Analyzing Activity of the Human Brain During Decision Making

Mari Liis Velner
The orbitofrontal cortex (OFC) is a region sitting at the front of the brain which function is not fully understood. However, it has been clearly implicated in decision making as shown by many neuroimaging studies over the last decades. Recent work by Saez et al. [1] has found evidence that OFC activity of high frequency (HFA) between 70-200 Hz is directly related to behavioral responses during decision making tasks. In particular, Saez et al. showed that some modulations of HFA correlated with the human choice and outcome in a simple betting game. Saez et al. conducted their analysis with univariate linear regression, predicting HFA values from one task-related parameter at a time to find electrodes which encode decision making information. This Thesis focused on extending these results and analyses of Saez et al. by applying multivariate methods to discover complex signals and important patterns in the neural data. For this, canonical correlation analysis and biclustering were used on 600 different datasets to find evidence of patterns in electrode responses and complicated combinations of behavioral responses encoded in the human brain signals. In addition, machine learning methods were used to analyze the patients' behavioral tendencies towards risk-taking in a gambling task and to predict task-related events such as winning, losing and gambling from the neural data. Moderate to good performance was achieved with most methods, but in-depth analysis is still necessary to gain a full understanding of how activity in orbitofrontal cortex gives rise to human behavior in decision making tasks.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Raul Vicente Zafra
Defence year