Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. Hover, previously proposed deterministic methods are limited to unighted graphs. ,  propose to employ the resistance distance matrix of ighted graphs as the distance matrix for transforming networks to signals based on classical multidimensional scaling.  present a framework for obtaining information about the network's structure through the mapped signals and recovering the original network using properties of the resistance matrix. Finally, the proposed method is applied to characterizing functional connectivity networks constructed from electroencephalogram data.
['Marisel Villafane-Delgado', 'Selin Aviyente']
I am writing a research paper on a idea Functional connectivity brain network analysis through network to signal transform based on the resistance distance
[{"from": "human", "value": "I am writing a research paper on a idea Functional connectivity brain network analysis through network to signal transform based on the resistance distance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. Hover, previously proposed deterministic methods are limited to unighted graphs. ,  propose to employ the resistance distance matrix of ighted graphs as the distance matrix for transforming networks to signals based on classical multidimensional scaling.  present a framework for obtaining information about the network's structure through the mapped signals and recovering the original network using properties of the resistance matrix. Finally, the proposed method is applied to characterizing functional connectivity networks constructed from electroencephalogram data."}]