Information theoretic interpretation of frequency domain connectivity measures

Area of Science:

• Neuroscience
• Information Theory
• Computational Neuroscience

Background:

• Assessing information flow between neural structures is crucial for understanding brain function.
• Existing multivariate connectivity measures like Partial Directed Coherence (PDC) and Directed Transfer Function (DTF) have limitations.
• A formal link between connectivity measures and information theory is needed.

Purpose of the Study:

• To introduce modified expressions for Partial Directed Coherence (PDC) and Directed Transfer Function (DTF).
• To establish a formal relationship between these modified connectivity measures and mutual information rates.
• To provide adequate multivariate measures for quantifying information flow in neural systems.

Main Methods:

• Development of modified mathematical expressions for PDC and DTF.
• Formal mathematical proofs establishing the relationship between the modified measures and mutual information rates.
• Application of information theory principles to neuroscience connectivity analysis.

Main Results:

• Modified expressions for PDC and DTF were successfully derived.
• The formal relationship between the enhanced PDC/DTF and mutual information rates was mathematically proven.
• The study provides a theoretically grounded framework for multivariate neural connectivity.

Conclusions:

• The introduced modifications enhance the interpretability of PDC and DTF.
• These findings offer a more robust approach to quantifying information flow in neural networks.
• The established link to mutual information rates advances the theoretical underpinnings of neuroscience connectivity measures.