PurposeThe 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 methodsThree-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.ResultsPatients 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.ConclusionThese 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.

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The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.

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