Literature DB >> 23290495

Resting state functional connectivity in preclinical Alzheimer's disease.

Yvette I Sheline1, Marcus E Raichle.   

Abstract

There has been a dramatic increase in the number of studies using resting state functional magnetic resonance imaging (rs-fMRI), a recent addition to imaging analysis techniques. The technique analyzes ongoing low-frequency fluctuations in the blood oxygen level-dependent signal. Through patterns of spatial coherence, these fluctuations can be used to identify the networks within the brain. Multiple brain networks are present simultaneously, and the relationships within and between networks are in constant dynamic flux. Resting state fMRI functional connectivity analysis is increasingly used to detect subtle brain network differences and, in the case of pathophysiology, subtle abnormalities in illnesses such as Alzheimer's disease (AD). The sequence of events leading up to dementia has been hypothesized to begin many years or decades before any clinical symptoms occur. Here we review the findings across rs-fMRI studies in the spectrum of preclinical AD to clinical AD. In addition, we discuss evidence for underlying preclinical AD mechanisms, including an important relationship between resting state functional connectivity and brain metabolism and how this results in a distinctive pattern of amyloid plaque deposition in default mode network regions.
Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Amyloid; BOLD; default mode network (DMN); fMRI; glycolysis; precuneus

Mesh:

Year:  2013        PMID: 23290495      PMCID: PMC3537262          DOI: 10.1016/j.biopsych.2012.11.028

Source DB:  PubMed          Journal:  Biol Psychiatry        ISSN: 0006-3223            Impact factor:   13.382


  94 in total

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  167 in total

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Review 7.  Obesity and Aging: Consequences for Cognition, Brain Structure, and Brain Function.

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9.  Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.

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