Author ORCID Identifier
https://orcid.org/0000-0002-7860-0896
Date of Award
Spring 6-28-2023
Document Type
Thesis (Ph.D.)
Department or Program
Quantitative Biomedical Sciences
First Advisor
Luke J Chang
Abstract
Background
Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life contexts. Moreover, most work has relied on non-invasive neuroimaging tools, which are prone to noise and have limited spatial and temporal resolution.
Methods
In this thesis, we explore the neural basis of emotions with a contextualized naturalistic paradigm and high-resolution intracranial EEG. We developed several novel methods combining tools from computer vision, time frequency analysis, and state-space models to enable effective and efficient analysis of fully naturalistic intracranial EEG data. Notably, our work is among the first to explore effective ways to analyze intracranial EEG in naturalistic paradigms.
Results
With the newly developed methods, we investigated the response in two key regions implicated in emotion processing: the ventromedial Prefrontal Cortex (vmPFC) and the Amygdala. Through our investigations with naturalistic paradigms, we delineated their contextual roles and functions. Specifically, we observed that the vmPFC is active in processing affective processing of contents to ongoing experiences in a state-like manner, but the specific states and temporal sequences are idiosyncratic to each individual. Furthermore, found a rapid broadband gamma (BBG) response in bilateral amygdala when exposed to affective facial cues.
Conclusions
Collectively, this work provides significant advancements in improving our understanding of how the brain generates emotions. More broadly, this work required developing innovative methodological solutions to analyze intracranial EEG in a naturalistic experimental context. While this work has focused on emotions, the methods can be applied to study other domains of cognitive neuroscience.
Original Citation
Xie, T., Cheong, J. H., Manning, J. R., Brandt, A. M., Aronson, J. P., Jobst, B. C., ... & Chang, L. J. (2021). Minimal functional alignment of ventromedial prefrontal cortex intracranial EEG signals during naturalistic viewing. bioRxiv, 2021-05. Jolly, E., Cheong, J. H., Xie, T., Byrne, S., Kenny, M., & Chang, L. J. (2021). Py-feat: Python facial expression analysis toolbox. arXiv preprint arXiv:2104.03509.
Recommended Citation
Xie, Tiankang, "Unraveling the neural basis of emotions: Advancing understanding with ecologically valid paradigms and high-resolution intracranial EEG" (2023). Dartmouth College Ph.D Dissertations. 181.
https://digitalcommons.dartmouth.edu/dissertations/181
Included in
Biostatistics Commons, Cognitive Neuroscience Commons, Computational Neuroscience Commons, Data Science Commons, Personality and Social Contexts Commons