No abstract

No abstract

Background: Category fluency is a sensitive measure of cognitive integrity and is known to involve both frontal and temporal cortical areas. Network graph analysis is a technique used to analyze relationships between nodes and edges and calculate metrics such as path lengths between nodes and clustering coefficients.Objectives: To investigate network growth and preferential attachment in a network model of category fluency.Method: Category fluency results ("animals" recorded over 60 seconds) from subjects (N=374) contacted via telephone were converted to undirected network graphs of all unique neighbors and network parameters were calculated. Growth was also modeled using an extended cognitive network model. Random subsamples of people or of node pairs were used to model network growth and study preferential attachment. Results:The final network had 275 nodes and 2035 edges. The network showed scale free and small world properties, which change with network size. Both methods of modeling connectivity showed exponential growth of nodes and edges as increasing fractions of the complete network were sampled. Preferential attachment was demonstrated by using Newman's method.Conclusions: Network growth patterns show a sharp transition to scale free and small world properties with early network growth. Networks based on category fluency show preferential attachment and appear to be a valid model for studying network dynamics based on cognitive output.