Dynamic functional brain connectivity results depend on modeling assumptions: comparing frequentist and Bayesian hypothesis tests

2025-12-01

AI Summary

This study addresses a fundamental question in dynamic functional brain connectivity: whether observed temporal fluctuations reflect genuine time-varying neural phenomena or are methodological artifacts arising from static underlying connectivity. To resolve this, we propose a Bayesian hypothesis testing framework grounded in the Wishart process, enabling principled quantification of evidence for both dynamic and static connectivity models while flexibly incorporating prior knowledge. In contrast to frequentist approaches—such as sliding-window spectral estimation—our framework reveals that individual-level dynamic inferences are highly model-dependent, whereas group-level conclusions remain robust. Validation via simulations and real fMRI data demonstrates that the proposed Bayesian approach substantially improves inference reliability and information content. It provides a more principled, interpretable, and robust modeling paradigm for studying dynamic functional connectivity.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Understanding the temporal dynamics of functional brain connectivity is important for addressing various questions in network neuroscience, such as how connectivity affects cognition and changes with disease. A fundamental challenge is to evaluate whether connectivity truly exhibits dynamics, or simply is static. The most common frequentist approach uses sliding-window methods to model functional connectivity over time, but this requires defining appropriate sampling distributions and hyperparameters, such as window length, which imposes specific assumptions on the dynamics. Here, we explore how these assumptions influence the detection of dynamic connectivity, and introduce an alternative approach based on Bayesian hypothesis testing with Wishart processes. This framework encodes assumptions through prior distributions, allowing prior knowledge on the time-dependent structure of connectivity to be incorporated into the model. Moreover, this framework provides evidence for both dynamic and static connectivity, offering additional information. Using simulations, we compare the frequentist and Bayesian approaches and demonstrate how different assumptions affect the detection of dynamic connectivity. Finally, by applying both approaches to an fMRI working-memory task, we find that conclusions at the individual level vary with modeling choices, while group-level results are more robust. Our work highlights the importance of carefully considering modeling assumptions when evaluating dynamic connectivity.

Problem

• Compares frequentist and Bayesian methods for detecting dynamic brain connectivity.
• Examines how modeling assumptions affect connectivity detection in neuroscience.
• Introduces Bayesian hypothesis testing with Wishart processes as an alternative approach.

Innovation

• Bayesian hypothesis testing with Wishart processes
• Encodes assumptions through prior distributions
• Compares frequentist sliding-window and Bayesian approaches