Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis

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.

Microstructural and Cerebral Blood Flow Abnormalities in Subjective Cognitive Decline Plus: Diffusional Kurtosis Imaging and Three-Dimensional Arterial Spin Labeling Study

“…It may also be the earliest region susceptible to pathological changes in patients with aMCI/AD (Gordon et al, 2013 ; Luo et al, 2014 ). The detection of reduced MK/CBF in the left Hip is also consistent with the previous results of DKI/ASL perfusion studies (Dai et al, 2009 ; Binnewijzend et al, 2013 ; Gong et al, 2017 ; Cheng et al, 2018 ; Song et al, 2019 ). As a limbic cortical region, the PCC is affected relatively early in the pathological progression to AD.…”

“…In a previous series of studies, patients with aMCI and patients with AD demonstrated aberrant microstructures according to DKI (Struyfs et al, 2015 ; Chen et al, 2017 ; Cheng et al, 2018 ; Song et al, 2019 ) or CBF changes according to ASL (Yoshiura et al, 2009 ; Haller et al, 2016 ; Fällmar et al, 2017 ; Riederer et al, 2018 ) in many brain regions. In this study, we also observed CBF and microstructural abnormalities in the Hip, PCC, Pr, OLWM, DT, FLWM, and CNC regions in patients with aMCI, a finding that agrees with findings from the previous study describing the association of changes in these regions with the likelihood of AD.…”

“…In a previous series of studies, patients with aMCI and patients with AD demonstrated aberrant microstructures according to DKI (Struyfs et al, 2015;Chen et al, 2017;Cheng et al, 2018;Song et al, 2019) or CBF changes according to ASL (Yoshiura…”

Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

“…We may be able to further disentangle questions of orientation coherence (dispersing and “kissing” fibers), fiber diameter, fiber density, membrane permeability, and myelination, which all influence classic anisotropy and diffusivity measures derived from DTI. Several AD studies have already used multi-shell protocols to compute diffusion indices from models that do not assume mono-exponential decay, such as diffusion kurtosis imaging (DKI; Jensen et al, 2005; Chen et al, 2017; Cheng et al, 2018; Wang M.-L. et al, 2018), and multi-compartment models such as neurite orientation dispersion and density imaging (NODDI; Zhang et al, 2012; Colgan et al, 2016; Slattery et al, 2017; Parker et al, 2018). To date, approximately 20 participants in ADNI have been scanned with multi-shell diffusion protocols; in a future report, we will relate multi-shell measures to those examined here.…”

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

“…We may be able to further disentangle questions of orientation coherence (dispersing and 'kissing' fibers), fiber diameter, fiber density, membrane permeability, and myelination, which all influence classic anisotropy and diffusivity measures derived from DTI. Several AD studies have already used multi-shell protocols to compute diffusion indices from models that do not assume monoexponential decay, such as diffusion kurtosis imaging (DKI; Jensen et al, 2005;Chen et al, 2017;Cheng et al, 2018;, and multi-compartment models such as neurite orientation dispersion and density imaging (NODDI; Zhang et al, 2012;Colgan et al, 2016;Slattery et al, 2017;Parker et al, 2018). To date, approximately 20 participants in ADNI have been scanned with multishell diffusion protocols; in a future report, we will relate these measures to those examined here.…”