A method for the estimation of functional brain connectivity from time-series data

被引:12
作者
Wilmer, A. [1 ]
de Lussanet, M. H. E. [1 ]
Lappe, M. [1 ]
机构
[1] Univ Munster, Otto Creutzfeldt Ctr Cognit & Behav Neurosci OCC, Dept Psychol, Munster, Germany
关键词
Functional connectivity; Time-delayed phase synchronization; MEG magnetencephalography; Network analysis; PHASE SYNCHRONIZATION; GRANGER CAUSALITY; EEG; MEG; RESPONSES; NETWORKS; MODELS;
D O I
10.1007/s11571-010-9107-z
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
A central issue in cognitive neuroscience is which cortical areas are involved in managing information processing in a cognitive task and to understand their temporal interactions. Since the transfer of information in the form of electrical activity from one cortical region will in turn evoke electrical activity in other regions, the analysis of temporal synchronization provides a tool to understand neuronal information processing between cortical regions. We adopt a method for revealing time-dependent functional connectivity. We apply statistical analyses of phases to recover the information flow and the functional connectivity between cortical regions for high temporal resolution data. We further develop an evaluation method for these techniques based on two kinds of model networks. These networks consist of coupled Rossler attractors or of coupled stochastic Ornstein-Uhlenbeck systems. The implemented time-dependent coupling includes uni- and bi-directional connectivities as well as time delayed feedback. The synchronization dynamics of these networks are analyzed using the mean phase coherence, based on averaging over phase-differences, and the general synchronization index. The latter is based on the Shannon entropy. The combination of these with a parametric time delay forms the basis of a connectivity pattern, which includes the temporal and time lagged dynamics of the synchronization between two sources. We model and discuss potential artifacts. We find that the general phase measures are remarkably stable. They produce highly comparable results for stochastic and periodic systems. Moreover, the methods proves useful for identifying brief periods of phase coupling and delays. Therefore, we propose that the method is useful as a basis for generating potential functional connective models.
引用
收藏
页码:133 / 149
页数:17
相关论文
共 68 条
[1]   Granger causality for circular variables [J].
Angelini, Leonardo ;
Pellicoro, Mario ;
Stramaglia, Sebastiano .
PHYSICS LETTERS A, 2009, 373 (29) :2467-2470
[2]  
[Anonymous], 2009, Stochastic methods: a handbook for the natural and social sciences, volume 13 of Springer series in synergetics
[3]   Synchrony and clustering in heterogeneous networks with global coupling and parameter dispersion [J].
Assisi, CG ;
Jirsa, VK ;
Kelso, JAS .
PHYSICAL REVIEW LETTERS, 2005, 94 (01)
[4]   Interpreting neurodynamics: concepts and facts [J].
Atmanspacher, Harald ;
Rotter, Stefan .
COGNITIVE NEURODYNAMICS, 2008, 2 (04) :297-318
[5]   Realistic spatial sampling for MEG beamformer images [J].
Barnes, GR ;
Hillebrand, A ;
Fawcett, IP ;
Singh, KD .
HUMAN BRAIN MAPPING, 2004, 23 (02) :120-127
[6]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[7]   Granger causality and information flow in multivariate processes [J].
Blinowska, KJ ;
Kus, R ;
Kaminski, M .
PHYSICAL REVIEW E, 2004, 70 (05) :4-1
[8]   The synchronization of chaotic systems [J].
Boccaletti, S ;
Kurths, J ;
Osipov, G ;
Valladares, DL ;
Zhou, CS .
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2002, 366 (1-2) :1-101
[9]   A unifying definition of synchronization for dynamical systems [J].
Brown, R ;
Kocarev, L .
CHAOS, 2000, 10 (02) :344-349
[10]   Analyzing multiple nonlinear time series with extended Granger causality [J].
Chen, YH ;
Rangarajan, G ;
Feng, JF ;
Ding, MZ .
PHYSICS LETTERS A, 2004, 324 (01) :26-35