Structured Code Review in Translational Neuromodeling and Computational Psychiatry

Errors in scientific code pose a significant risk to the accuracy of research. Yet, formal code review remains uncommon in academia. Drawing on our experience implementing code review in Translational Neuromodeling and Computational Psychiatry, we present a pragmatic framework for improving code quality, reproducibility, and collaboration. Our structured checklist (organized by priority and type of work: experimental, theoretical, machine learning) offers a practical guide for identifying common coding issues, from data handling errors to flawed statistical analyses. We also integrate best practices from software engineering and highlight the emerging role of large language models in automating aspects of review. We argue that, despite perceived costs, code review significantly enhances scientific reliability and fosters a culture of transparency and continuous learning. Our proposal provides an adaptable model for integrating code review into research workflows, helping mitigate errors before publication and strengthening trust in scientific results.