This study received approval from the Ethics Committee, and all subjects signed informed consent forms. A total of 44 CKD patients diagnosed were recruited in the study. The exclusion criteria were as follows: (1) obvious neurological disease, mental disease; (2) Traumatic brain injury, cerebral infarction, brain tumor; (3) Magnetic resonance imaging (MRI) to examine contraindications; (4) Audio-visual impairment, (5) poor-quality MR images due to movement artifacts. The inclusion criteria were as follows: (1) CKD patients diagnosed in the Department of Nephrology of our hospital followed the kidney disease outcomes quality initiative (K/DOQI) criteria with eGFR<59 mL/min/1.73 m²; (2) No history of kidney transplantation; (3) No drug abuse. In addition, 35 healthy volunteers, matched for sex and age, were recruited in this study.

All subjects underwent structural MRI and DSI on a 3.0 T MRI scanner (MAGNETOM VIDA, Siemens Healthcare, Erlangen, Germany) with a 64-channel head coil. The DSI sequence was obtained in the axial plane using a half q-space Cartesian grid sampling procedure under the following parameters: TR/TE = 4400/97 ms; FOV = 220 × 220 mm²; SMS factor = 2; slice thickness = 2.0 mm; voxel size = 2.0× 2.0× 2.0mm³; number of slices = 60; a total of 18 b-values = 0, 200, 350, 400, 550, 750, 950, 1,150, 1,500, 1,700, 1,850, 1,900, 2,050, 2,100, 2,250, 2,450, 2,650, and 3,000 s/mm²; a total of 128 diffusion sampling were acquired. High resolution 3D T1-weighted imaging was performed using the MPRAGE sequence, with the following parameters: TR = 2,300 ms, TE = 2.45 ms, flip angle = 9°, FOV = 256 × 256 mm², thickness = 0.9 mm, number of slices = 176, voxel size = 1 × 1 × 1 mm³, and the total scan time was 4 min 8 s.

Multiple parameter maps of DTI, MAP-MRI and NODDI were obtained from DSI data calculated using an in-house prototype software developed by MR Station (Chengdu Zhongying Medical Technology Co., Ltd.). All raw diffusion imaging data need to be converted to NIFTI format and subjected to eddy current correction (FSL). The maps of quantitative microstructural parameters are shown in Figure 1. The quantitative microstructural parameters are as follows:

DTI: axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), Radial diffusivity (RD).

NODDI: extracellular volume fraction (ECVF), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), orientation dispersion index (ODI).

MAP-MRI: mean squared displacement (MSD), non-Gaussianity (NG), Axial non-Gaussianity (NGAx), radial non-Gaussianity (NGRad), q-space inverse variance (QIV), return-to-axis probability (RTAP), return-to-origin probability (RTOP), return-to-plane probability (RTPP).

In order to evaluate the difference between CKD patients and healthy controls, diffusion parameter maps underwent processing through the Tract-Based Spatial Statistics (TBSS) pipeline within the FSL toolbox from the Oxford Center for Functional MRI of the Brain.¹ This involved: (a) nonlinear registration of FA maps for CKD and healthy controls to the FMRIB58_FA template; (b) affine alignment of all subjects to the 1 × 1 × 1 mm³ Montreal Neurological Institute (MNI152) standard space; (c) averaging, skeletonizing, and creating a mean FA skeleton from aligned images; (d) projecting FA and other parameter maps onto this mean FA skeleton. The brain was segmented into 50 regions using Johns Hopkins University (JHU) White Matter-labels as masks.

For group difference analysis, nonparametric permutation tests were performed utilizing the randomize tool. The random permutations were set to 5,000. Threshold-free cluster enhancement (TFCE) was used for multiple comparison correction. The statistical threshold was set at p < 0.001. Finally, the results of comparison between groups were overlaid on JHU-White Matter-labels templates to localization of significant brain regions.

Statistical analyses were performed by using the IBM SPSS 23.0 (IBM Corp, Armonk, NY, United States) software package. Pearson correction analysis was used to evaluate relationship between clinical data and diffusion parameter maps. The criterion for statistical significance was p < 0.05, with false discovery rate (FDR) used to correct for multiple comparisons.

Receiver operating characteristic (ROC) curves were obtained and the area under the receiver operating characteristic curves (AUC) was calculated to measure the predictive performance of the diffusion MRI models in identifying structural changes in the CKD brain. The AUCs of different diffusion models were compared using Delong’s method. A significance threshold of p < 0.05 was applied, with differences reaching this level considered statistically significant.

44 patients of CKD (27 males and 17 females; mean age, 61.205 ± 11.496 years; age range, 43–86 years) and 35 healthy volunteers (12 males and 23 females; mean age, 63.286 ± 11.052 years; age, 42–87 years) were included in our study. There were no significant differences observed in terms of age and gender between the two groups. The demographic data results are presented in Table 1.

For the DTI model, brain areas with significantly higher AD values were mainly located in the Superior corona radiata L, Superior longitudinal fasciculus L, Inferior fronto-occipital fasciculus L in the CKD group compared to the HC group (p < 0.001). The FA values were significantly lower in the Middle cerebellar peduncle, Genu of corpus callosum, Body of corpus callosum, Splenium of corpus callosum, Anterior corona radiata R/L, Superior corona radiata R/L, Posterior thalamic radiation R/L in the CKD group compared to the HC group. However, the MD and RD values of these brain areas were increased (p < 0.001; Figure 2A).

For the NODDI model, brain areas with significantly higher ECVF values were mainly located in the Genu of corpus callosum, Body of corpus callosum, Splenium of corpus callosum, and bilateral Posterior limb of internal capsule, Retrolenticular part of internal capsule, Anterior corona radiata, Superior corona radiata, Posterior corona radiata, Posterior thalamic radiation, Superior longitudinal fasciculus. The ICVF values of these brain areas were decreased (p < 0.001; Figure 2B).

For the MAP-MRI model, the MSD and QIV values of frontal and occipital lobes were significantly higher in the CKD group compared to the HC group (p < 0.001). Compared with HC group, the NG, NGAx, NGRad values of the occipital and parietal lobes were lower in the CKD group (p < 0.001). Meanwhile, the RTAP and RTOP values of frontal, occipital and parietal lobes were also significantly decreased in the CKD (p < 0.001). The RTPP values of the left Superior corona radiata and Superior longitudinal fasciculus were lower in the CKD compared to the HC group (p < 0.001; Figure 2C).

Associations were found between Serum Uric Acid (SUA) and diffusion parameters, including MD, ECVF, ICVF, NGRad, QIV and RTOP across multiple fiber tracts. MD values in Superior cerebellar peduncle L were negatively correlated with SUA (r = −0.522, p < 0.001). ECVF values in Cerebral peduncle L, Retrolenticular part of internal capsule R, and Fornix L were negatively correlated with SUA (r = −0.431 to-0.496, p ≤ 0.003). ICVF values in Cerebral peduncle R/L and Fornix L showed positive correlations with SUA (r = 0.483 to 0.546, p < 0.001). NGRad values in Corticospinal tract L, Cerebral peduncle R/L, and Fornix L were positively correlated with SUA (r = 0.444 to 0.535, p ≤ 0.001). QIV values in Cerebral peduncle R had a negative correlation with SUA (r = −0.511, p < 0.001). RTOP values in Cerebral peduncle R/L also correlated positively with SUA (r = 0.461 to 0.540, p ≤ 0.002; Figure 3).

ROC analysis evaluated the diagnostic capabilities of diffusion parameters in six brain regions linked to SUA, namely Corticospinal tract L, Superior cerebellar peduncle R, Cerebral peduncles R/L, Retrolenticular part of internal capsule R, and Fornix L. The AUC values varied across these regions. The DTI model’s FA parameter showed comparable performance in the above brain regions (AUC = 0.707 to 0.793, p < 0.05). In addition, RD of the DTI performed well in Cerebral peduncle L, with an AUC value of 0.814. In the NODDI model, the AUC values of the ECVF and ICVF of the Retrolenticular part of internal capsule R were 0.771 and 0.743, respectively (p < 0.05). In the MAP-MRI model, NG and NGAx in the Retrolenticular part of internal capsule R performed well (AUC = 0.80, 0.843, p < 0.05). MSD, QIV, and RTAP in Cerebral peduncle L, and RTOP and RTPP in Fornix L performed well (AUC = 0.714 to 0.793, p < 0.05; Figure 4). The results of DeLong’s tests showed that MAP_NGAx (the highest AUC value) had no statistical significance with other diffusion parameters (p > 0.05). Detailed DeLong’s statistical analysis results are provided in Supplementary Material 1.

For DTI, the brain of CKD patients displayed alterations in the diffusion characteristics of corpus callosum, anterior and superior corona radiata, and posterior thalamic radiation, marked by reduced FA and elevated MD and RD. In addition, we found that AD values increased in Superior corona radiata L, Superior longitudinal fasciculus L, Inferior fronto-occipital fasciculus L. The decrease of FA signifies a disruption in the microstructural integrity of the white matter. The increase in MD, which represents the mean diffusivity of water molecules, may be due to the loss of axons and myelin sheaths and the increase in extracellular fluid. Such alterations align with previous studies that reported decreased FA and increased MD in CKD patients’ white matter (8, 23, 24). The inclusion of AD and RD in our analysis offers a more comprehensive understanding, as they indicate water molecules dispersion parallel and perpendicular to the fiber orientation, hinting at axon integrity and myelin damage. Moreover, our study uniquely identified reduced FA in the middle cerebellar peduncle, with only one similar finding reported previously (24).

In our NODDI analysis, CKD patients showed higher ECVF and lower ICVF values compared to controls. The increase of ECVF is related to the diffusion of neurite blocking molecules. ICVF, correlating with neurite density (NDI) (25), decreases when nerve cells are destroyed or lost, resulting in a smaller volume of the intracellular space. In our results, NODDI detected microstructural changes in areas not identified by DTI, like the posterior limb of the internal capsule and posterior radiative crown. A study comparing DTI and NODDI to assess brain white matter abnormalities found significant differences in NDI in certain voxels not detected by DTI_FA (26). And they suggested that NODDI should be used in addition to DTI. Another previous research has also demonstrated that FA is less effective in areas with complex fiber structures (27). Obviously, our result is the support for their proposal. However, no significant changes in ODI were observed, possibly due to the limited statistical power resulting from the small sample size. This warrants further investigation through future longitudinal studies.

In complex biological tissues, water molecules’ diffusion often shows a non-Gaussian distribution due to cellular constraints (28). These microstructures cannot be adequately obtained by DTI and NODDI, but MAP-MRI can overcome this limitation. Our study revealed higher MSD and QIV values in CKD patients than in healthy individuals. MSD, is closely related to the MD metric (29, 30). But MSD is more sensitive to restricted diffusion components than MD (21, 29). The lower NG value in CKD patients, indicating neurite density, suggests reduced tissue complexity, possibly signifying axonal loss and demyelination. At present, there are limited studies on the direct relevance of MAP-MRI for chronic kidney disease. However, our findings are similar to a study on MAP-MRI in hemodialysis patients (22). Supporting this, research on Alzheimer’s disease has indicated that a decreased NG value could be indicative of axonal loss and demyelination (31). Changes in axial (NGAx) and radial (NGRad) neurite density also indicate changes in NG (11). Additionally, CKD patients had lower RTAP and RTOP values across most of the cerebrum. A previous study has reported that the family of zero displacement probabilities, including RTOP, RTAP, and RTPP, might better represent tissue microstructure changes than DTI metrics (32). The comprehensive results highlight the potential of MAP-MRI in advancing our understanding of CKD’s structural changes.

Our study delineates a moderate relationship between SUA levels and diffusion parameters in brain regions, particularly in the cerebral peduncle and fornix L, suggesting a link with CKD. SUA is one of the indicators of renal function. Elevated SUA correlates negatively with MD in the superior cerebellar peduncle R, hinting enhanced water molecule diffusion, a marker of diminished tissue density or microstructural impairment. Such changes are frequently linked to oxidative stress from high SUA (33, 34), potentially triggering neuronal damage and compromising brain integrity (35, 36). Furthermore, SUA demonstrated a negative correlation with ECVF in the cerebral peduncle L and fornix L, coupled with a positive correlation with ICVF. This underlines the potential role of SUA in influencing cellular stress responses or metabolic alterations. The NGRad values in the bilateral cerebral peduncles and fornix L were positively correlated with SUA. This may be due to cellular stress and metabolic changes caused by high SUA levels, leading to changes in neurons and nerve fibers, thereby increasing NGRad values. Elevated SUA also associates positively with RTOP values in these regions, implying effects on cerebral microcirculation, possibly from SUA-induced microvascular damage or hemodynamic changes (37, 38). Conversely, SUA exhibits a negative correlation with QIV in the superior cerebral peduncle R, indicating its adverse effects on vascular function, possibly reducing blood flow or causing vasoconstriction.

Overall, our results are similar to previous studies. These parallels underscore the role of elevated SUA, often resulting from impaired renal function, in inducing oxidative stress and vascular changes, thereby affecting brain structure in CKD patients. Although our results show that the correlation between SUA and brain microstructural changes in CKD patients is not obvious, our future work will address this issue by expanding the sample size.

The ROC curve analysis elucidates the diagnostic capabilities of various diffusion imaging parameters for CKD, highlighting the differential effectiveness of models in detecting CKD-related brain changes. The DTI model, particularly its FA parameter, demonstrated consistent diagnostic accuracy across multiple brain regions associated with SUA levels, with AUC values between 0.70 and 0.793. This consistency emphasizes DTI’s reliability in identifying brain microstructural alterations due to CKD, though it has limitations in capturing the full complexity of these changes compared to advanced techniques. The good diagnostic performance (0.814) of RD in Cerebral peduncle L was shown in our analysis, which can also help provide a more comprehensive understanding.

For the NOODI model, although it did not showcase outstanding AUC values in multiple brain regions, it provided critical insights into neurite density and orientation dispersion, complementing DTI and MAP-MRI results. This may be due to the small sample size of our study.

In our results, we observed that MAP-MRI’s prowess extended to multiple brain regions. In specific regions of the brain, such as the retrolenticular part of the internal capsule R, the AUC value of MAP_NGAx (0.843), was higher than other diffusion parameters, indicating potential regions where MAP-MRI may offer enhanced diagnostic sensitivity for detecting CKD microstructural changes. However, it is important to note that the DeLong’s test results did not provide robust statistical support for the overall superiority of MAP-MRI over DTI and NODDI. Therefore, while the results are promising for certain parameters in specific regions, they do not conclusively demonstrate that MAP-MRI is superior to DTI and NODDI across all regions or in a general clinical setting. But we cannot deny the ability of MAP-MRI to detect CKD-related brain changes. The advantage of MAP-MRI in predicting CKD’s brain microstructural changes needs to be verified in future studies with larger sample sizes. We will continue this work in the future to validate these preliminary findings and explore the underlying reasons for the variability in diagnostic performance between different brain regions.

These findings suggest that the three advanced diffusion MRI models could provide more detailed information in certain brain regions. Their detailed parameterization offers an additional view of CKD’s neurological changes, potentially guiding more precise and effective clinical interventions in the future.

Our study encountered several limitations. Firstly, owing to the small sample size and medication use, we found no significant differences in serum creatinine and blood urea nitrogen levels between groups. Larger future studies or those involving varied populations could further explore the relationships between these biomarkers and CKD. In addition, the cross-sectional design of our study limits causal inferences about the severity of CKD and white matter integrity. Longitudinal studies tracking the progression of CKD will enhance our understanding of its impact on white matter microstructures. Additionally, future studies requiring more refined statistical models may help elucidate the differences and potential advantages of various diffusion MRI models, such as MAP-MRI, in detecting microstructural changes associated with CKD. Moreover, future research should integrate broader clinical indicators for a more comprehensive correlation analysis with microstructural changes.