It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Area of Science:

• Neuroscience
• Dynamical Systems Theory
• Cognitive Science

Background:

• Current human neuroscience research primarily uses statistical methods to analyze localized, static patterns of neural activity.
• These static, local approaches present challenges in directly connecting neuroimaging findings to underlying neural mechanisms.
• Interpreting static patterns through dynamic concepts highlights a gap in mechanistic understanding.

Purpose of the Study:

• To propose dynamical systems theory as a mechanistic framework for human neuroimaging research.
• To demonstrate how this perspective can improve the interpretation of neuroimaging results and their link to behavior.
• To outline strategies for integrating dynamical systems concepts into neuroimaging analyses.

Main Methods:

• Reviewing key dynamical systems theory terminology relevant to neuroscience.
• Identifying three core shifts in neuroimaging analysis: global perspective, focus on dynamics, and forward modeling.
• Applying dynamical systems principles to the interpretation of neuroimaging data.

Main Results:

• Dynamical systems theory provides a framework to characterize the brain's time-varying nature and stability.
• Shifting to a global perspective and focusing on neural dynamics, rather than static snapshots, offers richer insights.
• Forward modeling approaches can map neural dynamics, bridging the gap between data and theory.

Conclusions:

• Adopting a dynamical systems perspective can profoundly impact the interpretation of human neuroimaging results.
• This approach offers opportunities to better understand the dynamic neural mechanisms supporting brain functions.
• The framework is applicable to both healthy brain function and psychopathology research.