March 2018
Volume 59, Issue 3
Open Access
Glaucoma  |   March 2018
White Matter Abnormalities and Correlation With Severity in Normal Tension Glaucoma: A Whole Brain Atlas-Based Diffusion Tensor Study
Author Affiliations & Notes
  • Rong Wang
    Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Zuohua Tang
    Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Xinghuai Sun
    Department of Ophthalmology & Visual Science, Eye & ENT Hospital of Shanghai Medical School, State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Key Laboratory of Myopia, NHFPC (Fudan University), and Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai, China
  • Lingjie Wu
    Department of Otolaryngology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Jie Wang
    Department of Otolaryngology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Yufeng Zhong
    Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
    Department of Radiology, Jinshan Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Zebin Xiao
    Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
  • Correspondence: Zuohua Tang, Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, P.R. China; tzh518sunny@163.com
  • Xinghuai Sun, Department of Ophthalmology & Visual Science, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai 200031, P.R. China; xhsun@shmu.edu.cn
  • Footnotes
     RW, ZT, and XS contributed equally to the work presented here and should therefore be regarded as equivalent authors.
Investigative Ophthalmology & Visual Science March 2018, Vol.59, 1313-1322. doi:10.1167/iovs.17-23597
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      Rong Wang, Zuohua Tang, Xinghuai Sun, Lingjie Wu, Jie Wang, Yufeng Zhong, Zebin Xiao; White Matter Abnormalities and Correlation With Severity in Normal Tension Glaucoma: A Whole Brain Atlas-Based Diffusion Tensor Study. Invest. Ophthalmol. Vis. Sci. 2018;59(3):1313-1322. doi: 10.1167/iovs.17-23597.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To detect injury of whole brain white matter (WM) in normal tension glaucoma (NTG) patients by using diffusion tensor imaging (DTI) and to analyze the correlations between DTI parameters and glaucoma indices.

Methods: Twenty mild, 17 moderate, and 18 severe NTG patients as well as 25 normal subjects were enrolled in this study. Atlas-based diffusion tensor analysis was performed to measure the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). One-way analyses of variance were used for comparisons of DTI parameters between NTG subgroups and normal control (NC) group. The relationships between DTI parameters and glaucoma indices were also assessed by Pearson's correlation and “broken-stick” analyses.

Results: As compared with NC subjects, significantly decreased FA and AD and increased MD and RD were observed in the bilateral posterior thalamic radiation, bilateral sagittal stratum, bilateral cingulum-hippocampus, and bilateral fornix/stria terminalis in NTG patients. The DTI parameters of these WM regions correlated with the mean deviation of visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness. Additionally, there was a tipping point between MDVF and DTI parameters as well as between MDVF and RNFL thickness.

Conclusions: Atlas-based DTI analysis was capable of indicating WM damage in the four regions associated with visual and visual-related functions in NTG patients, and it could also be used for investigating disease progression and pathologic changes. In addition, WM impairment and RNFL thinning occurred before patients showed detectable visual field loss.

Glaucoma has become the second leading cause of blindness, affecting approximately 70 million people globally.1 Generally, glaucoma is considered to be an eye disease characterized by progressive optic nerve damage and loss of neurons in the visual pathway.2 Normal tension glaucoma (NTG), which is more prevalent in the Asian population, is a common type of primary open angle glaucoma (POAG), while its mechanisms and brain microstructural changes have not been well described.3,4 
Diffusion tensor imaging (DTI) is a noninvasive magnetic resonance imaging (MRI) technique for detecting microstructural alterations in the white matter (WM) in vivo.5 Fractional anisotropy (FA) and mean diffusivity (MD) are the most widely used DTI parameters that are sensitive to the pathologic changes of WM regions.6 In addition, axial diffusivity (AD) is a biomarker for axonal damage,7 and radial diffusivity (RD) can reflect demyelination, inflammation, or gliosis, among other processes.8,9 Manual tracing of the region of interest (ROI) is the most commonly used DTI method in glaucoma, but this method only includes limited regions, such as the optic nerve and optic radiation, and is not able to evaluate the entire WM region. Recent studies10,11 have shown that in addition to the visual pathway, glaucoma also can cause morphologic and pathophysiologic changes to the vison-related regions. Thus, we attempted to use other DTI methods to investigate the abnormalities of whole brain WM fibers. Atlas-based diffusion tensor analysis (ABA) is a novel method in which each brain is parcellated into 50 anatomic units,12,13 which can effectively detect the integrity of the whole brain WM. Because it can reduce measurement error14 and improve statistical power15 when compared with ROI-based and voxel-based DTI analyses, respectively, the ABA method has recently been applied to investigate normal or abnormal neurodevelopment.1618 In addition, mean deviation of visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness can be measured by standard automated perimetry and optical coherence tomography (OCT), respectively, with a sensitivity that reflects the degree of glaucoma.19 Previous studies2026 have demonstrated that DTI parameters of the visual pathway are correlated with MDVF and RNFL thickness via ROI analysis. However, to the best of our knowledge, this is the first study to explore the abnormalities of whole brain WM regions with DTI parameters in NTG patients by using the ABA method, and the correlations between them and MDVF and RNFL thickness. 
Materials and Methods
Subjects
The study was approved by the Institutional Review Board of our hospital and was conducted in accordance with the tenets of the Declaration of Helsinki. Written informed consent was obtained from all participants. 
The prospective observational study included patients with NTG as study group and a control group of healthy subjects. Fifty-five patients with bilateral symmetrical disease from NTG and 25 healthy controls who were age and sex matched were enrolled in this study from July 2016 to March 2017. All patients and healthy controls were examined by a specialized ophthalmologist with 10 years' experience in glaucoma. The subjects underwent comprehensive ophthalmologic examinations, including optic intraocular pressure, slit-lamp microscopy, standard automated perimetry using the 30-2 program of the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA, USA) and RNFL thickness using the SD-OCT (Carl Zeiss Meditec). The diagnostic criteria for NTG patients included an untreated peak intraocular pressure ≤ 21 mm Hg in 24-hour intraocular pressure profiles, an open anterior chamber, typical glaucomatous optic disc damage with nerve fiber bundle defects, and the exclusion of other causes of optic neuropathy. Then, NTG patients were further divided into three subgroups (mild, moderate, and severe NTG) by MDVF. Mild NTG is characterized by an MDVF of −0.01 to −6.00 dB. Moderate NTG is characterized by an MDVF of −6.01 to −12.00 dB. Severe NTG is defined with MDVF greater than −12 dB. The exclusion criteria were as follows: (1) age <18 years; (2) secondary glaucoma; (3) any history or suspicion of diseases that could affect the visual field; (4) central nervous system (CNS) diseases found on the initial MRI; and (5) any contraindications to the MRI examinations. 
MRI Acquisition
Magnetic resonance imaging was performed on a 3.0-Tesla scanner (Verio; Siemens, Erlangen, Germany) equipped with a 32-channel head coil. DTI was performed with a single-shot spin-echo echo planar imaging (SE-EPI) sequence with the following parameters: repetition time = 7425 ms, echo time = 84 ms, matrix size = 110 × 110, field of view = 220 × 220 mm, slice number = 50 slices, and slice thickness = 2.5 mm. Diffusion-weighted images were acquired along 30 noncollinear directions with a b value of 1000 s/mm2. Additionally, a volume without diffusion weighting (b = 0 s/mm2) was acquired. 
DTI Data Processing
All DTI data were processed by using a pipeline toolbox for analysing brain diffusion images (PANDA).27 The main procedures of PANDA are described below. First, DICOM files were converted into NIfTI images. Second, the brain mask was estimated. Third, the raw images were cropped to reduce the image size. Fourth, the eddy current was corrected by coregistering each diffusion-weighted image to the b0 image. Fifth, DTI parameters (FA, MD, AD, and RD) were calculated for each subject. Then, the DTI parameters were normalized into the MNI space and the average values within each region of the ICBM DTI-81 atlas28 were calculated. This version of the atlas did not suffer from the flaws pointed out by Rohlfing.29 Because the hippocampus is not included in the ICBM-DTI-81 labels, DTI parameters of bilateral hippocampus are additionally evaluated on the basis of the Automated Anatomical Labeling (AAL) atlas.30 Then DTI parameters of this region were also evaluated for each subject. 
The WM regions with differences in DTI parameters between NC and NTG groups were shown with Mricron (http://www.nitrc.org/projects/mricron; provided in the public domain) and BrainNet Viewer31 (http://www.nitrc.org/projects/bnv; provided in the public domain), respectively. 
Statistical Analysis
All statistical analyses were conducted by using IBM SPSS software (version 24; Chicago, IL, USA). The demographic characteristics, ophthalmologic examinations, and DTI parameters were compared between the NTG and NC groups by using the independent two-sample Student's t-test for continuous variables, and the χ2 test was used for the sex proportion. The significant statistical threshold was set at a two-tailed P < 0.05. For DTI parameters, the false discovery rate (FDR) method was used to correct for multiple comparisons, and statistical significance was set at P (FDR-corrected) < 0.05. The WM regions were considered to be abnormal when both FA and MD values were statistically different after FDR correction between NTG and NC groups. In addition, all quantitative data were further tested by 1-way analyses of variance (ANOVAs) with Dunnett's post hoc tests for comparisons between the NTG subgroups and NC group. After Bonferroni correction, P (FDR-corrected) < 0.0167 (0.05/3, because intergroup comparisons were performed among three groups) was considered statistically significant. Finally, Pearson's correlation analyses adjusted for age were used to explore the associations between the mean MDVF and RNFL thickness for both eyes as well as the mean FA, MD, AD, and RD values of the WM regions for all patients. 
We also compared MDVF with DTI parameters of the four WM regions and RNFL thickness by using a “broken-stick” model. Davies' test was used to calculate the tipping point.32 This analysis was conducted by using R Language and Environment for Statistical Computing program33 and the segmented R package.34 P < 0.05 was considered statistically significant. 
Results
Demographic and Ophthalmologic Measurements
The demographic characteristics and ophthalmologic examinations are presented in Table 1. The normal controls (NCs), mild (mi-NTG), moderate (mo-NTG), and severe (se-NTG) NTG patients did not differ significantly regarding age or sex. Compared with the NC group, all of the NTG subgroups had a significantly lower RNFL thickness (P < 0.001, using Dunnett's post hoc test) and MDVF (P < 0.001, using Dunnett's post hoc test). 
Table 1
 
Clinical Characteristics of the Participants
Table 1
 
Clinical Characteristics of the Participants
FA and MD Comparisons
Significant differences in DTI parameters were found in the four WM regions that were linked to visual and visual-related functions between NTG patients and normal subjects (Fig 1); they are bilateral posterior thalamic radiation (B.PTR, with visual function), bilateral sagittal stratum (B.SS, with visual function), bilateral cingulum-hippocampus (B.CgH, with visual memory), and bilateral fornix/stria terminalis (B.FX/ST, with visual discrimination). In addition, DTI parameters of bilateral hippocampus (B.Hip) were also found to be different between the two groups. 
Figure 1
 
Through atlas-based analysis, white matter regions with differences in DTI parameters (FA, MD) between NC and NTG groups were marked by different colors in the axial (a, d), coronal (b, e), and sagittal (c, f) planes, as well as in the spatial patterns (g). These white matter regions include B.PTR (red), B.SS (blue), B.CgH (green), and B.FX/ST (violet). L, left; R, right.
Figure 1
 
Through atlas-based analysis, white matter regions with differences in DTI parameters (FA, MD) between NC and NTG groups were marked by different colors in the axial (a, d), coronal (b, e), and sagittal (c, f) planes, as well as in the spatial patterns (g). These white matter regions include B.PTR (red), B.SS (blue), B.CgH (green), and B.FX/ST (violet). L, left; R, right.
The means and standard deviations of the FA and MD values and the statistical results across the groups are presented in Figure 2 and Table 2. Relative to the NC subjects, the NTG subjects showed significantly lower FA values and higher MD values in B.PTR, B.SS, B.CgH, B.FX/ST, and B.Hip. 
Figure 2
 
Bar charts of the mean FA, MD, AD, and RD in regions with differences between the NC and NTG groups. The comparisons between the NC group and NTG subgroups indicated that there was a gradual decline in the mean FA and RD values and increase in the mean MD and AD values with increasing severity of NTG, respectively. *P < 0.0167, **P < 0.01, ***P < 0.001 for comparisons of NTG subgroups to normal controls (1-way ANOVA with Dunnett's post hoc test, FDR-corrected).
Figure 2
 
Bar charts of the mean FA, MD, AD, and RD in regions with differences between the NC and NTG groups. The comparisons between the NC group and NTG subgroups indicated that there was a gradual decline in the mean FA and RD values and increase in the mean MD and AD values with increasing severity of NTG, respectively. *P < 0.0167, **P < 0.01, ***P < 0.001 for comparisons of NTG subgroups to normal controls (1-way ANOVA with Dunnett's post hoc test, FDR-corrected).
Table 2
 
Group Comparisons of the Mean DTI Parameters in Normal Controls As Well As the NTG Group and Subgroups
Table 2
 
Group Comparisons of the Mean DTI Parameters in Normal Controls As Well As the NTG Group and Subgroups
Compared with the NC group, the mi-NTG subgroup demonstrated decreased FA and increased MD in B.PTR and B.SS. The mo-NTG subgroup showed lower FA and higher MD in B.PTR, B.SS, B.CgH, and B.FX/ST than the NC group. Moreover, the se-NTG subgroup had significantly lower FA and higher MD values than the NC group in B.PTR, B.SS, B.CgH, B.FX/ST, and B.Hip. 
AD and RD Comparisons
The AD and RD values of these regions are shown in Figure 2 and Table 2. Compared to the controls, the mi-NTG subgroup had decreased AD in B.PTR and B.SS, and increased RD in B.PTR. The mo-NTG subgroup had decreased AD in B.PTR and B.SS, and increased RD in B.PTR and B.FX/ST. Furthermore, the se-NTG subgroup had lower AD than the NC group in B.PTR, B.SS, B.CgH, and and B.Hip, with higher RD in B.PTR, B.SS, B.CgH, B.FX/ST, and B.Hip. 
As shown in Figures 3 and 4, the FA and AD values in B.PTR, B.SS, B.CgH, and B.FX/ST were positively correlated with the MDVF (P < 0.05) and RNFL thickness (P < 0.05). The MD and RD values in these four regions were negatively correlated with MDVF (P < 0.05) and RNFL thickness (P < 0.01). 
Figure 3
 
Scatterplots with regression lines showing the correlations between the MDVF scores and FA, MD, AD, and RD values in the four WM regions.
Figure 3
 
Scatterplots with regression lines showing the correlations between the MDVF scores and FA, MD, AD, and RD values in the four WM regions.
Figure 4
 
Scatterplots with regression lines showing correlations between the RNFL thickness and FA, MD, AD, and RD values in the four WM regions.
Figure 4
 
Scatterplots with regression lines showing correlations between the RNFL thickness and FA, MD, AD, and RD values in the four WM regions.
Relationships between visual field function and DTI parameters of the four WM regions were described by a “broken-stick” model (Fig. 5). The tipping point was existent at 0.496 (95% confidence interval [CI], 0.476–0.516), 0.457 (95% CI, 0.438–0.477), 0.389 (95% CI, 0.371–0.407), and 0.603 (95% CI, 0.585–0.621) for FA values; 0.928 (95% CI, 0.820–1.035), 0.946 (95% CI, 0.816–1.017), 0.905 (95% CI, 0.803–1.002), and 0.822 (95% CI, 0.752–0.892) for MD values; 1.242 (95% CI, 1.060–1.424), 1.261 (95% CI, 1.198–1.323), 1.333 (95% CI, 1.165–1.501), and 1.148 (95% CI, 0.918–1.379) for AD values; and 0.585 (95% CI, 0.454–0.716), 0.621 (95% CI, 0.555–0.687), 0.633 (95% CI, 0.545–0.721), and 0.710 (95% CI, 0.624–0.796) for RD values in the B.PTR, B.SS, B.CgH, and B.FX/ST, respectively. In addition, relationship between visual field function and retinal structure was also estimated at the tipping point of 68.39 μm (95% CI, 62.10–75.67). 
Figure 5
 
Relationships between visual field function, DTI parameters of these four WM regions (a), and retinal structures (b) in NTG and NC groups. Using the “broken-stick” model to determine the tipping points. Red line represents the spline fit, and the black line represents the “broken-stick” model. *P < 0.05, **P < 0.01, ***P < 0.001.
Figure 5
 
Relationships between visual field function, DTI parameters of these four WM regions (a), and retinal structures (b) in NTG and NC groups. Using the “broken-stick” model to determine the tipping points. Red line represents the spline fit, and the black line represents the “broken-stick” model. *P < 0.05, **P < 0.01, ***P < 0.001.
Discussion
Our preliminary study showed that WM changes caused by NTG can be found not only in the visual pathway, but also in the visual-related areas via the ABA method, as compared with previous ROI-based DTI studies on glaucoma. WM abnormalities were demonstrated in the four regions associated with visual (B.PTR, B.SS) and visual-related function (B.CgH, B.FX/ST) in NTG patients, as evaluated by lower FA and AD and higher MD and RD values. Further subgroup (mi-, mo-, and se-NTG) analyses revealed that these WM regions were damaged at different disease stages. In addition, DTI parameters of these four WM areas were well correlated with glaucoma indices (MDVF, RNFL thickness). 
Few studies have assessed the microstructural integrity of the PTR or SS in glaucoma. Zikou et al.35 have observed decreased FA values in the PTR of POAG patients when using voxel-based DTI. In the current study, we found lower FA and AD and higher MD and RD values in the PTR and SS in NTG patients than in NC subjects. Decreased FA and increased MD in our study suggested that the WM integrity of PTR and SS was damaged in NTG.36 This outcome probably occurred because both the PTR and SS primarily contain optic radiation.37,38 Optic radiation is one of the major components of the visual pathway,39 and previous studies have demonstrated that neurodegeneration of the optic radiation is a critical pathologic change in glaucoma.21,26,40 Subgroup analyses showed that significantly decreased AD and increased RD in the PTR and SS were present in the mi-NTG subgroup compared with normal group, and this effect became greater in the mo-NTG and se-NTG subgroups. Decreased AD and increased RD can reflect axonal degeneration and demyelination, inflammation or gliosis, respectively,79 and therefore we can further infer that these pathologic changes may occur in the PTR and SS during the early period of NTG and they would become more obvious as the disease progresses. 
In this study, we found that FA decreased and MD increased in the B.CgH in NTG patients by using the ABA method. The CgH is a WM tract connection between cingulum and hippocampus, so the damage of this tract may impede the transmission of memory information from cingulum to hippocampus.41 The hippocampus is part of the Papez circuit and its main function is to integrate visual memory.42 To determine whether it is impaired in NTG patients, we additionally measured the DTI parameters of the bilateral hippocampus in our study. Interestingly, we also found significant differences in FA, MD, AD, and RD values in the B.Hip between NTG and NC groups, implying that the hippocampus was damaged in NTG patients. Therefore, an injury to the hippocampus may lead to visual memory deficit in NTG patients, which was consistent with a previous finding that patients with glaucoma may have memory impairments.43 Moreover, further subgroup analyses showed that the significant differences in FA, MD, AD, and RD values only appeared in the se-NTG subgroup. Our results indicated that the impairment of bilateral hippocampus occurred in the late stage of NTG, which may be related to axonal damage, demyelination, inflammation, gliosis, or a combination of different pathologic events. However, Wang et al.44 have not found significant structural alterations of the hippocampus in POAG patients compared with healthy controls. This different finding compared to our results may be due to the following reasons. On the one hand, the type of glaucoma studied by Wang et al44 (POAG) is different from that in our study (NTG); thereby, different mechanisms may contribute to different brain structural changes. On the other hand, our results revealed that the hippocampus was damaged only in the se-NTG patients, but patients with different stages of POAG were analyzed together in the study of Wang and colleagues,44 meaning that the presence of mild and moderate POAG patients enrolled in their study could reduce the differences in the hippocampus. In future studies, it would be worthwhile to study the microstructural changes in the hippocampus in patients with glaucoma of different types and clinical severities. 
There has been no prior report that has assessed damage to the integrity of the FX/ST in glaucoma patients. However, in our study, we found significant decreased FA and increased MD in the FX/ST in both mo-NTG and se-NTG patients compared with NCs, indicating damage of FX/ST in the middle and late stage of NTG. The FX/ST is part of the limbic system, which is primarily responsible for visual discrimination.45 Thus, we suggested that NTG patients may have reduced function of visual discrimination, which was consistent with the study of Adams et al.46 Furthermore, RD was significantly increased in mo-NTG and se-NTG subgroups compared with normal subjects, while there was no significant difference in AD. The results indicated potential pathologic changes, such as demyelination, inflammation, or gliosis, occurred in the FX/ST in the middle and late stage of NTG, while the integrity of the axon was not destroyed.7 Some studies have demonstrated that the disruption of the FX/ST is associated with Alzheimer's disease.4749 Our results supported the notion that glaucoma and Alzheimer's disease may share similar underlying mechanisms.50,51 
In addition, the current study also analyzed the relationship between the DTI parameters of these four WM regions (B.PTR, B.SS, B.CgH, and B.FX/ST) and ophthalmologic examinations (MDVF and RNFL thickness). We found that the FA and AD values in these four WM regions were moderately to strongly (r = 0.323 ∼ 0.702) positively correlated with the MDVF and RNFL thickness, whereas the MD and RD values were moderately (r = −0.365 ∼ −0.544) negatively correlated with them, respectively. The MDVF and RNFL thickness are sensitive parameters that reflect the severity of glaucoma.19,52 Therefore, the DTI parameters of these four WM regions, which were extracted from the ABA method, could serve as potential markers for assessing the WM (visual and visual-related functions) microstructural changes of early, middle, and late stages of NTG, and that may be helpful for monitoring the progression of NTG. Furthermore, for NTG patients with different severities, DTI parameters can indicate the pathologic changes in these WM regions, such as integrity damage, axonal degeneration, demyelination, inflammation, or gliosis, which cannot be acquired by MDVF or RNFL thickness. 
To further determine the relationships between visual field function, WM regions, and retinal structures, we compared MDVF with DTI parameters of the four abnormal WM regions and RNFL thickness by using the “broken-stick” model. We detected tipping points between visual field function and FA, MD, AD, and RD values of the four abnormal WM regions. These results suggested that visual field function remained stable before DTI parameters of these four WM regions reached the tipping points, but visual field loss was detectable when the DTI parameters advanced beyond a tipping point. Additionally, a tipping point was also found between MDVF and RNFL thickness, which was in accordance with previous studies,53,54 indicating that RNFL thinning occurred before significant visual field loss. Thus, we observed that both retinal structure and WM regions were altered before detectable visual field loss in NTG patients, which was similar to a previous finding reported by Murphy et al.55 
Our study had some limitations. First, some brain abnormalities could affect the quality of template matching. Therefore, we excluded patients with obvious encephalatrophy to improve the accuracy of the ABA method. Second, the sample size of NTG patients was relatively small because the patients were further divided into three subgroups. Therefore, a larger sample size will be needed in our further studies. 
In conclusion, atlas-based DTI analysis demonstrated that significant WM abnormalities were found in the regions associated with visual and visual-related functions in NTG patients. In addition, the injury severity in these WM regions correlated with the MDVF and RNFL thickness, which can help elucidate the potential pathologic changes in these regions during NTG progression by the alterations of DTI parameters. Furthermore, deteriorations may be already present in these WM regions and retinal structure before clinical visual field loss. 
Acknowledgments
Supported by the State Key Program of National Natural Science Foundation of China (Grant No. 81430007) and the Grant of Science and Technology Commission of Shanghai Municipality (No.14411962000). The sponsor or funding organization had no role in the design or conduct of this research. 
Disclosure: R. Wang, None; Z. Tang, None; X. Sun, None; L. Wu, None; J. Wang, None; Y. Zhong, None; Z. Xiao, None 
References
Pinchuk L, Riss I, Batlle JF, et al. The development of a micro-shunt made from poly(styrene-block-isobutylene-block-styrene) to treat glaucoma. J Biomed Mater Res B Appl Biomater. 2017; 105: 211–221.
Tamada K, Machida S, Oikawa T, Miyamoto H, Nishimura T, Kurosaka D. Correlation between photopic negative response of focal electroretinograms and local loss of retinal neurons in glaucoma. Curr Eye Res. 2010; 35: 155–164.
Yasumura R, Meguro A, Ota M, et al. Investigation of the association between SLC1A3 gene polymorphisms and normal tension glaucoma. Mol Vis. 2010; 17: 792–796.
Shields MB. Normal-tension glaucoma: is it different from primary open-angle glaucoma? Curr Opin Ophthalmol. 2008; 19: 85–88.
Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci. 2008; 34: 51–61.
Nordly P, Agger EM, Andersen P, Nielsen HM, Foged C. Mean diffusivity and fractional anisotropy as indicators of disease and genetic liability to schizophrenia. J Psychiatr Res. 2011; 45: 980–988.
Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003; 20: 1714–1722.
Wang X, Cusick MF, Wang Y, et al. Diffusion basis spectrum imaging detects and distinguishes coexisting subclinical inflammation, demyelination and axonal injury in experimental autoimmune encephalomyelitis mice. NMR Biomed. 2014; 27: 843–852.
Watanabe M, Liao JH, Jara H, Sakai O. Multispectral quantitative MR imaging of the human brain: lifetime age-related effects. Radiographics. 2013; 33: 1305–1319.
Chen WW, Wang N, Cai S, et al. Structural brain abnormalities in patients with primary open-angle glaucoma: a study with 3T MR imaging. Invest Ophthalmol Vis Sci. 2013; 54: 545–554.
Bizrah M, Guo L, Cordeiro MF. Glaucoma and Alzheimer's disease in the elderly. Aging Health. 2011; 7: 719–733.
Ciulli S, Citi L, Salvadori E, et al. Prediction of impaired performance in Trail Making Test in MCI patients with small vessel disease using DTI data. IEEE J Biomed Health Inform. 2016; 20: 1026–1033.
Lin L, Xue Y, Duan Q, et al. Microstructural white matter abnormalities and cognitive dysfunction in subcortical ischemic vascular disease: an atlas-based diffusion tensor analysis study. J Mol Neurosci. 2015; 56: 363–370.
Marenco S, Rawlings R, Rohde GK, et al. Regional distribution of measurement error in diffusion tensor imaging. Psychiatry Res. 2006; 147: 69–78.
Qiu A, Brown T, Fischl B, Ma J, Miller MI. Atlas generation for subcortical and ventricular structures with its applications in shape analysis. IEEE Trans Image Process. 2010; 19: 1539–1547.
Yoshida S, Faria AV, Oishi K, et al. Anatomical characterization of athetotic and spastic cerebral palsy using an atlas-based analysis. J Magn Reson Imaging. 2013; 38: 288–298.
Oishi K, Faria AV, Yoshida S, Chang L, Mori S. Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int J Dev Neurosci. 2013; 31: 512–524.
Wang Y, Gupta A, Liu Z, et al. DTI registration in atlas based fiber analysis of infantile Krabbe disease. Neuroimage. 2011; 55: 1577–1586.
Lalezary M, Medeiros FA, Weinreb RN, et al. Baseline optical coherence tomography predicts the development of glaucomatous change in glaucoma suspects. Am J Ophthalmol. 2006; 142: 576–582.
El-Rafei A, Engelhorn T, Wärntges S, Dörfler A, Hornegger J, Michelson G. Glaucoma classification based on visual pathway analysis using diffusion tensor imaging. Magn Reson Imaging. 2013; 31: 1081–1091.
Murai H, Suzuki Y, Kiyosawa M, Tokumaru AM, Ishii K, Mochizuki M. Positive correlation between the degree of visual field defect and optic radiation damage in glaucoma patients. Jpn J Ophthalmol. 2013; 57: 257–262.
Wang MY, Wu K, Xu JM, et al. Quantitative 3-T diffusion tensor imaging in detecting optic nerve degeneration in patients with glaucoma: association with retinal nerve fiber layer thickness and clinical severity. Neuroradiology. 2013; 55: 493–498.
Tellouck L, Durieux M, Coupé P, et al. Optic radiations microstructural changes in glaucoma and association with severity: a study using 3Tesla-magnetic resonance diffusion tensor imaging. Invest Ophthalmol Vis Sci. 2016; 57: 6539–6547.
Michelson G, Engelhorn T, Wärntges S, Rafei AE, Hornegger J, Doerfler A. DTI parameters of axonal integrity and demyelination of the optic radiation correlate with glaucoma indices. Graefes Arch Clin Exp Ophthalmol. 2013; 251: 243–253.
Nucci C, Mancino R, Martucci A, et al. 3-T Diffusion tensor imaging of the optic nerve in subjects with glaucoma: correlation with GDx-VCC, HRT-III and Stratus optical coherence tomography findings. Br J Ophthalmol. 2012; 96: 976–980.
Sidek S, Ramli N, Rahmat K, Ramli NM, Abdulrahman F, Tan LK. Glaucoma severity affects diffusion tensor imaging (DTI) parameters of the optic nerve and optic radiation. Eur J Radiol. 2014; 83: 1437–1441.
Cui Z, Zhong S, Xu P, He Y, Gong G. PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci. 2013; 7: 42.
Mori S, Oishi K, Jiang H, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage. 2008; 40: 570–582.
Rohlfing T. Incorrect ICBM-DTI-81 atlas orientation and white matter labels. Front Neurosci. 2013; 7: 4.
Tzouriomazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15: 273–289.
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 2013; 8: e68910.
Davtes RB. Hypothesis testing when a nuisance parameter is present only under the alternative: linear model case. Biometrika. 2002; 89: 484–489.
R Development Core Team. R: a language and environment for statistical computing. Computing. 2014; 14: 12–21.
Muggeo VMR. Segmented: an R Package to fit regression models with broken-line relationships. R News. 2008; 8: 20–25.
Zikou AK, Kitsos G, Tzarouchi LC, Astrakas L, Alexiou GA, Argyropoulou MI. Voxel-based morphometry and diffusion tensor imaging of the optic pathway in primary open-angle glaucoma: a preliminary study. Am J Neuroradiol. 2012; 33: 128–134.
Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Chiro GD. Diffusion tensor MR imaging of human brain. China Contemp Med. 2001; 201: 637–648.
Aralasmak A, Ulmer JL, Kocak M, Salvan CV, Hillis AE, Yousem DM. Association, commissural, and projection pathways and their functional deficit reported in literature. J Comput Assist Tomogr. 2006; 30: 695–715.
Sincoff EH, Tan Y, Abdulrauf SI. White matter fiber dissection of the optic radiations of the temporal lobe and implications for surgical approaches to the temporal horn. J Neurosurg. 2004; 101: 739–746.
Martínezheras E, Varriano F, Prčkovska V, et al. Improved framework for tractography reconstruction of the optic radiation. PLoS One; 2015; 10: e0137064.
Garaci FG, Bolacchi F, Cerulli A, et al. Optic nerve and optic radiation neurodegeneration in patients with glaucoma: in vivo analysis with 3-T diffusion-tensor MR imaging. Radiology. 2009; 252: 496–501.
Delano-Wood L, Stricker NH, Sorg SF, et al. Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimers Dis. 2012; 29: 589–603.
Goodrich NJ. Word memory test performance in amnesic patients with hippocampal damage. Neuropsychology. 2009; 23: 529–534.
Yochim BP, Mueller AE, Kane KD, Kahook MY. Prevalence of cognitive impairment, depression, and anxiety symptoms among older adults with glaucoma. J Glaucoma. 2012; 21: 250–254.
Wang J, Li T, Sabel BA, et al. Structural brain alterations in primary open angle glaucoma: a 3T MRI study. Sci Rep. 2016; 6: 18969.
Lech RK, Koch B, Schwarz M, Suchan B. Fornix and medial temporal lobe lesions lead to comparable deficits in complex visual perception. Neurosci Lett. 2016; 620: 27–32.
Adams AJ, Rodic R, Husted R, Stamper R. Spectral sensitivity and color discrimination changes in glaucoma and glaucoma-suspect patients. Invest Ophthalmol Vis Sci. 1982; 23: 516–524.
Zhang S, Chen Y, Liu Z, et al. Association of white matter integrity and cognitive functions in Chinese non-demented elderly with the APOE ϵ4 allele. J Alzheimers Dis. 2015; 48: 781–791.
Mielke MM, Okonkwo OC, Oishi K, et al. Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease. Alzheimers Dement. 2012; 8: 105–113.
Zhu QY, Bi SW, Yao XT, et al. Disruption of thalamic connectivity in Alzheimer's disease: a diffusion tensor imaging study. Metab Brain Dis. 2015; 30: 1295–1308.
Inoue T, Kawaji T, Tanihara H. Elevated levels of multiple biomarkers of Alzheimer's disease in the aqueous humor of eyes with open-angle glaucoma. Invest Ophthalmol Vis Sci. 2013; 54: 5353–5358.
Sivak JM. The aging eye: common degenerative mechanisms between the Alzheimer's brain and retinal disease. Invest Ophthalmol Vis Sci. 2013; 54: 871–880.
Taliantzis S, Papaconstantinou D, Koutsandrea C, Moschos M, Apostolopoulos M, Georgopoulos G. Comparative studies of RNFL thickness measured by OCT with global index of visual fields in patients with ocular hypertension and early open angle glaucoma. Clin Ophthalmol. 2009; 3: 373–379.
Wollstein G, Kagemann L, Bilonick RA, et al. Retinal nerve fibre layer and visual function loss in glaucoma: the tipping point. Br J Ophthalmol. 2012; 96: 47–52.
Alasil T, Wang K, Yu F, et al. Correlation of retinal nerve fiber layer thickness and visual fields in glaucoma: a broken stick model. Am J Ophthalmol. 2014; 157: 953–959.
Murphy MC, Conner IP, Teng CY, et al. Retinal structures and visual cortex activity are impaired prior to clinical vision loss in glaucoma. Sci Rep. 2016; 6: 31464.
Figure 1
 
Through atlas-based analysis, white matter regions with differences in DTI parameters (FA, MD) between NC and NTG groups were marked by different colors in the axial (a, d), coronal (b, e), and sagittal (c, f) planes, as well as in the spatial patterns (g). These white matter regions include B.PTR (red), B.SS (blue), B.CgH (green), and B.FX/ST (violet). L, left; R, right.
Figure 1
 
Through atlas-based analysis, white matter regions with differences in DTI parameters (FA, MD) between NC and NTG groups were marked by different colors in the axial (a, d), coronal (b, e), and sagittal (c, f) planes, as well as in the spatial patterns (g). These white matter regions include B.PTR (red), B.SS (blue), B.CgH (green), and B.FX/ST (violet). L, left; R, right.
Figure 2
 
Bar charts of the mean FA, MD, AD, and RD in regions with differences between the NC and NTG groups. The comparisons between the NC group and NTG subgroups indicated that there was a gradual decline in the mean FA and RD values and increase in the mean MD and AD values with increasing severity of NTG, respectively. *P < 0.0167, **P < 0.01, ***P < 0.001 for comparisons of NTG subgroups to normal controls (1-way ANOVA with Dunnett's post hoc test, FDR-corrected).
Figure 2
 
Bar charts of the mean FA, MD, AD, and RD in regions with differences between the NC and NTG groups. The comparisons between the NC group and NTG subgroups indicated that there was a gradual decline in the mean FA and RD values and increase in the mean MD and AD values with increasing severity of NTG, respectively. *P < 0.0167, **P < 0.01, ***P < 0.001 for comparisons of NTG subgroups to normal controls (1-way ANOVA with Dunnett's post hoc test, FDR-corrected).
Figure 3
 
Scatterplots with regression lines showing the correlations between the MDVF scores and FA, MD, AD, and RD values in the four WM regions.
Figure 3
 
Scatterplots with regression lines showing the correlations between the MDVF scores and FA, MD, AD, and RD values in the four WM regions.
Figure 4
 
Scatterplots with regression lines showing correlations between the RNFL thickness and FA, MD, AD, and RD values in the four WM regions.
Figure 4
 
Scatterplots with regression lines showing correlations between the RNFL thickness and FA, MD, AD, and RD values in the four WM regions.
Figure 5
 
Relationships between visual field function, DTI parameters of these four WM regions (a), and retinal structures (b) in NTG and NC groups. Using the “broken-stick” model to determine the tipping points. Red line represents the spline fit, and the black line represents the “broken-stick” model. *P < 0.05, **P < 0.01, ***P < 0.001.
Figure 5
 
Relationships between visual field function, DTI parameters of these four WM regions (a), and retinal structures (b) in NTG and NC groups. Using the “broken-stick” model to determine the tipping points. Red line represents the spline fit, and the black line represents the “broken-stick” model. *P < 0.05, **P < 0.01, ***P < 0.001.
Table 1
 
Clinical Characteristics of the Participants
Table 1
 
Clinical Characteristics of the Participants
Table 2
 
Group Comparisons of the Mean DTI Parameters in Normal Controls As Well As the NTG Group and Subgroups
Table 2
 
Group Comparisons of the Mean DTI Parameters in Normal Controls As Well As the NTG Group and Subgroups
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