{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-empirical-evaluation-of-functional","title":"An empirical evaluation of functional alignment using inter-subject decoding","arxiv_id":null,"date":"2020-12-08","proceeding":null,"authors":["Thomas Bazeille","Elizabeth Dupre","Jean-Baptise Poline","Bertrand Thirion"],"abstract":"Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment—a class of methods that matches subjects’ neural signals based on their functional similarity—is a promising strategy for addressing this variability. At present, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider piecewise Procrustes, searchlight Procrustes, piecewise Optimal Transport, Shared Response Modelling (SRM), and intra-subject alignment; as well as associated methodological choices such as ROI definition. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM performs best within a region-of-interest while piecewise Optimal Transport performs best at a whole-brain scale. We also benchmark the computational efficiency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of the methods used.","url_abs":"https://www.biorxiv.org/content/10.1101/2020.12.07.415000v1","url_pdf":"https://www.biorxiv.org/content/10.1101/2020.12.07.415000v1.full.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-empirical-evaluation-of-functional","repo_url":"https://github.com/neurodatascience/fmralign-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[{"method_slug":"procrustes","method_name":"Procrustes"},{"method_slug":"srm","method_name":"SRM"}],"datasets_introduced":[{"slug":"ibc","name":"IBC","full_name":"Individual Brain Charting"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}