How do different emotional states represent in human brain?—Evidence from multi-variate pattern analysis based on functional MRI

Abstract

When studying emotion, there has been a longstanding controversy whether emotion should be better divided into different categories or into dimensions. There are a number of behavioral and physiological studies supporting both the categorical view and the dimension view. With the development of observational and analytical instrument, affective neuroscience is emerging as a new field that attracts researchers to explore how the brain generates and represents emotion. In this review, firstly we will introduce the superiority of a method called multi-variate pattern analysis (MVPA) in studying how different emotional states are represented in human brain. Traditional univariate fMRI data analysis characterizes the relationship between mental states and each individual brain voxel. Voxels have been seen as independent from each other and are isolated from different mental states. Due to its lack of sensitivity, there is no evidence shown that different emotional states have their own representational pattern in specific brain regions. Unlike univariate method, MVPA utilizes multi-voxel pattern to decode the information of mental states and have more sensitivity than univariate analysis. Secondly, the studies using MVPA to investigate the dimension view and category view will be briefly summarized. These two theories are both partly approved, as there are some evidences which support one view and some evidences support the other. We will illustrate two research orientations separately and comment on the shortcomings of the existing research. For example, the dimension view has often been studied on one singular dimension during which the other dimension is kept constant. However, emotion is constructed of both valence and arousal. Therefore, researchers should integrate both valence and arousal to examine the dimension view in the brain. Next, we will discuss what impact does MVPA bring on traditional emotion theory. Because of its incomparable advantages, MVPA could solve some controversial issues which cannot be studied only by behavioral measures, such as questions like whether positive and negative emotions are two sides of a same dimension or they are in different dimensions. or questions like the number of basic emotional states. What is more, MVPA can directly compare different emotion theories. Last but not least, we highlight the future directions about the representation of emotion in the brain. For example, some studies have shown that emotion perception could be divided into different processing stages. Therefore, we propose that the way our brain representing emotional states is different during different time course. Future studies may utilize MVPA to decode different emotional states in the time domain by using spatiotemporal pattern similarity analysis with EEG or MEG data. In addition, it is also a good direction to investigate how different brain regions interact with each other to represent emotions by resorting to network method and multi-connection pattern analysis.