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. Author manuscript; available in PMC: 2020 Oct 22.
Published in final edited form as: J Neuroimaging. 2017 Oct 24;28(2):199–205. doi: 10.1111/jon.12478

Diffusion Tensor Imaging Shows Corpus Callosum Differences between High-grade Gliomas and Metastases

Nicholas S Cho 1, Mehrnaz Jenabi 1, Julio Arevalo-Perez 1, Nicole Brennan 1, Robert J Young 1, Sasan Karimi 1, Andrei I Holodny 1, Kyung K Peck 1,2
PMCID: PMC7579570  NIHMSID: NIHMS1636377  PMID: 29064137

Abstract

Background and Purpose:

The corpus callosum (CC) has an important role in regulating interhemispheric transfer and is thought to be instrumental in contralateral brain reorganization in patients with brain tumors, as suggested by a previous study reporting callosal differences between language dominance groups through diffusion tensor imaging (DTI) characteristics. The purpose of this study was to explore the structural differences in the CC between high-grade gliomas (HGG) and metastatic tumors (MET) using the DTI characteristics of fractional anisotropy (FA), mean diffusivity (MD), and axial diffusivity (AD).

Methods:

HGG (n = 30) and MET (n = 20) subjects with MRI scans including DTI were retrospectively studied. The tumor and corpus callosum were segmented using the 3D T1-weighted scans to determine their volumes. The Region of Interest (ROI) (mean volume of the ROI = 3090 ± 464 mm3) of the body of the corpus callosum was overlaid onto the DTI parametric maps to obtain the averaged FA, MD, and AD values.

Results:

There were significant differences in the distributions of FA and MD values between the two patient groups (mean FA for HGG / MET = 0.691 / 0.646; p<0.05; mean MD for HGG / MET = 0.894 × 10−3 mm2/s / 0.992 × 10−3 mm2/s; p<0.01), while there was no correlation between the DTI parameters and the anatomical volumes.

Conclusion:

These results suggest that there is more contralateral brain reorganization in HGG patients than MET patients and that neither the tumor nor callosal volume impact the degree of contralateral brain reorganization.

Keywords: Corpus callosum, DTI, brain tumor, fractional anisotropy, brain reorganization

Background

The corpus callosum (CC) is the largest of the commissural fibers connecting the two homologous cortical areas of the human brain.1 Recent DTI studies exploring the degree and efficiency of interhemispheric transfer through the CC have indicated that high fractional anisotropy (FA) values in the CC are associated with more interhemispheric transfer2—regardless of whether the signals are inhibitory3 or excitatory4—and faster interhemispheric transfer time.5 Other studies have also evaluated the size of the CC and its association with interhemispheric transfer; whereas the size of the CC has been found to increase with the number of transcallosal fibers,6 no relation has been found with interhemispheric transfer time.7 Additionally, FA values have been found to decrease with age, and there have been mixed results on sex differences in FA values across the CC,8–10 which must be considered when conducting DTI studies in order to prevent confounded results.

A number of recent studies have demonstrated a capacity of the brain for transcallosal interhemispheric transfer of function from a damaged hemisphere to the contralateral side. Such brain plasticity has been described in patients with amputation,11 stroke,12–14 and brain tumors.15–24 In patients with brain cancer, there have also been mixed results if tumor volume impacts the degree of contralateral brain reorganization,20–22 while improved neurocognitive capabilities have been associated with increased contralateral brain reorganization.23, 24

In addition to inducing functional activation changes from contralateral brain reorganization, neuropathologies are known to induce structural changes specifically in the CC. Studies using DTI involving patients and healthy controls have shown that the patients had lower FA values in the CC than healthy controls for many conditions including lower-limb amputations,25 stroke,26 epilepsy,27 migraines,28 idiopathic scoliosis,29 and brain cancer.30 One study also found increased mean diffusivity (MD) values along with decreased FA values in the CC in patients with malformations of cortical development.27 Some studies have interpreted the decreased FA values in white matter structures of patients to be an indicator of Wallerian Degeneration, which is white matter degeneration in distal structures that are connected to the lesion.30, 31

A previous study by Saksena et al. found that FA values were lower and MD values were higher in brain cancer patients compared to healthy controls, but they were unable to find significant differences in DTI characteristics between patient groups.30 However, Tantillo et al. studied a population of brain cancer patients alone with fMRI and DTI data and found differences in FA values among two groups of patients.32 Left-hemispheric brain tumor patients who exhibited co-dominance of language function had higher FA values in the anterior CC compared to those who exhibited lateralized language function; this was suspected to occur due to higher interhemispheric transfer of excitatory signals for contralateral brain reorganization in the former group of patients.32 Moreover, they found evidence that different types of brain tumors can cause different amounts of contralateral brain reorganization, since those with more malignant tumors showed more co-dominant activation.32 This study focused on the anterior CC in particular because it was thought to serve the language areas of the brain, so structural changes in the CC caused by contralateral brain reorganization of language areas should be localized to that specific region of the CC.

The purpose of this study was to further investigate the structural differences in the CC that may arise from different amounts of contralateral brain reorganization as suggested by Tantillo et al.32 While Tantillo et al. investigated for differences based on language lateralization patterns, this study investigates whether different pathologies may result in structural changes indicative of different degrees of contralateral brain reorganization using the DTI parameters of FA, MD, and axial diffusivity (AD). The two patient groups studied were those with either a high-grade glioma (HGG) or metastatic tumor (MET) with the tumor located in a brain region served by the body of the CC. Additionally, the tumor had to not be infiltrating the CC because CC infiltration has been shown to impact the DTI characteristics of the CC by lowering FA and increasing MD values.33 Since HGG patients are known to have longer median survival times than MET patients,34–36 our hypothesis was that HGG patients would have a higher degree of contralateral brain reorganization because their longer median survival time affords the brain more time to undergo reorganization. This reorganization may also be potentiated by the insidious, infiltrative growth of HGGs that is absent in METs. DTI characteristics may suggest this through higher corpus callosal FA and AD values in HGG patients and higher corpus callosal MD values in MET patients. In addition, certain high-grade gliomas originate as low-grade gliomas, currently estimated at approximately 10%.37 The time spent as a low-grade glioma would give this subclass of high-grade gliomas additional time to reorganize. We also hypothesized that there would be statistically significant relationships between the DTI parameters and the volumes of the tumor, body of the CC, and whole CC.

Methods

Patients

Our institutional review board approved this retrospective study and issued a waiver of informed consent. This study was also compliant with the Health Insurance Portability and Accountability Act. Consecutive patients who had completed MRI scans including DTI between February 2011 to June 2014 and who were histologically diagnosed with either a high-grade glioma or a metastatic brain tumor not infiltrating the CC were included in the study. Patients with tumor infiltration of the CC were excluded; tumor infiltration was evaluated by an interpreting neuroradiologist with 15 years of experience and was considered when there was abnormal T2 or FLAIR signal hyperintensity in the CC that was contiguous with the tumor and the peritumoral abnormality. For inclusion, the neuroradiologist also determined that the tumor is located as being in one of the following regions in either brain hemisphere served by the body of the CC: fronto-parietal, parietal, temporo-parietal, and anterior temporal regions.

Image Acquisition

MRI data were acquired with a 3-Tesla General Electric scanner (GE Healthcare, Milwaukee, WI, USA) with an 8-channel head coil. For anatomical images, T1-weighted (TR / TE = 400 / 14 ms, 256 × 256 matrix, 240 mm × 240 mm FOV, 0.9 × 0.9 × 4 mm3 voxel size), FLAIR (TR / TE = 10000 / 106 ms, inversion time 220 ms, 256 × 256 matrix, 240 × 240 mm2 FOV, 0.9 × 0.9 × 4.0 mm3 voxel size), T2-weighted (TR / TE = 4000 / 102 ms, 256 × 256 matrix, 240 × 240 mm2 FOV, 0.9 × 0.9 × 4.0 mm3 voxel size), and T1 post-contrast (TR / TE = 600 / 20 ms, 256 × 256 matrix, 240 × 240 mm2 FOV, 0.9 × 0.9 × 4.0 mm3 voxel size) images were obtained. 3D T1-weighted anatomical images were also acquired with a spoiled gradient recalled (SPGR) sequence (TR / TE = 22 / 4 ms, 256 × 256 matrix, 240 × 240 mm2 FOV, 0.9 × 0.9 × 1.5 mm3 voxel size).

Whole brain DTI were acquired using a single shot spin echo echo-planar imaging sequence (TR / TE = 11000 / 64 ms, 128 × 128 matrix, 240 × 240 mm2 FOV, 1.9 × 1.9 × 3 mm3 voxel size, 25 gradient directions, 1000 s / mm2 b value, covering 32–34 slices).

DTI Analysis

Diffusion Toolkit and TrackVis (both from Ruopeng Wang, Van J. Wedeen, TrackVis.org, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA) were used to analyze the DTI data and generate the corresponding DTI maps. Firstly, a correction for eddy current distortion was applied and then motion corrected was performed by applying affine alignment of each diffusion-weighted image to the first volume of the diffusion data without gradient (b=0). Then the diffusion tensor was calculated for each voxel, and DTI maps for FA, color encoded Fractional Anisotropy (colorFA), MD, and AD were generated. The body of the CC was drawn by a neuroradiologist (SK) on axial and sagittal T1 anatomical slices by taking the rectangular-shaped, mid-axial slice and including the entire region between the genu and the splenium (Figure 1). The b0 DTI maps were then co-registered with the T1 anatomical images, and then the ROI (mean volume of the ROI = 3090 ± 464 mm3) was overlaid onto the FA, MD, and AD maps. The FA, MD and AD values were measured for each voxel and then averaged per the entire ROI.

Figure 1.

Figure 1

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Drawing the body of the corpus callosum (CC) region of interest on the color coded red-green-blue fractional anisotropy map (a) and the overlaid vector map on axial (b) and sagittal slices (c). The genu and splenium are delineated on the sagittal slices (c) and the entire region between them is considered to be the body of the CC.

Corpus Callosum / Tumor Segmentation and Volume Analysis

To investigate any correlation of the DTI parameters with the volumes of the tumor, body of the CC, and whole CC, the three anatomical ROIs of the tumor, the body of the CC, and whole CC, were segmented using 3D T1-weighted scans. All segmentation was performed manually using the open-source, cross-platform software ITK-SNAP (http://www.itksnap.org/)38. Once the MR images were loaded onto ITK-SNAP, the three structures were segmented from the axial, sagittal, and coronal slices by shading in the voxels of each structure in a different color (Figure 2). For segmentation purposes, the body of the CC was considered to be the entire region between the splenium and the genu, and the tumor was considered to include necrotic centers but not resection cavities. After segmentation was completed, the volume of each structure was calculated using the voxel count for each structure’s segmentation.

Figure 2.

Figure 2

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An example showing segmentation of the corpus callosum (CC) (a), body of the CC (b), and tumor (c) using the software ITK-SNAP for a patient with high-grade glioma. Starting from the left, the first, second, and third columns show the axial, sagittal, and coronal slices, respectively, of the 3D Spoiled Gradient Recalled sequence MR images retrieved for the patient. The voxels of each structure are shaded with a different color. A 3D rendering of each segmented structure is shown in the rightmost column.

Statistical Methods

The Spearman Rank Correlation test was initially used to test for age-matching differences within our patient groups, and a two-tailed Wilcoxon Rank-Sum test was also used to test for sex-matching differences within our patient groups. Additionally, a Chi-Square Test was used to test for overall differences in the proportion of male and female patients between the patient groups. The one-tailed Wilcoxon Rank-Sum test was used to test for any differences in the DTI parameters between patient groups. Additionally, the Spearman Rank Correlation test was used to test for any correlations between the DTI parameters and structure volumes. For all analyses, the significance level was set at α = 0.05, with Holm-Sidak corrected p-values used for analyses involving multiple comparisons. For graph analysis, a single asterisk (*) was used to denote significant results of p < 0.05 and a double asterisk (**) was used for significant results of p < 0.01.

Results

Study Population

A total of 50 patients (22 male, 28 female; mean age = 59.5 years) were included in this study. Patients were divided into two groups based on their pathology: 1) HGG group of 30 patients (18 male, 12 female; mean age = 60.2 ± 13.0 years) or 2) MET group of 20 patients (4 male, 16 female; mean age = 58.5 ± 13.9 years). Clinical data for the HGG and MET groups are summarized in Table 1. While there was a significant difference in the proportion of males and females between the patient groups (χ2 = 7.79, p < 0.01), there were no associations within the patient groups between the DTI parameters and either age (p > 0.2) or sex (p > 0.05).

Table 1.

Clinical Data of Both High-Grade Glioma and Metastasis Groups

Characteristic HGG Group (n=30) MET Group (n=20)
Sex (Male/Female) 18/12 4/16
Average Age (years ± standard deviation) 60.2 ± 13.0 58.5 ± 13.9
Pathology
Glioblastoma grade IV Tumor 25 N/A
Anaplastic grade III Tumor 5 N/A
Metastatic squamous cell carcinoma N/A 1
Metastatic pleomorphic spindle cell sarcomatoid N/A 1
Metastatic melanoma N/A 4
Metastasis of lung origin N/A 9
Metastasis of breast origin N/A 2
Metastasis of ovary origin N/A 1
Metastasis of colon origin N/A 2
Tumor Location
Fronto-parietal 11 12
Parietal 6 4
Temporo-parietal 3 2
Anterior temporal 10 2
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HGG = high-grade glioma; MET = metastatic tumor; n = number; N/A = not applicable

Summary of the clinical data (sex, age, pathology, and tumor location) for the HGG and MET groups.

DTI Parameters and Tumor Type

There was a significant difference in the distribution of FA and MD values between the two patient groups (U = 204, p < 0.05; U = 179, p < 0.01; respectively) (Figure 3). The mean FA and MD values for the HGG group were 0.691 and 0.894 × 10−3 mm2/s, respectively, while the mean FA and MD values for the MET group were 0.646 and 0.992 × 10−3 mm2/s, respectively. There was no significant difference in the distribution of AD values between the two patient groups (U = 261, p > 0.75). The mean DTI parameters for each patient group along with reference metrics of healthy control data presented by Li et al.28 are summarized in Table 2.

Figure 3.

Figure 3

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FA = fractional anisotropy; MD = mean diffusivity; AD = axial diffusivity; HGG = high-grade glioma; MET = metastatic tumor

Box-and-whisker plot analysis for the diffusion tensor imaging parameters of FA (a), MD (b), and AD (c) within each patient group. Significant differences were found for FA (a) (p < 0.05) and MD values (b) (p < 0.01).

Table 2.

Mean DTI Parameters in Patient Groups.

Mean FA Mean MD (10−3 mm2/s) Mean AD (10−3 mm2/s)
Controls (Reported by Li et al.28) 0.81 0.72 N/A
HGG 0.691 0.894 1.75
MET 0.646 0.992 1.79
p-value 0.029 0.0083 0.78
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DTI = diffusion tensor imaging; FA = fractional anisotropy; MD = mean diffusivity; AD = axial diffusivity; HGG = high-grade glioma; MET = metastatic tumor

The mean FA, MD, and AD values for both patient groups. Mean FA and MD were found to be statistically significant between the HGG and MET groups. The previously reported mean FA and MD values for healthy controls as determined by Li et al.28 are included for reference.

Volume Analysis

The volumes of the tumor, body of the CC, and whole CC were not found to be significantly correlated with FA, MD, or AD in either patient group after using Holm-Sidak corrected p-values (p > 0.04).

Discussion

Because HGG patients are known to have longer median survival times than MET patients,34–36 we hypothesized that DTI parameters would suggest increased contralateral brain reorganization in the HGG group that the corpus callosal FA and AD values would be higher in HGG patients while the corpus callosal MD values would be higher in MET patients. Our results demonstrate that the distribution of FA values was significantly higher and that of MD values was significantly lower in the HGG patients compared to MET patients. There was no significant difference in the distribution of AD values between the two patient groups. Despite the significant difference in the proportion of males and females between the patient groups, there were no impacts of sex distribution or age on the DTI parameters within our study population that would have confounded our findings.

In both of our patient groups, the mean FA value was increased and the mean MD was decreased relative to previously reported FA and MD values in healthy subjects,28 which are presented as reference metrics in Table 2. High FA values are associated with higher axonal density3 while high MD values are associated with low myelination and decreased tissue organization.5, 39 Therefore, our results suggest that the patients’ brains have decreased myelination and fiber orientation in the body of the CC. These overall differences in DTI characteristics between healthy subjects and our patient population provide evidence that the CC is impacted by distal neuropathologies,25–30 potentially due to Wallerian Degeneration caused by the tumors.30, 31

However, between each patient group, the differences in DTI characteristics suggest an altered response in the patients’ brains that is determined by the type of the tumor. Metastases are non-infiltrating tumors40 and are anatomically limited by their localization on MRI, while gliomas are known to be infiltrating tumors. This infiltration extends beyond the enhancement and FLAIR hyperintense regions on MRI scans.41 Since infiltration of the CC causes lower FA and higher MD values,33 it was expected the HGG group to have these DTI characteristics even in the absence of tumor infiltration on MR (as our inclusion criteria stipulated). Nevertheless, the opposite results were observed. While functional analysis was not conducted as part of this study, we speculate that the differing DTI parameters could be due to an increse in fiber density and organization in the CC in glioma patients comapred to patients with metastases. This increase in fiber density and organziation would be the consequence of brain reorganization to the contralateral side throughout the CC. The main function of the CC is interhemispheric transfer of brain signals,1 and it does so by acting as a brain commissure, or a neural bridge, that connects homologous cortical areas across the two hemispheres. Contralateral brain reorganization of eloquent brain areas affected by pathology would then have to occur through the CC, which may result in structural changes within the CC that are detectable by DTI.

Our data then might indicate that there is more contralateral brain reorganization in the HGG group than in the MET group, indicated by the former’s higher mean FA and lower mean MD values. This is in line with the recent study done by Tantillo et al.32 which observed that FA values were higher in left-hemispheric brain tumor patients that were co-dominant for language tasks than those that were left-dominant, and this was suspected to be due to contralateral brain reorganization in the co-dominant group. Although contralateral reorganization is usually associated with low-grade gliomas42, 43 because of their typically longer median survival time compared to HGGs, contralateral reorganization has been suggested to occur in HGG patients as well.22, 32 In addition, the median survival time for HGG patients is known to be longer than that for MET patients,34–36 so our results provide further evidence that a higher degree of contralateral brain reorganization can occur if the brain is given more time to undergo reorganization.

The increase in mean FA and decrease in mean MD values in the HGG group in our data also indicate that the fibers of the body of the CC are more oriented and myelinated in HGG patients than in MET patients. Higher FA values have already been shown to signify faster interhemispheric transfer times and an overall increase in interhemispheric transfer in the CC.2–5 More contralateral brain reorganization would be facilitated by interhemispheric transfer through the CC, which is why we believe this shows that the HGG group exhibits this process more than the MET group.

When contralateral brain reorganization occurs, the other brain hemisphere is recruited and activated to compensate for the damaged eloquent area. Since this seems to occur more in the patients of the HGG group according to their DTI characteristics, this raises the possibility that the increase in interhemispheric transfer would be due to more excitatory signals being sent to the contralateral side through the CC.44, 45 Higher FA values of the CC have been shown to occur in those with bimanual motor skills or codominant language tasks as compared to more-lateralized individuals, which further supports this speculation.4, 32, 39

In a previous study comparing the FA values of the CC of healthy controls, patients with glioblastomas, and patients with metastatic brain tumors, Saksena et al. were unable to find statistically significant differences in the DTI parameters between the patient groups.30 This can be due to their inclusion criteria, which included patients whose tumors could infiltrate the CC as long as infiltration did not go beyond the midline CC. Infiltration of the CC has been shown to cause lower FA and higher MD values,33 so their inclusion of tumors that infiltrated portions of the CC could have confounded their results because their measured DTI characteristics would reflect a combination of structural changes in the CC from contralateral brain reorganization and the impacts of tumor infiltration. By excluding patients whose tumors infiltrated any portion of the CC, our study only focused on the structural changes in the CC potentially from contralateral brain reorganization and this is why we may have found significant differences between patient groups unlike Saksena et al.30

In this study, there were also no significant correlations between the DTI parameters with the volumes of the tumor, CC, or the body of the CC, so our study suggests the degree of contralateral brain reorganization appears to be related to tumor type and not anatomical volumes. Previous studies on patients with gliomas have had mixed results on the impact of tumor volume and contralateral brain reorganization as well.20–22 This could be a result of the differing degrees of Wallerian Degeneration30, 31 and potential infiltration of the CC by gliomas that is not detected by the FLAIR hyperintense regions on MRI scans,41 which would cause variable DTI characteristics between tumors of the same size. Previous studies have also shown that the size of the CC has an impact on some variables suspected to be important for contralateral brain reorganization, such as the number of fibers connecting the two hemispheres,6 but no relation has been found in other variables such as interhemispheric transfer time.7 Thus, the tumor effects described above along with the uncertainty of the impact of callosal volume on contralateral brain reorganization can explain why it appears that tumor type and not the anatomical volumes impact the degree of contralateral brain reorganization.

The main limitation of our study was that while our data support the notion of a previous study discussing the relationship between contralateral brain reorganization, tumor type, and DTI parameters, no functional imaging data was analyzed in this study. In the future, a combined study of fMRI and DTI within these patient groups would allow for stronger conclusions as to whether the differences in DTI parameters between the patient groups are due to contralateral brain reorganization. Additionally, neurocognitive assessments may be potentially included in the future to further study the brain plasticity differences between patient groups. It would also be favorable to study patient groups with more evenly distributed proportions of males and females, although this metric is dependent on the patient cohort that is admitted to the cancer center. Lastly, another limitation was that healthy controls were unable to be included in this study, and healthy controls may be included in future studies in order to further study how DTI parameters are altered in the presence of pathology.

Conclusion

Our data suggest that there are structural differences in the CC between patients with HGG and those with MET as shown by DTI, with the former having higher FA values and lower MD values. While Wallerian Degeneration will impact the DTI paremeters overall within both patient groups, we speculate that the differences in DTI parameters between both patient groups are due to an increase in interhemispheric transfer caused by more contralateral brain reorganization in HGG patients. Future studies combining DTI and fMRI will be required to further study the relationship between tumor type and degree of brain plasticity as shown through contralateral brain reorganization.

Acknowledgements and Disclosure:

This research was funded in part through the National Institutes of Health / National Cancer Institute Cancer Center Support Grant P30 CA008748. The authors declare that they have no conflict of interest. The abstract of this manuscript has been presented as an e-poster at the 2017 ASNR 55th Annual Meeting in April. We would like to thank Joanne Chin for her editorial assistance and Dr. Zhigang Zhang for his statistical consulting.

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