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. 2018 Apr 27:7:10.
doi: 10.1186/s40035-018-0115-y. eCollection 2018.

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

Jia-Xing Cheng  1 , Hong-Ying Zhang  2 , Zheng-Kun Peng  2 , Yao Xu  1 , Hui Tang  3 , Jing-Tao Wu  2 , Jun Xu  4   5
Affiliations

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

Jia-Xing Cheng et al. Transl Neurodegener. .

Abstract

Background: Brain 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).

Methods: Magnetic 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.

Results: We 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.

Conclusions: Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.

Keywords: Alzheimer’s disease; Brain networks; Diffusion kurtosis imaging; Small world.

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Conflict of interest statement

The study was approved by the Ethics Committee of Northern Jiangsu People's Hospital, Yangzhou University, with informed consent provided by all participants.The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The interregional correlation matrix (90 × 90) in the AD and NC groups using DKI metrics of MK, KFA, AK and RK. The color bar indicates the value of the interregional parameter correlation. The red color bar represents the higher positive correlation value. The blue color bar represents the higher negative correlation value. From the maps, a great degree of dispersion in DKI could be observed in AD patients. Note more strong positive coordinated effects existing in extensive brain regions labeled by red color for the metric of MK, KFA, AK and RK in the control group vs. AD group, and KFA is the typical. The higher KFA value meant more compact histological structure
Fig. 2
Fig. 2
Small world properties of GM networks in the AD and NC using DKI metrics of MK, KFA, AK and RK. Both networks demonstrated small world architectures over a wide range of sparsity (6% ~ S ~ 40%) in comparison with the matched random networks. γ value was calculated by Cp/mean (Cprand), λ value was calculated by Lp/mean (Lprand). The small-worldness value of σ was presented by γ/λ
Fig. 3
Fig. 3
Hub regions within groups for MK metric networks. Each ball represented corresponding brain region in AAL atlas, displayed in the center of the region. The size of balls represented the bi value. Only the hub regions with bi>1.5 were indicated by red. Note the hub regions in AD became sparse in the default mode areas. The figure was processed by BrainNet Viewer software. IFGoperc.L: Left inferior frontal gyrus, opercular part; PCUN.R: Right precuneus; IFGoperc.R: Right inferior frontal gyrus, opercular part; STG.L: Left superior temporal gyrus; STG.R: Right superior temporal gyrus; FFG.L: Left fusiform; FFG.R: Right fusiform; ORB.sup.L: Left superior frontal gyrus, medial orbital; HIP.L: Left hippocampus; MTG.L: Left middle temporal gyrus; MTG.R: Right middle temporal gyrus; TPOsup.R: Right temporal pole, superior temporal gyrus; INS.L: Left insula; HES.L: Left heschlgyrus
Fig. 4
Fig. 4
Between-group grey matter differences in Lp and Cp properties. The graph shows AD patients (blue lines) presented with increased Lp values and decreased Cp values in the brain networks than controls (red lines) over a wide range of thresholds. Statistical analysis of the between-group differences obtained by 1000 permutation tests further revealed significant differences (p< 0.05) in the Lp values at 10%
Fig. 5
Fig. 5
Comparison of small world properties in the WM networks between AD and NC group. a. Lp values of DKI metrics; b. Lp values of FN; c. Eg of DKI metrics; d. Ad: Eg of FN. Divergence network features in Lp and Eg were displayed by various DKI parameters
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References

    1. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR, Jr, Kaye J, Montine TJ, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):280–292. doi: 10.1016/j.jalz.2011.03.003. - DOI - PMC - PubMed
    1. Jack CR, Jr, Holtzman DM. Biomarker modeling of Alzheimer's disease. Neuron. 2013;80(6):1347–1358. doi: 10.1016/j.neuron.2013.12.003. - DOI - PMC - PubMed
    1. Delbeuck X, Collette F, Van der Linden M. Is Alzheimer's disease a disconnection syndrome? Neuropsychologia. 2007;45(14):3315–3323. doi: 10.1016/j.neuropsychologia.2007.05.001. - DOI - PubMed
    1. Arendt T. Synaptic degeneration in Alzheimer's disease. Acta Neuropathol. 2009;118(1):167–179. doi: 10.1007/s00401-009-0536-x. - DOI - PubMed
    1. Takahashi RH, Capetillo-Zarate E, Lin MT, Milner TA, Gouras GK. Co-occurrence of Alzheimer's disease ss-amyloid and tau pathologies at synapses. Neurobiol Aging. 2010;31(7):1145–1152. doi: 10.1016/j.neurobiolaging.2008.07.021. - DOI - PMC - PubMed

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