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 |
---|---|
Article number | 14998 |
Journal | Scientific reports |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
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In: Scientific reports, Vol. 12, No. 1, 14998, 12.2022.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - The trend of disruption in the functional brain network topology of Alzheimer’s disease
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 - Toga, Arthur W.
AU - Crawford, Karen
AU - Neu, Scott
AU - Schneider, Lon S.
AU - Pawluczyk, Sonia
AU - Beccera, Mauricio
AU - Teodoro, Liberty
AU - Spann, Bryan M.
AU - Beckett, Laurel
AU - Harvey, Danielle
AU - Fletcher, Evan
AU - Carmichael, Owen
AU - Olichney, John
AU - DeCarli, Charles
AU - Green, Robert C.
AU - Sperling, Reisa A.
AU - Johnson, Keith A.
AU - Marshall, Gad
AU - Frey, Meghan
AU - Lane, Barton
AU - Rosen, Allyson
AU - Tinklenberg, Jared
AU - Saykin, Andrew J.
AU - Foroud, Tatiana M.
AU - Shen, Li
AU - Faber, Kelley
AU - Kim, Sungeun
AU - Nho, Kwangsik
AU - Farlow, Martin R.
AU - Hake, Ann Marie
AU - Matthews, Brandy R.
AU - Herring, Scott
AU - Hunt, Cynthia
AU - Morris, John
AU - Raichle, Marc
AU - Holtzman, Davie
AU - Cairns, Nigel J.
AU - Householder, Erin
AU - Taylor-Reinwald, Lisa
AU - Ances, Beau
AU - Carroll, Maria
AU - Leon, Sue
AU - Mintun, Mark A.
AU - Schneider, Stacy
AU - Oliver, Angela
AU - Raudin, Lisa
AU - Sorensen, Greg
AU - Kuller, Lew
AU - Mathis, Chet
AU - Lopez, Oscar L.
AU - Oakley, Mary Ann
AU - Paul, Steven
AU - Relkin, Norman
AU - Chaing, Gloria
AU - Raudin, Lisa
AU - Davies, Peter
AU - Fillit, Howard
AU - Hefti, Franz
AU - Mesulam, M. Marcel
AU - Kerwin, Diana
AU - Mesulam, Marek Marsel
AU - Lipowski, Kristine
AU - Wu, Chuang Kuo
AU - Johnson, Nancy
AU - Grafman, Jordan
AU - Potter, William
AU - Snyder, Peter
AU - Schwartz, Adam
AU - Montine, Tom
AU - Peskind, Elaine R.
AU - Fox, Nick
AU - Thompson, Paul
AU - Apostolova, Liana
AU - Tingus, Kathleen
AU - Woo, Ellen
AU - Silverman, Daniel H.S.
AU - Lu, Po H.
AU - Bartzokis, George
AU - Koeppe, Robert A.
AU - Heidebrink, Judith L.
AU - Lord, Joanne L.
AU - Potkin, Steven G.
AU - Preda, Adrian
AU - Nguyenv, Dana
AU - Foster, Norm
AU - Reiman, Eric M.
AU - Chen, Kewei
AU - Fleisher, Adam
AU - Tariot, Pierre
AU - Reeder, Stephanie
AU - Potkin, Steven
AU - Mulnard, Ruth A.
AU - Thai, Gaby
AU - Mc-Adams-Ortiz, Catherine
AU - Buckholtz, Neil
AU - Hsiao, John
AU - Albert, Marylyn
AU - Albert, Marilyn
AU - Onyike, Chiadi
AU - D’Agostino, Daniel
AU - Kielb, Stephanie
AU - Simpson, Donna M.
AU - Frank, Richard
AU - Kaye, Jefrey
AU - Quinn, Joseph
AU - Lind, Betty
AU - Carter, Raina
AU - Dolen, Sara
AU - Doody, Rachelle S.
AU - Villanueva-Meyer, Javier
AU - Chowdhury, Munir
AU - Rountree, Susan
AU - Dang, Mimi
AU - Stern, Yaakov
AU - Honig, Lawrence S.
AU - Bell, Karen L.
AU - Marson, Daniel
AU - Grifth, Randall
AU - Clark, David
AU - Geldmacher, David
AU - Brockington, John
AU - Roberson, Erik
AU - Grossman, Hillel
AU - Mitsis, Efe
AU - de Toledo-Morrell, Leyla
AU - Shah, Raj C.
AU - Fleischman, Debra
AU - Arfanakis, Konstantinos
AU - Duara, Ranjan
AU - Varon, Daniel
AU - Greig, Maria T.
AU - Roberts, Peggy
AU - Galvin, James E.
AU - Cerbone, Brittany
AU - Michel, Christina A.
AU - Rusinek, Henry
AU - de Leon, Mony J.
AU - Glodzik, Lidia
AU - De Santi, Susan
AU - Doraiswamy, P. Murali
AU - Petrella, Jefrey R.
AU - Wong, Terence Z.
AU - James, Olga
AU - Smith, Charles D.
AU - Jicha, Greg
AU - Hardy, Peter
AU - Sinha, Partha
AU - Oates, Elizabeth
AU - Conrad, Gary
AU - Porsteinsson, Anton P.
AU - Goldstein, Bonnie S.
AU - Womack, Kyle
N1 - Funding Information: 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: “Bio-Mathematics with computational approach” 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’s 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’s Association; Alzheimer’s 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’s 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’s 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. Funding Information: 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: “Bio-Mathematics with computational approach” 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’s 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’s Association; Alzheimer’s 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’s 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’s 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. Publisher Copyright: © 2022, The Author(s).
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.
UR - http://www.scopus.com/inward/record.url?scp=85137169860&partnerID=8YFLogxK
U2 - 10.1038/s41598-022-18987-y
DO - 10.1038/s41598-022-18987-y
M3 - Article
C2 - 36056059
AN - SCOPUS:85137169860
SN - 2045-2322
VL - 12
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 14998
ER -