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Background End-stage kidney disease (ESKD) patients undergoing hemodialysis experience diverse neurological complications. This study investigated prefrontal cerebral blood volume (CBV) and cerebral blood flow (CBF) during hemodialysis using functional near-infrared spectroscopy (fNIRS) to analyze cerebral hemodynamic changes.Methods ESKD patients undergoing maintenance hemodialysis without a history of neurological disorders were enrolled prospectively. The fNIRS data were collected using a NIRSIT Lite device. The fNIRS values were recorded three times for each patient: before the start of hemodialysis (pre-HD), 1 h after the start of hemodialysis (mid-HD), and after the end of hemodialysis (post-HD). The average changes in oxy-hemoglobin (HbO2), deoxy-hemoglobin (HbR), total hemoglobin (HbT, calculated as HbO2 + HbR) concentrations, and in hemoglobin concentration difference (HbD, calculated as HbO2 − HbR) were analyzed. We then compared the differences in changes in HbO2, HbR, HbT, and HbD according to the hemodialysis period.Results Thirty hemodialysis patients were analyzed. The change in HbO2, HbT, and HbD levels showed significant differences according to the hemodialysis period. Between the pre-HD and post-HD periods, there were significant differences in changes in HbO2 (0.005 ± 0.001 µM vs. 0.015 ± 0.004 µM, p = .046) and HbT (0.006 ± 0.001 µM vs. 0.016 ± 0.008 µM, p = .029). Additionally, between pre-HD and post-HD periods, HbD tended to increase (0.005 ± 0.001 µM vs. 0.014 ± 0.004 µM, p = .094).Conclusions We demonstrated that during one hemodialysis session, the relative change in prefrontal CBV increased post-HD compared with pre-HD. These results are expected to help understanding the mechanisms underlying the effects of hemodialysis on brain function.

ObjectivesStudies have recently shown an alteration of the structural connectivity and a dysfunctional glymphatic system in patients with chronic kidney disease (CKD). In this study, we aimed to investigate the effects of the structural connectivity and glymphatic system on the cognitive function of patients with CKD.MethodsWe prospectively enrolled patients with CKD and healthy controls. The CKD group was divided into two regarding their cognitive function. All patients received brain magnetic resonance imaging, including diffusion tensor imaging (DTI). We calculated the measures of structural connectivity and diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, a neuroimaging marker of the glymphatic system function, and compared the indices between groups.ResultsThe mean clustering coefficient, local efficiency, and small-worldness index in patients with CKD were lower than those in healthy controls (0.125 ± 0.056 vs. 0.167 ± 0.082, p = 0.008; 1.191 ± 0.183 vs. 1.525 ± 0.651, p = 0.002; 0.090 ± 0.043 vs. 0.143 ± 0.102, p = 0.003; respectively). The DTI-ALPS index was lower in patients with CKD than in healthy controls (1.436 vs. 1.632, p

Abstract Introduction Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity. Methods We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)‐values were used in the connectometry analysis. The apnea‐hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false‐discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections. Results Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto‐occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI. Conclusion Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.