Using a multi variate pattern analysis (MVPA) approach to decode FMRI responses to fear and anxiety.

neuroimaging;machine learning

Sajjad Torabian Esfahani

University:University of Louisville

Department:Computer Engineering and Computer Science

关键词: neuroimaging;    machine learning;   

Others  :  https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=3808&context=etd

美国|英语

来源: The Universite of Louisville's Institutional Repository

【 摘 要 】

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.