==== Front Neuropsychiatr Dis TreatNeuropsychiatr Dis TreatNeuropsychiatric Disease and TreatmentNeuropsychiatric Disease and Treatment1176-63281178-2021Dove Medical Press 10.2147/NDT.S170106ndt-14-1929Original ResearchAberrant cortical thickness in neurologically asymptomatic patients with end-stage renal disease Dong Jianwei 1*Ma Xiaofen 2*Lin Wuhong 1Liu Mengchen 2Fu Shishun 2Yang Lihua 1Jiang Guihua 2 1 School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China 2 Department of Medical Imaging, Guangdong No. 2 Provincial People’s Hospital, Guangzhou, People’s Republic of China, jiangguihua177@163.comCorrespondence: Guihua Jiang, Department of Medical Imaging, Guangdong No. 2 Provincial People’s Hospital, No. 466, Xingang Central Road, Haizhu District, Guangzhou 510317, People’s Republic of China, Tel/fax +86 2 08 916 8701, Email jiangguihua177@163.com* These authors contributed equally to this work 2018 02 8 2018 14 1929 1939 © 2018 Dong et al. This work is published and licensed by Dove Medical Press Limited2018The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.Purpose The aim of this study is to investigate the morphology of cortical gray matter in patients with end-stage renal disease (ESRD) and the relationship between cortical thickness and kidney function. Patients and methods Three-dimensional high-resolution brain structural magnetic resonance imaging data were collected from 35 patients with ESRD (28 men, 18–61 years old) and 40 age- and gender-matched healthy controls (HCs, 32 men, 22–58 years old). Vertex-wise analysis was then performed to compare the brains of the patients with ESRD with those of HCs to identify abnormalities in the brains of the former. Multiple biochemical measures of renal metabolin, vascular risk factors, general cognitive ability, and dialysis duration were correlated with brain morphometry alterations for the patients. Results Patients with ESRD showed lesser cortical thickness than the HCs. The most significant cluster with decreased cortical thickness was found in the right prefrontal cortex (P<0.05, random-field theory correction). In addition, the four local peak vertices in the prefrontal cluster were lateral prefrontal cortex (Peaks 1 and 2), medial prefrontal cortex (Peak 3), and ventral prefrontal cortex (Peak 4). Significant negative correlations were observed between the cortical thicknesses of all four peak vertices and blood urea nitrogen; a negative correlation, between the cortical thickness in three of four peaks and serum creatinine; and a positive correlation, between cortical thickness in the medial prefrontal cortex (Peak 3) and hemoglobin. Conclusion These results provided compelling evidence for cortical abnormality of ESRD patients and suggested that kidney function may be the key factor for predicting changes of brain tissue structure. Keywords cortical thicknesskidney functionvertex-wise analysismultivariate analysisbrain morphometry alterations ==== Body Introduction End-stage renal disease (ESRD) typically occurs when chronic renal failure progresses to a point where the kidneys are permanently functioning at less than 10% of their capacities.1 Apart from central nervous system abnormalities and neurologic problems,2,3 ESRD significantly elevates the risk of developing cognitive impairments and death rate.4,5 Therefore, detecting brain abnormalities in neurologically asymptomatic patients with ESRD is crucial to early diagnosis, prognosis improvement, and ultimately the reduction of death rate. Neuroimaging techniques provide potential avenues for these goals and enable the detection of ESRD-related abnormalities in white matter (WM) integrity,6–8 gray matter (GM) volume,9 and functional architecture.10,11 These pioneering studies have largely contributed to our understanding of the disease. However, the neuropathologic substrate underlying ESRD cannot be established well, particularly at the structure level from a cortical GM morphology perspective. The morphology of cortical GM is commonly assessed using T1-weighted magnetic resonance imaging (MRI) with automated computerized methods, such as voxel-based morphometry (VBM). Most previous studies in this field analyzed regional alterations in the brain GM volume or density in patients with ESRD.10,12,13 For example, Ma et al10 found that the brain GM volumes in the bilateral medial prefrontal gyrus, anterior cingulate gyrus, superior temporal gyrus, middle temporal gyrus, lingual gyrus, parahippocampa gyrus, putamen, caudate, insula, and right middle cingulate gyrus were reduced in patients with ESRD. Zhang et al13 found that patients with ESRD showed diffusely decreased GM volume, which further decreased in the presence of encephalopathy. They also discovered that the GM volumes in these areas were negatively correlated with serum urea level. Qiu et al12 showed that the GM volume in the bilateral medial orbital-prefrontal cortices, bilateral dorsal lateral prefrontal cortices, and left middle temporal cortices of patients with ESRD considerably decreased. However, VBM is influenced by registration strategies and the choice of a normalization template.14,15 In addition, VBM analysis may provide a mixed measure of GM alteration combined with abnormalities in cortical thickness, cortical surface area, and cortical folding.16,17 These factors potentially reduce the sensitivity of VBM analysis for detecting significant effects of brain structural abnormalities in diseased brains. Structural neuroimaging studies have largely focused on volumetric measures of the cerebral cortex.12,13 This metric is, however, a composite measure derived from the product of cortical surface area and cortical thickness, each of which may in turn be differentially affected by a variety of factors. It is, therefore, surprising that there have been relatively few studies focusing on the analyses of cortical thickness or surface area as discrete measures in patients with ESRD. In this study, we investigated ESRD-related alterations in the morphology of cortical GM. These alterations might be a more appropriate measure than GM volume when evaluating morphologic abnormalities in the brain structure.18–21 Cortical thickness can provide valuable information of brain neuroanatomy, and it reflects the size, density, and arrangement of cells (neurons, neuroglia, and nerve fibers). Moreover, cortical thickness can represent a more sensitive and complementary measure of brain structure change than standard MRI-based volumetry in patients with ESRD. Based on previous structural and functional studies on ESRD, we hypothesized that cortical thickness in certain areas of the brain can be altered, especially in the orbito-prefrontal cortices, bilateral dorsal lateral prefrontal cortices, and the left middle temporal cortex10,12 of individuals with ESRD, and that the nephrotoxicity or duration of dialysis is associated with altered cortical thickness. To test this hypothesis, we calculated the cortical thickness in patients with ESRD and control groups using vertex-wise methods. Simultaneously, our aim is to determine the brain structure changes in patients with ESRD by calculating cortical thickness of the entire brain and to explore whether changes in renal function and cortical thickness may be predicted or correlated with each other. Our results provided direct evidence for cortical GM disturbances in ESRD at the structural level. Patients and methods Participants A total of 38 patients with ESRD (all right-handed) were recruited from the Renal Transplantation Department of Guangdong No. 2 Provincial People’s Hospital, Guangzhou, China, from August 2011 to July 2013. The exclusion criteria were as follows: 1) psychiatric disorders or major neurologic disorders (eg, severe head injury, stroke, epilepsy, or visible lesions); 2) ischemic diseases including acute ischemic cerebrovascular disease, acute peripheral arterial occlusion, and advanced liver or heart failure; 3) asymptomatic coronary ischemia by electrocardiogram testing; 4) a history of diabetes; and 5) substance abuse, particularly drugs, alcohol, and cigarettes. Conventional MR images were examined by an experienced radiologist who was blinded to whether the images were from the patient or control group. Three patients were excluded because of abnormal hyperintensities in their T2-weighted-fluid-attenuated inversion recovery (T2-FLAIR) MR images. Therefore, 35 patients with ESRD (28 males, 7 females; mean age 37.5±11.3 years, range 18–61 years) were included in the final analysis. Because Guangdong No. 2 Provincial People’s Hospital is an affiliated hospital of Southern Medical University, ethical approval was provided by the Research Ethics Review Board of the Institute of Mental Health at the Southern Medical University, and written informed consent was obtained from each participant. All the patients completed biochemical tests, such as serum creatinine, urea level, hemoglobin level, cholesterol level, serum albumin level, serum kalium, and serum calcium within 24 hours before the MRI. In this study, none of the patients were on erythropoiesis-stimulating agents (ESAs) and treated with vitamin D, calcitriol, and/or phosphorus-chelating agents, ASE, whereas they were treated with intravenous iron. Indeed, intravenous iron can raise levels of hemoglobin even without the use of ESAs and enhance the efficacy of ESAs, and also has non-erythropoietic effects including improvement in physical performance, cognition, and amelioration of restless leg syndrome.22 We did not check the serum parathyroid hormone (PTH) level for the patients with ESRD. The serum calcium levels were corrected with serum albumin levels using the Payne’s formula.23 The dialysis modality and duration (15.5±6.6 months) were also recorded from the medical history of patients. All the patients underwent a neuropsychological test involving a mini-mental state examination (MMSE).24 Forty age- and gender-matched healthy controls (HCs) (all right handed; 32 males, 8 females; mean age of 41.5±10.6 years, range 22–58 years) were recruited from the local community. All the HCs had normal renal functions, having no detected abnormalities in abdominal MRI and no physical disease or history of psychiatric or neurologic diseases. All the demographic and clinical information is summarized in Table 1. Data acquisition All participants were scanned using a 1.5 T MR scanner (Achieva Nova-Dual; Philips, Best, the Netherlands) at the Department of Medical Imaging, Guangdong No. 2 Provincial People’s Hospital. The conventional imaging sequences, including T1-weighted images and T2-FLAIR images, were obtained for each participant to detect clinically silent lesions. High-resolution anatomical images were acquired using a T1-weighted three-dimensional volumetric magnetization-prepared rapidly acquired gradient-echo (MPRAGE) sequence set at the following parameters: repetition time=1,600 ms; echo time=2.13 ms; flip angle=9°; inversion time (TI)=1,000 ms; slice thickness=1 mm; no gap; in-plane resolution=1×1 mm2; and matrix=256×224×176. For each participant, resting-state functional MRI (fMRI) data and diffusion-weighted imaging were also acquired but were not used in the current study. Image processing Cortical thickness was estimated using three-dimensional MPRAGE images from patients with ESRD and HCs with the use of FreeSurfer software package (Version 5.3.0, http://surfer.nmr.mgh.harvard.edu),62 a widely documented and automated program for reconstructing brain cortical surfaces.25–27 The brain cortical surface reconstruction included the following steps: 1) brain extraction using a hybrid watershed/surface deformation procedure; 2) automated segmentation to obtain the cerebrospinal fluid (CSF), WM, and deep GM structures; 3) generation of the cutting planes to separate two hemispheres and subcortical structures; 4) repairmen of the interior holes of the segmentation; 5) a triangular mesh tessellation of the GM–WM boundary and the mesh deformation over the GM–WM boundary to form the GM–WM interface (white surface) and the GM–CSF interface (pial surface); 6) automatic fixation of the topologic defects on the surface; 7) inflation of the individual mesh surface; and 8) registration of all individual cortical surfaces to a common surface template (fsaverage). Cortical thickness was measured during the reconstruction as follows: two distances were estimated for each vertex: the shortest distance between a given point on an estimated pial surface and white surface and that between a given point on the white surface and an estimated pial surface. These distances were averaged to estimate the cortical thickness. After the normalization to a common template, the cortical thickness was smoothed with a 25-mm full-width-half-maximum Gaussian kernel to improve the signal-to-noise ratio and statistical power. Statistical analysis We performed multivariate analysis to examine the differences between the cortical thicknesses of the patients with ESRD and those of HCs. SurfStat software was used for the surface-based analysis, and general linear models (http://www.math.mcgill.ca/keith/surfstat/)63 were used. Two-sample t-tests were performed to compare the cortical thickness between the two groups. The mean cortical thickness from each participant was used as covariate. The resulting T-maps were corrected for multiple comparisons using the random-field theory (RFT) correction method at a P-value of <0.05. To estimate whether demographic and clinical information affects the changes in cortical thickness in patients with ESRD, we calculated the Pearson correlations between the indices (demographic and clinical) and cortical thickness in a local peak vertex from the between-group difference results. The correlations were considered significant at a threshold of P<0.05. Results Demographic and clinical characteristics Thirty-five patients with ESRD (28 males, 7 females; mean age of 37.5±11.3 years, range of 18–61 years) and 40 HCs (32 males, 8 females; mean age of 41.5±10.6 years, range of 22–58 years) were included in the final analysis. As shown in Table 1, no significant difference was observed in gender (P=1), age (P=0.119), or education level (P=0.148) between the ESRD and HC groups. Patients with ESRD had lower MMSE score than HCs (P<0.001). The mean duration of hemodialysis for the patients was 15.5±6.6 months. The mean calcium, kalium, hemoglobin, creatinine, urea, and cholesterol levels for the patients were 2.3±0.2 mmol/L, 4.5±0.9 mmol/L, 102.5±23.5 g/L, 838.1±483.6 μmol/L, 17.8±8.2 mmol/L, and 5.1±1.4 mmol/L, respectively (Table 1). Between-group differences in cortical thickness Figure 1 displays the difference in cortical thickness between patients with ESRD and HCs. The patients with ESRD showed lower cortical thicknesses than HCs. The most significant cluster with decreased cortical thickness was found in the right prefrontal cortex (P<0.05, RFT correction). Four local peak vertices were observed in the prefrontal cluster as shown in Figure 2. Their MNI coordinates are Peak 1 (25.438, 42.1878, 23.7868), Peak 2 (27.191, 51.987, 8.085), Peak 3 (28.108, 34.569, −10.259), and Peak 4 (2.543, 39.725, −19.244), respectively. Peaks 1 and 2 were in the lateral prefrontal cortex, Peak 3 was located in the medial prefrontal cortex, and Peak 4 was in the ventral prefrontal cortex. No increase in cortical thickness was observed in the ESRD group at the same statistical threshold. Cortical thickness–clinical relationship For the ESRD patients, cortical thicknesses from the four local peak vertices were correlated with demographic and clinical information across subjects to delineate the demographic and clinical factor resulting in the observed differences in the cortical thickness. In patients with ESRD, correlation analysis revealed no significant correlations between the cortical thicknesses of all the four peak vertices and the MMSE scores (P>0.05), and the cortical thicknesses of all the four peak vertices showed negative correlation with blood urea nitrogen (BUN) (Peak 1: r=−0.384, P=0.023; Peak 2: r=−0.38, P=0.024; Peak 3: r=−0.47, P=0.004; and Peak 4: r=−0.44, P=0.009). The cortical thickness in three of the four peak vertices showed negative correlation with serum creatinine (Peak 1: r=−0.34, P=0.043; Peak 2: r=−0.335, P=0.049; and Peak 3: r=–0.39, P=0.021). By contrast, the cortical thickness in Peak 3 located in the medial prefrontal cortex was positively correlated with hemoglobin (r=0.34, P=0.046) and negatively correlated with serum kalium (r=−0.33, P=0.049). Another positive correlation was observed between the cortical thickness in Peak 2 and corrected serum calcium (r=0.35, P=0.037). The scatter plots of all these correlations are visualized in Figure 2, and all significant correlation results are shown in Table 2. Discussion Using vertex-wise methods, we explored the differences between the ESRD and HC group with respect to cortical thickness. The vertex-wise surface-based morphometry (SBM) analysis indicated that compared with HCs, ESRD showed significantly decreased cortical thickness in clusters primarily located in the right prefrontal cortex of the brain, which includes four local peak vertices in the prefrontal cluster, such as lateral prefrontal cortex (Peaks 1 and 2), medial prefrontal cortex (Peak 3), and ventral prefrontal cortex (Peak 4). Significant negative correlations were also observed between the BUN and cortical thicknesses of all four peak vertices in patients with ESRD. Cortical thickness in three peak vertices showed negative correlation with serum creatinine. Cortical thickness in Peak 3, located in the medial prefrontal cortex, was positively correlated with hemoglobin but negatively correlated with serum kalium. A positive correlation was observed between cortical thickness in Peak 2 and corrected serum calcium. Renal failure results in accumulation of numerous organic substances that possibly act as uremic neurotoxins, but no single metabolite has been identified as the sole cause of uremia.28 Accumulation of urea, guanidino compounds, uric acid, various amino acids, and “middle molecules” has been reported in the literature.29,30 Among the guanidino compounds, guanidine and creatinine were found to be highly increased in serum, CSF, and brain of uremic patients.31 De Deyn et al reported that CSF and brain levels of some guanidine compounds, such as creatinine, guanidine, guanidinosuccinic acid, and methylguanidine, are substantially elevated in uremic patients.32 Interestingly, these high toxin concentrations (up to ten-fold higher in chronic kidney disease (CKD) patients than in controls) were found in brain regions that play a determinant role in cognition, such as the thalamus, the mamillary bodies, and the cerebral cortex.32 Moreover, more than 30 years ago, Passer reported a high prevalence of cerebral atrophy (CA) in patients undergoing long-term hemodialysis.33 It was subsequently shown in such patients that the lesions were prominent in the frontal lobes.34,35 In addition, in patients with ESRD, alterations in the brain morphometry of the right prefrontal cortex are also supported by a recent histologic study by Frias et al.36 The reduced brain-derived neurotrophic factor is often associated with the atrophy and cellular death of glia and neurons in neurodegenerative disorders. Previous positron emission tomography (PET) studies also revealed abnormalities in these areas. In an F-18-fluorodeoxyglucose PET study, Song et al37 found several voxel clusters of significantly decreased cerebral glucose metabolism in patients with predialysis CKD. These clusters include the left prefrontal cortex (Brodmann’s area 9), right prefrontal cortex (Brodmann’s area 10), right basolateral prefrontal cortex (Brodmann’s area 46), left anterior cingulate gyrus (Brodmann’s area 32), left premotor cortex (Brodmann’s area 6), left transverse temporal gyrus (Brodmann’s area 41), left superior temporal gyrus (Brodmann’s area 42), right basolateral prefrontal cortex (Brodmann’s area 44), right inferior parietal lobule (Brodmann’s area 39), left middle temporal gyrus (Brodmann’s area 19), and left angular gyrus (Brodmann’s area 39).37 In the present study, VBM analysis results indicated a significant decrease in the GM volumes in the bilateral prefrontal cortices of the patients with ESRD.12 Further study by Zhang et al13 showed diffusely decreased GM volumes of the frontal lobe in patients with ESRD by comparing minimal nephron-encephalopathy (MNE) and non-MNE with controls. They found that serum urea was negatively associated with the changes in GM volume in many regions (bilateral occipital lobes, bilateral lingual lobes, bilateral calcarine, bilateral superior temporal gyri, bilateral temporal poles, bilateral uncus, posterior cingulate cortex/precuneus/cuneus, right fusiform, right parahippocampus, right amygdala, and left hippocampus/parahippocampus).13 However, VBM analysis may have reduced sensitivity to the significant effects of brain structural abnormalities in diseased brains because of the GM alterations and the abnormalities in cortical thickness, cortical surface area, and cortical folding.16,17 In this study, we used SBM analyses over VBM to provide more precise information with morphometry in ESRD. ESRD showed significantly decreased cortical thickness in clusters primarily located in the right prefrontal cortex. The prefrontal cortex subserves cognitive control. In the human brain, the areas of the prefrontal cortex are involved in the mediation of cognitive control processes for motor behavior38–40 and evaluation of motivational responses to behavioral and environmental feedback.39,41,42 Thus, our result may imply early imaging manifestations of ESRD-related functional impairment. Numerous clinical studies suggested that ESRD significantly elevates the risk of developing cognitive impairments.43–45 The brain morphologic changes detected in our study may serve as brain structural bases of cognitive impairment in ESRD. Several fMRI studies have a similar finding, that is, disrupted functional integration in ESRD, particularly in the prefrontal cortex.10,11 This evidence suggests that prefrontal cortex plays a decisive role in the progress of cognitive impairment in ESRD and is a potential observation indicator of cognitive change in ESRD. In addition, certain correlations were observed between morphometry alterations and blood biochemical indicators. The four local peak vertices in the prefrontal cluster were lateral prefrontal cortex (Peaks 1 and 2), medial prefrontal cortex (Peak 3), and ventral prefrontal cortex (Peak 4). Significant negative correlations were observed between BUN and cortical thicknesses of all the four peak vertices. A negative correlation was observed between serum creatinine and cortical thicknesses in three peaks, whereas a positive correlation was observed between cortical thickness in the medial prefrontal cortex (Peak 3) and hemoglobin. ESRD is typically accompanied by low hemoglobin levels, which could further lead to malnutrition. Previous studies showed that chronic malnutrition can result in insufficient nutrient supply to the brain, thereby triggering a series of problems, including brain tissue hypoxia and blood viscosity reduction and eventual hypoperfusion and/or hypometabolism.46,47 Recent studies highlighted the importance of normal metabolism in establishing and retaining interregional coordination in the brain,48,49 which is the basis of cognitive processing.50 Given that low hemoglobin is associated with poor mental health in ESRD,51,52 morphometry alterations of the prefrontal cortex may contribute to cognitive disturbances in ESRD because of insufficient energy metabolism in the brain because of low hemoglobin levels. We also found significant negative correlations between BUN and the cortical thicknesses of all four peak vertices and a negative correlation between serum creatinine and cortical thicknesses in three peaks. Galons et al53 reported the major role of urea in the pathogenesis of cerebral edema in dialysis disequilibrium syndrome. Zhang et al13 found that serum urea levels play an important role in ESRD MNE using VBM method; this result was consistent with our results. Uremic toxins, such as serum urea and creatinine, accumulated in vivo in the brains of patients with ESRD because of renal failure.54,55 Our results may provide several objective evidence of neuronal damage due to nephrotoxic substances. Our study has several limitations. First, the sample size was relatively small, and the MRI scanning parameters were suboptimal. Thus, the detection of subtle effects was limited. Future studies must use a large cohort of participants, advanced techniques, and optimized parameters. Second, depression may complicate ESRD. A recent study found that depression itself can affect the alterations in the neurons.56,57 Thus, a more rigorous experiment is necessary to exclude the influence of depression in the future study. Third, another main limitation of the study is that all of the ESRD patients received regular hemodialysis at the time of the fMRI study. Whether and how hemodialysis itself can affect the brain is unknown; however, it can affect the patient’s cognitive function.58,59 Although we did not find significant correlation between the abnormal cortical thickness and hemodialysis duration, a more detailed experiment with CKD (stages 4–5) without hemodialysis is required in the future study. Finally, we did not collect clinical information on Kt/V and episodes of dialytic hypotension for the patients. Consequently, we could not exclude the possibility that the current findings may be confounded by other factors, such as hemodialysis modality. As our project progresses, we will address these interesting questions with more rigorous experimental design and stricter enrollment criteria. In addition, a study has shown that all three tested dialysis modes did not differ in urea dialysis dose (Kt/V) as a parameter of small molecular weight removal.60 The patients showed mild CA on the basis of cortical sulci exceeding 3 mm in breadth and an Evans ratio exceeding 0.31, for a total of 14 degrees of CA (mean 0.9±1), and further conclusions indicated that CA in this patient group can only be attributed to uremia-related pathology and that it tends to worsen as regular hemodialysis treatment continues.61 In conclusion, we detected alterations in the cortical thicknesses of the prefrontal cortices of patients with ESRD through a surface-based morphometric analysis. Our findings were consistent with those of previous studies, which reported the presence of abnormalities in key brain regions in the brains of patients with ESRD. To the best of our knowledge, this is one of the first studies to evaluate the difference between patients with ESRD and HCs with respect to cortical thickness. Our findings suggested that abnormal cortical thickness in the prefrontal cortex has more considerable effect on cognitive capacities of patients in the ESRD group than in the normal group. We detected negative correlations between the cortical thickness in the lateral prefrontal cortex, medial prefrontal cortex, and ventral prefrontal cortex, and BUN and serum creatinine. By contrast, a positive correlation was observed between cortical thickness in the medial prefrontal cortex and hemoglobin. These results may contribute vital information to the limited literature on brain structural alterations in ESRD. Conclusion In summary, this study provided evidence for the first time that ESRD showed thinner cortical thickness than the HCs by combining resting-state fMRI technique and vertex-wise analysis. ESRD had the most significant cluster with decreased cortical thickness, which was found in the right prefrontal cortex (P<0.05, RFT corrected) than HCs. Moreover, significant negative correlations were observed between the cortical thicknesses of all four peak vertices and BUN; negative correlations, between the cortical thickness in three of four peaks and serum creatinine; and a positive correlation, between cortical thickness in the medial prefrontal cortex (Peak 3) and hemoglobin. These results provided compelling evidence for cortical abnormality in ESRD and also suggested that kidney function may be a critical factor associated with the brain alteration. Acknowledgments This study is supported by National Natural Science Foundation of China (Nos. 11771458, 81471639, 81771807), Guangdong Province Key grant (No. 2016B030307003), and Natural Science Foundation of Guangdong (Nos. 2015A030313723, 2016A020215125, 201607010056, 2017A020215077). Author contributions All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work. Disclosure The authors report no conflicts of interest in this work. Figure 1 Cortical thickness comparison between patients with ESRD and HCs. (A) Group difference in cortical thickness shown as a T-value map, positive T-values (red, yellow) indicate thicker cortical thickness in HC group. (B) Two P-values (P-values for each vertex and P-values for each cluster) are shown simultaneously. The P-value for clusters indicates significant corrected P-values with the lightest blue color, and the P-value for vertices indicates significant corrected P-values with the lightest yellow color. Only one cluster (blue, right prefrontal cortex) showed thinner cortical thickness in patients with ESRD. Abbreviations: ESRD, end-stage renal disease; HC, healthy control. Figure 2 Cortical thickness–clinical correlation. There were four local peak vertices in right prefrontal cortex (white dots in the figure, 1, 2, 3, and 4 represent Peak 1, Peak 2, Peak 3, and Peak 4 separately). The correlations were performed between the cortical thickness in four peak vertices and clinical information across patients with ESRD. The correlations were considered significant at a threshold of P<0.05. Abbreviations: ESRD, end-stage renal disease; HC, healthy control. Table 1 Demographics and clinical characteristics of all participants Variables ESRD (n=35) HC (n=40) P-value Gender (M/F) 28/7 32/8 1a Age (years) 37.5±11.3 (18–61) 41.5±10.6 (22–58) 0.119b Education level (years) 11.8±3.2 (3–16) 10.8±2.8 (6–18) 0.148b MMSE 26.9±1.9 (22–30) 29.5±0.9 (26–30) <0.001b Dialysis duration (months) 15.5±6.6 (6–30) Blood systolic pressure 159±19 (120–190) Blood diastolic pressure 90±11 (60–110) Serum calcium (corrected) 9.3±1.3 (6.7–12.3) Serum kalium (mmol/L) 4.5±0.9 (2.9–6.3) Hemoglobin (g/L) 102.5±23.5 (56–158) Serum creatinine (μmol/L) 838.1±483.6 (80–2,030) Blood urea nitrogen (mmol/L) 17.8±8.2 (4.1–30.2) Cholesterol (mmol/L) 5.1±1.4 (3.7–9.9) Serum calcium×phosphorus (mg/dL) 54±22.8 (22–98) Notes: Values are represented as mean±SD (min−max). a The P-value was obtained by chi-squared test. b The P-value was obtained by two-sided two-sample t-test. Abbreviations: ESRD, end-stage renal disease; HC, healthy control; MMSE, mini-mental state examination; M, male; F, female. Table 2 Correlation between cortical thicknesses of four peak vertices and clinical information (P<0.05) Vertices Blood urea nitrogen Serum creatinine Hemoglobin Serum calcium Serum kalium Peak 1 −0.38 −0.34 Peak 2 −0.38 −0.335 0.35 Peak 3 −0.47 −0.39 0.33 −0.33 Peak 4 −0.44 ==== Refs References 1 Foley RN Collins AJ End-stage renal disease in the United States: an update from the United States Renal Data System J Am Soc Nephrol 2007 18 10 2644 2648 17656472 2 De Deyn PP Saxena VK Abts H Clinical and pathophysiological aspects of neurological complications in renal failure Acta Neurol Belg 1992 92 4 191 206 1332359 3 Brouns R De Deyn PP Neurological complications in renal failure: a review Clin Neurol Neurosurg 2004 107 1 1 16 15567546 4 Kurella Tamura M Yaffe K Dementia and cognitive impairment in ESRD: diagnostic and therapeutic strategies Kidney Int 2011 79 1 14 22 20861818 5 Raphael KL Wei G Greene T Baird BC Beddhu S Cognitive function and the risk of death in chronic kidney disease Am J Nephrol 2012 35 1 49 57 22189005 6 Hsieh TJ Chang JM Chuang HY End-stage renal disease: in vivo diffusion-tensor imaging of silent white matter damage Radiology 2009 252 2 518 525 19528357 7 Kim HS Park JW Bai DS Diffusion tensor imaging findings in neurologically asymptomatic patients with end stage renal disease NeuroRehabilitation 2011 29 1 111 116 21876303 8 Chou MC Hsieh TJ Lin YL Widespread white matter alterations in patients with end-stage renal disease: a voxelwise diffusion tensor imaging study AJNR Am J Neuroradiol 2013 34 10 1945 1951 23788598 9 Prohovnik I Post J Uribarri J Lee H Sandu O Langhoff E Cerebro-vascular effects of hemodialysis in chronic kidney disease J Cereb Blood Flow Metab 2007 27 11 1861 1869 17406658 10 Ma X Jiang G Li S Aberrant functional connectome in neurologically asymptomatic patients with end-stage renal disease PLoS One 2015 10 3 e0121085 25786231 11 Ma X Tian J Wu Z Spatial disassociation of disrupted functional connectivity for the default mode network in patients with end-stage renal disease PLoS One 2016 11 8 e0161392 27560146 12 Qiu Y Lv X Su H Jiang G Li C Tian J Structural and functional brain alterations in end stage renal disease patients on routine hemodialysis: a voxel-based morphometry and resting state functional connectivity study PLoS One 2014 9 5 e98346 24854737 13 Zhang LJ Wen J Ni L Predominant gray matter volume loss in patients with end-stage renal disease: a voxel-based morphometry study Metab Brain Dis 2013 28 4 647 654 24065440 14 Bookstein FL “Voxel-based morphometry” should not be used with imperfectly registered images Neuroimage 2001 14 6 1454 1462 11707101 15 Jones DK Symms MR Cercignani M Howard RJ The effect of filter size on VBM analyses of DT-MRI data Neuroimage 2005 26 2 546 554 15907311 16 Hester R Nestor L Garavan H Impaired error awareness and anterior cingulate cortex hypoactivity in chronic cannabis users Neuropsychopharmacology 2009 34 11 2450 2458 19553917 17 Voets NL Hough MG Douaud G Evidence for abnormalities of cortical development in adolescent-onset schizophrenia Neuroimage 2008 43 4 665 675 18793730 18 Clarkson MJ Cardoso MJ Ridgway GR A comparison of voxel and surface based cortical thickness estimation methods Neuroimage 2011 57 3 856 865 21640841 19 Hutton C Draganski B Ashburner J Weiskopf N A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging Neuroimage 2009 48 2 371 380 19559801 20 Klein A Ghosh SS Avants B Evaluation of volume-based and surface-based brain image registration methods Neuroimage 2010 51 1 214 220 20123029 21 Lüsebrink F Wollrab A Speck O Cortical thickness determination of the human brain using high resolution 3T and 7T MRI data Neuroimage 2013 70 122 131 23261638 22 Singh N Agarwal AK Pumping iron: revisiting risks, benefits and strategies in treatment of iron deficiency in end-stage renal disease Clin Nephrol 2012 77 3 188 194 22377249 23 Payne RB Little AJ Williams RB Milner JR Interpretation of serum calcium in patients with abnormal serum proteins Br Med J 1973 4 5893 643 646 4758544 24 Schultz-Larsen K Lomholt RK Kreiner S Mini-Mental Status Examination: a short form of MMSE was as accurate as the original MMSE in predicting dementia J Clin Epidemiol 2007 60 3 260 267 17292020 25 Dale AM Fischl B Sereno MI Cortical surface-based analysis. I. Segmentation and surface reconstruction Neuroimage 1999 9 2 179 194 9931268 26 Fischl B Sereno MI Dale AM Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system Neuroimage 1999 9 2 195 207 9931269 27 Fischl B Sereno MI Tootell RB Dale AM High-resolution intersubject averaging and a coordinate system for the cortical surface Hum Brain Mapp 1999 8 4 272 284 10619420 28 Vanholder R De Smet R Glorieux G European Uremic Toxin Work Group (EUTox) Review on uremic toxins: classification, concentration, and interindividual variability Kidney Int 2003 63 5 1934 1943 12675874 29 Enomoto A Takeda M Taki K Interactions of human organic anion as well as cation transporters with indoxyl sulfate Eur J Pharmacol 2003 466 1–2 13 20 12679137 30 Topczewska-Bruns J Pawlak D Chabielska E Tankiewicz A Buczko W Increased levels of 3-hydroxykynurenine in different brain regions of rats with chronic renal insufficiency Brain Res Bull 2002 58 4 423 428 12183021 31 De Deyn PP D’Hooge R van Bogaert PP Marescau B Endogenous guanidino compounds as uremic neurotoxins Kidney Int Suppl 2001 78 S77 S83 11168988 32 De Deyn PP Vanholder R Eloot S Glorieux G Guanidino compounds as uremic (neuro) toxins Semin Dial 2009 22 4 340 345 19708978 33 Passer JA Cerebral atrophy in end-stage uremia Proc Clin Dial Transplant Forum 1977 7 91 94 616005 34 Kamata T Hishida A Takita T Morphologic abnormalities in the brain of chronically hemodialyzed patients without cerebrovascular disease Am J Nephrol 2000 20 1 27 31 10644864 35 Savazzi GM Cusmano F Musini S Cerebral imaging changes in patients with chronic renal failure treated conservatively or in hemodialysis Nephron 2001 89 1 31 36 11528229 36 Frias B Santos J Morgado M The role of brain-derived neurotrophic factor (BDNF) in the development of neurogenic detrusor overactivity (NDO) J Neurosci 2015 35 5 2146 2160 25653370 37 Song SH Kim IJ Kim SJ Kwak IS Kim YK Cerebral glucose metabolism abnormalities in patients with major depressive symptoms in pre-dialytic chronic kidney disease: statistical parametric mapping analysis of F-18-FDG PET, a preliminary study Psychiatry Clin Neurosci 2008 62 5 554 561 18950375 38 Ridderinkhof KR van den Wildenberg WP Segalowitz SJ Carter CS Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning Brain Cogn 2004 56 2 129 140 15518930 39 Ridderinkhof KR Ullsperger M Crone EA Nieuwenhuis S The role of the medial frontal cortex in cognitive control Science 2004 306 5695 443 447 15486290 40 Polli FE Barton JJ Cain MS Thakkar KN Rauch SL Manoach DS Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission Proc Natl Acad Sci U S A 2005 102 43 15700 15705 16227444 41 Schnider A Bonvallat J Emond H Leemann B Reality confusion in spontaneous confabulation Neurology 2005 65 7 1117 1119 16217071 42 Diekhof EK Falkai P Gruber O The orbitofrontal cortex and its role in the assignment of behavioural significance Neuropsychologia 2011 49 5 984 991 21262242 43 Kurella M Chertow GM Luan J Yaffe K Cognitive impairment in chronic kidney disease J Am Geriatr Soc 2004 52 11 1863 1869 15507063 44 Murray AM Tupper DE Knopman DS Cognitive impairment in hemodialysis patients is common Neurology 2006 67 2 216 223 16864811 45 Kurella Tamura M Yaffe K Dementia and cognitive impairment in ESRD: diagnostic and therapeutic strategies Kidney Int 2011 79 1 14 22 20861818 46 Bornivelli C Aperis G Giannikouris I Paliouras C Alivanis P Relationship between depression, clinical and biochemical parameters in patients undergoing haemodialysis J Ren Care 2012 38 2 93 97 22429274 47 Kuwabara Y Sasaki M Hirakata H Cerebral blood flow and vasodilatory capacity in anemia secondary to chronic renal failure Kidney Int 2002 61 2 564 569 11849397 48 Liang X Zou Q He Y Yang Y Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain Proc Natl Acad Sci U S A 2013 110 5 1929 1934 23319644 49 Tomasi D Wang GJ Volkow ND Energetic cost of brain functional connectivity Proc Natl Acad Sci U S A 2013 110 33 13642 13647 23898179 50 Sporns O Zwi JD The small world of the cerebral cortex Neuroinformatics 2004 2 2 145 162 15319512 51 Kalender B Ozdemir AC Koroglu G Association of depression with markers of nutrition and inflammation in chronic kidney disease and end-stage renal disease Nephron Clin Pract 2006 102 3–4 c115 c121 16282695 52 Teles F Azevedo VF Miranda CT Miranda MP Teixeira MC Elias RM Depression in hemodialysis patients: the role of dialysis shift Clinics 2014 69 3 198 202 24626947 53 Galons JP Trouard T Gmitro AF Lien YH Hemodialysis increases apparent diffusion coefficient of brain water in nephrectomized rats measured by isotropic diffusion-weighted magnetic resonance imaging J Clin Invest 1996 98 3 750 755 8698867 54 Owen WF Lew NL Liu Y Lowrie EG Lazarus JM The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis N Engl J Med 1993 329 14 1001 1006 8366899 55 De Deyn PP Vanholder R Eloot S Glorieux G Guanidino compounds as uremic (neuro)toxins Semin Dial 2009 22 4 340 345 19708978 56 Murphy ML Frodl T Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression Biol Mood Anxiety Disord 2011 1 1 3 22738088 57 Kimmel PL Cukor D Cohen SD Peterson RA Depression in end-stage renal disease patients: a critical review Adv Chronic Kidney Dis 2007 14 4 328 334 17904499 58 Nasser MT Shawki S El Shahawy Y Sany D Assessment of cognitive dysfunction in kidney disease Saudi J Kidney Dis Transpl 2012 23 6 1208 1214 23168850 59 Kurella Tamura M Unruh ML Nissenson AR Effect of more frequent hemodialysis on cognitive function in the frequent hemodialysis network trials Am J Kidney Dis 2013 61 2 228 237 23149295 60 Vára F Lopot F Valkovský I Phosphorus removal in low-flux hemodialysis, high-flux hemodialysis and hemodiafiltration ASAIO Journal 2015 62 2 176 61 Savazzi GM Cusmano F Vinci S Allegri L Progression of cerebral atrophy in patients on regular hemodialysis treatment: long-term follow-up with cerebral computed tomography Nephron 1995 69 1 29 33 7891794 62 A Software Package for the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies [homepage on the Internet] Massachusetts The Athinoula A. Martinos Center for Biomedical Imaging 2013 [updated May 15, 2013]. Available from: http://surfer.nmr.mgh.harvard.edu/ Accessed May, 2013 63 A Matlab Toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory [homepage on the Internet] Canada Department of Mathematics and Statistics, McGill University 2008 [updated September 26, 2008]. Available from: http://www.math.mcgill.ca/keith/surfstat/ Accessed September, 2008