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Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals.
D Hemmelmann1, M Ungureanu, W Hesse
1Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University Jena, Germany. dirk.hemmelmann@mti.uni-jena.de
Neuroimage
|March 13, 2009
Summary
Time-variant Granger Causality Index (tvGCI) reliably identifies brain connectivity using fMRI data. This method detected pre-tapping brain activity, showing dynamic interrelations between motor areas before movement execution.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Systems Neuroscience
Background:
- Functional Magnetic Resonance Imaging (fMRI) measures blood-oxygen-level-dependent (BOLD) signals to infer brain activity.
- Identifying directed interrelations between brain areas is crucial for understanding brain function.
- Time-variant analysis approaches are needed to capture the dynamic nature of brain connectivity.
Purpose of the Study:
- To investigate the reliability of time-variant analysis for identifying directed brain interrelations using fMRI data.
- To assess the performance of the Time-variant Granger Causality Index (tvGCI) in simulated and measured BOLD signals.
- To explore pre-movement brain activity and connectivity patterns.
Main Methods:
- Application of tvGCI and time-variant Partial Directed Coherence (tvPDC) to simulated and measured BOLD signals.
- Simulations using Dynamic Causal Modelling (DCM) with Generalized Dynamic Neural Network (GDNN) and autoregressive (AR) processes.
- Analysis of fMRI data from healthy subjects during a finger-tapping task, focusing on pre-supplementary motor area (preSMA), supplementary motor area (SMA), and primary motor cortex (M1).
- Statistical evaluation of identified couplings using shuffled data and confidence tubes.
Main Results:
- tvGCI successfully identified modelled connectivity networks in simulations with signal-to-noise-ratios comparable to measured fMRI data.
- A significant time-variant connection from preSMA to SMA was observed up to 3 seconds before finger tapping in some subjects.
- Preceding interrelations from preSMA to M1 were also detected, consistent across subjects.
- The study demonstrated the capability of tvGCI to reveal the time-evolution of individual connectivity networks.
Conclusions:
- Time-variant analysis, specifically tvGCI, is a reliable method for identifying directed interrelations in fMRI data.
- tvGCI can detect anticipatory brain activity and dynamic connectivity changes preceding motor tasks.
- The findings support the use of tvGCI for a better interpretation of fMRI-based connectivity analyses.
