Tor Wager, PhD

Address:
Diana L. Taylor Distinguished Professor
Presidential Cluster in Neuroscience and
Department of Psychological and Brain Sciences
Dartmouth College


“A New Look at Self-Regulation”

One of humans’ most important capacities is self-regulation, the ability to use goals and contextual knowledge to influence our feelings, decisions, and sometimes perception and physiology. Early neuroimaging studies suggest that goal-directed regulation of pain and emotion can have substantial impacts on the brain generators of negative affect, particularly the amgydala. However, these studies have been small and subject to potential selection and analytic biases. It remains unclear to what degree humans can regulate responses in core affective brain systems, and what psychological and behavioral ingredients lead to their effective regulation. This talk takes a new look at self-regulation of negative emotion and pain through the lens of comparative effects across over 30 fMRI studies of self regulation and  new large-scale fMRI studies (N = 350 on emotion regulation, N = 400 twins on pain regulation) that test established affective neuromarkers.The findings point to different mechanisms from those suspected in the literature, and a need to revise current theories of how self-regulation influences the neural underpinnings of emotion and pain.


Tor Wager is the Diana L. Taylor Distinguished Professor in Neuroscience at Dartmouth College, and the Director of Dartmouth’s Cognitive and Affective Neuroscience laboratory, the Dartmouth Brain Imaging Center, and the Dartmouth Center for Cognitive Neuroscience.  Professor Wager’s research centers on the neurophysiology of affective processes—pain, emotion, stress, and empathy—and how they are shaped by cognitive and social influences. One focus area is the impact of thoughts and beliefs on learning, brain function, and brain-body communication. Another focus is the development of brain biomarkers that track and predict affective experience, including pain and other clinical symptoms. A third focus is on statistical, machine learning, and computational techniques that provide a foundation for new models of the affective brain. Professor Wager’s laboratory conducts basic research in these focus areas and applies the resulting techniques and models  to collaborative, translational research on clinical disorders and interventions. In support of these goals, Professor Wager and his group have developed several publicly available software toolboxes (see canlab.github.io). He also teaches courses and workshops on fMRI analysis and has co-authored a book, Principles of fMRI.   More information about Dr. Wager and his lab’s activities, publications, and software can be found at canlab.science 


Reading List

Ashar, Y. K., Andrews-Hanna, J. R., Halifax, J., Dimidjian, S., & Wager, T. D. (2021). Erratum to: Effects of compassion training on brain responses to suffering others. Social Cognitive and Affective Neuroscience, 16(10), 1111–1111. https://doi.org/10.1093/scan/nsab068

Čeko, M., Kragel, P. A., Woo, C.-W., López-Solà, M., & Wager, T. D. (2022). Common and stimulus-type-specific brain representations of negative affect. Nature Neuroscience, 25(6), 760–770. https://doi.org/10.1038/s41593-022-01082-w

Koban, L., Gianaros, P. J., Kober, H., & Wager, T. D. (2021). The self in context: Brain Systems Linking Mental and physical health. Nature Reviews Neuroscience, 22(5), 309–322. https://doi.org/10.1038/s41583-021-00446-8

Koban, L., Wager, T. D., & Kober, H. (2022). A neuromarker for drug and food craving distinguishes drug users from non-users. Nature Neuroscience, 26(2), 316–325. https://doi.org/10.1038/s41593-022-01228-w

Zunhammer, M., Spisák, T., Wager, T. D., Bingel, U., Atlas, L., Benedetti, F., Büchel, C., Choi, J. C., Colloca, L., Duzzi, D., Eippert, F., Ellingsen, D.-M., Elsenbruch, S., Geuter, S., Kaptchuk, T. J., Kessner, S. S., Kirsch, I., Kong, J., Lamm, C., … Zeidan, F. (2021). Meta-analysis of neural systems underlying placebo analgesia from individual participant fmri data. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21179-3