Decoding negative affect personality trait from patterns of brain activation to threat stimuli

Area of Science:

• Neuroscience
• Cognitive Psychology
• Psychiatry

Background:

• Pattern recognition analysis (PRA) with functional magnetic resonance imaging (fMRI) is a powerful tool for decoding cognitive processes and identifying potential biomarkers for mental health conditions.
• Understanding the neural underpinnings of personality traits, such as positive affect (PA) and negative affect (NA), is crucial for advancing mental health research.

Purpose of the Study:

• To investigate if personality traits, specifically negative affect (NA) and positive affect (PA), can be decoded from brain activation patterns.
• To explore the neural correlates of personality traits in response to threat stimuli using fMRI and PRA.

Main Methods:

• fMRI data were collected from 34 healthy volunteers viewing threat and neutral stimuli.
• A General Linear Model (GLM) was used to define spatial patterns of brain activation.
• Multiple Kernel Learning (MKL) regression was applied to decode NA and PA from these brain patterns.

Main Results:

• The MKL model successfully decoded negative affect (NA) from brain activity patterns when viewing threat stimuli directed away, achieving a significant correlation (0.52, p=0.01).
• Positive affect (PA) could not be decoded above chance levels.
• The decoding model identified a network of 37 brain regions involved in perception and emotional evaluation.

Conclusions:

• The findings suggest an interaction between an individual's NA and their brain's response to perceived threat, enabling NA decoding from neural patterns.
• This study provides the first evidence that PRA can decode personality traits from brain activation during emotional contexts.