Growth and evolution of category fluency network graphs

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.

Graph Theory Analysis of Semantic Fluency in Russian–English Bilinguals

“…The results from that initial study (Lerner et al, 2009) showed that the number of nodes decreased from normal to mild cognitive impairment to AD, as did the edges, and measures of complexity such as clustering coefficient and mean shortest path length also changed consistently between the groups. Further analysis using this method of graph construction showed that this method produced scale-free networks with small-world properties, thereby providing validation for the methodology (Lenio et al, 2016;Lerner et al, 2009;Shrestha et al, 2015).…”

“…It is clear from our previous work and the results presented here that group size is an important variable. Using subsets from a study of ∼350 individuals, Shrestha et al (2015) modeled network growth and demonstrated the emergence and change of network parameters with group size. Although our cohort was relatively small, comparisons with Shrestha and colleagues (2015) indicate that network parameters are relatively stable and that our group size appears acceptable for the initial analysis presented here.…”

“…Verbal fluency testing is generally tested over a short time interval, most often 60 seconds, although intervals up to 10 minutes have been reported (Rosen et al, 2005). The medical literature has often focused primarily on the animal naming task, which is a large category with an estimated lexicon size of several hundred animals (Shrestha et al, 2015).…”

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

“…Then, the nodes and edges of each learner are aggregated in a LAG by simply grouping all together. See also Lerner et al ( 2009 ), Shrestha et al ( 2015 ), Lenio et al ( 2016 ) for a similar methodology of graph construction by simple association. Sinha et al ( 2023 ) believe that if scale-free networks with small-world properties are produced with this method, then it is a valid method for graph construction.…”

Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

“…This threshold is the only parameter in the SemNeT package's implementation of the Naïve Random Walk method and it defaults to 3. The Naïve Random Walk has demonstrated validity by finding exponential growth in the evolution of verbal fluency networks (Shreestha et al, 2015) as well as differences in semantic structure of people with mild cognitive impairments (Lerner et al, 2009).…”