Limits...
How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network.

Toppi J, De Vico Fallani F, Vecchiato G, Maglione AG, Cincotti F, Mattia D, Salinari S, Babiloni F, Astolfi L - Comput Math Methods Med (2012)

Bottom Line: The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks.However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern.The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.

ABSTRACT
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

Show MeSH

Related in: MedlinePlus

Experimental setup employed for the simulated electrical recording on a mannequin head by means of a 61-channel EEG cap. The polystyrene mannequin head was posed in front of a screen to include the interferences on signals due to the presence of a monitor.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3420234&req=5

fig1: Experimental setup employed for the simulated electrical recording on a mannequin head by means of a 61-channel EEG cap. The polystyrene mannequin head was posed in front of a screen to include the interferences on signals due to the presence of a monitor.

Mentions: We simulated an EEG recording on a head of a synthetic mannequin by using a 61-channel system (Brain Amp, Brain-Products GmbH, Germany). The sampling frequency was set to 200 Hz. In order to keep the impedance below the 10 kΩ, the mannequin was equipped with a cap positioned over a humidified towel. It must be noted that there were not electromagnetic sources inserted within the mannequin's head, that is instead composed only by polystyrene. Thus, the mannequin head cannot produce any possible electromagnetic signals on the electric sensors disposed on the recording cap. Figure 1 presents the experimental setup employed for the electrical recordings. The mannequin was put in front of a screen to take into account the interferences of a monitor on EEG recording. To avoid any differences between the two datasets we used the same number of trials and samples per trial of simulated data.


How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network.

Toppi J, De Vico Fallani F, Vecchiato G, Maglione AG, Cincotti F, Mattia D, Salinari S, Babiloni F, Astolfi L - Comput Math Methods Med (2012)

Experimental setup employed for the simulated electrical recording on a mannequin head by means of a 61-channel EEG cap. The polystyrene mannequin head was posed in front of a screen to include the interferences on signals due to the presence of a monitor.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3420234&req=5

fig1: Experimental setup employed for the simulated electrical recording on a mannequin head by means of a 61-channel EEG cap. The polystyrene mannequin head was posed in front of a screen to include the interferences on signals due to the presence of a monitor.
Mentions: We simulated an EEG recording on a head of a synthetic mannequin by using a 61-channel system (Brain Amp, Brain-Products GmbH, Germany). The sampling frequency was set to 200 Hz. In order to keep the impedance below the 10 kΩ, the mannequin was equipped with a cap positioned over a humidified towel. It must be noted that there were not electromagnetic sources inserted within the mannequin's head, that is instead composed only by polystyrene. Thus, the mannequin head cannot produce any possible electromagnetic signals on the electric sensors disposed on the recording cap. Figure 1 presents the experimental setup employed for the electrical recordings. The mannequin was put in front of a screen to take into account the interferences of a monitor on EEG recording. To avoid any differences between the two datasets we used the same number of trials and samples per trial of simulated data.

Bottom Line: The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks.However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern.The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.

ABSTRACT
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

Show MeSH
Related in: MedlinePlus