Individualized prediction of trait self-control from whole-brain functional connectivity.

Area of Science:

• Neuroscience
• Psychology

Background:

• Self-control is vital for societal adaptation, success, and happiness.
• Neural mechanisms underlying self-control are not fully understood.

Purpose of the Study:

• To explore the predictive power of intrinsic functional brain connections for trait self-control.
• To identify neural networks and key brain regions associated with individual differences in self-control.

Main Methods:

• Employed relevance vector regression (RVR), a machine-learning approach.
• Utilized resting-state functional MRI (fMRI) in a large sample of 390 healthy adults.
• Analyzed whole-brain functional connectivity patterns.

Main Results:

• Identified significant functional connections across multiple neural networks predicting trait self-control.
• Highlighted the involvement of the fronto-parietal network (FPN), salience network (SAL), sensorimotor network (Mot), and medial frontal network (MF).
• Found key predictive nodes including the dorsolateral prefrontal cortex (dlPFC) and middle frontal gyrus (MFG).

Conclusions:

• Self-control is a multidimensional construct.
• Neural underpinnings of self-control involve interactions across various brain networks.
• Functional brain connectivity patterns can predict individual differences in trait self-control.