Open Access
Single‐trial EEG ‐informed fMRI analysis of emotional decision problems in hot executive function
Author(s) -
Guo Qian,
Zhou Tiantong,
Li Wenjie,
Dong Li,
Wang Suhong,
Zou Ling
Publication year - 2017
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.728
Subject(s) - electroencephalography , psychology , function (biology) , executive functions , executive dysfunction , eeg fmri , cognitive psychology , cognition , neuroscience , neuropsychology , evolutionary biology , biology
Abstract Background Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Objective Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG ‐ fMRI fusion technology to reveal brain neural activity related with feedback stimuli. Methods In this study, an EEG ‐informed fMRI analysis was applied to process simultaneous EEG ‐ fMRI data. First, relative power‐spectrum analysis and K‐means clustering method were performed separately to extract EEG ‐ fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. Results The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum ( VS ), the orbital frontal cortex ( OFC ), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG ‐ fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC . The VS and the medial prefrontal cortex ( mPFC ) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback‐related negativity ( FRN ) as regressors. Furthermore, the brain region activation intensity was the strongest when theta‐band power was used as a regressor compared with the other two fusion results. Conclusions The EEG ‐based fMRI analysis can more accurately depict the whole‐brain activation map and analyze emotional decision problems.