Biology subjects

Tiwary, U. S.

Publications and source records attributed to Tiwary, U. S..

3 recordsLinked to original sources

bioRxiv2021

The degree of context un/familiarity impacts the emotional feeling and preaware cardiac-brain activity: a study with emotionally salient naturalistic paradigm using DENS Dataset

Emotion experiments with naturalistic paradigms are emerging and giving new insights into dynamic brain activity. Context familiarity is considered as an important dimensions of emotion processing by appraisal theorists. However, how the context un/familiarity of the naturalistic stimuli influences the central and autonomic activity is not probed yet [check it]. Hence, we tried to address this issue in this work by breaking it down into three questions. 1) What is the relation between context un/familiarity with the neural correlates of self-assessment affective dimensions viz. valence and arousal; 2) the influence of context un/familiarity in cardiac-brain mutual interaction during emotion processing; 3.) brain network reorganization to accommodate the degree of context familiarity. We found that the less-context familiarity is primarily attributed to negative emotion feeling mediated by lack of predictability of sensory experience. Whereas, with high-context familiarity, both positive and negative emotions are felt. For less-context familiarity, the arousal activity is negatively correlated with EEG power. In addition, the cardiac activity for both high and less context familiarity is modulated before the reported self-awareness of emotional feeling. The correlation of cortical regions with cardiac activity and connectivity patterns reveals that ECG is modulated by salient feature during pre-awareness and correlates with AIC and conceptual hub in high-familiarity. Whereas, for the low familiarity, the cardiac activity is correlated with the exteroceptive sensory regions. In addition, we found that OFC and dmPFC have high connectivity with less-context familiarity, whereas AIC has high connectivity with high-context familiarity. To the best of our knowledge, the context familiarity and its influence on cardiac and brain activity have never been reported with a naturalistic paradigm. Hence, this study significantly contributes to understanding automatic processing of emotions by analyzing the effect of context un/familiarity on affective feelings, the dynamics of cardiac-brain mutual interaction, and the brains effective connectivity during pre-awareness.

neuroscience↗

bioRxiv2021

Dataset on Emotions using Naturalistic Stimuli (DENS)

Emotions are constructed and emerge through the dynamic interaction of multiple components. It is difficult to capture the dynamics using static or artificial stimuli. Hence, there is a need for an experiment paradigm using ecologically valid film stimuli. The data set described in this work results from an attempt to capture felt emotional experience at a particular point in time using physiological measures like EEG, ECG and EMG as well as self-reported scales. Sixteen emotional film stimuli were used from the film stimuli dataset validated in the Indian population. Participants self-reported the felt emotional category. Both the raw and pre-processed data are provided along with the pre-processing pipeline. The paradigm we have adopted is new which we have termed as Emotional Event Marker Paradigm (EEMP). Hence, the dataset has unique information about temporal markers of emotional experiences while watching the film stimuli, which is not available with any data to date. It is the first EEG data with emotional film stimuli on the Indian population. This data can be utilized to study dynamic activation and connectivity in a whole-brain source localization study, understand the mutual interactions between the central and autonomic nervous system, understand temporal hierarchy using multi-resolution tools, and perform machine learning-based classification and complex networks analysis associated with emotions.

neuroscience↗

bioRxiv2021

A study of spatio-temporal dynamics of emotion processing usingDENS dataset

The emotion research with artificial stimuli does not represent the dynamic processing of emotions in real-life situations. The lack of data on emotion with the ecologically valid naturalistic paradigm hinders the knowledge of emotion mechanism in a real-world interaction. To this aim, we collected the emotional multimedia clips, validated them with the university students, recorded the neuro-physiological activities and self-assessment ratings for these stimuli. Participants localized their emotional feelings (in time) and were free to choose the best emotion for describing their feelings with minimum distractions and cognitive load. The obtained electrophysiological and self-assessment responses were analyzed with functional connectivity, machine learning and source localization techniques. We observed that the connectivity patterns in the theta and beta band could differentiate emotions better. Using machine learning, we observed that the classification of affective self-assessment features, namely dominance, familiarity, and self-relevance, involves midline brain regions responsible for mentalization and event construction activity compared to valence and arousal, which were mainly associated with lateral brain regions. This finding advocates the need for more than two dimensions for emotion representation. In addition, the channels with high predictability were source localized to the brain regions in default-mode, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.

neuroscience↗