Dataset for Systematic Review: Computational Enhancement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers

This dataset supports the systematic review titled "Computational Enhabcement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers" (Tallón Fuentes et al.). It contains metadata for 65 included studies (updated search to February 1, 2026), extracted from PubMed, Scopus, ACM Digital Library, and Web of Science. The review focuses on the integration of artificial intelligence (AI) and computational methods with behavioral activation (BA) interventions, neurobiological mechanisms (e.g., hippocampal changes, BDNF, HPA axis), frailty considerations, and advanced detection/monitoring approaches. Files included:- dataset.csv: Main metadata table with 65 studies, including Authors, Year, Title, Journal, Volume/Pages, DOI, and Full Citation.- S1_File.docx: Retrospective review protocol.- Online Resource 1.pdf: Full search terms, strings, and strategy (including Boolean operators and variants).- S2_File.docx: Completed PRISMA 2020 checklist for systematic reviews. The data were curated following PRISMA 2020 guidelines and support reproducibility of the review's findings on AI-enhanced BA, multimodal neuroimaging, digital phenotyping, and frailty moderation in depression. License: CC BY 4.0 (Creative Commons Attribution 4.0 International). Users are free to share and adapt the material with appropriate credit to the authors. Keywords: depression, behavioral activation, artificial intelligence, machine learning, neuroimaging, frailty, digital mental health, multimodal fusion, systematic review, PRISMA DOI: 10.5281/zenodo.18451337