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| Research article summary (published 27 Feb 2009): |
Network graph analysis of category fluency testing.
Full Abstract
BACKGROUND: Category fluency is impaired early in Alzheimer disease (AD). Graph theory is a technique to analyze complex relationships in networks. Features of interest in network analysis include the number of nodes and edges, and variables related to their interconnectedness. Other properties important in network analysis are "small world properties" and "scale-free" properties. The small world property (popularized as the so-called "6 degrees of separation") arises when the majority of connections are local, but a number of connections are to distant nodes. Scale-free networks are characterized by the presence of a few nodes with many connections, and many more nodes with fewer connections. OBJECTIVE: To determine if category fluency data can be analyzed using graph theory. To compare normal elderly, mild cognitive impairment (MCI) and AD network graphs, and characterize changes seen with increasing cognitive impairment. METHODS: Category fluency results ("animals" recorded over 60 s) from normals (n=38), MCI (n=33), and AD (n=40) completing uniform data set evaluations were converted to network graphs of all unique cooccurring neighbors, and compared for network variables. RESULTS: For Normal, MCI and AD, mean clustering coefficients were 0.21, 0.22, 0.30; characteristic path lengths were 3.27, 3.17, and 2.65; small world properties decreased with increasing cognitive impairment, and all graphs showed scale-free properties. Rank correlations of the 25 commonest items ranged from 0.75 to 0.83. Filtering of low-degree nodes in normal and MCI graphs resulted in properties similar to the AD network graph. CONCLUSIONS: Network graph analysis is a promising technique for analyzing changes in category fluency. Our technique results in nonrandom graphs consistent with well-characterized properties for these types of graphs.
Author information
Author/s: Lerner, Alan J (AJ); Ogrocki, Paula K (PK); Thomas, Peter J (PJ);
Affiliation: Department of Neurology, University Hospitals Case Medical Center, Cleveland, OH 44120, USA. alan.lerner(-atsign-)case.edu
Grants: P50 AG08012 (Agency:NIA NIH HHS)
Journal and publication information
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
Journal: Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology (Cogn Behav Neurol), published in United States. (Language: eng)
Reference: 2009-Mar; vol 22 (issue 1) : pp 45-52
Dates: Created 2009/04/17; Completed 2009/06/16;
PMID: 19372770, status: MEDLINE (last retrieval date: 6/16/2009, IMS Date: 16 Jun 2009 00:00:00)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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