Analysing connectivity with Granger causality and dynamic causal modelling.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding functional integration in neuronal macrocircuits is crucial for deciphering brain function.
• Distinguishing between functional and effective connectivity is key to interpreting brain network dynamics.

Purpose of the Study:

• To review state-of-the-art analyses of functional integration in neuronal macrocircuits.
• To compare Granger causality (GC) and dynamic causal modeling (DCM) for detecting and estimating directed connectivity.

Main Methods:

• Focus on Granger causality (GC) for detecting functional connectivity.
• Focus on dynamic causal modeling (DCM) for estimating effective connectivity.
• Comparative evaluation of GC and DCM within the context of functional segregation and integration.

Main Results:

• GC and DCM offer distinct yet complementary approaches to analyzing neuronal networks.
• Recent developments in GC and DCM have accelerated the discovery and quantification of brain architectures.
• Both methods are valuable for understanding directed connectivity and modeling effective connectivity.

Conclusions:

• GC and DCM are powerful tools for investigating functional and effective connectivity in the brain.
• Further research is needed to address outstanding issues in the application of these methods.
• These analytical approaches are vital for advancing our understanding of complex brain architectures.