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Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues.

ABSTRACT: INTRODUCTION:The present study explored the role of resting state and personality component (BIS/BAS measure) on prefrontal cortical responsiveness to emotional cues. Indeed, we supposed that lateralized resting activity (right vs. left) and approach (BAS) versus avoidance (BIS) attitude may explain the successive emotional processing within the prefrontal cortex (PFC) based on the stimulus valence (positive and negative emotional cues). METHODS:Hemodynamic (functional near-infrared spectroscopy, fNIRS) and electroencephalographic (EEG) measures were considered. The resting and experimental brain activity were registered when subjects (N = 21) viewed emotional positive versus negative stimuli (International Affective Picture System, IAPS). LIR eeg and LIR nirs (lateralized Index Response) during resting state, and LI eeg and LI nirs during emotional processing were acquired. RESULTS:A set of regression analyses was applied to the multiple measures. The predictive effect of resting activity and approach/avoidance dichotomy were elucidated. Indeed, more left/right resting activity (for both LIR eeg and LIR nirs) predicted the successive more brain left/right response (LI eeg and LI nirs) to emotional cues. Second, significant effects were revealed as a function of valence (increased right response to negative stimuli; increased left response to positive stimuli) during emotion processing. Third, higher BAS values explained an increased left cortical activity in resting state and in experimental condition for positive cues. In contrast, higher BIS values marked an increased right activity in resting state and in experimental condition in response to negative cues. CONCLUSION:The significance of trait component for both resting and emotional cue processing was discussed at light of the present results.

SUBMITTER: Balconi M

PROVIDER: S-EPMC5434191 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues.

Balconi Michela M Vanutelli Maria E ME Grippa Elisabetta E

Brain and behavior 20170405 5

<h4>Introduction</h4>The present study explored the role of resting state and personality component (BIS/BAS measure) on prefrontal cortical responsiveness to emotional cues. Indeed, we supposed that lateralized resting activity (right vs. left) and approach (BAS) versus avoidance (BIS) attitude may explain the successive emotional processing within the prefrontal cortex (PFC) based on the stimulus valence (positive and negative emotional cues).<h4>Methods</h4>Hemodynamic (functional near-infrar ...[more]

PMID: 28523228

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Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Dataset Information

Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues.

Publications

Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues.

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