CME JOURNAL OF MAGNETIC RESONANCE IMAGING 40:1041–1053 (2014)

Review Article

Diffusion Tensor Imaging and Tractography of the Human Language Pathways: Moving Into the Clinical Realm Prakash Muthusami, MD,* Jija James, PhD, Bejoy Thomas, DNB, T.R. Kapilamoorthy, MD, and Chandrasekharan Kesavadas, MD The functional correlates of anatomical derangements are of interest to the neurological clinician. Diffusion tensor tractography (DTT) is a relatively new tool in the arsenal of functional neuroimaging, by which to assess white matter tracts in the brain. While much import has been given to tracking corticospinal tracts in neurological disease, studying language pathway interconnections using DTT has largely remained in the research realm. Hardware and software advances have allowed this tool to ease into clinical practice, with several radiologists, neurologists, and neurosurgeons now familiar with its applications. DTT images, although visually appealing, are founded in mathematical equations and assumptions, and require a more than basic understanding of principles and limitations before they can be integrated into routine clinical practice. Cognitive pathways like that of language, that are normally hard to assess and especially more so when pathologically affected, have been at the receiving end of several opposing and often controversial hypotheses, and the past decade has seen the clarification, validation or rejection of several of these by the in vivo charting of functional connectivity using DTT. The focus of this review is to illustrate DTT of the language pathways with emphasis on practical considerations, clinical applications, and limitations. Key Words: diffusion tensor imaging; tractography; language pathways; arcuate fasciculus; hemispheric dominance; language plasticity J. Magn. Reson. Imaging 2014;40:1041–1053. C 2013 Wiley Periodicals, Inc. V

THE DIRECTIONAL ANISOTROPY measured by diffusion tensor imaging (DTI) permits the tracking of intact fibers in the living brain (1–3), potentially enaDepartment of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum. Additional Supporting Information may be found in the online version of this article. *Address reprint requests to: P.M., Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India 695011. E-mail: [email protected] Received May 17, 2013; Accepted November 14, 2013. DOI 10.1002/jmri.24528 View this article online at wileyonlinelibrary.com. C 2013 Wiley Periodicals, Inc. V

bling the characterization of structural connectivity between functional cortical regions (1,4–6). Being a noninvasive imaging method, diffusion tensor tractography (DTT) of white matter tracts to decipher the organization of and interconnections between cognitive functional areas in the brain has recently caught the interest of the research community. One such area of interest is the brain’s language network. Until recently in the research domain, the translation of findings of language mapping into clinical practice is nascent but has exciting potential. One of the oldest and most enduring network models of language organization, propounded by Wernicke in the 19th century consisted of a center for recognizing sound in the posterior temporal lobe (this would later be named the Wernicke’s area) and a center in the inferior frontal lobe which was responsible for the production of sound (later named the Broca’s area) (7–9). Lichtheim later formulated, as an extension of this “classic model,” a three-component interconnected language model that included a hypothetical “concept center” for semantic processing (10,11). More recently, Hickok and Poeppel (12,13) proposed a dual stream model for language processing which, as an analogy of the visual processing model, consisted of ventral and dorsal pathways, the former serving in auditory-motor integration (the so-called “how” function) and the latter in sound-meaning interpretation (the so-called “what” function). There is at present extensive ongoing research into how exactly functional language centers in the brain are interrelated, and endeavors like the Human Connectome Project (HCP) can potentially provide these answers (14). Not surprisingly, white matter DTT forms the basis of projects such as the HCP. In this study, we elucidate the performance and various practical applications of DTT of the language pathways in the clinical setting.

FUNCTIONAL ANATOMY OF BASIS OF NEUROIMAGING

LANGUAGE:

THE

At a very fundamental level, language involves the association of auditory inputs to meaningful thought and speech output, thereby enabling us to interact

1041

1042

with our environment. While the concept of “input” and “output” centers for language have always been recognized, it is an undeniable fact that at a cognitive level, language encoding necessitates the interplay of memory, attention, auditory, visual, and motor networks (15,16). The popular language models devised in the 19th century were “neurological” models and were largely based on lesional studies (9,17). The most popular of these, reported in 1861, was the brain of Leborgne, one of Broca’s patients, who had developed a motor aphasia from an ischemic insult to the left inferior frontal gyrus. By deduction, this area would represent the area controlling, or at least included in, the circuitry for speech articulation. Approximately a decade after this, Wernicke reported a patient who had speech comprehension difficulty, with damage to the left posterior superior temporal gyrus (10). Yet another decade later, Lichtheim propounded his concept of conduction aphasia, borne from injury to the arcuate fasciculus, which, by then had been established, connected the eponymous Broca’s and Wernicke’s areas (7,18). It is interesting but not wholly surprising that the pathways for processing of language from visual inputs (e.g., when a word is read) are not exactly the same as those involved in processing auditory signals (e.g., when the same word is heard). Dejerine, from his work on alexia syndromes, in 1891 deduced that visual input merges into the language pathways by means of a center in the angular gyrus that traffics signals into the Wernicke’s area (10,18,19). Figure 1a and 1b show the cognitive components of these models. The perceived shortcomings of the lesion deficit model fueled the development of more recent behavioral-based models of language processing, focusing on explaining linguistic functionality rather than the lack of it (18,20– 22). These cognitive models were largely developed by psychologists in the second half of the 20th century. These information processing systems consist of several interacting subcomponents and describe neurofunction in terms of different levels of organization rather than in isolation. These still-evolving concepts form the basis of present neuroimaging-driven language models. With the importance placed on connectionist and/or disconnectionist themes in several of these hypotheses, it behooves us to be familiar with the following white matter tracts in the brain that have an important and often complementary role in language processing. a. Superior Longitudinal Fasciculus (SLF) and Arcuate Fasciculus Previously considered synonymous in humans, these are a group of tracts located lateral to the corona radiata above the insula, connecting portions of frontal lobe to inferior parietal lobule and temporal gyri. In the classical associationist models, the terms superior longitudinal fasciculus (SLF) and arcuate tracts were used interchangeably. Anatomical studies in primates (23), and more recently neuroimaging studies in humans, have shown the subcomponent nature of this bundle (24). It has been shown that the SLF in

Muthusami et al.

Figure 1. The components of the connectionist model of language which forms the basis of most diffusion tensor tractographic interpretation. a: Pathway for analyzing the spoken word, by means of the auditory lexicon. After auditory input, sounds are processed in Wernicke area, labeled 3. From here, information transfer occurs to 4, Broca’s area, and subsequently a motor plan is formed and sent to the premotor cortex, labeled 5. b: Pathway for analyzing the written word. Note that both these pathways have common integral throughputs from the superior temporal gyrus, labeled 3 in this representation, and share output modules through the inferior frontal gyrus, here labeled 4. Visual processing of language transits through the angular gyrus, labeled 2, which serves as a way-station, so to speak, of the orthographic to auditory lexicon, probably through other components of human cognition. Separate connections from the superior temporal gyrus to the inferior frontal region have been depicted by means of the upwardly convex arrow in (A) and downward convex arrow in (B), to represent that different ventral and dorsal streams exist, although the basis of their functional segregation is yet speculative. c: Schematic diagram depicting the dissociation of the temporofrontal language connections, as elucidated by recent functional neuroimaging studies (See text). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

the nonhuman primate is a four component tract system, connecting the homologue of the human inferior parietal lobule (SLF I, II, and III), and superior temporal gyrus (arcuate fasciculus) to the frontal language areas. The SLF I is the medial and dorsal component, extending to the dorsal premotor and dorsolateral prefrontal cortices (24). The SLF II and arcuate fascicule have largely a similar trajectory above the insula, projecting anteriorly to the dorsolateral frontal cortex (25). The SLF III extends from the supramarginal gyrus to the ventral premotor and prefrontal regions. In a recent q-ball–based interrogation of connections between anterior and posterior language areas in humans, the authors demonstrated a similar dissociation between the trajectory of fiber tracts extending posteriorly from areas 44 compared with those from area 45 of Broca’s area (26). The fibers from area 44, they stated, connect to the inferior parietal lobule while fibers from area 45 connect with a more ventral

Tractography of Language Pathways

trajectory to the superior and middle temporal gyri. The former of these tracts, corresponding to the aforementioned SLF III, was in turn ventral in its trajectory to the arcuate fasciculus (and theoretically, SLF II) which projected from the traditional location of the Wernicke’s area in the superior temporal gyrus to areas 6 and 8 on the dorsolateral frontal cortex. Although increasing spatial and angular resolution of diffusion imaging studies make the in vivo partitioning of SLF tract components a reality, for clinical purposes it is reasonable to consider this collection of fibers in unison. b. Uncinate Fasciculus (UF) The UF is a ventral associative bundle that connects the anterior temporal lobe with the medial and lateral orbitofrontal cortex. It was initially shown in nonhuman primates as the anterior-most of the temporofrontal projection systems (25), and later shown in humans as a fan-like array of fibers arising from the anterior temporal region and coalescing into a single solid bundle of fibers that runs in the external and extreme capsules deep to the insula before terminating in the inferior frontal regions (27,28). Its functions are far from fully known, but it has been shown to play a role in certain types of learning and memory, and also supposedly mediates social-emotional driven behavior. It is believed to be pathologically involved in several neurological and psychiatric disorders such as schizophrenia, bipolar disorder, and anxiety disorder (29). Its role in the language network has largely been reported to be naming of objects, action, and people (30,31). Recent studies have also shown that the UF is involved in semantic control during word comprehension (32). In one study describing intraoperative subcortical stimulation, the authors found no language perturbations brought about by stimulating the region of the UF (33). They also reported no postoperative language deficits on surgical partial removal of the UF. However, a more recent study showed an impairment in naming objects and famous faces after surgical resection of the UF (31), and another combined functional MRI (fMRI) DTI study suggested that the fibers through the extreme capsule are relevant for comprehension (34). c.

Inferior Longitudinal Fasciculus (ILF)

The ILF is a ventral associative bundle with long and short fibers connecting the occipital and temporal lobes, and for long has been believed to be concerned with the transfer of visual objects to the language areas in the temporal region. Accordingly, several clinical syndromes were attributed to a disconnection in this fiber pathway, including visual agnosia and prosopagnosia (35). Several other neurological disorders have also been correlated with ILF involvement, such as thought disorders, visual emotion, and cognitive impairments (36). In the literature, however, there is no consensus on the role, or, indeed, requirement of the ILF in serving language subcomponents, with opinions varying from its role in object naming (37), language semantics, visual object recognition (38), and

1043

even no absolute independent function (39). Needless to say, the role of the ILF in language, like the UF, is yet to be fully defined, and functional imaging can potentially provide us with some of the answers. d.

Inferior Fronto Occipito Fasciculus (IFOF)

The IFOF is a ventral associative bundle that connects the ventral occipital lobe to the orbitofrontal cortex by means of the temporal lobe. In the temporal stem, the IFOF runs medial and superior to the UF, and in this region these tracts are in close association with the optic radiations; in the extreme capsule, the IFOF runs superior to the UF. The IFOF is believed to be a direct pathway from the frontal to the occipital lobes (40), the UF and ILF together forming a complementary indirect pathway, and these pathways are linked to the perisylvian network at least in two different regions, posteriorly to the superior temporal language area and anteriorly to the inferior frontal language area. Accordingly, and given the strong correlations between the ILF and IFOF white matter bundles (41), it is likely that these tracts play a similar role in the execution of language processes. ROLE OF FUNCTIONAL NEUROIMAGING The past decade has seen the elucidation as well as alteration of several of the language models by the use of functional neuroimaging (Fig. 1), particularly fMRI and DTT (26,34,42–44). Unlike lesional studies, functional neuroimaging allows probing of function in vivo and has the fundamental advantage of studying the interaction of functional areas. This pertains both to residual function in the setting of structural damage as well as to the loss of function despite structural integrity, thereby not being limited to an assumption of their relation. fMRI maps out the brain areas associated with function by contrasting the blood oxygen level dependent (BOLD) signal on “task” versus “rest” states (45,46). Depicting whether and how these discrete functional areas are structurally related to each other is in the realm of DTT (2,3,47,48). Functional neuroimaging, and in particular DTT, is causing a gradual shift in the classical disconnectionist paradigm of language toward a cognitive model founded on connectivity (1). The 21st century will indubitably witness an increasing interest in, and investigation into, these anatomicofunctional coalitions of assumed neurological prototype using state-of-the-art neuroimaging. DIFFUSION TRACKING

TENSOR

IMAGING

AND

FIBER

DTI is a relatively new imaging technique based on measuring water diffusivity in brain tissue. Water diffusivity is considerably more anisotropic (directional) in white matter than in gray matter, owing to the arrangement of axonal membranes and microskeletal components that necessitate motion parallel to axonal orientation (2,47,49,50). By applying multiple directional motion probing gradients, DTI quantifies this

1044

Muthusami et al.

Figure 2. The concept of fiber allocation by continuous tracking (FACT) used for deterministic diffusion tensor tractography. a: shows a schematic representation of this line propagation technique. The squares represent voxels, and the oblique lines within them, the direction of the largest eigenvector in that voxel. Discrete co-ordinates are converted to a continuous coordinate which yields a “tract” depicted here by the white line. Interpolation is then performed with distance-averaged vector orientation using a predetermined step size to obtain a continuous smooth tract, depicted here by the curved line. b: FACT performed over several voxels, with predefined track-termination parameters, showing an array of extracted tracts.

anisotropy and provides an assessment of the directionality of white matter within individual voxels. The most widely used DTI parameters are mean diffusivity (MD), as a measure of the average motion of water molecules independent of tissue directionality, and fractional anisotropy (FA), which reflects the directionality of water diffusion within fiber tracts, and thereby the degree of alignment within them and their structural integrity. MD is supposed to be mainly affected by cellular size, integrity, and myelination, whereas FA is indicative of fiber integrity and alignment (51– 53). In normal fiber tracts, water diffusion is directional (high FA), whereas in degenerated or disrupted tracts, FA decreases substantially. DTI parameters are becoming increasingly important in various pathologies of the white matter, including ischemia (54– 57), demyelination (51,55,58,59), as well as in tumor imaging (60–62). Diffusion-sensitive MRI sequences are used to generate virtual two- or three-dimensional representations of the white matter fiber tracts. One can then, with prior anatomical knowledge of white matter tracts, chart out the likely course of particular tracts. These tracts are based on similarities between the diffusion tensor properties of neighboring voxels, and several mathematical algorithms have been devised to translate diffusion properties into putative tracts (3,47,48,63,64). The approaches to reconstructing white matter tracts from vector directionality are roughly divided into two types: line propagation techniques and global energy minimization techniques (47,65–71). As their name suggests, line propagation methods link neighboring voxels that conform to predefined specifications to produce a continuous tract. The fiber allocation by continuous tracking (FACT) algorithm is a popular method used by several tractography software systems. In this technique of deterministic tracking, each voxel is represented by its maximum eigenvector and fiber construction is performed by tracking along consecutive principal eigenvectors such that the resultant fiber at every point

represents the direction of the eigenvector in that voxel. A schematic representation of this method is shown in Figure 2. The global energy minimization methods attempt to find the energetically most favorable path between two predetermined voxels, and represent paths thus delineated as tracts. For example, the “fast marching” method uses likelihood of connectivity of a seed point to another voxel by assessing the arrival times if tracked along different possible paths (47). Similarly, other probability-based algorithms propagate a wavefront of “likely” tracts, that permit analysis of possible diverging or crossing fibers (72). These tracts, being based on the directional movement of water through imaged voxels, are dependent upon, determined by, and thus act as a surrogate for, brain microstructure. It is important to recognize that tract trajectories thus delineated are an instantiation of a conglomerate of individual vector fields. The obvious and significant size disparity between voxels and axons requires that a favorable trade-off exist between spatial resolution and the anisotropy experienced by water molecules due to cytoarchitectural inhomogeneity. While being at present the only noninvasive in vivo method available for mapping white matter fiber tract trajectories in the human brain, this process is technically and mathematically demanding and requires several further advances in the fields of diffusion weighted imaging (DWI) and data processing before a simplistic association between extracted and actual neuronal tracts can be assumed. DISSOCIATING THE LANGUAGE PATHWAYS USING DTT Several tracts pertaining to language networks have been described, and can be described under the following two broad categories (13,34): (A) Dorsal pathways. (B) Ventral pathways. The dorsal stream projects from the temporal cortex toward inferior parietal and posterior frontal cortices

Tractography of Language Pathways

1045

the SLF. A simpler, but cruder parcellation of the SLF can be performed by DTI for clinical purposes using selective tracking of fibers, for example, to the inferior or middle frontal gyri (see Fig. 5b). The reconstruction protocol (Fig. 4a) is as follows: A single region of interest (ROI) method can be used with a region selected on a coronal section in which the fornix can be identified as a single intense structure. Here the projections are located superior to the sagittal stratum and are seen as green bundles lateral to the blue corona radiata. A two ROI method can also be used where the first ROI is drawn on the inferior and/or middle frontal gyrus on a coronal section and a second ROI is drawn on a region, which includes the posterior superior temporal gyrus on a sagittal slice of the DTI color map. b. Uncinate Fasciculus (UF)

Figure 3. Maps generated from the diffusion tensor images with Neuro 3D software. a: Directional color-coded fractional anisotropy (FA) maps, coded as follows: blue ¼ cephalocaudad, green ¼ anteroposterior, red ¼ left–right. Tensor maps (b), tensor map aligned with anatomical images (c), and tensor map aligned with FA map of the same patient (d). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

and is involved in auditory-motor integration, thus being involved in the control of tasks such as repeating an auditory input. The ventral stream projects from frontal areas to the middle and inferior temporal cortices and occipital cortex, and functions as an “interpreter” for language inputs to abstract thought and thence to formulation of a motor output. The DTI tract correlates of the ventral stream consist of the inferior longitudinal, the inferior fronto-occipital, and the uncinate fasciculi; the correlates of the dorsal pathways consist of the large arcuate and superior longitudinal fasciculi. The process of DTT for the language pathways consist of the following steps: sequence acquisition, postprocessing, region of interest (ROI) placement, and fiber tracking. Our institution protocol for DTI data acquisition is detailed in the online supplement (Supp. Material S1, which is available online). We perform reconstruction of the language white matter pathways as explained below. Figure 3 shows postprocessed DTI maps and Figure 4 shows how ROIs are placed on these maps for generating various language tracts. a. Superior Longitudinal Fasciculus (SLF) and Arcuate Fasciculus Due to the resolution-size mismatch between DTI and nerve bundles, separation of the SLF into its subcomponents is not wholly possible, however, newer techniques like q-ball imaging have been able to dissociate

The most posterior coronal slice in which the temporal lobe is separated from the frontal lobe is selected (Fig. 4b). The first ROI is drawn in the antero-inferior temporal lobe, and the second ROI is drawn in the posterior aspect of the lateral orbitofrontal region on a coronal slice. Alternately, the ROIs can also be conveniently drawn on axial slices that include the inferior temporal and frontal gyri. c.

Inferior Longitudinal Fasciculus (ILF)

A coronal slice is selected that identifies the posterior edge of cingulum, which includes the first ROI in the medial occipital white matter (Fig. 4c). Next, the most posterior coronal slice, which includes the entire temporal lobe, is selected and the second ROI is drawn in the green bundle on the far lateral aspect of the sagittal stratum. Alternately, both ROIs can be drawn on an axial slice of the color DTI map that shows the ILF in its entirety.

Figure 4. Region-of-interest placement for generating the language tracts. Also see text for description. Superior longitudinal fasciculus and arcuate fasciculus (a), uncinate fasciculus (b), inferior longitudinal fasciculus (c), and inferior frontooccipital fasciculus (d). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

1046

Muthusami et al.

individual comparisons or for serial follow-up. Tracing tract anatomy in clinically normal individuals can also provide novel information on the functional neuroanatomy of language. Most of the clinical applications of language DTT, however, are in the setting of a pathological process and especially where a clinical question needs answering (6,56,57,77–79). While the concepts we describe here are also relevant for other functional tracts such as the visual and corticospinal tracts, we shall limit our discussion to the DTT of language tracts. Presurgical Tract Localization in Tumors and Lesional Epilepsy

Figure 5. Parcellation of tracts for clinical demonstration. Color-based tract separation has been performed with overlaying on three-dimensional anatomical images. a: Parcellation of the ventral (uncinate fasciculus in dark blue, inferior longitudinal fasciculus in green, and inferior fronto-occipital fasciculus in red) and dorsal (superior longitudinal and arcuate fascicule in sky blue and yellow) tracts connecting the temporal region with the frontal region. b: Parcellation showing the uncinate fasciculus in green and the arcuate fibers to inferior frontal gyrus in red. Superior longitudinal fibers to the middle frontal gyrus are shown here in yellow, showing the possibility of separation of fibers by tracking to source or destination gyri, a clinically very relevant application. c: Bilateral inferior occipitofrontal fibers. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

d.

Inferior Fronto Occipito Fasciculus (IFOF)

For the first ROI, a coronal slice is selected at the midpoint between the posterior edge of cingulum and posterior edge of parieto-occipital sulcus (Fig. 4d). For the second ROI, a coronal slice is selected at the anterior edge of the genu of corpus callosum. In both these sections, the IFOF can be seen as a green bundle in the medial frontal and occipital regions. Alternately, both ROIs can be conveniently drawn on an axial slice of DTI color map that shows the IFOF in its entirety. Tracts thus generated can be color-parcellated to better depict the different connections. In our experience, this last step allows the surgeon to better appreciate tract orientations and implications before planning treatment. Figure 5 shows various tracts after parcellation.

FROM THE LAB TO THE FIELD: CLINICAL APPLICATIONS OF LANGUAGE TRACTOGRAPHY In the clinical setting, language tractography has a role in both normal subjects as well as in patients. In normal subjects, DTT can be used as a qualitative tool to investigate tract location or presence (73–76). Quantitative parameters such as tract volume, tract density, or mean FA value can be used for accurate inter-

This is at present the area where DTT of the language tracts has the maximum clinical applicability and usefulness. Preservation of eloquent function with elimination of seizures is a priority during tumor resection surgeries. While fMRI localizes functional cortical language areas, it cannot elucidate language tracts in the peritumoral white matter. The ability to overlay in three dimensions information regarding tumor location and extent along with DTT information regarding tract course and orientation enables a detailed understanding of their relations and potential postsurgical deficits (Fig. 6). Notwithstanding brain shift during tumor surgery, DTT delineation provides a good assessment of the actual location of these functional tracts intraoperatively and increases the ability to define a safe margin around the tumor (61,62,80,81). This provides a dual advantage of reduced aggressiveness to preserve function as well as of more precise resections. A few studies have described the usefulness of DTT in preoperative planning with the categorization of tracts into affections such as “disruption”, “displacement,” “displacement with infiltration,” etc. (82–84). Although in routine clinical practice, several factors like tissue-type, tumoral mass effect, perilesional edema, pharmacotherapy, and inherent technical variations lead to significant imprecision in categorically assigning such labels (Fig. 7), this does not preempt the application of DTT information in neurosurgical planning. Similarly, DTT can be useful in the follow-up of the language tracts in low grade lesions (Fig. 8) and postoperatively (Fig. 9). Intraoperative electrophysiological mapping with intracranial electrodes, which requires “awake craniotomy,” has been used for the resection of tumors involving or in proximity to eloquent cortex (62,85–87). Expectedly, this places high technical, cost, and expertise demands. Analyzing the relation of tumor margins to tracts as delineated by DTT can help in planning resections, and a good correlation has been found between intraoperative cortical stimulation and DTT (88,89). Similarly, surgical planning, procedure modification, and prognostication using DTT have been described in lesional epilepsy (90). DTT can also depict the language network reorganization that often accompanies temporal lobe epilepsies (TLE), and can also provide important information before and after anterior temporal lobe resection. Based on DTT data, the cutoff distance for a safe margin of resection has been variously reported to be 5 mm and 10 mm (88–90). In our

Tractography of Language Pathways

Figure 6. Clinical usefulness of three-dimensional anatomical overlay of tractographic data. a: Axial fluid attenuated inversion recovery (FLAIR) image showing a low-grade glioma in the right temporo-insular region. b: Preoperative diffusion tractographic data overlaid on a composite coronal-axial three-dimensional FLAIR image showing the close proximity of arcuate fibers on the right side to the posterior margin of the tumor. c: Preoperative sagittal FLAIR image of a lowgrade glioma in the left inferior temporal gyrus. d: Diffusion tractographic information overlaid on three-dimensional FLAIR image showing close proximity of the ventral tracts (uncinate fasciculus in blue and inferior longitudinal fasciculus in red) to the tumor, while the fibers of the arcuate fasciculus (in yellow) are well away from anatomical tumor margins. Images such as these increase confidence in surgical planning.

practice, we coregister DTI images with a 3D-FLAIR sequence for anatomic information regarding lesion margin, and report distances less than 10 mm to represent increased risk of postoperative neurological deficit (Fig. 10). It has been argued, and with good reason, that fMRI-driven DTT can provide more accurate estimates of the location of white matter tracts related to the lesion of surgical interest (79,91,92). Using fMRI nodes as seed points for tract propagation, tracts in intimate association of the lesion can be determined. A recent study using fMRI-based tractography imported into a neuronavigation system and compared with electrocortical stimulation, showed the location of the stimulus points to be within 6mm of the DTT-predicted arcuate fasciculus (93). While still largely in the research arena, wider routine availability of, and clinician familiarity with functional imaging software will permit its integration into daily practice.

Determining Hemispheric Language Dominance Language lateralization in the human brain was recognized by both Broca and Wernicke. Broca described

1047

Figure 7. Information regarding tract involvement by diffusion tensor tractography (DTT) including fallacies in interpretation. a: Axial FLAIR image of a high-grade glioma in the left parieto-occipital region. b: DTT showing abrupt cut-off (red arrow) of the left inferior fronto-occipital fasciculus well ahead of the tumor margins. This could be due to several physiological, pathological, and pharmacological factors, as discussed in the text. c: Axial FLAIR image of a high-grade glioma in the left temporo-insular region. d: DTT of the left arcuate fasciculus shows incomplete tracts. This was due to inability of proper ROI placement anteriorly, and significant white matter edema posteriorly.

Figure 8. Diffusion tensor tractography in a case of glioma progression. Upper row, (left to right) shows, in temporal sequence, axial FLAIR images of a left fronto-insular glioma extending into the deep gray structures. Lower row shows the corresponding arcuate fasciculus tractography overlaid on three-dimensional anatomical images. Note the progressive diminution and disarray of left arcuate fibers (red) with increasing grade of the tumor. Also noted is the accompanying increasing volume and density of right arcuate fibers (yellow), possibly denoting plasticity in this patient whose clinical language scores remained preserved over time.

1048

Muthusami et al.

essing (26,101,102). DTT-based data have shown stronger frontotemporal connections on the left by means of the IFOF and UF and also from language receptive areas to the supramarginal gyrus (101,103). Using DTT, an “indirect” pathway by means of the inferior parietal lobule has been secerned separate from the “direct” arcuate fasciculus (7), suggesting a greater complexity of language connectivity between the frontal and temporal lobes regions than previously supposed. Other authors have, using fMRI-based DTT, reported distinct “ventral” and “dorsal” pathways serving as parallel streams of language processing (34). Whereas a visual assessment is often useful in the clinical setting to show asymmetrical tract patterns, quantitative comparisons can be performed using tract volume, fiber density, and mean FA (Fig. 11). However, given the considerable individual variability in language component processing, establishing the risk of aphasia in surgical therapy of dominant hemisphere using tractography is presently more accurate when preceded by fMRI to localize relevant functional cortex. Figure 9. A middle-aged right-handed man with left temporofronto-insular high grade glioma. a: Initial T2-weighted axial MRI image showing the extent of the lesion. b: Tractography showing a superior longitudinal fasciculus (SLF) between the posterior temporal and middle frontal gyri. Demonstration of this permitted tumor margins to be confidently defined. c: Postoperative T2-weighted axial MRI image showing postoperative defect. There was no postsurgical language deficit. d: Tractography shows maintained volume of SLF, explaining this functional preservation. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

the left-hemispheric language dominance in righthanded patients, and the corollary, that is, the right hemisphere being dominant in left-handed patients was the popular belief (76,94). This conjecture was rendered fallible with the subsequent description of several left-handed patients who suffered language deficits with left hemispheric strokes. Research in the second half of the 20th century, conducted primarily on patients with aphasias and recent-onset epilepsy, had resulted in postulations on the relationship between handedness and lateralization. Nevertheless, deriving hypotheses solely from neurological patients becomes a knotty issue when we factor in shifts in function after a stroke, the possibility of prior contralateral stroke, and the clinical heterogeneity that underlies several presentations. Several recent studies using functional imaging methods have convincingly corroborated these hypotheses (95–98), and provided findings whereon to base new hypotheses. Two fMRI studies demonstrated that 94% (99) and 96% (100) of right-handed subjects had their left hemispheres dominant for language function. Left-handed patients, conversely, showed greater bilateral and right hemispheric patterns of dominance. By providing a noninvasive method to study in vivo connectivity, DTT can provide a structural framework for this functional asymmetry. DTT has also shown several subtler structural hemispheric asymmetries that conform to patterns of variance in semantic proc-

Studying Language Plasticity in the Human Brain The phenomenon of neuroplasticity, the ability of nervous system reorganization in response to

Figure 10. DTT to guide surgical tumor resection. DTT data are coregistered with three-dimensional FLAIR images to assess tract relation to tumor margins. We report distances less than 10mm to represent increased risk of postoperative deficit. a: Preoperative coronal FLAIR image of a medial right temporal dysembroplastic neuroectodermal tumor in a patient with seizures who had right dominant language function. b,c: Arcuate fasciculus overlaid on 3D FLAIR image shows that the fibers are sufficiently far from the tumor margins to allow complete tumor resection without fear of neurological deficit. The patient underwent surgery which was uneventful. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Tractography of Language Pathways

1049

Figure 11. Language asymmetry in health and disease, as illustrated by diffusion tractography. a: Top row shows tractography of the arcuate fasciculus overlaid on anatomical images of a patient with a left parietotemporal high grade glioma. Cutoff of the temporal end of tracts is noted (yellow arrow). fMRI showed a right hemispheric language lateralization. Bottom row shows arcuate fasciculus tractography in another patient with seizures due to focal cortical dysplasia (yellow arrow) and right-lateralized language by fMRI. A visual inspection shows the reduced tract volume ipsilateral to the lesion, and this is useful in the clinical setting. b: Quantitative assessments are more accurate. The upper image shows values of tract number, length, and volume, as well as mean fractional anisotropy (FA) in a healthy volunteer with left-sided language lateralization. The bottom image is of a patient with focal cortical dysplasia (same patient as in Fig. 11a) showing increased right-sided tract volume and mean FA, revealing the basis for the clinical, fMRI, and visual findings of lateralization. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

environmental triggers, has piqued the interest of the research community and has recently been shown to have the potential to translate to effective clinical therapy (104,105). Such plasticity is considered “adaptive” when associated with a gain in function (106) or “maladaptive” when associated with loss of function, as has been illustrated by animal and human studies (107). Understandably, the ability to follow, noninvasively and in vivo, dynamic morphological changes brought about by pathological or physiological processes in the brain will have far-reaching consequences for cognitive neuroscience. Most of our knowledge of neuroplasticity comes from the study of motor recovery after stroke (108– 111). There is less experience with cognitive recovery after stroke, and functional neuroimaging plays a key role in the understanding of the structural processes underlying the functional metaphysis. The functional complexity of language stems from a widely distributed interactive network with task-dependent differential activation. Functional imaging data, largely from positron emission tomography and fMRI studies, suggest that the mechanism for language plasticity is for the most part due to recruitment of previously inhibited transcallosal and collateral secondary cortical modules (112). How these modules interconnect to supplant lost function can potentially be assessed

and followed by DTT (Fig. 12). Such cortical reorganization can be either or both ipsilateral or contralateral. Numerous reports elucidate both perilesional activation and contralateral homotopic activation following injury to language areas, leading to the description of recruited neural circuits as being either “compensatory” (i.e., from contralateral language areas) or “restorative” (i.e., from perilesional language areas) (113,114). Postsurgical language plasticity, while a wellrecognized phenomenon, has little representation in clinical neuroimaging literature. The limited data that exist appear to suggest that postoperative language recovery is dependent on the rate of tumor growth, such that recruitment of distant language networks is more efficient in a slowly growing than a rapidly growing tumor (112,115,116). It has thus been argued that low-grade tumors involving language areas may be surgically resected safely without resultant significant postoperative functional deficits, and also that iterative resection strategies may be considered instead of single definitive resection to reduce the risk and extent of postsurgical deficits. It has also been postulated that unlike ipsilateral recruitment in stroke-related aphasia, in tumors there is a more significant recruitment of contralateral functional networks, but this is not the rule. Whereas fMRI can

1050

Figure 12. Language connectional plasticity as depicted by tractography. A young right-handed woman with left middle cerebral artery (MCA) stroke 2 years back, had severe initial language affection, and significantly improving language scores over time. a,b: Axial section of T2-weighted MRI shows the chronic left MCA infarct. A voluminous superior longitudinal fasciculus is noted on the right side, yielding a possible explanation. Initial MRI was however not available for comparison. This case denotes recruitment, or activation, as it were, of contralateral connections. c,d: A young male with right-dominant language function, being followed up for right insular cavernoma. Axial T2-weighted image shows the cavernoma with peripheral hemosiderin staining, also extending into the inferior frontal region. Tractography of the right arcuate fasciculus shows a large volume tract, with significant connections to the middle and even superior frontal gyri, suggesting recruitment of perilesional functional cortex. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Muthusami et al.

(i.e., along the principal eigenvector) in a voxel (47,61,74). The complexity of the brain’s neural circuits suggests that this simplistic correlation is not precise and is therefore prone to confounding or altogether overlooking fibers that cross, meet or diverge. Tract information as portrayed by DTT must be interpreted with a prior knowledge of normal anatomy and expected pathological changes. The finesse provided by diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI) can potentially resolve a few of these issues and permit a more exact delineation of fiber orientation and relation (26,120,121). It is also important to remember that DTT – derived information (like neuronal density, orientation, or connections) pertaining to hemispheric dominance, plasticity, deficits in stroke etc., are based on factors that are susceptible to several conditions that could provide spurious results. The anisotropy of the diffusion tensor being a function of the intravoxel FA, a change in the latter could result in inconsistent fiber tracking. Edema, for one, is a common confounder while tracking neuronal fibers (Fig. 13) and its presence might cause substantial alteration not only in the process of fiber tracking but also in the finally derived tract. Similarly, posttreatment changes might cause several apparent changes, and deciding the veracity of these is an important clinical consideration. Using DTT information to plan surgical procedures, while providing a roadmap for excision, has the caveat that the surgeon be aware of the inherent limitations of the technique. As seen in Figure 7, when a tract close to a tumor is depicted by DTT to be

reliably illustrate newly functional cortical areas, DTI has an inherent advantage over fMRI in those patients who are unable to follow instructions due to severe pathological cognitive insult. An ideal situation would be to synthesize information from both these modalities in every case.

LIMITATIONS OF APPLYING LANGUAGE TRACTOGRAPHY IN THE CLINICAL CONTEXT DTI and tractography, while a robust and powerful tool capable of providing important information in the clinical setting, are not without limitations. While several excellent expositions are available detailing technical limitations in the performance of DTI and tractography (48,117–119), here we shall discuss these in their practical contexts. The principal limiting factor of DTI is the assumption of a single Gaussian diffusion function within each voxel, which translates to a single fiber direction

Figure 13. Limitation of fiber tracking in the presence of white matter edema. Coronal T2-weighted (a) and axial contrast-enhanced T1-weighted (b) images showing a right temporal meningioma, with severe underlying edema. c,d: Note the “cut-off” appearance of right-sided arcuate fibers, similar to the appearance of infiltrated fibers. This drawback of tractography, due to its dependence on fractional anisotropy (FA) and thus on factors that affect FA at the intravoxel level must be taken into account in tractographic interpretation. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Tractography of Language Pathways

“cut off,” it remains to be answered whether this actually represents tumor infiltration or is an aberration borne from the limitations of the fiber tracking process itself. Increasing familiarity and availability coupled with more reports on clinical applicability will allow these tools to become integrated into routine clinical practice. In this context, it is important that there is increasing use of quantitative DTI data, which would require software standardization across different vendors – the logistic difficulty of this being considerable, parametric maps of relative values (e.g., relative FA) might prove useful. Testing relevance for normal populations and patients against gold standards is necessary before widespread clinical acceptance. Trials with large numbers are required that compare DTI-derived hemispheric dominance against fMRI, DTI-derived plasticity against serial clinical scores and DTI-predicted tracts against intraoperative findings. There could also be a case made for reporting dominance and plasticity as quantitative values, validated against clinical function and outcome. As we have discussed earlier, the addition of functional information from fMRI, can serve to supplement DTT-derived information. THE FUTURE Several newer diffusion models have shown ways to circumvent the limitations of classical tensor-based tractographic models. DSI was one of the first successors to DTI, which allowed the measurement of microscopic diffusion function, thus permitting the resolution of intravoxel fiber crossing. DSI depends on the sampling of the diffusion signal on a threedimensional Cartesian lattice, and this, apart from being time-intensive, also requires large pulsed-field gradients (118,121). An alternative method, based on sampling the diffusion signal from a spherical shell of specified radius, rather than the three-dimensional lattice has also been used, called High angular resolution diffusion imaging (HARDI). Several models for reconstructing HARDI data have been described, as well as model-independent schemes like q-ball imaging (26,122–125). Notwithstanding that these methods pose new challenges and require validation and optimization before they can become clinically viable, it is inevitable that these advances in DTI will eventually spill over into the clinical arena and enable more useful information to be derived for neurological patients. In conclusion, DTT of the language pathways as a clinical tool, although not yet universal in clinical practice, is an area of growing interest. With newer developments in data models and software that push the limits of the yield from tractography, one hopes to eventually achieve a technique of noninvasive, in vivo dissection of white matter tracts. The magnitude of gain that the neurological sciences can expect from this will then be left only to one’s imagination. ACKNOWLEDGMENT Recipient of certificate of honor for Educational Exhibit at RSNA 2011.

1051

REFERENCES 1. Gong G, He Y, Concha L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 2009;19:524–536. 2. Nucifora PGP, Verma R, Lee SK, Melhem ER. Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. Radiology 2007;245:367–384. 3. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PCM, Mori S. Fiber tract-based atlas of human white matter anatomy. Radiology 2004;230:77–87. 4. Sporns O, Tononi G, Kotter R. The human connectome: a structural description of the human brain. PLoS Comput Biol 2005;1: e42. 5. Friston K. Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Annu Rev Neurosci 2002;25:221–250. 6. 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;7:715–727. 7. Catani M, Jones DK. Perisylvian language networks of the human brain. Ann Neurol 2005;57:8–16. 8. Anderson JM, Gilmore R, Roper S, et al. Conduction aphasia and the arcuate fasciculus: a reexamination of the WernickeGeschwind model. Brain Lang 1999;70:1–12. 9. Dronkers NF, Plaisant O, Iba-Zizen MT, Cabanis EA. Paul Broca’s historic cases: high resolution MR imaging of the brains of Leborgne and Lelong. Brain 2007;130:1432–1441. 10. Graves RE. The legacy of the Wernicke-Lichtheim model. J Hist Neurosci 1997;6:3–20. 11. Lichtheim L. On aphasia. Brain 1885;7:433–484. 12. Poeppel D, Hickok G. Towards a new functional anatomy of language. Cognition 2004;92:1–12. 13. Hickok G, Poeppel D. Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language. Cognition 2004;92:67–99. 14. Human Connectome Project: Mapping the human brain connectivity. Available at: http://www.humanconnectomeproject.org. Accessed: February 7, 2013. 15. Jackendoff R. What is the human language faculty?: two views. Language 2011;87:586–624. 16. Hauser MD. The faculty of language: what is it, who has it, and how did it evolve? Science 2002;298:1569–1579. 17. Hari R. Broca’s region: from action to language Nobuyuki Nishitani. J Neurophysiol 2005;94:2251–2254. 18. Price CJ. The anatomy of language: contributions from functional neuroimaging. J Anat 2000;197:335–359. 19. Catani M. The rises and falls of disconnection syndromes. Brain 2005;128:2224–2239. 20. Levelt WJM. Speaking: from intention to articulation. Cambridge, MA: MIT Press; 1993. 21. McClelland JL, Rumelhart DE. An interactive activation model of context effects in letter perception: an account. Psychol Rev 1981;88:375–407. 22. Seidenberg MS, McClelland JL. A distributed, developmental model of word recognition and naming. Psychol Rev 1989;96: 523–568. 23. Petrides M, Pandya DN. Projections to the frontal cortex from the posterior parietal region in the rhesus monkey. J Comp Neurol 1984;228:105–116. 24. Makris N, Kennedy DN, McInerney S, et al. Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study. Cereb Cortex 2004;15: 854–869. 25. Petrides M, Pandya DN. Association fiber pathways to the frontal cortex from the superior temporal region in the rhesus monkey. J Comp Neurol 1988;273:52–66. 26. Frey S, Campbell JSW, Pike GB, Petrides M. Dissociating the human language pathways with high angular resolution diffusion fiber tractography. J Neurosci 2008;28:11435–11444. 27. Highley JR. Asymmetry of the uncinate fasciculus: a postmortem study of normal subjects and patients with schizophrenia. Cereb Cortex 2002;12:1218–1224. 28. Kier EL, Staib LH, Davis LM, Bronen RA. MR imaging of the temporal stem: anatomic dissection tractography of the uncinate fasciculus, inferior occipitofrontal fasciculus, and Meyer’s loop of the optic radiation. AJNR Am J Neuroradiol 2004;25: 677–691.

1052 29. Kawashima T, Nakamura M, Bouix S, et al. Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: a diffusion tensor imaging study. Schizophr Res 2009; 110:119–126. 30. Von Der Heide RJ, Skipper LM, Klobusicky E, Olson IR. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain 2013;136:1692–707. 31. Papagno C, Miracapillo C, Casarotti A, et al. What is the role of the uncinate fasciculus? Surgical removal and proper name retrieval. Brain 2010;134:405–414. 32. Harvey DY, Wei T, Ellmore TM, Hamilton AC, Schnur TT. Neuropsychological evidence for the functional role of the uncinate fasciculus in semantic control. Neuropsychologia 2013;51:789–801. 33. Duffau H, Gatignol P, Moritz-Gasser S, Mandonnet E. Is the left uncinate fasciculus essential for language? A cerebral stimulation study. J Neurol 2009;256:382–389. 34. Saur D, Kreher BW, Schnell S, et al. Ventral and dorsal pathways for language. Proc Natl Acad Sci U S A 2008;105:18035–18040. 35. Tusa RJ, Ungerleider LG. The inferior longitudinal fasciculus: a reexamination in humans and monkeys. Ann Neurol 1985;18: 583–591. 36. Chanraud S, Zahr N, Sullivan EV, Pfefferbaum A. MR diffusion tensor imaging: a window into white matter integrity of the working brain. Neuropsychol Rev 2010;20:209–225. 37. Shinoura N, Suzuki Y, Tsukada M, et al. Deficits in the left inferior longitudinal fasciculus results in impairments in object naming. Neurocase 2010;16:135–139. 38. Ortibus E, Verhoeven J, Sunaert S, Casteels I, De Cock P, Lagae L. Integrity of the inferior longitudinal fasciculus and impaired object recognition in children: a diffusion tensor imaging study: ILF and object recognition in children. Dev Med Child Neurol 2012;54:38–43. 39. Mandonnet E, Nouet A, Gatignol P, Capelle L, Duffau H. Does the left inferior longitudinal fasciculus play a role in language? A brain stimulation study. Brain 2007;130:623–629. 40. Ashtari M. Anatomy and functional role of the inferior longitudinal fasciculus: a search that has just begun. Dev Med Child Neurol 2012;54:6–7. 41. Wahl M, Li Y-O, Ng J, et al. Microstructural correlations of white matter tracts in the human brain. Neuroimage 2010;51:531–541. 42. Kreher B, Schnell S, Mader I, et al. Connecting and merging fibres: pathway extraction by combining probability maps. Neuroimage 2008;43:81–89. 43. Scott SK, Blank CC, Rosen S, Wise RJS. Identification of a pathway for intelligible speech in the left temporal lobe. Brain 2000; 123:2400–2406. 44. Friederici AD, Bahlmann J, Heim S, Schubotz RI, Anwander A. The brain differentiates human and non-human grammars: functional localization and structural connectivity. Proc Natl Acad Sci U S A 2006;103:2458–2463. 45. Amaro E, Barker GJ. Study design in fMRI: basic principles. Brain Cognition 2006;60:220–232. 46. Huettel SA. Functional magnetic resonance imaging. Sunderland, MA: Sinauer Associates; 2004. 47. Mori S, van Zijl P. Fiber tracking: principles and strategies-a technical review. NMR in Biomedicine 2002;15:468–480. 48. Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 2006;51: 527–539. 49. Song S-K, Yoshino J, Le TQ, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005;26:132–140. 50. Pierpaoli C, Barnett A, Pajevic S, et al. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage 2001;13:1174–1185. 51. Cercignani M, Inglese M, Pagani E, Comi G, Filippi M. Mean Diffusivity and Fractional Anisotropy Histograms of Patients with Multiple Sclerosis. American Journal of Neuroradiology 2001; 22:952–958. 52. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994;103:247–254. 53. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996;201:637–648. 54. Liang Z, Zeng J, Liu S, et al. A prospective study of secondary degeneration following subcortical infarction using diffusion tensor imaging. J Neurol Neurosurg Psychiatry 2007;78:581–586.

Muthusami et al. 55. Werring DJ, Clark CA, Barker GJ, Thompson AJ, Miller DH. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 1999;52:1626. 56. Thomalla G, Glauche V, Weiller C, Rother J. Time course of wallerian degeneration after ischaemic stroke revealed by diffusion tensor imaging. J Neurol Neurosurg Psychiatry 2005;76:266–268. 57. Kim SH, Jang SH. Prediction of aphasia outcome using diffusion tensor tractography for arcuate fasciculus in stroke. AJNR Am J Neuroradiol 2012;34:785–790. 58. Droogan A, Clark C, Werring D, Barker G, McDonald W, Miller D. Comparison of multiple sclerosis clinical subgroups using navigated spin echo diffusion-weighted imaging. Magn Reson Imaging 1999;17:653–661. 59. Nusbaum AO, Tang CY, Wei T-C, Buchsbaum MS, Atlas SW. Whole-brain diffusion MR histograms differ between MS subtypes. Neurology 2000;54:1421–1427. 60. Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol 2004;25:356–369. 61. Nimsky C, Ganslandt O, Hastreiter P, et al. Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 2005;56:130–137; discussion 138. 62. Richter M, Zolal A, Ganslandt O, Buchfelder M, Nimsky C, Merhof D. Evaluation of diffusion-tensor imaging-based global search and tractography for tumor surgery close to the language system. PLoS One 2013;8:e50132. 63. Poupon C, Clark C, Frouin V, et al. Regularization of diffusion based direction maps for the tracking of brain white matter fascicles. Neuroimage 2000;12:184–195. 64. Basser P, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med 2000;44:625. 65. Feigl GC, Hiergeist W, Fellner C, et al. Magnetic resonance imaging diffusion tensor tractography: evaluation of anatomic accuracy of different fiber tracking software packages. World Neurosurg 2013;pii:S1878–8750(13)00011-9. 66. Mandelli M, Berger M, Berman J, Amirbekian B, Maccagnano E, Henry R. Probabilistic tracking can improve the delineation of cortico-spinal tract for neurosurgical planning. Proc. Intl. Soc. Mag. Reson. Med. 17 (2009). p. 1190. 67. Danielian LE, Iwata NK, Thomasson DM, Floeter MK. Reliability of fiber tracking measurements in diffusion tensor imaging for longitudinal study. Neuroimage 2010;49:1572–1580. 68. O’Donnell LJ, Westin C-F. An introduction to diffusion tensor image analysis. Neurosurg CLIN NORTH Am 2011;22:185–196. 69. Leclercq D, Delmaire C, Menjot de Champfleur N, Chiras J, Lehericy S. Diffusion tractography: methods, validation and applications in patients with neurosurgical lesions. Neurosurg Clin North Am 2011;22:253–68. 70. Jiang H, van Zijl P, Kim J, Pearlson GD, Mori S. Dti Studio: resource program for diffusion tensor computation and fiber bundle tracking. Computer methods and programs in biomedicine 2006;81:106–116. 71. Mukherjee P, Chung S, Berman J, Hess C, Henry R. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol 2008;29:843–852. 72. Paolo PG, Verma R, Lee SK, Melhem ER. Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. Radiology 245:367–384. 73. Hofer S, Frahm J. Topography of the human corpus callosum revisited? Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage 2006;32:989–994. 74. Mukherjee P, Berman J, Chung S, Hess C, Henry R. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am J Neuroradiol 2008;29:632–641. 75. Okada T, Miki Y, Fushimi Y, et al. Diffusion-tensor fiber tractography: intraindividual comparison of 3.0-T and 1.5-T MR imaging. Radiology 2006;238:668–678. 76. Knecht S, Drager B, Deppe M, et al. Handedness and hemispheric language dominance in healthy humans. Brain 2000; 123:2512–2518. 77. Galantucci S, Tartaglia MC, Wilson SM, et al. White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain 2011;134:3011–3029.

Tractography of Language Pathways 78. Heller SL, Heier LA, Watts R, et al. Evidence of cerebral reorganization following perinatal stroke demonstrated with fMRI and DTI tractography. Clin Imaging 2005;29:283–287. 79. 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;52:37–46. 80. Berman J. Diffusion MR Tractography As a Tool for Surgical Planning. Magn reson Imaging Clin North Am 2009;17:205–214. 81. Leclercq D, Duffau H, Delmaire C, et al. Comparison of diffusion tensor imaging tractography of language tracts and intraoperative subcortical stimulations. J Neurosurg 2010;112:503–511. 82. 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;20:555–562. 83. Witwer BP, Moftakhar R, Hasan KM, et al. Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 2002;97:568–575. 84. 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;232:221–228. 85. Taylor MD, Bernstein M. Awake craniotomy with brain mapping as the routine surgical approach to treating patients with supratentorial intraaxial tumors: a prospective trial of 200 cases. J Neurosurg 1999;90:35–41. 86. Meyer FB, Bates LM, Goerss SJ, et al. Awake craniotomy for aggressive resection of primary gliomas located in eloquent brain. Amsterdam: Elsevier; 2001. p 677–687. 87. Gupta DK, Chandra P, Ojha B, Sharma B, Mahapatra A, Mehta V. Awake craniotomy versus surgery under general anesthesia for resection of intrinsic lesions of eloquent cortex—a prospective randomised study. Clin Neurol Neurosurg 2007;109:335–343. 88. Berman JI, Berger MS, Chung SW, Nagarajan SS, Henry RG. Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging. J Neurosurg 2007;107:488–494. 89. Nimsky C, Ganslandt O, Merhof D, Sorensen AG, Fahlbusch R. Intraoperative visualization of the pyramidal tract by diffusion-tensorimaging-based fiber tracking. Neuroimage 2006;30:1219–1229. 90. Radhakrishnan A, James JS, Kesavadas C, et al. Utility of diffusion tensor imaging tractography in decision making for extratemporal resective epilepsy surgery. Epilepsy Res 2011;97:52–63. 91. Ulmer J, Salvan C, Mueller W, et al. The role of diffusion tensor imaging in establishing the proximity of tumor borders to functional brain systems: implications for preoperative risk assessments and postoperative outcomes. Technol Cancer Res Treat 2004;3:567. 92. Staempfli P, Reischauer C, Jaermann T, Valavanis A, Kollias S, Boesiger P. Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTIbased fiber tractography results. Neuroimage 2008;39:119–126. 93. Kamada K, Todo T, Masutani Y, et al. Visualization of the frontotemporal language fibers by tractography combined with functional magnetic resonance imaging and magnetoencephalography. J Neurosurg 2007;106:90–98. 94. Harris LJ. Broca on cerebral control for speech in right-handers and left-handers: a note on translation and some further comments. Brain Lang 1993;45:108–120. 95. Szaflarski JP, Binder JR, Possing ET, McKiernan KA, Ward B, Hammeke TA. Language lateralization in left-handed and ambidextrous people fMRI data. Neurology 2002;59:238–244. 96. Woermann FG, Jokeit H, Luerding R, et al. Language lateralization by Wada test and fMRI in 100 patients with epilepsy. Neurology 2003;61:699–701. 97. Rutten G, Ramsey N, Van Rijen P, Van Veelen C. Reproducibility of fMRI-determined language lateralization in individual subjects. Brain Lang 2002;80:421–437. 98. Fernandez G, Specht K, Weis S, et al. Intrasubject reproducibility of presurgical language lateralization and mapping using fMRI. Neurology 2003;60:969–975. 99. Springer JA, Binder JR, Hammeke TA, et al. Language dominance in neurologically normal and epilepsy subjects A functional MRI study. Brain 1999;122:2033–2046.

1053 100. Pujol J, Deus J, Losilla JM, Capdevila A. Cerebral lateralization of language in normal left-handed people studied by functional MRI. Neurology 1999;52:1038. 101. Parker GJM, Luzzi S, Alexander DC, Wheeler-Kingshott CAM, Ciccarelli O, Lambon Ralph MA. Lateralization of ventral and dorsal auditory-language pathways in the human brain. Neuroimage 2005;24:656–666. 102. Catani M, Allin MPG, Husain M, et al. Symmetries in human brain language pathways correlate with verbal recall. Proc Natl Acad Sci U S A 2007;104:17163–17168. 103. Powell HW, Parker GJM, Alexander DC, et al. Hemispheric asymmetries in language-related pathways: a combined functional MRI and tractography study. Neuroimage 2006;32:388–399. 104. Cramer SC, Sur M, Dobkin BH, et al. Harnessing neuroplasticity for clinical applications. Brain 2011;134:1591–1609. 105. Goto Y, Yang CR, Otani S. Functional and dysfunctional synaptic plasticity in prefrontal cortex: roles in psychiatric disorders. Biol Psychiatry 2010;67:199–207. 106. Cohen LG, Celnik P, Pascual-Leone A, et al. Functional relevance of cross-modal plasticity in blind humans. Nature 1997; 389:180–182. 107. Nudo RJ. Plasticity. Neurotherapeutics 2006;3:420–427. 108. Murphy TH, Corbett D. Plasticity during stroke recovery: from synapse to behaviour. Nature Rev Neurosci 2009;10:861–872. 109. Hermann DM, Chopp M. Promoting brain remodelling and plasticity for stroke recovery: therapeutic promise and potential pitfalls of clinical translation. Lancet Neurol 2012;11:369–380. 110. Di Lazzaro V, Profice P, Pilato F, et al. Motor cortex plasticity predicts recovery in acute stroke. Cereb Cortex 2010;20:1523– 1528. 111. Yu C, Zhu C, Zhang Y, et al. A longitudinal diffusion tensor imaging study on Wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 2009;47:451–458. 112. Pillai JJ. Insights into adult postlesional language cortical plasticity provided by cerebral blood oxygen level–dependent functional mr imaging. AJNR Am J Neuroradiol 2010;31:990–996. 113. Heiss WD. WSO Leadership in Stroke Medicine Award Lecture Vienna, September 26, 2008: functional imaging correlates to disturbance and recovery of language function. Int J Stroke 2009;4:129–136. 114. Landi D, Rossini PM. Cerebral restorative plasticity from normal ageing to brain diseases: a “never ending story”. Restor Neurol Neurosci 2010;28:349–266. 115. Duffau H. Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 2005;4:476–486. 116. Wu CX, Pu S, Lin Y, et al. Fractionated resection on low grade gliomas involving Broca’s area and insights to brain plasticity. Chin Med J (Engl) 2008;121:2026–2030. 117. Catani M. From hodology to function. Brain 2007;130:602–605. 118. Masutani Y, Aoki S, Abe O, Hayashi N, Otomo K. MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. Eur J Radiol 2003;46:53–66. 119. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: from Scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 2006;26(Suppl 1):S205–S223. 120. Friederici AD. Pathways to language: fiber tracts in the human brain. Trends Cogn Sci 2009;13:175–181. 121. Schmahmann JD, Pandya DN, Wang R, et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 2007;130:630–53. 122. Tuch DS. Q-ball imaging. Magn Reson Med 2004;52:1358–1372. 123. Pontabry J, Rousseau F. Probabilistic tractography using Q-ball modeling and particle filtering. Med Image Comput Comput Assist Interv 2011;14:209–216. 124. Fernandez-Miranda JC, Pathak S, Engh J, et al. High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications. Neurosurgery 2012;71:430–53. 125. Sotiropoulos SN, Bai L, Morgan PS, Constantinescu CS, Tench CR. Brain tractography using Q-ball imaging and graph theory: improved connectivities through fibre crossings via a modelbased approach. Neuroimage 2010;49:2444–2456.