Supervised Learning Models of fMRI Data for Inferring Brain Function and Predicting Behavior

Abstract

The development of fMRI has revolutionized cognitive neuroscience. There are two related areas gaining increasing interest: 1) Investigating the directional interactions between different regions. 2) Predicting human behaviors from brain activities. In this thesis, supervised learning models were applied on fMRI data for solving these problems. Firstly, dynamic Granger causality, a regression based supervised learning model, was experimentally demonstrated to be capable of inferring stimulus-evoked sub-100ms timing difference in fMRI responses, providing a reliable data-driven method for effective connectivity analysis of fMRI data. Secondly, Patel’s τ – a method which performed best for inferring directional interactions in a previous simulation – was investigated using experimental fMRI data, highlighting the necessity of experimental validation of simulation results. Lastly, recursive cluster elimination based support vector machine, a classification based supervised learning model, was used to predict purchase decisions using spatio-temporal fMRI features, providing a reliable framework for using fMRI data to predict purchase-related decisions.