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Neuropsychology Review

, Volume 24, Issue 1, pp 49–62 | Cite as

Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

  • Emily L. Dennis
  • Paul M. Thompson
Review

Abstract

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.

Keywords

Alzheimer’s Aging Functional connectivity fMRI Resting state Graph theory ICA Seed-based 

Notes

Acknowledgments

ED was funded, in part, by an NIH Training Grant in Neurobehavioral Genetics (T32 MH073526-06), and by the Betty B. and James B. Lambert Scholarship from the Kappa Alpha Theta Foundation. The authors were also supported by NIH R01 grants EB008432, EB008281, EB007813 and P41 RR013642.

Author Disclosure Statement

The authors have no competing financial interests.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Imaging Genetics Center, Institute for Neuroimaging InformaticsUSC Keck School of MedicineLos AngelesUSA
  2. 2.Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics and OphthalmologyUSC Keck School of Medicine, University of Southern CaliforniaLos AngelesUSA
  3. 3.Department of Psychiatry & Biobehavioral Sciences, Semel Institute for Neuroscience & Human BehaviorUCLA School of MedicineLos AngelesUSA

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