Predicting risky choices from brain activity patterns

Sarah M. Helfinstein*, Tom Schonberg, Eliza Congdon, Katherine H. Karlsgodt, Jeanette A. Mumford, Fred W. Sabb, Tyrone D. Cannon, Edythe D. London, Robert M. Bilder, Russell A. Poldrack

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.

Original languageEnglish
Pages (from-to)2470-2475
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number7
StatePublished - 18 Feb 2014
Externally publishedYes


FundersFunder number
National Institute of Dental and Craniofacial ResearchUL1DE019580
National Institute of Dental and Craniofacial Research


    • Decision-making
    • FMRI
    • Machine learning


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