Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease

doi:10.1038/s41598-018-31794-8

Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease

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30 Citas (Scopus)

Resumen

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using ¹⁸F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using ¹⁸F-FDG PET are the key feature for differentiating disease groups in AD.

Idioma original	English
Número de artículo	13807
Publicación	Scientific Reports
Volumen	8
N.º	1
DOI	https://doi.org/10.1038/s41598-018-31794-8
Estado	Published - dic 1 2018

Nota bibliográfica

Publisher Copyright:
© 2018, The Author(s).

Financiación

This work was supported by grants BMRP 488 and CMRPD1E0301-3 from Chang Gung Memorial Hospital, and MOST 106-2314-B-182-017-MY3 from Ministry of Sciences and Technology, Taiwan, ROC. 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.; 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 Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. A complete listing of ADNI investigators can be found at: https://adni.loni.usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Financiadores	Número del financiador
National Institute on Aging	U01AG024904, P30AG010129
Ministry of Science and Technology, Taiwan	103-2314-B-182-010-MY3
Chang Gung Medical Foundation	CMRPD1E0301-3, BMRP 488
National Center for Advancing Translational Sciences (NCATS)	UL1TR002369

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title = "Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer{\textquoteright}s disease",

abstract = "The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.",

author = "Huang, \{Sheng Yao\} and Hsu, \{Jung Lung\} and Lin, \{Kun Ju\} and Liu, \{Ho Ling\} and Wey, \{Shiaw Pying\} and Hsiao, \{Ing Tsung\} and Michael Weiner and Paul Aisen and Ronald Petersen and Jack, \{Clifford R.\} and William Jagust and Trojanowki, \{John Q.\} and Toga, \{Arthur W.\} and Laurel Beckett and Green, \{Robert C.\} and Saykin, \{Andrew J.\} and John Morris and Shaw, \{Leslie M.\} and Enchi Liu and Tom Montine and Thomas, \{Ronald G.\} and Michael Donohue and Sarah Walter and Devon Gessert and Tamie Sather and Gus Jiminez and Danielle Harvey and Matthew Bernstein and Nick Fox and Paul Thompson and Norbert Schuff and Charles DeCarli and Bret Borowski and Jeff Gunter and Matt Senjem and Prashanthi Vemuri and David Jones and Kejal Kantarci and Chad Ward and Koeppe, \{Robert A.\} and Norm Foster and Reiman, \{Eric M.\} and Kewei Chen and Chet Mathis and Susan Landau and Cairns, \{Nigel J.\} and Erin Householder and Reinwald, \{Lisa Taylor\} and Greg Jicha and Richard King",

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AU - Hsu, Jung Lung

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AU - Liu, Ho Ling

AU - Wey, Shiaw Pying

AU - Hsiao, Ing Tsung

AU - Weiner, Michael

AU - Aisen, Paul

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AU - Jagust, William

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AU - Toga, Arthur W.

AU - Beckett, Laurel

AU - Green, Robert C.

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AU - Liu, Enchi

AU - Montine, Tom

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AU - Donohue, Michael

AU - Walter, Sarah

AU - Gessert, Devon

AU - Sather, Tamie

AU - Jiminez, Gus

AU - Harvey, Danielle

AU - Bernstein, Matthew

AU - Fox, Nick

AU - Thompson, Paul

AU - Schuff, Norbert

AU - DeCarli, Charles

AU - Borowski, Bret

AU - Gunter, Jeff

AU - Senjem, Matt

AU - Vemuri, Prashanthi

AU - Jones, David

AU - Kantarci, Kejal

AU - Ward, Chad

AU - Koeppe, Robert A.

AU - Foster, Norm

AU - Reiman, Eric M.

AU - Chen, Kewei

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AU - Landau, Susan

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AB - The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

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Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease

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Nota bibliográfica

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