Identifying Brain Connectivity Using Network-Based Statistics in Amnestic Mild Cognitive Impairment Stratified by β-Amyloid Positivity

Ji Eun Kim; Sung Woo Kim; Minsuk Choi; Joon Kyung Seong; Jae Hong Lee

doi:10.1177/1533317518813556

Identifying Brain Connectivity Using Network-Based Statistics in Amnestic Mild Cognitive Impairment Stratified by β-Amyloid Positivity

Ji Eun Kim, Sung Woo Kim, Minsuk Choi, Joon Kyung Seong, Jae Hong Lee

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Background: The aim of this study was to identify white matter structural networks of amnestic mild cognitive impairment (aMCI) dichotomized by β amyloid (Aβ) status and compare them using network-based statistics (NBS). Methods: Patients underwent whole-brain diffusion-weighted magnetic resonance imaging, detailed neuropsychological test and [ ¹⁸ F]-Florbetaben amyloid positron emission tomography. We performed the NBS analysis to compare the whole-brain white matter structural networks extracted from diffusion tensor images. Results: One hundred sixteen participants (Aβ− cognitively normal [CN], n = 35; Aβ− aMCI, n = 42; Aβ+ aMCI, n = 39) were included. There was no subnetwork showing significant difference between Aβ+ aMCI and Aβ− aMCI. However, by comparing each aMCI group with control group, we found that supplementary motor areas were common hub regions. Intriguingly, Aβ+ aMCI showed reduced connectivity mainly in the medial frontal regions, while Aβ− aMCI showed somewhat uniform disruption when compared to CN. Conclusion: Structural network analysis using network-based approach in aMCI may shed light on further understanding of white matter disruption in the prodromal stage of Alzheimer’s disease.

Original language	English
Pages (from-to)	104-111
Number of pages	8
Journal	American Journal of Alzheimer's Disease and other Dementias
Volume	34
Issue number	2
DOIs	https://doi.org/10.1177/1533317518813556
Publication status	Published - 2019 Mar 1

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1A2B4014398).

Publisher Copyright:
© The Author(s) 2018.

Keywords

Alzheimer’s disease
mild cognitive impairment
neural network
β amyloid peptide

ASJC Scopus subject areas

General Neuroscience

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10.1177/1533317518813556

Cite this

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title = "Identifying Brain Connectivity Using Network-Based Statistics in Amnestic Mild Cognitive Impairment Stratified by β-Amyloid Positivity",

abstract = " Background: The aim of this study was to identify white matter structural networks of amnestic mild cognitive impairment (aMCI) dichotomized by β amyloid (Aβ) status and compare them using network-based statistics (NBS). Methods: Patients underwent whole-brain diffusion-weighted magnetic resonance imaging, detailed neuropsychological test and [ 18 F]-Florbetaben amyloid positron emission tomography. We performed the NBS analysis to compare the whole-brain white matter structural networks extracted from diffusion tensor images. Results: One hundred sixteen participants (Aβ− cognitively normal [CN], n = 35; Aβ− aMCI, n = 42; Aβ+ aMCI, n = 39) were included. There was no subnetwork showing significant difference between Aβ+ aMCI and Aβ− aMCI. However, by comparing each aMCI group with control group, we found that supplementary motor areas were common hub regions. Intriguingly, Aβ+ aMCI showed reduced connectivity mainly in the medial frontal regions, while Aβ− aMCI showed somewhat uniform disruption when compared to CN. Conclusion: Structural network analysis using network-based approach in aMCI may shed light on further understanding of white matter disruption in the prodromal stage of Alzheimer{\textquoteright}s disease.",

keywords = "Alzheimer{\textquoteright}s disease, mild cognitive impairment, neural network, β amyloid peptide",

author = "Kim, {Ji Eun} and Kim, {Sung Woo} and Minsuk Choi and Seong, {Joon Kyung} and Lee, {Jae Hong}",

note = "Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1A2B4014398). Publisher Copyright: {\textcopyright} The Author(s) 2018.",

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AU - Seong, Joon Kyung

AU - Lee, Jae Hong

N1 - Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1A2B4014398). Publisher Copyright: © The Author(s) 2018.

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N2 - Background: The aim of this study was to identify white matter structural networks of amnestic mild cognitive impairment (aMCI) dichotomized by β amyloid (Aβ) status and compare them using network-based statistics (NBS). Methods: Patients underwent whole-brain diffusion-weighted magnetic resonance imaging, detailed neuropsychological test and [ 18 F]-Florbetaben amyloid positron emission tomography. We performed the NBS analysis to compare the whole-brain white matter structural networks extracted from diffusion tensor images. Results: One hundred sixteen participants (Aβ− cognitively normal [CN], n = 35; Aβ− aMCI, n = 42; Aβ+ aMCI, n = 39) were included. There was no subnetwork showing significant difference between Aβ+ aMCI and Aβ− aMCI. However, by comparing each aMCI group with control group, we found that supplementary motor areas were common hub regions. Intriguingly, Aβ+ aMCI showed reduced connectivity mainly in the medial frontal regions, while Aβ− aMCI showed somewhat uniform disruption when compared to CN. Conclusion: Structural network analysis using network-based approach in aMCI may shed light on further understanding of white matter disruption in the prodromal stage of Alzheimer’s disease.

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Identifying Brain Connectivity Using Network-Based Statistics in Amnestic Mild Cognitive Impairment Stratified by β-Amyloid Positivity

Abstract

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