Whole-brain network dynamics underlying intolerance of uncertainty

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Intolerance of uncertainty (IU) is a significant risk factor for various psychopathologies.
• The precise neural mechanisms underlying IU are not well understood.
• Understanding these mechanisms is crucial for developing targeted interventions.

Purpose of the Study:

• To investigate the neural underpinnings and network dynamics of intolerance of uncertainty (IU) using functional magnetic resonance imaging (fMRI).
• To identify brain networks predictive of IU and examine their temporal characteristics.
• To validate the generalizability of these findings in an independent dataset.

Main Methods:

• Employed connectome-based predictive modeling (CPM) and edge time-series analysis on fMRI data during a task involving surprised faces.
• Utilized a primary cohort (N=85) and a large independent dataset (N=878) for validation.
• Examined network dynamics during task performance, post-task resting-state, and in relation to clinical symptoms during a gambling task.

Main Results:

• CPM identified an IU-predictive network with specific visual-salience and subcortical-motor couplings.
• IU was associated with both positive and negative network couplings, with inhibitory IU linked specifically to the negative network.
• Sustained engagement of the IU-related network was observed during post-task resting-state in individuals with higher IU.
• The identified network dynamics generalized to internalizing symptoms in an independent dataset during a gambling task.

Conclusions:

• A distinct neural network sensitive to evoked uncertainty underlies intolerance of uncertainty.
• This IU-related network exhibits sustained temporal dynamics that are generalizable across different tasks and populations.
• These findings provide crucial insights into the neural basis of IU and its link to psychopathology.