Abstract
Approach-avoidance conflict arises when the drives to pursue reward and avoid harm are incompatible. Previous neuroimaging studies of approach-avoidance conflict have shown large variability in reported neuroanatomical correlates. These prior studies have generally neglected to account for potential sources of variability, such as individual differences in choice preferences and modeling of hemodynamic response during conflict. In the present study, we controlled for these limitations using a hierarchical Bayesian model (HBM). This enabled us to measure participant-specific per-trial estimates of conflict during an approach-avoidance task. We also employed a variable epoch method to identify brain structures specifically sensitive to conflict. In a sample of 28 human participants, we found that only a limited set of brain structures (inferior frontal gyrus, right dorsolateral prefrontal cortex and right pre-supplementary motor area) are specifically correlated with approach-avoidance conflict. These findings suggest that controlling for previous sources of variability increases the specificity of the neuroanatomical correlates of approach-avoidance conflict.
Significance Statement Approach-avoidance conflict is implicated in many psychiatric syndromes. Previous fMRI studies of this important process have potential biases caused by overlooking individual differences in the evaluation of reward and threat in their analyses. We present a method to model individual differences in approach-avoidance conflict and demonstrate how to incorporate this model into fMRI analyses. We found our approach to have greater specificity than previous studies, which highlights the importance of capturing large variability in participant behavior.
Footnotes
Over the past three years, DAP has received consulting fees or honoraria from Akili Interactive Labs, Alkermes, BlackThorn Therapeutics, Boehringer Ingelheim, Compass, and Takeda, for activities unrelated to the current paper. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors. All other authors report no biomedical financial interests.
This research was sponsored by the U.S. Army Research Office and Defense Advanced Research Projects Agency under Cooperative Agreement Number W911NF-14-2-0045. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the funding sources. Cluster computing resources made possible by Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043, and facilities were funded by grant P41 EB015896. DAP was partially supported by R37 MH068376 and R01 MH101521. KKE was partially supported by NIH National Institute of Neurological Disorders and Stroke (NINDS) Training Program in Recovery and Restoration of CNS Health and Function (T32 NS100663-01).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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