The trend of disruption in the functional brain network topology of Alzheimer’s disease

for the Alzheimer's Disease Neuroimaging Initiative

doi:10.1038/s41598-022-18987-y

The trend of disruption in the functional brain network topology of Alzheimer’s disease

for the Alzheimer's Disease Neuroimaging Initiative

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

18 Scopus citations

Abstract

Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

Original language	English (US)
Article number	14998
Journal	Scientific reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-18987-y
State	Published - Dec 2022

Funding

This work has been supported in part by a grant from the Cognitive Sciences and Technologies Council with grant No. 8226. In addition, the second author is indebted to the Research Core: \u201CBio-Mathematics with computational approach\u201D of Tarbiat Modares University, with Grant No IG-39706. We would like to express our great appreciation to Dr. Kazemi for her valuable and constructive suggestions during the revised manuscript.\u00A0Data collection and sharing for this project was funded by the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u2019s Association; Alzheimer\u2019s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u2019s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of this report. This work has been supported in part by a grant from the Cognitive Sciences and Technologies Council with grant No. 8226. In addition, the second author is indebted to the Research Core: \u201CBio-Mathematics with computational approach\u201D of Tarbiat Modares University, with Grant No IG-39706. We would like to express our great appreciation to Dr. Kazemi for her valuable and constructive suggestions during the revised manuscript. Data collection and sharing for this project was funded by the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u2019s Association; Alzheimer\u2019s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u2019s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu ). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of this report.

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Cite this

@article{15c75dd9361b415b92a432e819bb25eb,

title = "The trend of disruption in the functional brain network topology of Alzheimer{\textquoteright}s disease",

abstract = "Alzheimer{\textquoteright}s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain{\textquoteright}s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer{\textquoteright}s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.",

author = "{for the Alzheimer's Disease Neuroimaging Initiative} and Alireza Fathian and Yousef Jamali and Raoufy, {Mohammad Reza} and Weiner, {Michael W.} and Norbert Schuf and Rosen, {Howard J.} and Miller, {Bruce L.} and Thomas Neylan and Jacqueline Hayes and Shannon Finley and Paul Aisen and Zaven Khachaturian and Thomas, {Ronald G.} and Michael Donohue and Sarah Walter and Devon Gessert and Tamie Sather and Gus Jiminez and Leon Thal and James Brewer and Helen Vanderswag and Adam Fleisher and Melissa Davis and Rosemary Morrison and Ronald Petersen and Jack, {Cliford R.} and Matthew Bernstein and Bret Borowski and Jef Gunter and Matt Senjem and Prashanthi Vemuri and David Jones and Kejal Kantarci and Chad Ward and Mason, {Sara S.} and Albers, {Colleen S.} and David Knopman and Kris Johnson and William Jagust and Susan Landau and Trojanowki, {John Q.} and Shaw, {Leslie M.} and Virginia Lee and Magdalena Korecka and Michal Figurski and Arnold, {Steven E.} and Karlawish, {Jason H.} and David Wolk and Mesulam, {Marek Marsel} and Jordan Grafman",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41598-022-18987-y",

language = "English (US)",

volume = "12",

journal = "Scientific reports",

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AU - for the Alzheimer's Disease Neuroimaging Initiative

AU - Fathian, Alireza

AU - Jamali, Yousef

AU - Raoufy, Mohammad Reza

AU - Weiner, Michael W.

AU - Schuf, Norbert

AU - Rosen, Howard J.

AU - Miller, Bruce L.

AU - Neylan, Thomas

AU - Hayes, Jacqueline

AU - Finley, Shannon

AU - Aisen, Paul

AU - Khachaturian, Zaven

AU - Thomas, Ronald G.

AU - Donohue, Michael

AU - Walter, Sarah

AU - Gessert, Devon

AU - Sather, Tamie

AU - Jiminez, Gus

AU - Thal, Leon

AU - Brewer, James

AU - Vanderswag, Helen

AU - Fleisher, Adam

AU - Davis, Melissa

AU - Morrison, Rosemary

AU - Petersen, Ronald

AU - Jack, Cliford R.

AU - Bernstein, Matthew

AU - Borowski, Bret

AU - Gunter, Jef

AU - Senjem, Matt

AU - Vemuri, Prashanthi

AU - Jones, David

AU - Kantarci, Kejal

AU - Ward, Chad

AU - Mason, Sara S.

AU - Albers, Colleen S.

AU - Knopman, David

AU - Johnson, Kris

AU - Jagust, William

AU - Landau, Susan

AU - Trojanowki, John Q.

AU - Shaw, Leslie M.

AU - Lee, Virginia

AU - Korecka, Magdalena

AU - Figurski, Michal

AU - Arnold, Steven E.

AU - Karlawish, Jason H.

AU - Wolk, David

AU - Mesulam, Marek Marsel

AU - Grafman, Jordan

PY - 2022/12

Y1 - 2022/12

N2 - Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

AB - Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

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JF - Scientific reports

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The trend of disruption in the functional brain network topology of Alzheimer’s disease

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