Advancing functional connectivity research from association to causation.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Cognition and behavior arise from complex brain network interactions.
• Functional connectivity (FC) methods analyze statistical associations in neural activity but often infer correlation, not causation.
• Effective connectivity methods aim to infer causal interactions, but progress is hindered by diverse approaches.

Purpose of the Study:

• To refine functional connectivity (FC) methods for improved causal inference in brain network analysis.
• To establish a common ontology of causal neural interactions to foster cumulative progress across FC approaches.
• To demonstrate how common FC measures can be improved for better causal inferences.

Main Methods:

• Incorporating best practices from diverse FC research areas.
• Utilizing properties of causal neural interactions as a unifying framework.
• Analyzing the limitations of common FC measures like correlation and coherence.

Main Results:

• Refined FC methods enhance inferences about neural mechanisms and causal interactions.
• Common FC measures (correlation, coherence) constrain potential causal models, aiding inference despite limitations.
• Identified limitations of standard FC measures for causal inference.

Conclusions:

• Improving functional connectivity (FC) methods is crucial for understanding brain function and causal neural interactions.
• A unified approach to causal inference in brain networks facilitates scientific progress.
• Exploring alternative FC measures can advance causal inference beyond current common methods.