Application of diffusion tensor tractography in pediatric optic pathway glioma. J Neurosurg Pediatr 2012 Oct;10(4):273-280.

d

[40] Winston GP, Yogarajah M, Symms MR, McEvoy AW, Micallef C, Duncan JS. Diffusion tensor imaging tractography to visualize the relationship of the optic radiation to epileptogenic lesions prior to neurosurgery. Epilepsia 2011 Aug;52(8):1430-1438.

Ac ce pt e

[41] Winston GP, Daga P, Stretton J, Modat M, Symms MR, McEvoy AW, et al. Optic radiation tractography and vision in anterior temporal lobe resection. Ann Neurol 2012 Mar;71(3):334-341. [42] Tatsuzawa K, Owada K, Sasajima H, Yamada K, Mineura K. Surgical strategy of brain tumors adjacent to the optic radiation using diffusion tensor imaging-based tractography. Oncol Lett 2010 Nov;1(6):1005-1009. [43] Yogarajah M, Focke NK, Bonelli S, Cercignani M, Acheson J, Parker GJ, et al. Defining Meyer's looptemporal lobe resections, visual field deficits and diffusion tensor tractography. Brain 2009 Jun;132(Pt 6):16561668. [44] Mori S, Frederiksen K, van Zijl PC, Stieltjes B, Kraut MA, Solaiyappan M, et al. Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol 2002 Mar;51(3):377-380. [45] Kuhnt D, Bauer MH, Becker A, Merhof D, Zolal A, Richter M, et al. Intraoperative visualization of fiber tracking based reconstruction of language pathways in glioma surgery. Neurosurgery 2012 Apr;70(4):911-9; discussion 919-20. [46] Leclercq D, Duffau H, Delmaire C, Capelle L, Gatignol P, Ducros M, et al. Comparison of diffusion tensor imaging tractography of language tracts and intraoperative subcortical stimulations. J Neurosurg 2010 Mar;112(3):503-511. [47] Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986 Nov;161(2):401-407.

Page 13 of 23

[48] Einstein A. Über die von der molecularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Annals of Physics 1905;17:549-550-569. [49] Le Bihan D, Johansen-Berg H. Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage 2012 Jun;61(2):324-341. [50] Chenevert TL, Brunberg JA, Pipe JG. Anisotropic diffusion in human white matter: demonstration with MR techniques in vivo. Radiology 1990 Nov;177(2):401-405.

ip t

[51] Doran M, Hajnal JV, Van Bruggen N, King MD, Young IR, Bydder GM. Normal and abnormal white matter tracts shown by MR imaging using directional diffusion weighted sequences. J Comput Assist Tomogr 1990 Nov-Dec;14(6):865-873.

cr

[52] Le Bihan D, Turner R, Douek P. Is water diffusion restricted in human brain white matter? An echo-planar NMR imaging study. Neuroreport 1993 Jul;4(7):887-890.

us

[53] Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 1990 Aug;176(2):439-445.

an

[54] Neil JJ, Shiran SI, McKinstry RC, Schefft GL, Snyder AZ, Almli CR, et al. Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology 1998 Oct;209(1):57-66.

M

[55] Takeda K, Nomura Y, Sakuma H, Tagami T, Okuda Y, Nakagawa T. MR assessment of normal brain development in neonates and infants: comparative study of T1- and diffusion-weighted images. J Comput Assist Tomogr 1997 Jan-Feb;21(1):1-7.

d

[56] Toft PB, Leth H, Peitersen B, Lou HC, Thomsen C. The apparent diffusion coefficient of water in gray and white matter of the infant brain. J Comput Assist Tomogr 1996 Nov-Dec;20(6):1006-1011.

Ac ce pt e

[57] Vorisek I, Sykova E. Evolution of anisotropic diffusion in the developing rat corpus callosum. J Neurophysiol 1997 Aug;78(2):912-919. [58] Rutherford MA, Cowan FM, Manzur AY, Dubowitz LM, Pennock JM, Hajnal JV, et al. MR imaging of anisotropically restricted diffusion in the brain of neonates and infants. J Comput Assist Tomogr 1991 MarApr;15(2):188-198. [59] Stejskal,E.O., Tanner,J.E. Spin Diffusion Measurements: Spin echoes in the presence of a time-dependent field gradient. The Journal of Chemical Physics 1965;42:288. [60] Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994 Jan;66(1):259-267. [61] Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994 Mar;103(3):247-254. [62] Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999 Feb;45(2):265-269. [63] Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003 Nov;50(5):1077-1088. [64] Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 2008 Jan 1;39(1):336-347.

Page 14 of 23

[65] Lawes IN, Barrick TR, Murugam V, Spierings N, Evans DR, Song M, et al. Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. Neuroimage 2008 Jan 1;39(1):62-79. [66] Verhoeven JS, Sage CA, Leemans A, Van Hecke W, Callaert D, Peeters R, et al. Construction of a stereotaxic DTI atlas with full diffusion tensor information for studying white matter maturation from childhood to adolescence using tractography-based segmentations. Hum Brain Mapp 2010 Mar;31(3):470-486.

ip t

[67] Wassermann D, Bloy L, Kanterakis E, Verma R, Deriche R. Unsupervised white matter fiber clustering and tract probability map generation: applications of a Gaussian process framework for white matter fibers. Neuroimage 2010 May 15;51(1):228-241.

cr

[68] Thiebaut de Schotten M, Ffytche DH, Bizzi A, Dell'Acqua F, Allin M, Walshe M, et al. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. Neuroimage 2011 Jan 1;54(1):49-59.

us

[69] Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage 2013 Jun;73:239-254.

an

[70] Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 2006 Sep 7;51(5):527-539. [71] Hunsche S, Moseley ME, Stoeter P, Hedehus M. Diffusion-tensor MR imaging at 1.5 and 3.0 T: initial observations. Radiology 2001 Nov;221(2):550-556.

M

[72] Jaermann T, Crelier G, Pruessmann KP, Golay X, Netsch T, van Muiswinkel AM, et al. SENSE-DTI at 3 T. Magn Reson Med 2004 Feb;51(2):230-236.

d

[73] Nagae-Poetscher LM, Jiang H, Wakana S, Golay X, van Zijl PC, Mori S. High-resolution diffusion tensor imaging of the brain stem at 3 T. AJNR Am J Neuroradiol 2004 Sep;25(8):1325-1330.

Ac ce pt e

[74] Okada T, Miki Y, Fushimi Y, Hanakawa T, Kanagaki M, Yamamoto A, et al. Diffusion-tensor fiber tractography: intraindividual comparison of 3.0-T and 1.5-T MR imaging. Radiology 2006 Feb;238(2):668-678. [75] Lagana M, Rovaris M, Ceccarelli A, Venturelli C, Marini S, Baselli G. DTI parameter optimisation for acquisition at 1.5T: SNR analysis and clinical application. Comput Intell Neurosci 2010:254032. [76] Seo Y, Wang ZJ, Morriss MC, Rollins NK. Minimum SNR and acquisition for bias-free estimation of fractional anisotropy in diffusion tensor imaging - a comparison of two analytical techniques and field strengths. Magn Reson Imaging 2012 Jul 19. [77] Stadlbauer A, Nimsky C, Buslei R, Salomonowitz E, Hammen T, Buchfelder M, et al. Diffusion tensor imaging and optimized fiber tracking in glioma patients: Histopathologic evaluation of tumor-invaded white matter structures. Neuroimage 2007 Feb 1;34(3):949-956. [78] Farrell JA, Landman BA, Jones CK, Smith SA, Prince JL, van Zijl PC, et al. Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. J Magn Reson Imaging 2007 Sep;26(3):756-767. [79] Bastin ME, Armitage PA, Marshall I. A theoretical study of the effect of experimental noise on the measurement of anisotropy in diffusion imaging. Magn Reson Imaging 1998 Sep;16(7):773-785. [80] Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996 Dec;36(6):893-906.

Page 15 of 23

[81] Tomassini V, Jbabdi S, Klein JC, Behrens TE, Pozzilli C, Matthews PM, et al. Diffusion-weighted imaging tractography-based parcellation of the human lateral premotor cortex identifies dorsal and ventral subregions with anatomical and functional specializations. J Neurosci 2007 Sep 19;27(38):10259-10269. [82] Croxson PL, Johansen-Berg H, Behrens TE, Robson MD, Pinsk MA, Gross CG, et al. Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography. J Neurosci 2005 Sep 28;25(39):8854-8866.

ip t

[83] Cohen MX, Schoene-Bake JC, Elger CE, Weber B. Connectivity-based segregation of the human striatum predicts personality characteristics. Nat Neurosci 2009 Jan;12(1):32-34. [84] Beckmann M, Johansen-Berg H, Rushworth MF. Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization. J Neurosci 2009 Jan 28;29(4):1175-1190.

cr

[85] Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 1999 Sep;42(3):515-525.

us

[86] Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM, Wheeler-Kingshott CA, Boulby PA, et al. Noninvasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 2003 Jul;6(7):750-757.

an

[87] Farquharson S, Tournier JD, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 2013 Jun;118(6):1367-1377.

M

[88] Berman JI, Chung S, Mukherjee P, Hess CP, Han ET, Henry RG. Probabilistic streamline q-ball tractography using the residual bootstrap. Neuroimage 2008 Jan 1;39(1):215-222.

d

[89] Hess CP, Mukherjee P, Han ET, Xu D, Vigneron DB. Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med 2006 Jul;56(1):104-117.

Ac ce pt e

[90] Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol 2008 May;29(5):843-852. [91] Kuo LW, Chen JH, Wedeen VJ, Tseng WY. Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system. Neuroimage 2008 May 15;41(1):7-18. [92] Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010 Aug;23(7):803-820. [93] Burgel U, Madler B, Honey CR, Thron A, Gilsbach J, Coenen VA. Fiber tracking with distinct software tools results in a clear diversity in anatomical fiber tract portrayal. Cent Eur Neurosurg 2009 Feb;70(1):27-35. [94] Feigl GC, Hiergeist W, Fellner C, Schebesch KM, Doenitz C, Finkenzeller T, et al. Magnetic Resonance Imaging Diffusion Tensor Tractography: Evaluation of Anatomic Accuracy of Different Fiber Tracking Software Packages. World Neurosurg 2013 Jan 4. [95] Clark CA, Barrick TR, Murphy MM, Bell BA. White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning? Neuroimage 2003 Nov;20(3):1601-1608. [96] Guye M, Parker GJ, Symms M, Boulby P, Wheeler-Kingshott CA, Salek-Haddadi A, et al. Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo. Neuroimage 2003 Aug;19(4):1349-1360. [97] Krings T, Reinges MH, Thiex R, Gilsbach JM, Thron A. Functional and diffusion-weighted magnetic resonance images of space-occupying lesions affecting the motor system: imaging the motor cortex and pyramidal tracts. J Neurosurg 2001 Nov;95(5):816-824.

Page 16 of 23

[98] Nimsky C, Ganslandt O, Cerny S, Hastreiter P, Greiner G, Fahlbusch R. Quantification of, visualization of, and compensation for brain shift using intraoperative magnetic resonance imaging. Neurosurgery 2000 Nov;47(5):1070-9; discussion 1079-80. [99] Nabavi A, Black PM, Gering DT, Westin CF, Mehta V, Pergolizzi RS,Jr, et al. Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 2001 Apr;48(4):787-97; discussion 797-8. [100] Hartkens T, Hill DL, Castellano-Smith AD, Hawkes DJ, Maurer CR,Jr, Martin AJ, et al. Measurement and analysis of brain deformation during neurosurgery. IEEE Trans Med Imaging 2003 Jan;22(1):82-92.

ip t

[101] Duffau H, Capelle L, Denvil D, Sichez N, Gatignol P, Taillandier L, et al. Usefulness of intraoperative electrical subcortical mapping during surgery for low-grade gliomas located within eloquent brain regions: functional results in a consecutive series of 103 patients. J Neurosurg 2003 Apr;98(4):764-778.

us

cr

[102] Archip N, Clatz O, Whalen S, Kacher D, Fedorov A, Kot A, et al. Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. Neuroimage 2007 Apr 1;35(2):609-624. [103] Hall WA, Kowalik K, Liu H, Truwit CL, Kucharezyk J. Costs and benefits of intraoperative MR-guided brain tumor resection. Acta Neurochir Suppl 2003;85:137-142.

an

[104] Nossek E, Korn A, Shahar T, Kanner AA, Yaffe H, Marcovici D, et al. Intraoperative mapping and monitoring of the corticospinal tracts with neurophysiological assessment and 3-dimensional ultrasonographybased navigation. Clinical article. J Neurosurg 2011 Mar;114(3):738-746.

M

[105] Field AS, Alexander AL, Wu YC, Hasan KM, Witwer B, Badie B. Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor. J Magn Reson Imaging 2004 Oct;20(4):555-562.

d

[106] Price SJ, Burnet NG, Donovan T, Green HA, Pena A, Antoun NM, et al. Diffusion tensor imaging of brain tumours at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol 2003 Jun;58(6):455-462.

Ac ce pt e

[107] Wieshmann UC, Clark CA, Symms MR, Franconi F, Barker GJ, Shorvon SD. Reduced anisotropy of water diffusion in structural cerebral abnormalities demonstrated with diffusion tensor imaging. Magn Reson Imaging 1999 Nov;17(9):1269-1274. [108] Goebell E, Paustenbach S, Vaeterlein O, Ding XQ, Heese O, Fiehler J, et al. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology 2006 Apr;239(1):217-222. [109] Tropine A, Vucurevic G, Delani P, Boor S, Hopf N, Bohl J, et al. Contribution of diffusion tensor imaging to delineation of gliomas and glioblastomas. J Magn Reson Imaging 2004 Dec;20(6):905-912. [110] Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 2003 May;24(5):937-941. [111] Lu S, Ahn D, Johnson G, Law M, Zagzag D, Grossman RI. Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. Radiology 2004 Jul;232(1):221-228. [112] Deng Z, Yan Y, Zhong D, Yang G, Tang W, Lu F, et al. Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neurosci 2010 Dec;17(12):1530-1536. [113] Provenzale JM, McGraw P, Mhatre P, Guo AC, Delong D. Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 2004 Aug;232(2):451-460.

Page 17 of 23

[114] Price SJ, Pena A, Burnet NG, Jena R, Green HA, Carpenter TA, et al. Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas. Eur Radiol 2004 Oct;14(10):1909-1917. [115] Sinha S, Bastin ME, Whittle IR, Wardlaw JM. Diffusion tensor MR imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol 2002 Apr;23(4):520-527. [116] Morita K, Matsuzawa H, Fujii Y, Tanaka R, Kwee IL, Nakada T. Diffusion tensor analysis of peritumoral edema using lambda chart analysis indicative of the heterogeneity of the microstructure within edema. J Neurosurg 2005 Feb;102(2):336-341.

ip t

[117] Inoue T, Ogasawara K, Beppu T, Ogawa A, Kabasawa H. Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin Neurol Neurosurg 2005 Apr;107(3):174-180.

cr

[118] Kleiser R, Staempfli P, Valavanis A, Boesiger P, Kollias S. Impact of fMRI-guided advanced DTI fiber tracking techniques on their clinical applications in patients with brain tumors. Neuroradiology 2010 Jan;52(1):37-46.

us

[119] Stadlbauer A, Hammen T, Grummich P, Buchfelder M, Kuwert T, Dorfler A, et al. Classification of peritumoral fiber tract alterations in gliomas using metabolic and structural neuroimaging. J Nucl Med 2011 Aug;52(8):1227-1234.

an

[120] Stadlbauer A, Nimsky C, Gruber S, Moser E, Hammen T, Engelhorn T, et al. Changes in fiber integrity, diffusivity, and metabolism of the pyramidal tract adjacent to gliomas: a quantitative diffusion tensor fiber tracking and MR spectroscopic imaging study. AJNR Am J Neuroradiol 2007 Mar;28(3):462-469.

M

[121] Yen PS, Teo BT, Chiu CH, Chen SC, Chiu TL, Su CF. White Matter tract involvement in brain tumors: a diffusion tensor imaging analysis. Surg Neurol 2009 Nov;72(5):464-9; discussion 469.

d

[122] Kuroiwa T, Nagaoka T, Ueki M, Yamada I, Miyasaka N, Akimoto H, et al. Correlations between the apparent diffusion coefficient, water content, and ultrastructure after induction of vasogenic brain edema in cats. J Neurosurg 1999 Mar;90(3):499-503.

Ac ce pt e

[123] Stadlbauer A, Ganslandt O, Buslei R, Hammen T, Gruber S, Moser E, et al. Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 2006 Sep;240(3):803-810. [124] Yamada K, Kizu O, Mori S, Ito H, Nakamura H, Yuen S, et al. Brain fiber tracking with clinically feasible diffusion-tensor MR imaging: initial experience. Radiology 2003 Apr;227(1):295-301. [125] Melhem ER, Mori S, Mukundan G, Kraut MA, Pomper MG, van Zijl PC. Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am J Roentgenol 2002 Jan;178(1):3-16. [126] Ojemann JG, Miller JW, Silbergeld DL. Preserved function in brain invaded by tumor. Neurosurgery 1996 Aug;39(2):253-8; discussion 258-9. [127] Skirboll SS, Ojemann GA, Berger MS, Lettich E, Winn HR. Functional cortex and subcortical white matter located within gliomas. Neurosurgery 1996 Apr;38(4):678-84; discussion 684-5. [128] Black PM. Comment on: Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: a prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery 2007;61(5):949. [129] Duffau H. The Dangers of Magnetic Resonance Imaging Diffusion Tensor Tractography in Brain Surgery. World Neurosurg 2013 Feb 1. [130] Duffau H. Diffusion Tensor Imaging as A Research and Educational Tool, But Not Yet A Clinical Tool. World Neurosurg 2013 Sep 6.

Page 18 of 23

[131] Keles GE, Lundin DA, Lamborn KR, Chang EF, Ojemann G, Berger MS. Intraoperative subcortical stimulation mapping for hemispherical perirolandic gliomas located within or adjacent to the descending motor pathways: evaluation of morbidity and assessment of functional outcome in 294 patients. J Neurosurg 2004 Mar;100(3):369-375. [132] Jameson LC, Sloan TB. Monitoring of the brain and spinal cord. Anesthesiol Clin 2006 Dec;24(4):777791.

ip t

[133] Benzagmout M, Gatignol P, Duffau H. Resection of World Health Organization Grade II gliomas involving Broca's area: methodological and functional considerations. Neurosurgery 2007 Oct;61(4):741-52; discussion 752-3.

cr

[134] Duffau H. Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 2005 Aug;4(8):476-486.

us

[135] Sarubbo S, Le Bars E, Moritz-Gasser S, Duffau H. Complete recovery after surgical resection of left Wernicke's area in awake patient: a brain stimulation and functional MRI study. Neurosurg Rev 2012 Apr;35(2):287-92; discussion 292.

an

[136] Scholz J, Klein MC, Behrens TE, Johansen-Berg H. Training induces changes in white-matter architecture. Nat Neurosci 2009 Nov;12(11):1370-1371. [137] Berger MS. Comment on: Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 2005;56(1):138.

M

[138] Nimsky C. Fiber tracking--a reliable tool for neurosurgery? World Neurosurg 2010 Jul;74(1):105-106.

d

[139] De Witt Hamer PC, Robles SG, Zwinderman AH, Duffau H, Berger MS. Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis. J Clin Oncol 2012 Jul 10;30(20):25592565.

Ac ce pt e

[140] Ostry S, Belsan T, Otahal J, Benes V, Netuka D. Is Intraoperative Diffusion Tensor Imaging at 3.0T Comparable to Subcortical Corticospinal Tract Mapping? Neurosurgery 2013 Nov;73(5):797-807. [141] Ohue S, Kohno S, Inoue A, Yamashita D, Harada H, Kumon Y, et al. Accuracy of diffusion tensor magnetic resonance imaging-based tractography for surgery of gliomas near the pyramidal tract: a significant correlation between subcortical electrical stimulation and postoperative tractography. Neurosurgery 2012 Feb;70(2):283-93; discussion 294. [142] Zolal A, Hejcl A, Vachata P, Bartos R, Humhej I, Malucelli A, et al. The use of diffusion tensor images of the corticospinal tract in intrinsic brain tumor surgery: a comparison with direct subcortical stimulation. Neurosurgery 2012 Aug;71(2):331-40; discussion 340. [143] Prabhu SS, Gasco J, Tummala S, Weinberg JS, Rao G. Intraoperative magnetic resonance imaging-guided tractography with integrated monopolar subcortical functional mapping for resection of brain tumors. Clinical article. J Neurosurg 2011 Mar;114(3):719-726.

Page 19 of 23

Figure legend Fig. 1: Posterior view of a deterministic tractography of a 34-year-old patient with a right-sided frontotemporoparietal gemistocytic astrocytoma prior to a planned fourth resection. The tumor is highlighted in yellow. We placed a region of interest in the right cerebral peduncle, without further adjustment to get nicer results. The left image shows the close relationship of the tractography of the pyramidal tract (green) projected on a T1-weighted MRI scan. The right image shows the same deterministic tractography projected on the

ip t

fractional anisotropy map. The deterministic tractography approach follows only the principal diffusion direction. As a consequence, it is clearly visible that only part of the pyramidal tract is depicted, with the lateral and most

cr

medial aspect of the tract missing. Blue: superior-inferior tracts, green: antero-posterior tracts, red: right-left tracts.

us

Fig. 2: Preoperative DTI images of representative tracts of a 40-year-old patient with a large left-sided diffuse gemistocytic astrocytoma that presented with focal epileptic seizures (dysphasia). All images are made with

an

deterministic tractography with an FA threshold of 0.18. R denotes the right side of the brain. (A) 3D graphical lateral view of the tumor (blue/purple) and the relation with the left arcuate fascicle (yellow). These deep structures are projected on the surface of the cerebral cortex. (B) The same tract projected on a transversal T2-

M

weighted FLAIR MRI, showing that there is space between the arcuate fascicle and the tumor, although there seems to be a close relationship. (C) The cingulate fasciculi projected on a coronal T1-weighted MRI (without gadolinium contrast). (D) The pyramidal tracts projected on a coronal T2-weighted FLAIR MRI, which shows

d

that the left tract has a close relationship with the tumor, but does not seem to be displaced or disrupted. (E) This picture clearly shows that the left inferior fronto-occipital fasciculus is disrupted by the tumor and there is a stop

tumor.

Ac ce pt e

in the tractography. (F) 2D Illustration of the displacement and also disruption of the left uncinate fascicle by the

Table 1: Limitations and potential benefits of DTI in neurosurgical resections of tumors. Limitations

Potential benefits

Variability in data acquisition, possibly

Defining optimal surgical approach

leading to low SNR’s

Tractography depends on used software,

Potential beneficial role in multimodal

tracking method, seed region and other user-

navigation to maximize tumor resection,

specified variables

without causing more morbidity

Registration error

Synergy with other modalities

Brain shift affects tractography reliability

Possibility of intraoperative updating may guide in optimal resection strategies

Page 20 of 23

False negative tracts by white matter tract

Improved orientation for direct stimulation

alterations Comfirmation with direct stimulation

Ac ce pt e

d

M

an

us

cr

ip t

False positive tracts (tracts without function)

Page 21 of 23

ip t cr us an

Ac ce pt e

d

M

Figure 1 .

Page 22 of 23

ip t cr us an

Ac ce pt e

d

M

Figure 2 .

Page 23 of 23