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A new statistical approach for the extraction of adjacency matrix from effective connectivity networks
- Source :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2013, pp.2932-5, EMBC
- Publication Year :
- 2013
- Publisher :
- HAL CCSD, 2013.
-
Abstract
- International audience; 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.
- Subjects :
- Theoretical computer science
Computer science
Reliability (computer networking)
Models, Neurological
0206 medical engineering
Population
MESH: Algorithms
02 engineering and technology
computer.software_genre
Field (computer science)
03 medical and health sciences
Consistency (database systems)
MESH: Neural Networks (Computer)
0302 clinical medicine
MESH: Computer Simulation
MESH: Models, Neurological
Neural Pathways
MESH: Electroencephalography
Humans
Computer Simulation
Adjacency matrix
education
MESH: Brain Mapping
Brain Mapping
education.field_of_study
Models, Statistical
MESH: Humans
Phantoms, Imaging
Stochastic process
MESH: Neural Pathways
Neurosciences
Reproducibility of Results
Electroencephalography
Graph theory
MESH: Neurosciences
020601 biomedical engineering
MESH: Reproducibility of Results
MESH: Phantoms, Imaging
Data Interpretation, Statistical
[SDV.IB]Life Sciences [q-bio]/Bioengineering
Neural Networks, Computer
Data mining
computer
MESH: Data Interpretation, Statistical
Algorithms
030217 neurology & neurosurgery
MESH: Models, Statistical
Subjects
Details
- Language :
- English
- ISSN :
- 1557170X
- Database :
- OpenAIRE
- Journal :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2013, pp.2932-5, EMBC
- Accession number :
- edsair.doi.dedup.....f1555cce8c767764bd6eb45e80c183eb