Literature DB >> 34776436

Selective Impairment of Long-Range Default Mode Network Functional Connectivity as a Biomarker for Preclinical Alzheimer's Disease in People with Down Syndrome.

Natalie D DiProspero1,2, David B Keator3, Michael Phelan4, Theo G M van Erp5, Eric Doran5, David K Powell6, Kathryn L Van Pelt7, Frederick A Schmitt7,8, Elizabeth Head9, Ira T Lott5, Michael A Yassa1,2,3.   

Abstract

BACKGROUND: Down syndrome (DS) is associated with increased risk for Alzheimer's disease (AD). In neurotypical individuals, clinical AD is preceded by reduced resting state functional connectivity in the default mode network (DMN), but it is unknown whether changes in DMN connectivity predict clinical onset of AD in DS.
OBJECTIVE: Does lower DMN functional connectivity predict clinical onset of AD and cognitive decline in people with DS?
METHODS: Resting state functional MRI (rsfMRI), longitudinal neuropsychological, and clinical assessment data were collected on 15 nondemented people with DS (mean age = 51.66 years, SD = 5.34 years, range = 42-59 years) over four years, during which 4 transitioned to dementia. Amyloid-β (Aβ) PET data were acquired on 13 of the 15 participants. Resting state fMRI, neuropsychological, and clinical assessment data were also acquired on an independent, slightly younger unimpaired sample of 14 nondemented people with DS (mean age = 44.63 years, SD = 7.99 years, range = 38-61 years).
RESULTS: Lower functional connectivity between long-range but not short-range DMN regions predicts AD diagnosis and cognitive decline in people with DS. Aβ accumulation in the inferior parietal cortex is associated with lower regional DMN functional connectivity.
CONCLUSION: Reduction of long-range DMN connectivity is a potential biomarker for AD in people with DS that precedes and predicts clinical conversion.

Entities:  

Keywords:  Alzheimer’s disease; Down syndrome; biomarkers; default mode network; dementia; functional connectivity; resting state functional magnetic resonance imaging

Mesh:

Substances:

Year:  2022        PMID: 34776436      PMCID: PMC9017677          DOI: 10.3233/JAD-210572

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.160


  49 in total

1.  Methods to detect, characterize, and remove motion artifact in resting state fMRI.

Authors:  Jonathan D Power; Anish Mitra; Timothy O Laumann; Abraham Z Snyder; Bradley L Schlaggar; Steven E Petersen
Journal:  Neuroimage       Date:  2013-08-29       Impact factor: 6.556

Review 2.  The brain's default network: anatomy, function, and relevance to disease.

Authors:  Randy L Buckner; Jessica R Andrews-Hanna; Daniel L Schacter
Journal:  Ann N Y Acad Sci       Date:  2008-03       Impact factor: 5.691

3.  Florbetapir PET, FDG PET, and MRI in Down syndrome individuals with and without Alzheimer's dementia.

Authors:  Marwan N Sabbagh; Kewei Chen; Joseph Rogers; Adam S Fleisher; Carolyn Liebsack; Dan Bandy; Christine Belden; Hillary Protas; Pradeep Thiyyagura; Xiaofen Liu; Auttawut Roontiva; Ji Luo; Sandra Jacobson; Michael Malek-Ahmadi; Jessica Powell; Eric M Reiman
Journal:  Alzheimers Dement       Date:  2015-04-04       Impact factor: 21.566

4.  Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects.

Authors:  Hyun Kook Lim; Robert Nebes; Beth Snitz; Ann Cohen; Chester Mathis; Julie Price; Lisa Weissfeld; William Klunk; Howard J Aizenstein
Journal:  Brain       Date:  2014-09-29       Impact factor: 13.501

5.  The pathological association between Down syndrome and Alzheimer disease.

Authors:  D M Mann
Journal:  Mech Ageing Dev       Date:  1988-05       Impact factor: 5.432

6.  The changing survival profile of people with Down's syndrome: implications for genetic counselling.

Authors:  E J Glasson; S G Sullivan; R Hussain; B A Petterson; P D Montgomery; A H Bittles
Journal:  Clin Genet       Date:  2002-11       Impact factor: 4.438

7.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with alzheimer's disease and cognitively normal subjects.

Authors:  Abhinay D Joshi; Michael J Pontecorvo; Chrisopher M Clark; Alan P Carpenter; Danna L Jennings; Carl H Sadowsky; Lee P Adler; Karel D Kovnat; John P Seibyl; Anupa Arora; Krishnendu Saha; Jason D Burns; Mark J Lowrey; Mark A Mintun; Daniel M Skovronsky
Journal:  J Nucl Med       Date:  2012-02-13       Impact factor: 10.057

8.  A reproducible evaluation of ANTs similarity metric performance in brain image registration.

Authors:  Brian B Avants; Nicholas J Tustison; Gang Song; Philip A Cook; Arno Klein; James C Gee
Journal:  Neuroimage       Date:  2010-09-17       Impact factor: 6.556

9.  Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data.

Authors:  Douglas N Greve; Claus Svarer; Patrick M Fisher; Ling Feng; Adam E Hansen; William Baare; Bruce Rosen; Bruce Fischl; Gitte M Knudsen
Journal:  Neuroimage       Date:  2013-12-19       Impact factor: 6.556

Review 10.  NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease.

Authors:  Clifford R Jack; David A Bennett; Kaj Blennow; Maria C Carrillo; Billy Dunn; Samantha Budd Haeberlein; David M Holtzman; William Jagust; Frank Jessen; Jason Karlawish; Enchi Liu; Jose Luis Molinuevo; Thomas Montine; Creighton Phelps; Katherine P Rankin; Christopher C Rowe; Philip Scheltens; Eric Siemers; Heather M Snyder; Reisa Sperling
Journal:  Alzheimers Dement       Date:  2018-04       Impact factor: 21.566

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