Using a multi variate pattern analysis (MVPA) approach to decode FMRI responses to fear and anxiety.
Date on Master's Thesis/Doctoral Dissertation
Computer Engineering and Computer Science
Computer Science, MS
Committee Co-Chair (if applicable)
neuroimaging; machine learning
This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.
Torabian Esfahani, Sajjad, "Using a multi variate pattern analysis (MVPA) approach to decode FMRI responses to fear and anxiety." (2017). Electronic Theses and Dissertations. Paper 2652.