Title
Localizing complex neural circuits with MEG data.
Abstract
During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.
Year
DOI
Venue
2006
10.1007/s10339-005-0024-8
Cognitive processing
Keywords
Field
DocType
inverse problem,cognitive process,oscillations,special functions,magnetoencephalography,data processing,electroencephalography,information flow
Data processing,Associative property,Computer science,Cognitive psychology,Software,Artificial intelligence,Inverse problem,Electroencephalography,Pattern recognition,Coherence (physics),Biological neural network,Machine learning,Magnetoencephalography
Journal
Volume
Issue
ISSN
7
1
1612-4782
Citations 
PageRank 
References 
1
0.41
6
Authors
5
Name
Order
Citations
PageRank
P Belardinelli150.99
Luca Ciancetta271.49
V Pizzella320024.54
C Del Gratta4644.51
G L Romani511312.58