Date of Award

Spring 1-1-2016

Document Type


Degree Name

Doctor of Philosophy (PhD)


Psychology & Neuroscience

First Advisor

Tor D. Wager

Second Advisor

Alaa Ahmed

Third Advisor

June Gruber

Fourth Advisor

Yuko Munakata

Fifth Advisor

Randall C. O’Reilly


For the last two decades, functional Magnetic Resonance Imaging (fMRI) revolutionized how we study human cognition and emotion. However, as evidence accumulates, many of the human brain-function mappings that fMRI studies have produced appear to be flawed due to their poor sensitivity, specificity, and reproducibility. A new emerging paradigm, which we termed predictive modeling, has a potential to resolve these issues. This new approach is based on specific uses of machine learning techniques combined with experimental designs optimized for prediction, yielding well-defined neuroimaging signatures of brain-outcome relationships that can be prospectively tested in new individuals, studies, and translational applications.

In this dissertation, I first reviewed the state of translational neuroimaging and discussed how we can move the field forward using the predictive modeling approach (Chapter I). Then, I examined neural representations of pain and related emotions using pattern recognition techniques. More specifically, in Chapter II, I identified fMRI multivariate patterns that are sensitive and specific to physical pain and social rejection and assessed the relationship between two patterns. 60 participants who recently experienced an unwanted romantic break-up were scanned with fMRI while they experienced physical pain and social rejection stimuli on separate trials. I found that the multivariate patterns for pain and rejection are uncorrelated and separately modifiable at the whole-brain level and within many brain regions, such as the dorsal anterior cingulate, anterior and dorsal posterior insular, and secondary somatosensory cortices that have shown overlapping fMRI activity with univariate methods. In Chapter III, I developed an fMRI multivariate pattern signature that characterizes the cerebral processes that contribute to pain beyond the level of nociceptive input and mediate psychological and behavioral influences. The new signature was developed based on data from 4 independent studies that involved thermal pain stimulation (N = 137) and included patterns of fMRI activity in nucleus accumbens, lateral prefrontal, parahippocampal, and other regions. We then prospectively tested the new signature on 2 independent test datasets (Studies 5-6, N = 46), and showed that the signature responses explained variation in trial-by-trial pain ratings not reflected in a previous fMRI-based marker for nociceptive pain. In addition, the signature responses mediated the pain-modulating effects of three psychological manipulations of expectation and perceived control.

In Chapter IV and V, I further examined whether different psychological pain modulation methods influence primary nociceptive system or other cognitive and affective ones. In Chapter IV, 33 participants engaged in cognitive self-regulation to increase or decrease pain while experiencing multiple levels of painful heat. I found that both heat intensity and self-regulation strongly influenced reported pain, but they did so via two distinct brain pathways. The effects of stimulus intensity were mediated by the neurologic pain signature (NPS), an a priori distributed brain network shown to predict physical pain with over 90% sensitivity and specificity across four studies. Self-regulation did not influence NPS responses; instead, its effects were mediated through functional connections between the nucleus accumbens and ventromedial prefrontal cortex. This pathway was unresponsive to noxious input, and has been broadly implicated in valuation, emotional appraisal, and functional outcomes in pain and other types of affective processes. In Chapter V, 20 participants experienced painful heat while distracting cognitive tasks were crossed with an expectation-based placebo treatment. I found that distracting tasks reduced pain though increased activity of the fronto-parietal control network (FPN) and decreased activity of the NPS. However, placebo treatment had no