Name
Title | ||
|---|---|---|
A new statistical approach for the extraction of adjacency matrix from effective connectivity networks. |
Abstract | ||
|---|---|---|
Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610154 | EMBC |
Keywords | Field | DocType |
effective connectivity networks,medical signal detection,neurophysiology,uncorrelated random signals,random processes,statistical analysis,electroencephalography,signal acquisition,medical signal processing,mannequin head,adjacency matrix extraction,neuroscience,graph theory,eeg,ghaph indexes,statistical threshold,phantoms,brain models,phantom,analysis of variance,electrodes,indexes,correlation | Adjacency matrix,Graph theory,Population,Computer vision,Computer science,Imaging phantom,Uncorrelated,Stochastic process,Edge density,Artificial intelligence,Threshold model | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 3 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jlenia Toppi | 1 | 136 | 22.74 |
| F De Vico Fallani | 2 | 0 | 0.34 |
| M Petti | 3 | 13 | 5.99 |
| G Vecchiato | 4 | 0 | 0.34 |
| A G Maglione | 5 | 48 | 10.63 |
| F Cincotti | 6 | 54 | 7.76 |
| S Salinari | 7 | 0 | 0.34 |
| D Mattia | 8 | 9 | 3.70 |
| Fabio Babiloni | 9 | 315 | 40.23 |
| Laura Astolfi | 10 | 170 | 20.52 |