Identifying the Neural Correlates of Resting State Affect Processing Dynamics.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Affective Science

Background:

• Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to study brain dynamics and their link to behavior.
• Understanding individual differences in spontaneous neural activity is crucial for both healthy and clinical populations.

Purpose of the Study:

• To propose and validate a novel method for analyzing whole-brain rs-fMRI data by mapping it to a low-dimensional representation of affective valence and arousal.
• To establish a tractable state space for validating and visualizing neural dynamics related to affect processing.

Main Methods:

• Developed neural decoding models of affective valence and arousal using task-based fMRI data from 97 healthy subjects.
• Applied these models to decode moment-to-moment affect processing from out-of-sample rs-fMRI data.
• Computed temporal derivatives of valence and arousal time-series to create neurally constrained ordinary differential equations (ODEs) for affect dynamics.

Main Results:

• Simulations based on decoded ODEs showed significantly less group-level error compared to random sampling.
• Validated affective processing dynamics for up to four closed-loop simulation steps (2.0s intervals) for both valence and arousal.
• Identified significant brain activation clusters related to affective reactivity and regulation in ODE decoding representations.

Conclusions:

• The proposed method provides a tractable approach for analyzing rs-fMRI dynamics in terms of affective states.
• This methodology has implications for identifying biomarkers for psychopathology, especially mood and emotional instability disorders.