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 » Introduction
 » Subjects and Methods
 » Results
 » Discussion
 »  References
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 »  Article Tables

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ORIGINAL ARTICLE
Year : 2015  |  Volume : 63  |  Issue : 1  |  Page : 49-57

Analyzing functional, structural, and anatomical correlation of hemispheric language lateralization in healthy subjects using functional MRI, diffusion tensor imaging, and voxel-based morphometry


1 Department of Imaging Science and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
2 Department of Cognition and Behavioral Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
3 Department of Radiodiagnosis, Medical College, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
4 Madhavan Nayar Center for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India

Date of Web Publication4-Mar-2015

Correspondence Address:
Dr. Chandrasekharan Kesavadas
Department of Imaging Science and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram - 695 011, Kerala
India
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Source of Support: Kerala State Council for Science, Technology and Environment (KSCSTE), Conflict of Interest: None


DOI: 10.4103/0028-3886.152634

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 » Abstract 

Context: To evaluate the efficacy of diffusion fiber tractography (DFT) and voxel-based morphometry (VBM) for lateralizing language in comparison with functional magnetic resonance imaging (fMRI) to noninvasively assess hemispheric language lateralization in normal healthy volunteers.
Aims: The aim of the present study is to evaluate the concordance of language lateralization obtained by diffusion tensor imaging (DTI) and VBM to fMRI, and thus to see whether there exists an anatomical correlate for language lateralization result obtained using fMRI.
Settings and Design: This is an advanced neuroimaging study conducted in normal healthy volunteers.
Subjects and Methods: Fifty-seven normal healthy subjects (39 males and 18 females; age range: 15-40 years) underwent language fMRI and 30 underwent direction DTI. fMRI language laterality index (LI), fiber tract asymmetry index (AI), and tract-based statistics of dorsal and ventral language pathways were calculated. The combined results were correlated with VBM-based volumetry of Heschl's gyrus (HG), planum temporale (PT), and insula for lateralization of language function.
Statistical Analysis Used: A linear regression analysis was done to study the correlation between fMRI, DTI, and VBM measurements.
Results: A good agreement was found between language fMRI LI and fiber tract AI, more specifically for arcuate fasciculus (ArcF) and inferior longitudinal fasciculus (ILF). The study demonstrated significant correlations (P < 0.05) between blood-oxygen-level dependent (BOLD) fMRI activations, tract-based statistics, and PT and HG volumetry for determining language lateralization.
Conclusions: A strong one-to-one correlation between fMRI, laterality index, DTI tractography measures, and VBM-based volumetry measures for determining language lateralization exists.


Keywords: Diffusion fiber tractography; functional magnetic resonance imaging; language lateralization; voxel-based morphometry


How to cite this article:
James JS, Kumari SR, Sreedharan RM, Thomas B, Radhkrishnan A, Kesavadas C. Analyzing functional, structural, and anatomical correlation of hemispheric language lateralization in healthy subjects using functional MRI, diffusion tensor imaging, and voxel-based morphometry. Neurol India 2015;63:49-57

How to cite this URL:
James JS, Kumari SR, Sreedharan RM, Thomas B, Radhkrishnan A, Kesavadas C. Analyzing functional, structural, and anatomical correlation of hemispheric language lateralization in healthy subjects using functional MRI, diffusion tensor imaging, and voxel-based morphometry. Neurol India [serial online] 2015 [cited 2019 Aug 19];63:49-57. Available from: http://www.neurologyindia.com/text.asp?2015/63/1/49/152634



 » Introduction Top


Language is a lateralized brain function, with most activity usually found in the left (dominant) hemisphere. [1],[2] Brain asymmetry, language laterality, and handedness are complexly interrelated. Some individuals have the presence of "atypical language" representation (right hemisphere or bilateral representation). [3],[4],[5] The investigation of neuroanatomical substrates of linguistic processing in atypical language lateralization, particularly in non-right handers, is an interesting area among neurolinguistic researchers. [6],[7]

The morphological explanation of left hemisphere language lateralization has mostly focused on structural asymmetries of planum temporale (PT), Heschl's gyrus (HG), and insula. [8],[9] Several functional and structural neuroimaging studies [10],[11],[12] demonstrated the role of these structures in language processing.

Clinically, it is crucial to determine the dominant hemisphere for language function. [13] Therefore, for effective cognitive processing, the integrity and intactness of both cortical brain areas and their connectivity [14] should be well understood. While the asymmetries of white matter pathways can be studied by diffusion fiber tractography (DFT), [15],[16] the functional relationships between cortical brain regions responsible for linguistic processing can be measured using blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI). [17],[18]

The main objective of the present study is to evaluate the concordance of language lateralization obtained by diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) to fMRI, and thus to see whether an anatomical 
correlate for language lateralization result obtained using 
fMRI exists. Considering fMRI as a noninvasive imaging technique for language lateralization, we wanted to test our hypothesis that DTI and VBM are as sensitive as fMRI in language lateralization.


 » Subjects and Methods Top


The Institutional Ethics Committee of our institute approved the study and a written informed consent was obtained from 57 normal, healthy volunteers (39 males and 18 females; age range: 15-40 years; 51 = right handed, 6 = left handed), with no history of any disorder affecting the brain function.

Data acquisition

The MRI was performed using 1.5 T magnetic resonance scanner (Avanto SQ engine, Siemens, Erlangen, Germany) with echo-planar imaging (EPI) capabilities. During fMRI session, high-resolution three-dimensional (3D) T1-weighted images of the entire head were obtained with a 3D spoiled gradient-recalled acquisition in the steady-state sequence [(3D fast low-angle shot (FLASH); time of repetition/time of echo (TR/TE) 11/4.94 ms, flip angle 15°, field of view (FOV) 256 mm, slice thickness 1 mm, matrix 256 × 256)] in 6.3 min. A gradient-echo echo-planar sequence based on BOLD effects (TR/TE 3580/50 ms, flip angle 90°, FOV 250 mm, matrix 64 × 64, slice thickness 3 mm) was applied after the gradient field mapping to acquire T2-weighted functional images. The scanning time for each fMRI session was 6.02 min.

For the acquisition of DTI data, a spin-echo echo-planar DTI sequence was acquired in 7 min, with diffusion gradients along 30 noncollinear directions with the following imaging parameters: TR 3500 ms, TE 105 ms, matrix 192 × 192, FOV 230 mm 2 , 2 mm slice thickness with 1.5 mm gap averaged twice and with a and b factor of 0 and 1000 s/mm 2 , respectively. A high-resolution T1-weighted 3D magnetization prepared rapid gradient echo (MPRAGE) image with excellent gray matter-white matter contrast was acquired in 10.16 min for VBM-based volumetric analysis with the following imaging parameters: TR 2000 ms, TE2.91 ms, flip angle 8°, slice thickness 2 mm, FOV 256 mm, and matrix 256 × 256. The total time for whole data acquisition was around 48 min.

fMRI language paradigms

We designed four different boxcar model language paradigms (visual verb generation (VG), syntactic task (ST), semantic decision task, and word pair task [WP]), which consisted of sequence of blocks (five blocks each) [that constitutes an active or a baseline condition and typically lasts 30 s]. All stimuli were presented visually in the subject's native language and the rest condition was same for all paradigms, during which a checker board was shown to the subjects.

fMRI data analysis

fMRI data analysis was done using Statistical Parametric Mapping (SPM5, Wellcome Trust Department of Cognitive Neurology, London, UK, www.fil.ion.ucl.ac.uk/spm) [19] software implemented in MATLAB (Matlab 7.1, MathWorks, Natick, MA) environment. The fMRI data from each subject were slice acquisition-corrected, motion artifacts removed, normalized, smoothed, and coregistered with the coplanar anatomical image and represented in a stereotaxic template. [20],[21] The conventional SPM approach-generated statistical parametric maps (SPM) of t-statistics, reflecting the differences between active and baseline states at each voxel location with a probability threshold of P < 0.05 (family-wise error (FWE)-corrected), were chosen.

For the quantification of hemispheric dominance, [22] fMRI language laterality index (LI) was calculated based on equality, LI = L−R/L + R, where L and R correspond to number of activated voxels on the left and right hemisphere, respectively. The positive (+) value denotes left hemisphere lateralization, negative (−) value denotes right hemisphere lateralization, and zero (0) represents bilateral activation of language function.

DTI analysis and fiber tractography

DFT was done by using DTI studio software (http://Ibam.med.jhmi.edu). [23] For reconstruction of arcuate fasciculus (ArcF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and inferior fronto-occipital fasciculus (IFOF), a deterministic approach based on Fiber-Assignment 
by Continuous-Tracking (FACT) algorithm [24] was employed. A multiple ROI approach was performed [Figure 1]a and b based on manually defined ROIs on the axial, coronal, or sagittal color fractional anisotropy (FA) map of each subject. By the cross-referencing DTI studies [13],[23] and earlier tractography works, [14],[25] regions of interest (ROIs) were defined manually and used as targeting seed regions for fiber tracking.
Figure 1: (A) The trajectory of dorsal and ventral language pathways and its identification in color FA maps; (a) ArcF, (b) UF, (c) ILF, and (d) IFOF. (B) Reconstructed language pathways overlaid on FA maps. ArcF = Arcuate fasciculus, ILF = inferior longitudinal fasciculus, UF = uncinate fasciculus, IFOF = inferior fronto-occipital fasciculus, L = left, R = right


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An FA threshold of 0.2 and a turning angle of 30°were selected to terminate the tract propagation. For each white matter tract, following quantitative parameters were calculated: (a) FA, (b) mean diffusivity (MD), (c) fiber tract volumes (FTV), and (d) relative fiber density (RFD). In order to determine the hemispheric dominance based on structural asymmetry values, a fiber symmetry index (AI) was calculated by using the formula, AI = (RFD in left− RFD in right)/(RFD in left + RFD in right). If AI > 0.20, it indicated a leftward structural asymmetry and if AI < −0.20, it indicated rightward structural asymmetry, and a value close to "0" represented bilateral fiber tract symmetry.

VBM-based volumetric analysis

The volumetric measurements of PT, HG, and insula were obtained by voxel-based morphometric approach [Figure 2]a using SPM based on automated anatomical labeling (AAL- Anatomical Automatic Labeling; http://www.cyceron.fr/web/aal). [26] The normalized, segmented, modulated gray matter images were then smoothed with a 12-mm isotropic Gaussian kernel. Individual gray matter structures were segmented out based on the AAL  Atlas More Details with the help of Matlab-based segmentation algorithm.
Figure 2: Segmentation and volumetry of HG, PT, and insula using automated VBM approach. (a) Segmentation, (b) AAL-based parcellation of subcortical structures, (c) 3D overlay using MRIcron, (d) BOLD SPM t-maps (3D rendered) showing activated clusters for different language paradigms. HG = Heschl's gyrus, PT = planum temporale, VBM = voxel-based morphometry, AAL = anatomical automatic labeling, 3D = three-dimensional, BOLD = blood-oxygen-level dependent, SPM = Statistical Parametric Mapping


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Analysis of imaging data was performed by two independent operators. The operator who performed the DTI and VBM processing was blinded to the results of fMRI data processing.

Statistical analysis

All statistical analyses were performed using SPSS (Statistical Package for Social Sciences, www.ibm.com/software/analytics/spss/) [27] software version 17.0. To assess the reproducibility of fMRI and DTI measurements, an inter-rater agreement between the two independent operators was calculated using kappa statistics. A linear regression analysis was performed to test the correlation between the relative fiber AI and fMRI LI and volumetric measurements. Statistical significance was considered at P < 0.05 level.


 » Results Top


Visual verb generation and semantic decision task (VG and SeT) tasks generated significant (P < 0.05) BOLD activations in classic language processing areas. Obviously the VG task produced more prominent activation patterns (P < 0.001) in language expression and comprehension areas in the dominant hemisphere. During the SeT task, greater activation was found in speech comprehension areas compared to the speech production area [Figure 2]b. Comparatively less BOLD activations were found during Syn and WP tasks. Montreal neurologic institute (MNI) coordinates of fMRI activations are depicted in [Table 1]. The hemodynamic response function (HRF) plots [Figure 3] for VG and SeT paradigms were found to be fitting well with the experimental boxcar model compared to Syn and WP tasks in all subjects.
Figure 3: Plots showing hemodynamic response functions for different language paradigms. ArcF = arcuate fasciculus, ILF = inferior longitudinal fasciculus, UF = uncinate fasciculus, IFOF = inferior fronto-occipital fasciculus, AI = asymmetry index


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Table 1: BOLD fMRI activation results and MNI coordinates of cortical brain activations for different language tasks


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Significant differences (P = 0.004) were found in the LI values between the right- and left-handed subjects. Of 51 right-handed subjects, the overall functional LI showed left-sided functional hemispheric language lateralization (LI ≥ 0. 10) in 42 subjects, right-sided language representation in 4, and bilateral language representation in 5 subjects. Out of six left-handed subjects, one subject showed significant left-sided language lateralization, two subjects showed right-sided, and three subjects showed bilateral language lateralization.

In right-handed control subjects, with the predominant left-sided fMRI activation, an asymmetry of left ArcF, and ILF was observed with high FA. The RFD and tract volume of left ArcF and ILF were also found to be higher than the right side, and no significant differences (P = 1.210) were noted in MD. An increased FA and RFD were found in right ArcF, and ILF for subjects with right-sided fMRI activation. No significant differences in any of the tract-based quantitative parameters were found for subjects with bilateral representation [Table 2]. A good inter-rater agreement (K = 0.72) was found between the two operators for the fMRI and DTI measurements.
Table 2: Mean values of tract-based quantitative parameters


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fMRI-VBM correlation for language lateralization

An increased left PT and HG volume was found in dominant (left) hemisphere-activated subjects. However, in this group, no significant volume differences (P = 0.210) were observed for the left insula. In the right hemisphere dominant groups, in addition to high right PT and HG, an increased insular volume (P = 0.021) was observed in the right hemisphere. No significant volume differences (P = 0.104) were found for any gray matter structures for bilateral subjects [Table 3].
Table 3: Correlation between fMRI, fiber tractography, and volumetric observations


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fMRI-DTI correlation for language lateralization

The study tested the correlations between the lateralization scores of fMRI activation measures (fMRI LI) and asymmetry scores of DTI-derived parameters (fiber AI). The linear regression analysis demonstrated a good correlation between language fMRI LI and fiber tract AI, more specifically, for ArcF (R ArcF: r 2 = 0.240, P = 0. 015; L ArcF: r 2 = 0.280, P = 0. 020) and ILF (R ILF: r 2 = 0.221, P = 0.004; L ILF: r 2 = 0. 214, P = 0.013). No correlations were found between (a) UF AI and fMRI LI (R UF: r 2 = 0.014, P = 0.210; L UF: r 2 = 0.122, P = 0.231) and (b) IFOF AI and fMRI measurements in any of the control subjects (R IFOF: r 2 = 2.245, P = 0.205; L IFOF: r 2 = 2.331, P = 0.146) [Figure 4].
Figure 4: Scatter plots showing relation between language fMRI LI and AI of white matter tracts. (a and b) Positive correlation was found for ArcF and ILF. (c and d) No correlation was found for UF and IFOF. fMRI = Functional magnetic resonance imaging, ArcF = Arcuate fasciculus, LI = laterality index, ILF = inferior longitudinal fasciculus, UF = uncinate fasciculus, IFOF = inferior fronto-occipital fasciculus, AI = asymmetry index


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 » Discussion Top


Functional hemisphere language lateralization by fMRI

We have investigated the relative effectiveness of four different commonly used clinical language fMRI paradigms. The results of this study demonstrate that VG and SeT paradigms are the best among this group of paradigms for the lateralization of expressive and receptive language areas. The main language components involved in our VG task are the orthography, lexico-semantic, and sublexical processes. In line with the observed results, the performance of VG and SeT tasks predominantly engaged in the posterior superior temporal gyrus, the area of early cortical stage responsible for speech perception. This area subsequently diverges into two processing streams [the dorsal (ArcF) and ventral (IFG, IFOF, and UF) 10 language pathways] [12] and is mainly involved in auditory-motor 11 integrations and sound-to-meaning interfaces. Our findings confirm the best lateralizing capability of the above-mentioned tasks for lateralizing expressive and receptive language functions.

A similar study by Binder et al., [28] found 100% concordance in 22 cases for a semantic decision task. Furthermore, Szaflarski et al., [29] reported a high correlation (r < 0.75, P < 0.001) between the fMRI and the signal intensity changes for a semantic decision. In spite of these findings, a variable agreement was found in a study by Baciu et al., [30] using four semantic tasks (categorical judgment, word stem completion, and semantic and phonological association). The functional LI in our study was strictly based on positive fMRI activation in the anatomically predefined regions (both frontal and temporoparietal language areas). This is consistent with the previous observations [31],[32],[33] suggesting that language function restricted to the right hemisphere is found in less than 2% of the normal subjects.

Structural hemispheric language lateralization by tractography

The mean FA and RFD of dorsal language network were found to be dependent on tract size and tract volume. Apart from MD, significant variations in the FA values were detected in ArcF and ILF pathway, suggesting that a strong degree of asymmetry was explicitly found for dorsal and ventral language pathways.

We also noticed quantitative differences between the right and left hemispheres, with overall tract volumes being significantly higher on the left than the right hemisphere for majority of right-handed subjects. Examination of these white matter tract volumes as a function of commonality showed that the degree of lateralization was higher for left ArcF and ILF than UF and IFOF. Moreover, the mean FA of the estimated tracts was also significantly higher on the left than on the right. Our findings in the control population support the results of a study by Parker et al., [34] which has used tractography to investigate the lateralization of language pathways, demonstrating the stronger connection in the left hemisphere.

In spite of fiber count, the nearest RFD is the ratio of reconstructed fiber pathways based on mathematically derived tensor directions. Therefore, it reflects a theoretical representation of the structural density of anatomical connections. However, it can be influenced by methodological limitations that are inherent to the tractography.

Agreement between lateralization from the fiber tractography and fMRI results was sought as a validation for the clinical utility of fMRI and DTI. The study found a good agreement (92%) between the two methods, suggesting that the DTI method could be used in a similar way to the fMRI for language lateralization. This could be useful, especially in children who fail to perform complex language tasks.

Anatomical correlation of language function by VBM

Our results for the volume of PT and HG indicate the relatively larger volume on the left sided compared to the right in 86% of control subjects. A similar MRI-based study by Pierre et al., [9] found that the left HG was associated with larger white matter volume than the right HG. Our volumetric results showed the asymmetries favoring the left hemisphere for both HG and PT for control subjects are in concordance with a previous finding by Woods et al,.[11] There is also much neuroanatomical evidence to suggest that structural differences between the two hemispheres exist. Foundas et al. [3] were among the first to attempt to relate the PT asymmetry directly to language lateralization by means of IAT. Our measurement of PT volumetry supports these results and 93% of our control subjects showed higher PT volume in the language-dominant hemisphere. Although it is well-known that PT is larger in the left hemisphere than in the right, few studies have reported on HG in this regard. [35]

Another gray matter structure we included in the present study was insula. Chee et al. (2004) [36] reported in a functional neuroimaging study that among the regions showing language-dependent increments in activation, the left insula showed greater activation in subjects with equal proficiency in both Chinese and English languages. However, the measured insular volumes in our control subjects were comparable on both sides. It was clearly understood from the study that morphological asymmetries of PT and HG may be predetermined, are more resistant to change and the morphological asymmetry of the insula may be related to user-dependent factors.

Moreover, our study also evaluated the efficacy of VBM segmentation algorithm by applying the same in high-resolution structural MRI data (MPRAGE). We found that for segmentation-based morphometric analysis, the MPRAGE worked better than FLASH 3D sequence because of a better gray and white matter differentiation. So, for a routine clinical set up, a single structural MRI only needs to be done, which will bring down the total scanning time.

The main limitation of the study was the relatively small sample size. More studies need to be performed using higher Tesla magnets and larger sample size to find out the correlation between the results of fMRI, DTI, and VBM. Such studies are needed before we can conclude that there is a consistent one-to-one correlation between these techniques and that the structural imaging techniques can be used for language lateralization in a clinical scenario.

In conclusion, our study has shown that there exists a one-to-one correlation between fMRI, VBM-based volumetry, and DTI-based structural connectivity. The observations from the present study demonstrated that tractography of white matter pathways and volumetry of corresponding gray matter anatomical correlates can be combined with BOLD signal changes during fMRI to noninvasively explore the in vivo structure-function relationship of language function in the human brain.

 
 » References Top

1.
Broca P. On the seat of the faculty of articulated language. Bull Anthropol Soc Paris 1865;6:377-93.  Back to cited text no. 1
    
2.
Frost JA, Binder JR, Springer JA, Hammeke TA, Bellgowan PS, Rao SM, et al. Language processing is strongly left lateralized in both sexes. Evidence from functional MRI. Brain 1999;122:199-208.  Back to cited text no. 2
    
3.
Foundas AL, Leonard CM, Gilmore RL, Fennell EB, Heilman KM. Pars triangularis asymmetry and language dominance. Proc Natl Acad Sci U S A 1996;93:719-22.  Back to cited text no. 3
    
4.
Powell HW, Parker GJ, Alexander DC, Symms MR, Boulby PA, Wheeler-Kingshott CA, et al. Hemispheric asymmetries in language-related pathways: A combined functional MRI and Tractography Study. Neuroimage 2006;32:388-99.  Back to cited text no. 4
    
5.
Vernooij MW, Smits M, Wielopolski PA, Houston GC, Krestin GP, van der Lugt A. Fiber density asymmetry of the arcuate fasciculus in relation to functional hemispheric language lateralization in both right- and left-handed healthy subjects: A combined fMRI and DTI Study. NeuroImage 2007;35:1064-76.  Back to cited text no. 5
    
6.
Khedr EM, Hamed E, Said A, Basahi J. Handedness and language cerebral lateralization. Eur J Appl Physiol 2002;87:469-73.  Back to cited text no. 6
    
7.
James JS, Kesavadas C. Functional magnetic resonance imaging of language in patients with epilepsy. In: Kar BK, editor. Cognition and brain development: Converging evidence from various methodologies. 1 st ed. Washington: American Psychological Association; 2013. p. 289-310.  Back to cited text no. 7
    
8.
Geschwind N, Levitsky W. Human brain: Left-right asymmetries in temporal speech region. Science 1968;161:186-7.  Back to cited text no. 8
    
9.
Dorsaint-Pierre R, Penhune VB, Watkins KE, Neelin P, Lerch JP, Bouffard M, et al. Asymmetries of the planum temporale and Heschl′s gyrus: Relationship to language lateralization. Brain 2006;129:1164-76.  Back to cited text no. 9
    
10.
Smith KM, Mecoli MD, Altaye M, Komlos M, Maitra R, Eaton KP, et al. Morphometric differences in the Heschl′s gyrus of hearing impaired and normal hearing infants. Cereb Cortex 2011;21:991-8.  Back to cited text no. 10
    
11.
Woods DL, Herron TJ, Cate AD, Kang X, Yund EW. Phonological processing in human auditory cortical fields. Front Hum Neurosci 2011;5:42.  Back to cited text no. 11
    
12.
Hickok G, Poeppel D. Dorsal and ventral streams: A framework for understanding aspects of the functional anatomy of language. Cognition 2004;92:67-99.  Back to cited text no. 12
    
13.
Catani M, Jones DK, ffytche DH. Perisylvian language networks of the human brain. Ann Neurol 2005;57:8-16.  Back to cited text no. 13
    
14.
Muthusami P, James J, Thomas B, Kapilamoorthy TR, Kesavadas C. Diffusion tensor imaging and tractography of the human language pathways: Moving into the clinical realm. J Magn Reson Imaging 2014;40:1041-53.  Back to cited text no. 14
    
15.
Le Bihan D. Molecular diffusion, tissue microdynamics and microstructure. NMR Biomed 1995;8:375-86.  Back to cited text no. 15
    
16.
Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med 2011;65:1532-56.  Back to cited text no. 16
    
17.
Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 1990;87:9868-72.  Back to cited text no. 17
    
18.
Price CJ. The anatomy of language: Contributions from functional neuroimaging. J Anat 2000;197:335-59.  Back to cited text no. 18
    
19.
Friston KJ. Statistical parametric mapping. In: Kötter R, editor. Neuroscience Databases. 1 st ed. New York: Springer; 2003. p. 237-50.  Back to cited text no. 19
    
20.
Van Essen DC. A Population-Average, Landmark- and Surface-Based (PALS) atlas of human cerebral cortex. Neuroimage 2005;28:635-62.  Back to cited text no. 20
    
21.
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273-89.  Back to cited text no. 21
    
22.
MarsBaR region of interest toolbox for SPM [Internet] - MarsBaR v0.43 Documentation. Available from: http://marsbar.sourceforge.net/[Last cited on 2013 Mar 20].  Back to cited text no. 22
    
23.
Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S. Dti Studio: Resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006;81:106-16.  Back to cited text no. 23
    
24.
Mori S, van Zijl PC. Fiber tracking: Principles and strategies - a technical review. NMR Biomed 2002;15:468-80.  Back to cited text no. 24
    
25.
Sreedharan RM, Menon AC, James JS, Kesavadas C, Thomas SV. Arcuate fasciculus laterality by diffusion tensor imaging correlates with language laterality by functional MRI in preadolescent children. Neuroradiology 2014.  Back to cited text no. 25
    
26.
UMR 5296 [Internet] - Neurofunctional Imaging Group (GIN) - AAL. Available from: http://www.gin.cnrs.co.in/[Last cited on 2014 Jul 02].  Back to cited text no. 26
    
27.
Spss, Data Mining, Statistical Analysis Software, Predictive Analysis, Predictive Analytics, Decision Support Systems [Internet]. Available from: http://www.spss.co.in/[Last cited on 2014 july 15].  Back to cited text no. 27
    
28.
Binder JR, Swanson SJ, Hammeke TA, Sabsevitz DS. A comparison of five fMRI protocols for mapping speech comprehension systems. Epilepsia 2008;48:1980-97.  Back to cited text no. 28
    
29.
Szaflarski JP, Holland SK, Schmithorst VJ, Byars AW. An fMRI study of language lateralization in children and adults. Hum Brain Mapp 2006;27:202-12.  Back to cited text no. 29
    
30.
Baciu MV, Rubin C, Décorps MA, Segebarth CM. fMRI assessment of hemispheric language dominance using a simple inner speech paradigm. NMR Biomed 1999;12:293-8.  Back to cited text no. 30
    
31.
Foundas AL, Leonard CM, Gilmore R, Fennell E, Heilman KM. Planum temporale asymmetry and language dominance. Neuropsychologia 1994;32:1225-31.  Back to cited text no. 31
    
32.
Moffat SD, Hampson E, Lee DH. Morphology of the planum temporale and corpus callosum in left handers with evidence of left and right hemisphere speech representation. Brain 1998;121:2369-79.  Back to cited text no. 32
    
33.
Loring DW, Meador KJ, Lee GP, Murro AM, Smith JR, Flanigin HF, et al. Cerebral language lateralization: Evidence from intracarotid amobarbital testing. Neuropsychologia 1990;28:831-8.  Back to cited text no. 33
    
34.
Parker GJ, Luzzi S, Alexander DC, Wheeler-Kingshott CA, Ciccarelli O, Lambon Ralph MA. Lateralization of ventral and dorsal auditory-language pathways in the human brain. Neuroimage 2005;24:656-66.  Back to cited text no. 34
    
35.
Musiek FE, Reeves AG. Asymmetries of the auditory areas of the cerebrum. J Am Acad Audiol 1990;1:240-5.  Back to cited text no. 35
    
36.
Chee MW, Choo WC. Functional imaging of working memory after 24 hr of total sleep deprivation. J Neurosci 2004;12:4560-7.  Back to cited text no. 36
    


    Figures

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    Tables

  [Table 1], [Table 2], [Table 3]

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