Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools.

Area of Science:

• Neuroscience
• Cognitive Psychology
• Psychiatry

Background:

• Understanding brain activity during decision-making is crucial, especially in psychiatric disorders.
• Previous research often relied on fMRI, but electroencephalography (EEG) offers complementary insights.

Purpose of the Study:

• To investigate electroencephalographic (EEG) activity in specific brain areas during decision-making in psychiatric patients and healthy controls.
• To identify differences in brain activity patterns and explore their potential as biomarkers.

Main Methods:

• 30 psychiatric patients and 41 healthy controls performed the Iowa Gambling Task.
• Dense array EEG recorded brain activity across various frequency bands (alpha, beta, gamma, delta, theta).
• Photogrammetry and source localization identified active Brodmann Areas; electric charge flow was calculated.

Main Results:

• Significant differences in Brodmann Area activity were observed between groups across task variants.
• Hyperactivity in the amygdala was noted in both patients and controls.
• Somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex showed significant involvement.

Conclusions:

• EEG reveals distinct decision-making-related cortical activity patterns in psychiatric disorders.
• Findings support the role of specific brain regions and offer potential for developing EEG-based biomarkers for psychiatric conditions.