Heejung Jung
Cognitive Neuroscience, PhD
I’m a postdoctoral researcher at the Laboratory of Behavioral and Cognitive Neuroscience, led by Dr. Josef Parvizi. My research investigates how we construct representations of future states – specifically, how are we able to think of a time point that never occured before, and how do these imagined future states influence our current thoughts and behavior?
Currently, I explore how expectations about certain individuals shape subsequent face perception. Using intracranial brain recordings, I aim to examine the temporal dynamics through which expectations modulate sensory processing. During my PhD training, I spearheaded a large scale fMRI dataset project and examined the relationship between expectations and pain processing at the Cognitive Affective Neuroscience Lab advised by Tor Wager and co-advised by McKell Carter. I’m also broadly interested in macroscale neural dynamics across various cognitive processes, particularly in how these processes converge to facilitate complex cognitive functions. (I discuss the role of such convergence zones in supporting higher-order representations, such as imagining future states or thinking about other people’s thoughts, in my review paper.
heejung [dot] jung [dot] [at] stanford [dot] edu
Neurology & Neurological Sciences
Stanford University
Alway Bldg, Stanford, CA
A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks
In prep
Heejung Jung, Maryam Amini, Bethany A. Hunt, Eilis I. Murphy, Patrick Sadil, Yaroslav O. Halchenko, Zizhuang Miao, Philip Al. Kragel, Xiaochun Han, Mickela Heilicher, Bogdan Petre, Michael Sun, Owen G. Collins, Martin A. Lindquist, Tor D. Wager
Domain-general and specific effects of expectation on multimodal negative affect
In prep
Heejung Jung, Aryan Yazdanpanah, Alireza Soltani, Martin A. Lindquist, Tor D. Wager
Grounding High Dimensional Representation Similarity by Comparing Decodability and Network Performance
Lucas Hayne, Heejung Jung, Abhijit Suresh, R. McKell Carter
Submitted to Transactions on Machine Learning Research
Divergent effects of expectations on behavior and brain
2023
Heejung Jung, Aryan Yazdanpanah, Alireza Soltani, & Tor D. Wager
Proceedings of 2023 Conference on Cognitive Computational Neuroscience
How can we reduce the climate costs of OHBM? A vision for a more sustainable meeting
2023
Samira Epp, Heejung Jung, Valentina Borghesani, Milan Klöwer, Marie-Eve Hoeppli, Maria Misiura, Elinor Thompson, Niall W Duncan, Anne E Urai, Michele Veldsman, Sepideh Sadaghiani, Charlotte L Rae
Aperture Neuro,
3,
August,
1-16
Unpacking placebo and working memory training effects on cognitive performance
2022
Tor D. Wager, Heejung Jung
Proceedings of the National Academy of Sciences of the United States of America,
119,
42,
Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients
2022
Heejung Jung, Tor D. Wager, R. McKell Carter
Journal of Cognitive Neuroscience,
34,
3,
381–396
A Nexus Model of Restricted Interests in Autism Spectrum Disorder
2020
R. McKell Carter, Heejung Jung, J. Reaven, A. Blakeley-Smith, G. S. Dichter
Frontiers in human neuroscience,
14,
212,
1-11
Neural correlates of expectantions across multiple domains
Expectations can shape experiences. Perceiving a shot as more painful if warned by a doctor, or finding a midterm easy after a friend’s heads-up are everyday examples. I investigate how expectations affect experiences of across multiple domains of somatic pain, vicarious pain, and cognitive effort. Check out my analysis notebook; please note that it’s a working document for reproducibility and documentation, not a final product → Cue-expectancy Rbookdown
BIDS-ifying and analyzing skin conductance response
This is a repository that converts raw physiological data, collected from Acknowledge Biopac system, into BIDS compatible tsv files. It identifies boundaries between runs and splits them into separate runs. This tool box also extract event files based on TTL signals and digital channel toggles. This is work in collaboration with Isabel Neumann - check out our walkthrough here → Physio Walkthrough Google Colab
A python library for preprocessing everything in my dataset
For reproducibility purposes, I keep track of my preprocessing code for any quality control step. This includes mriqc, fmriprep, but also custom-made code, such as plotting the voxel wise correlations across runs. This extends to organizing behavioral data and maintaining questionnaire databases. The goal is to provide a comprehensive, open-source codebase alongside the opensource dataset for others to use.
Published code for data collection
Reproducibility is fundamental and begins at the very outset with data collection. To ensure this, I meticulously froze and packaged all code before initiating data collection. Each distinct repository, like ‘task-faces’ and ‘task-narratives’, is time-stamped at data collection launch