RSS-Feed abonnieren
DOI: 10.1055/s-0038-1634129
Time-variant Analysis of Fast-fMRI and Dynamic Contrast Agent MRI Sequences as Examples of 4-dimensional Image Analysis
Publikationsverlauf
Received 03. August 2005
accepted 27. März 2006
Publikationsdatum:
08. Februar 2018 (online)
Summary
Objectives: Image sequences with time-varying information content need appropriate analysis strategies. The exploration of directed information transfer (interactions) between neuronal assemblies is one of the most important aims of current functional MRI (fMRI) analysis. Additionally, we examined perfusion maps in dynamic contrast agent MRI sequences of stroke patients. In this investigation, the focus centers on distinguishing between brain areas with normal and reduced perfusion on the basis of the dynamics of contrast agent inflow and washout.
Methods: Fast fMRI sequences were analyzed with time-variant Granger causality (tvGC). The tvGC is based on a time-variant autoregressive model and is used for the quantification of the directed information transfer between activated brain areas. Generalized Dynamic Neural Networks (GDNN) with time-variant weights were applied on dynamic contrast agent MRI sequences as a nonlinear operator in order to enhance differences in the signal courses of pixels of normal and injured tissues.
Results: A simple motor task (self-paced finger tapping) is used in an fMRI design to investigate directed interactions between defined brain areas. A significant information transfer can be determined for the direction primary motor cortex to supplementary motor area during a short time period of about five seconds after stimulus. The analysis of dynamic contrast agent MRI sequences demonstrates that the trained GDNN enables a reliable tissue classification. Three classes are of interest: normal tissue, tissue at risk for death, and dead tissue.
Conclusions: The time-variant multivariate analysis of directed information transfer derived from fMRI sequences and the computation of perfusion maps by GDNN demonstrate that dynamic analysis methods are essential tools for 4D image analysis.
-
References
- 1 Baccala LA, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern 2001; 84 (06) 463-74.
- 2 Winterhalder M, Schelter B, Hesse W, Schwab K, Leistritz L, Klan D. et al Comparison directed of linear signal processing techniques to infer interactions in multivariate neural systems. Signal Processing 2005; 85 (011) 2137-60.
- 3 Hesse W, Möller E, Arnold M, Schack B. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J Neurosci Methods 2003; 124 (01) 27-44.
- 4 Kaminski M, Ding MZ, Truccolo WA, Bressler SL. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 2001; 85 (02) 145-57.
- 5 Granger CWJ. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 1969; 37 (03) 424-38.
- 6 Geweke J. Measurement of Linear-Dependence and Feedback between Multiple Time-Series. J Am Stat Assoc 1982; 77 (378) 304-13.
- 7 Bernasconi C, König P. On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biol Cybern 1999; 81 (03) 199-210.
- 8 Bernasconi C, von Stein A, Chiang C, König P. Bi-directional interactions between visual areas in the awake behaving cat. Neuroreport 2000; 11 (04) 689-92.
- 9 Galicki M, Leistritz L, Witte H. Learning continuous trajectories in recurrent neural networks with time-dependent weights. IEEE Trans Neural Netw 1999; 10 (04) 741-56.
- 10 Möller E, Schack B, Arnold M, Witte H. Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models. J Neurosci Methods 2001; 105 (02) 143-58.
- 11 Morgan VL, Price RR. The effect of sensorimotor activation on functional connectivity mapping with MRI. Magnetic Resonance Imaging 2004; 22 (08) 1069-75.
- 12 Fukunaga K. Introduction to Statistical Pattern Recognition. 2nd ed: Academic Press 1990
- 13 Goebel R, Roebroeck A, Kim DS, Formisano E. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magnetic Resonance Imaging 2003; 21 (010) 1251-61.
- 14 Roebroeck A, Formisano E, Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage. 2005; 25 (01) 230-42.
- 15 Hesse W, Wüstenberg T, Witte H. Untersuchung zeitvarianter gerichteter Interaktionen im fast fMRI unter Nutzung der adaptiven Granger- Kausalität. Biomedizinische Technik, Supplement 2, 2004; 49: 314-5.
- 16 Arnold M, Günther R. Adaptive parameter estimation in multivariate self-exciting threshold autoregressive models. Communications in Statistics - Simulation and Computation 2001; 30 (02) 257-75.
- 17 Leistritz L, Galicki M, Kochs E, Zwick EB, Fitzek C, Reichenbach JR. et al Application of Generalized Dynamic Neural Networks to Biomedical Data. IEEE Transactions on Biomedical Engineering. 2006 accepted.
- 18 Galicki M, Leistritz L, Zwick EB, Witte H. Improving Generalization Capabilities of Dynamic Neural Networks. Neural Computation 2004; 16 (06) 1253-82.
- 19 Cunnington R, Windischberger C, Deecke L, Moser E. The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the Bereitschafts-BOLD response. Neuroimage 2003; 20 (01) 404-12.
- 20 Wildgruber D, Erb M, Klose U, Grodd W. Sequential activation of supplementary motor area and primary motor cortex during self-paced finger movement in human evaluated by functional MRI. Neuroscience Letters 1997; 227 (03) 161-4.
- 21 Yousry TA, Schmid UD, Alkadhi H, Schmidt D, Peraud A, Buettner A. et al Localization of the motor hand area to a knob on the precentral gyrus - A new landmark. Brain 1997; 120: 141-57.
- 22 Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith C, Frackowiak RSJ. Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping 1995; 2: 189-210.