Divergent topological networks in Alzheimer\u2019s disease: a diffusion kurtosis imaging analysis

Jia-Xing Cheng,Jun Xu,Hui Tang,Jing-Tao Wu,Hong-Ying Zhang,Zheng-Kun Peng,Yao Xu

doi:10.1186/s40035-018-0115-y

Abstract

BackgroundBrain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD).MethodsMagnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches.ResultsWe found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks.ConclusionsOur results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.

Highlights

Alzheimer’s disease (AD) is the most frequent neurodegenerative disorder causing cognitive impairment, disabilities and death in aged people
All the participants were assessed referring to a standard clinical protocol, which involved the inquiry of the medical history, an interview with a spouse or close family member, blood tests, MRI of the brain according to a standard protocol and a set of neuropsychological assessments including Mini Mental State Examination (MMSE) and Montreal cognitive assessment (MoCA)
The images of the group level interregional correlation matrices using Diffusion kurtosis imaging (DKI) metrics of mean kurtosis (MK), kurtosis fractional anisotropy (KFA), axial kurtosis (AK) and radial kurtosis (RK) are shown in Fig. 1, and KFA in the control group presented the strongest positive coordinated effects during observations among these metrics

Summary

Introduction

Alzheimer’s disease (AD) is the most frequent neurodegenerative disorder causing cognitive impairment, disabilities and death in aged people. Structural networks are based on brain anatomical features such as the grey matter (GM) volume, cortical thickness, surface area, and the correlations between different regional features [7, 19, 20]. Diffusion networks can be calculated by the metric of fiber number (FN), density, strength, probability, and mean diffusion measures from the data of diffusion tensor imaging (DTI) [9, 21,22,23]. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD)

Methods

Results

Discussion

Conclusion