Functional–structural relationship in large‐scale brain networks of patients with end stage renal disease after kidney transplantation: A longitudinal study

Abstract

It is unclear how the brain network changed after kidney transplantation (KT). We explored the patterns of large‐scale complex network after KT in end‐stage renal disease (ESRD) patients with resting‐state functional MRI (rs‐fMRI) and diffusion tensor imaging (DTI). Twenty‐one ESRD patients (14 men; mean age, 31.5 ± 9.9 years) scheduled for KT and 17 age‐ and gender‐matched healthy controls (HC) (8 men; mean age, 28.9 ± 7.2 years) were enrolled in this study. Each participant underwent rs‐fMRI and DTI scans in three time points (pre‐KT, 1 and 6 months after KT [for ESRD]). Graph theory analysis was used to characterize the topological properties by using functional and structural network connectivities intergroup correlation analysis was performed between functional/structural MR indexes and clinical markers. Compared with HC, pre‐KT ESRD patients showed an altered topological organization in both functional and structural networks. Compared with pre‐KT, increased node degree and node efficiency were observed for both functional and structural networks at 1 month after KT (all p < .05), which were further increased at 6 months after KT (p < .05). Both functional and structural networks did not recover completely at 6 months after KT (all p < .05). The patients showed an increased functional–structural connectivity coupling at 1 month after KT compared with HC (p = .041). A trend of progressive recovery of functional and structural connectivity networks was observed in ERSD patients after KT, which did not recover to the normal levels even in 6 months after KT. The study results underlie cognitive function recovery in ESRD patients following KT in the neuropathophysiological perspective.

Artificial Intelligence Algorithm‐Based MRI in the Diagnosis of Complications after Renal Transplantation

“…e vascular empty signal of MRI can rule out venous thrombosis and provide a strong basis for the development of a safe surgical plan [22]. Based on the U-Net network, a separation attention residual module and a compression excitation attention module were introduced to improve the feature mining ability of multichannel information [23,24].…”

“…The vascular empty signal of MRI can rule out venous thrombosis and provide a strong basis for the development of a safe surgical plan [ 22 ]. Based on the U-Net network, a separation attention residual module and a compression excitation attention module were introduced to improve the feature mining ability of multichannel information [ 23 , 24 ]. The proposed DRSA-U-Net network-based abdominal multimodal image synthesis algorithm can obtain synthetic images very close to the real images in terms of visual effects and image accuracy.…”

Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

“…Honey et al [28] directly examined relationships between the two modalities and found that in many cases, despite high RSFC, the measures were unrelated, with indirect connections playing an important role in explaining RSFC. Other studies in healthy samples and populations with medical illness have used summary measures of structural-functional connectivity across the whole brain [29,30] or across neural networks (e.g., DMN, salience network (SN) and central executive networks (CEN), which comprise the triple network [31]) [32], rather than within ROI pairs comprising a network.…”

Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

“…Honey et al [ 28 ] directly examined relationships between the two modalities and found that, in many cases, despite high RSFC, the measures were unrelated, with indirect connections playing an important role in explaining RSFC. Other studies, in both healthy samples and populations with medical illness, have used summary measures of structural-functional connectivity across the whole brain [ 29 , 30 ] or across neural networks (e.g., DMN, salience network (SN), and central executive networks (CEN), which comprise the triple network [ 31 , 32 ]), rather than within ROI pairs comprising a single network.…”