Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals

Time-variant Granger Causality Index (tvGCI) was applied to simulated and measured BOLD signals to investigate the reliability of time-variant analysis approaches for the identification of directed interrelations between brain areas on the basis of fMRI data. Single-shot fMRI data of a single image slice with short repetition times (200 ms, 16000 frames/subject, 64x64 voxels) were acquired from 5 healthy subjects during an externally-driven, self-paced finger-tapping paradigm (57-59 single taps for each subject). BOLD signals were derived from the pre-supplementary motor area (preSMA), the supplementary motor area (SMA), and the primary motor cortex (M1). The simulations were carried out by means of a Dynamic Causal Modelling (DCM) approach. The tvGCI as well as time-variant Partial Directed Coherence (tvPDC) were used to identify the modelled connectivity network (connectivity structure - CS - of the DCM). Different CSs were applied by using dynamic systems (Generalized Dynamic Neural Network - GDNN) and trivariate autoregressive (AR) processes. The influence of the low-pass characteristics of the simulated hemodynamic response (Balloon model) and of the measuring noise was tested. Additionally, our modelling strategy considered "spontaneous" BOLD fluctuations before, during, and after the appearance of the event-related BOLD component. Couplings which were extracted from the simulated signals were statistically evaluated (tvGCI for shuffled data, confidence tubes for tvGCI courses). We demonstrate that connections of our CS models can be correctly identified during the event-related BOLD component and with signal-to-noise-ratios corresponding to those of the measured data. The results based on simulations can be used to examine the reliability of connectivity identification based on BOLD signals by means of time-variant as well as time-invariant connectivity measures and enable a better interpretation of the analysis results using fMRI data. A readiness-BOLD response was only detected in one subject. However, in two subjects a strong time-variant connection (tvGCI) from preSMA to SMA was observed 3 s before the tapping was executed. This connection was accompanied by a weaker rise of the tvGCI from preSMA to M1. These preceding interrelations were confirmed in the other subjects by the dynamics of tvGCI courses. Based on the results of tvGCI analysis, the time-evolution of an individual connectivity network is shown for each subject.

[1]  W. Hesse,et al.  The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies , 2003, Journal of Neuroscience Methods.

[2]  John E. W. Mayhew,et al.  Investigating neural–hemodynamic coupling and the hemodynamic response function in the awake rat , 2006, NeuroImage.

[3]  J. Martinerie,et al.  Statistical assessment of nonlinear causality: application to epileptic EEG signals , 2003, Journal of Neuroscience Methods.

[4]  R. Buxton,et al.  A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[5]  M. Breakspear "Dynamic" connectivity in neural systems: theoretical and empirical considerations. , 2004, Neuroinformatics.

[6]  Ewald Moser,et al.  Premovement activity of the pre-supplementary motor area and the readiness for action: studies of time-resolved event-related functional MRI. , 2005, Human movement science.

[7]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

[8]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[9]  Ewald Moser,et al.  The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the Bereitschafts-BOLD response , 2003, NeuroImage.

[10]  W Hesse,et al.  Time-variant analysis of fast-fMRI and dynamic contrast agent MRI sequences as examples of 4-dimensional image analysis. , 2006, Methods of information in medicine.

[11]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[12]  Herbert Witte,et al.  Improving Generalization Capabilities of Dynamic Neural Networks , 2004, Neural Computation.

[13]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[14]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[15]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[16]  G. Lindinger,et al.  Supplementary Motor Area Activation Preceding Voluntary Movement Is Detectable with a Whole-Scalp Magnetoencephalography System , 2000, NeuroImage.

[17]  Sourabh Bhattacharya,et al.  A Bayesian approach to modeling dynamic effective connectivity with fMRI data , 2006, NeuroImage.

[18]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[19]  João Ricardo Sato,et al.  A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality , 2006, NeuroImage.

[20]  Thomas Weiss,et al.  How do brain areas communicate during the processing of noxious stimuli? An analysis of laser-evoked event-related potentials using the Granger causality index. , 2008, Journal of neurophysiology.

[21]  Karl J. Friston,et al.  Comparing dynamic causal models , 2004, NeuroImage.

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[24]  Karl J. Friston,et al.  Comparing hemodynamic models with DCM , 2007, NeuroImage.

[25]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[26]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[27]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[28]  H. Alkadhi,et al.  Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. , 1997, Brain : a journal of neurology.

[29]  Herbert Witte,et al.  Learning continuous trajectories in recurrent neural networks with time-dependent weights , 1999, IEEE Trans. Neural Networks.

[30]  L. Deecke,et al.  The Preparation and Execution of Self-Initiated and Externally-Triggered Movement: A Study of Event-Related fMRI , 2002, NeuroImage.

[31]  Herbert Witte,et al.  Development of interaction measures based on adaptive non-linear time series analysis of biomedical signals / Entwicklung von Interaktionsmaßen auf der Grundlage adaptiver, nichtlinearer Zeitreihenanalyse von biomedizinischen Signalen , 2006, Biomedizinische Technik. Biomedical engineering.

[32]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[33]  Hans-Jochen Heinze,et al.  Causal visual interactions as revealed by an information theoretic measure and fMRI , 2006, NeuroImage.

[34]  R. Buxton,et al.  Modeling the hemodynamic response to brain activation , 2004, NeuroImage.

[35]  Karin Schwab,et al.  Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems , 2005, Signal Process..

[36]  H. Akaike A new look at the statistical model identification , 1974 .

[37]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[38]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[39]  M. Arnold,et al.  Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models , 2001, Journal of Neuroscience Methods.

[40]  E. J. Hannan,et al.  Multivariate linear time series models , 1984, Advances in Applied Probability.