NetworkToolbox (version 1.1.2)

neuralstat: Local and Global Neural Network Characteristics

Description

Obtains a global or local network characteristic from neural network data

Usage

neuralstat(filarray, statistic = c("CC", "ASPL", "Q", "S", "transitivity",
  "conn", "BC", "LC", "deg", "inDeg", "outDeg", "degRI", "str", "inStr",
  "outStr", "strRI", "comm", "EC", "lev", "rspbc", "hybrid", "impact"),
  progBar = TRUE, ...)

Arguments

filarray

Filtered array from neuralnetfilter function

statistic

A statistic to compute

progBar

Should progress bar be displayed? Defaults to TRUE. Set FALSE for no progress bar

...

Additional arguments for statistics functions

Value

Returns vector of global characteristics (rows = participants, columns = statistic) or a matrix of local characteristics (rows = ROIs, columns = participants)

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.

Joyce, K. E., Laurienti, P. J., Burdette, J. H., & Hayasaka, S. (2010). A new measure of centrality for brain networks. PLoS One, 5(8), e12200.

Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features of semantic networks: Evidence from the Hebrew mental lexicon. PloS one, 6(8), e23912.

Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6(19668), 1-15.

Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to invest in the peripheries. Scientific Reports, 3(1655), 1-7.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059-1069.

Examples

# NOT RUN {
neuralarray <- convertConnBrainMat()

filteredneuralarray <- neuralnetfilter(neuralarray, method = "threshold", thres = .50)

ClusteringCoefficient <- neuralstat(filteredneuralarray, statistic = "CC")

AverageShortestPathLength <- neuralstat(filteredneuralarray, statistic = "ASPL")

Modularity <- neuralstat(filteredneuralarray, statistic = "Q")

Smallworldness <- neuralstat(filteredneuralarray, statistic = "S")

Trasitivity <- neuralstat(filteredneuralarray, statistic = "transitivity")

Connectivity <- neuralstat(filteredneuralarray, statistic = "conn")

BetweennessCentrality <- neuralstat(filteredneuralarray, statistic = "BC")

ClosenessCentrality <- neuralstat(filteredneuralarray, statistic = "LC")

Degree <- neuralstat(filteredneuralarray, statistic = "deg")

NodeStrength <- neuralstat(filteredneuralarray, statistic = "str")

Communities <- neuralstat(filteredneuralarray, statistic = "comm")

EigenvectorCentrality <- neuralstat(filteredneuralarray, statistic = "EC")

LeverageCentrality <- neuralstat(filteredneuralarray, statistic = "lev")

RandomShortestPathBC <- neuralstat(filteredneuralarray, statistic = "rspbc")

HybridCentrality <- neuralstat(filteredneuralarray, statistic = "hybrid")

NodeImpact <- neuralstat(filteredneuralarray, statistic = "impact")
# }