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Chu S, Hutcherson C, Ito R, Lee ACH. Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model. Cereb Cortex 2023; 33:7797-7815. [PMID: 36944537 PMCID: PMC10267625 DOI: 10.1093/cercor/bhad080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 03/23/2023] Open
Abstract
The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.
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Affiliation(s)
- Sonja Chu
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Cendri Hutcherson
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON M5S 3E6, Canada
| | - Rutsuko Ito
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Cell and Systems Biology, University of Toronto, 25 Harbord Street, Toronto, ON M5S 3G5, Canada
| | - Andy C H Lee
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Rotman Research Institute, Baycrest Centre, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada
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Letkiewicz AM, Kottler HC, Shankman SA, Cochran AL. Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches. Neurosci Biobehav Rev 2023; 147:105103. [PMID: 36804398 PMCID: PMC10023482 DOI: 10.1016/j.neubiorev.2023.105103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.
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Affiliation(s)
- Allison M Letkiewicz
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
| | - Haley C Kottler
- Department of Mathematics, University of Wisconsin, Madison, WI, USA
| | - Stewart A Shankman
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Amy L Cochran
- Department of Mathematics, University of Wisconsin, Madison, WI, USA; Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA
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