Neuroimaging of Delay Discounting in Cocaine Use Disorder: Task-Related Map Evaluation and Predictive Modeling

Abstract

Delay discounting, the tendency to devalue delayed rewards, is strongly linked to addiction. In cocaine use disorder (CUD), it remains unclear which fMRI-derived task-related maps and analytic choices best capture individual differences in this behavior. This thesis tested that question directly by comparing map type, mask definition, label construction, and cross-validation strategy within one coherent predictive framework. We analyzed fMRI data from an individualized in-scanner delay-discounting task for 61 CUD participants across multiple task-related maps, brain masks, and cross-validation schemes, using support vector machines for both regression and classification. Predictive performance varied substantially across pipeline choices. Some task contrasts supported above-chance prediction of individual discount rates across multiple analyses, while others were consistent only within a single analysis or did not replicate. This pattern indicates that methodological choices influence not only overall performance, but also which maps appear most informative. A linearizing transformation of the discount rate reduced the influence of extreme values, produced more stable label estimates, and improved predictive performance for task contrasts most plausibly related to delay-discounting computations. These improvements were selective rather than uniform across all maps, supporting the interpretation that label refinement helps most when the neural feature set captures behavior-relevant signal. Taken together, the results support the discount rate k as a behavioral marker of delay discounting in CUD and show that brain-based prediction depends on map selection, label transformation, and CV design.