Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Neuroscience

Background:

• Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) typically analyzes single region of interest (ROI) activity.
• Traditional univariate analysis of NF training effects overlooks distributed brain changes.
• Learning self-regulation via NF involves complex, widespread neural network alterations.

Purpose of the Study:

• To develop and apply a multivariate decoding model for assessing whole-brain NF training effects.
• To identify patterns of coactivation across functional atlas regions associated with NF-induced self-regulation.
• To explore individual differences in NF learning strategies through brain activity patterns.

Main Methods:

• Deployment of a posthoc multivariate decoding model to analyze NF training effects.
• Utilizing a 90-region functional atlas to map patterns of coactivation.
• Application of cross-validation to identify optimal brain region sets and assess model generalizability.
• Analysis of data from an rt-fMRI NF study targeting auditory cortex downregulation.

Main Results:

• An optimal model comprising 16 brain regions accurately described NF training effects over time.
• The multivariate model demonstrated generalizability across participants via cross-validation.
• Participants were clustered into two distinct groups based on coactivation patterns, suggesting varied learning strategies.
• Identified distributed brain regions involved in learning to self-regulate a single ROI.

Conclusions:

• Multivariate analysis provides a more comprehensive understanding of NF training effects than univariate methods.
• NF-induced self-regulation involves coordinated activity across multiple, distributed brain regions.
• Distinct patterns of brain coactivation may reflect different individual approaches to NF learning.