Mar 17, 2014

Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

BioRxiv : the Preprint Server for Biology
Traver HartJason Moffat

Abstract

This paper adapts a method for detecting influential nodes and communities in networked systems to reveal changes in functional networks of subjects with Alzheimer's disease (AD) and amnestic Mild Cognitive Impairment (aMCI). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all the functional connectivity changes on the alignment of any two regions. This is achieved by comparing their placement in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment reveals that key regions in the Default Mode Network lack clear and consistent alignments in healthy control subjects. This results in relatively few significant alignment changes in AD, despite clear reductions in functional connectivity within this network. For AD subjects, compared with healthy controls, the posterior superior temporal gyrus displays the most si...Continue Reading

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Mentioned in this Paper

Classification
Gene Knockdown Techniques
Genome
Genes
CRISPR-Cas Systems
Tumor Tissue Sample
Knock-out
Neoplasms
Genomic Screening
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