A common framework for the problem of deriving estimates of dynamic functional brain connectivity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Brain Imaging Analysis

Background:

• Dynamic functional connectivity (dFC) research investigates the temporal dynamics of brain networks.
• Numerous dFC methods exist, often based on disparate assumptions, hindering result comparability.
• A common theoretical framework is needed to reconcile and evaluate diverse dFC techniques.

Purpose of the Study:

• To develop a unified theoretical framework for dynamic functional connectivity methods.
• To enable comparison and evaluation of various dFC techniques within a single framework.
• To highlight the critical assumptions and methodological nuances of different dFC approaches.

Main Methods:

• Development of a generalized theoretical framework for dFC analysis.
• Formulation of diverse dFC methods within this common framework.
• Illustration of key features and assumptions of individual techniques.

Main Results:

• A versatile framework capable of encompassing a wide range of dFC methods.
• Facilitation of direct comparison between different analytical approaches.
• Clearer understanding of the underlying assumptions and potential drawbacks of each method.

Conclusions:

• The proposed framework provides a unified approach to understanding and comparing dFC methods.
• It aids researchers in selecting appropriate methods and interpreting results.
• This work promotes methodological transparency and advancement in dFC research.