Network-based biomarkers in Alzheimer's disease

Review and future directions

Jaime Gomez-Ramirez, Jinglong Wu

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

By 2050 it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Original languageEnglish
Article numberArticle 12
JournalFrontiers in Aging Neuroscience
Volume6
Issue numberFEB
DOIs
Publication statusPublished - 2014

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Alzheimer Disease
Biomarkers
Early Diagnosis
Research
Direction compound
Autopsy
Therapeutics

Keywords

  • Alzheimer's disease
  • Default-mode network DMN
  • Network degeneration hypothesis
  • Network-based biomarkers
  • Resting-state functional connectivity

ASJC Scopus subject areas

  • Ageing
  • Cognitive Neuroscience

Cite this

Network-based biomarkers in Alzheimer's disease : Review and future directions. / Gomez-Ramirez, Jaime; Wu, Jinglong.

In: Frontiers in Aging Neuroscience, Vol. 6, No. FEB, Article 12, 2014.

Research output: Contribution to journalArticle

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