Choong-Wan (Wani) is a postdoctoral researcher in the Cognitive and Affective Neuroscience Laboratory (Mentor: Tor D. Wager) at the University of Colorado Boulder. Choong-Wan's research focuses on understanding how the human brain represents, processes, and regulates pain and emotions. His recent publications span the topics from developing neuroimaging-based biomarker for pain using fMRI to understanding the neural mechanisms of cognitive self-regulation of pain. Recent research focuses on quantifying cerebral contributions to pain above and beyond external, nociceptive input. He has received many awards including the Fulbright Graduate Study Award from Korea and US government, the Carol B. Lynch Graduate Fellowship from the graduate school of the University of Colorado Boulder, and the Heyer Award from the Psychology and Neuroscience department.
2016 Oct I'm giving talks at Seoul National University (at Psych department and BCS [Prof. SH Lee's lab]), Seoul National University Hospital (Psychiatry Grand Round; PGR), and Korea University (Psych department, 뉴로비어)."Talk titles:  “Pain is inevitable. Suffering is optional.”: Brain mechanisms of pain and pain regulation (SNU Psych, KU Psych).  Brain signatures and models in translational neuroimaging (SNUH PGR).  Decoding pain and emotion using generalizable and extensible functional neuroimaging signatures (SNU BCS)"
2016 Oct I gave a talk at 2016 Young Computational Neuroscientist Workshop, which is supported by Asia Pacific center for Theoretical Physics (APCTP) and KAIST. "Talk title: Brain signatures and models in translational neuroimaging" Information
2016 Sep In collaboration with Marina López-Solà, we published a paper about fMRI-based signatures for fibromyalgia on PAIN. I very much enjoyed working with Marina on this project, where I helped with coding and making some figures. The signatures we identified discriminated patients with fibromyalgia from healthy controls with 92% sensitivity and 94% specificity. This study includes many good features: First, it uses multiple component process signatures to identify disease cases. Second, we were able to achieve quite high classification accuracy using data from a short duration of scanning (the multisensory task took less than 5 mins). I believe these features show us a promising future direction of translational neuroimaging. Publication
2016 July I passed my PhD defense.
I finally got PhD. I want to thank Tor, my lab colleagues, and family for their support throughout my graduate school life. Now, I'm excited to find out what's on the next page of my life. For now, I will keep doing research as a postdoc in the same lab in Boulder.
2016 June OHBM 2016 poster presentation
"Distinct neural mechanisms of pain modulation through distraction and placebo" | PDF
I am selected to receive an Abstract Travel Award ($1,000 USD) for the OHBM 2016 Annual Meeting in Geneva, Switzerland. In the notification email, they said that "the funding for these special awards was provided by the National Science Foundation and only the top rated abstracts were selected for this travel award." Very excited!
2016 June In collaboration with Anjali Krishnan and others, we published a paper about comparing the neural representations of somatic vs. vicarious pain on eLife. It's been a long journey from the first submission until its publication partly because the paper disproves a very popular theory on pain empathy. However, we need to listen to what the data tell us. This paper is a product of a highly collaborative effort, esp. among Anjali, Tor, Luke, and me. Publication
2016 June I am giving talks at KAIST, KNU, KU, and SKKU in South Korea. "Talk title: Decoding pain and emotion using generalizable and extensible functional neuroimaging signatures"
2016 May I am also selected to receive an Travel Award ($1,450 USD) for the 2016 IASP Annual Meeting in Yokohama, Japan. Feeling grateful.
2016 April A review paper (Title: "Brain signatures and models in translational neuroimaging") has been submitted and is under review now.
2016 April In collaboration with Etienne Vachon-Presseau at Northwestern University, we published a paper on neural mediators of pain-related facial expression in patients with chronic back pain on PAIN. I helped mostly with the mediation analyses in the paper and implemented bootstrap tests for moderation variables. Publication
2016 March A research article (Title: "Quantifying cerebral contributions to pain beyond nociception") has been submitted and is under review.
2016 February Tor and I together wrote a series of commentaries on the assessment of test-retest reliability in pain neuroimaging. These have been published on PAIN. Publications
2015 December In collaboration with Martin Lindquist, we developed a new method that can improve single-subject predictions with population-level priors. We termed the method as GRIP (Group-Regularized Individual Prediction). In the future, we'd want to see neuroimaging-based predictions being GRIPped. ;) This has been published on NeuroImage. Publication
2015 July I passed the comprehensive exam and now am a PhD candidate.
Topic: Facilitating neuroimaging marker discovery and validation: The predictive mapping approach PPT
2015 June I gave a talk at National Institute of Standards and Technology (NIST, the “biophotonics and bioimaging” seminar series).
Talk: Imaging pain and emotion in the human brain to answer psychological questions
2015 June I presented a poster at OHBM.
Poster: Cerebral contributions to pain independent of nociceptive stimulus intensity
2015 May I was awarded Carol B. Lynch Graduate Fellowship.
Graduate School, University of Colorado at Boulder
2015 May I received Heyer Award.
Department of Psychology and Neuroscience, University of Colorado at Boulder
2015 February I gave a talk at CogLunch, CU Boulder.
Talk: Understanding pain in the human brain
- In the Machine Learning class, our team won the second place in a Kaggle competition (When they buzz, Machine learning final project).
- In the Network Analysis and Modeling class, I implemented network analysis algorithms in Matlab. The tools include centrality measures, sophisticated random network models (e.g., configuration model), the modularity-maximization community detection method, and Stochastic Block Model (including degree-corrected version of SBM).
Keywords: Neuroimaging; fMRI; Biomarker; Pain; Emotion; Brain Decoding; Mind reading; Machine learning; Network analysis; Prediction; Translational research; Chronic pain; Depression; Drug discovery
How does the brain represent, process, and regulate pain and affective experiences? My research goal is to answer this research question to provide a better understanding of pain and emotional distress, and eventually a better help for our lives, our families, and our neighbors suffering from those.
FMRI-based biomarker: Functional brain measures, which show the brain in action, can offer important, unique contributions to making pain and emotion visible by providing their objective measures. Neuroimaging-based biomarkers could support better assessment, diagnosis, and prognosis and provide a rich window through which we can integrate psychological knowledge with neuroscience and medicine. My research primarily focuses on developing fMRI-based biomarkers for pain and emotion (and relevant clinical conditions) using machine learning and other advanced statistical methods, such as network analysis (Wager et al., 2013; Woo et al., 2014).
Pain regulation, Emotion regulation: My research also focuses on the neural mechanisms of the psychological influences on pain (Woo et al., 2015). Pain is more than just responding to noxious stimuli. For example, severely injured soldiers in a battle report minimal or less pain than normally expected. Conversely, chronic pain patients report intense pain with no evident physical causes. Psychological factors such as expectation, placebo, distraction, and reappraisal can effectively change pain experience, as used in various forms of psychotherapy for pain. Pain is also a multi-dimensional experience with sensory, cognitive, and evaluative aspects. Therefore, identifying brain systems for different aspects of pain, and understanding the neural mechanisms of the top-down influences on pain are crucial for assessing chronic pain patients and predicting which type of therapy will be effective. This line of research will eventually help accomplish my long-term research goal, establishing a foundation of the personalized medicine for patients suffering from pain and emotional distress.
Translational research: In spite of growing interests in using neuroimaging as predictive tools (i.e., biomarkers) for the clinical purpose, such tools have not yet made their way into clinical practice. However, given the potential uses of fMRI-based biomarkers in multiple aspects of prevention and treatment—such as risk and symptom assessment, diagnosis, prognosis, treatment selection, drug discovery, and more—efforts to discover neuroimaging-based biomarkers have been expanding. Particularly, recent advances in human neuroimaging, combined with machine learning techniques, are bringing us closer to the goal. However, translations from basic research to clinical use have been more difficult and slower than initially expected. Indeed, a big gap, sometimes called the ‘valley of death’ (Butler, 2008), between basic and clinical research is common across fields, and neuroimaging appears to be no exception. My research focuses on facilitating bidirectional "translations" (bench-to-bedside and bedside-to-bench) by developing a new approach, a predictive mapping approach, to study human cognition, emotions, and clinical conditions (Woo et al., in preparation).
Machine Learning, Network Analysis, Natural Language Processing: To accomplish the goals that are mentioned above, interdisciplinary efforts are necessary. By pursuing dual PhD both in Cognitive Sciences and Psychology/Neuroscience, I found great interests in sub-fields of Computer Science, such as machine learning, network analysis, and natural language processing, and it became apparent that these techniques are crucial to solve my research problems. I developed my knowledge on these topics by taking classes (e.g., CSCI 5352, CSCI 5622) and actually using them in research (e.g., Woo et al., 2014). I truly believe that these techniques will serve building blocks in my future research.
2016 "Somatic and vicarious pain are represented by dissociable multivariate brain patterns."
Anjali Krishnan, Choong-Wan Woo, Luke J Chang, Luka Ruzic, Xiaosi Gu, Marina López-Solà, Philip L Jackson, Jesús Pujol, Jin Fan, Tor D Wager, eLife eLife | PDF
2016 "Multiple faces of pain: Effects of chronic pain on the brain regulation of facial expression."
Etienne Vachon-Presseau, Mathieu Roy, Choong-Wan Woo, Miriam Kunz, Marc-Olivier Martel, Michael J. Sullivan, Philip L. Jackson, Tor D. Wager, Pierre Rainville, PAIN PDF
2016 "Issues in assessing reliability in pain neuroimaging"
Tor D. Wager, Choong-Wan Woo, PAIN PDF
2015 "Group-regularized individual prediction: Theory and application to pain"
Martin A. Lindquist, Anjali Krishnan, Marina Lopez-Sola, Marieke Jepma, Choong-Wan Woo, Leonie Koban, Mathieu Roy, Lauren Y. Atlas, Luke J. Chang, Elizabeth A.R. Losin, Hedwig Eisenbarth, Yoni K. Ashar, Zeb Delk, Tor D. Wager, NeuroImage, S1053-8119(15)00998-2. doi:10.1016/j.neuroimage.2015.10.074 PDF | Pubmed
2015 "Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain"
Choong-Wan Woo, Mathieu Roy, Jason T. Buhle, Tor D. Wager, PLoS Biology, 13(1): e1002036. doi:10.1371/journal.pbio.1002036 PDF | PLOS | OpenfMRI | Eprime
Commentaries: Nat Rev Neurosci | TICS | PLoS Biol
Press (selected): CU | DailyCamera | Newscientist | NeuroscientistNews | PsychologyToday | Examiner | Nuritas
2015 "fMRI in analgesic drug discovery"
Tor D. Wager, Choong-Wan Woo, Science Translational Medicine, 7, 274fs6, doi:10.1126/scitranslmed.3010342 PDF
2015 "Brain and psychological mediators of imitation: Sociocultural versus physical traits"
Elizabeth A.R. Losin, Choong-Wan Woo, Anjali Krishnan, Tor D. Wager, Marco Iacoboni, Mirella Dapretto, Culture and Brain, doi:10.1007/s40167-015-0029-9 PDF
2015 "Influence of dorsolateral prefrontal cortex and ventral striatum on risk avoidance in addiction: a mediation analysis"
Dorothy J. Yamamoto, Choong-Wan Woo, Tor D. Wager, Michael F. Regner, Jody Tanabe, Drug and Alcohol Dependence, 149, 10-17. doi: 10.1016/j.drugalcdep.2014.12.026 PDF
2014 "Separate neural representations for physical pain and social rejection"
Choong-Wan Woo, Leonie Koban, Ethan Kross, Martin A. Lindquist, Marie T. Banich, Luka Ruzic, Jessica R. Andrews-Hanna, Tor D. Wager, Nature Communications, 5, 5380. doi: 10.1038/ncomms6380 NCOMMS | PDF | Supplementary
Commentary: J Neurophys
Press (selected): CU | NY Magazine | Discover (Neuroskeptic) | Refinery29 | 동아사이언스 | 한겨레사이언스온 | 한빛사인터뷰
2014 "Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations"
Choong-Wan Woo, Anjali Krishnan, Tor D. Wager, NeuroImage, 91, 412-419. doi: 10.1016/j.neuroimage.2013.12.058 PDF
2013 "An fMRI-based Neurologic Signature of Physical Pain"
Tor D. Wager, Lauren, Y. Atlas, Martin, A. Lindquist, Mathieu Roy, Choong-Wan Woo, Ethan Kross, The New England Journal of Medicine, 368 (15), 1388-1397. doi: 10.1056/NEJMoa1204471 PDF | NEJM
Commentary: NEJM | TICS
Press (selected): NPR | Scientific American | Science | VOX | AP | Daily Camera
2010 "The obsessive-compulsive inventory-revised (OCI-R): psychometric properties of the Korean version and the order, gender, and cultural effects"
Choong-Wan Woo, Seok-Man Kwon, Young-Jin Lim, Min-Sup Shin, Journal of Behavior Therapy and Experimental Psychiatry, 41, 220-227. doi:10.1016/j.jbtep.2010.01.006 PDF
2010 "Cognitive impairments in schizophrenia and psychotic bipolar disorder and their relation to psychotic symptoms"
Choong-Wan Woo, Min-Sup Shin, Korean Journal of Clinical Psychology, 29, 471-489.
2010 "Are obsessive beliefs specific to obsessive-compulsive symptoms?"
Choong-Wan Woo, Min-Sup Shin, Seok-Man Kwon, Korean Journal of Clinical Psychology, 29, 35-52.
NeuroimagingCANlab Github Repository: CANlab fMRI analysis tools
Neurosynth: Large-scale, automated meta-analysis tool for fMRI
Neurovault: A repository of brain activation maps
Data analysisIPython (Jupyter): Interactive computing tool for Python
Scikit-learn: Machine Learning in Python
Kaggle: A great platform for machine learning and prediction
Webweb: A good Network visualization tool
PodcastTalking Machines: My favorite podcast
Invisibilia: Another favorite podcast