[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86081-en":3,"doc-seo-86081-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86081,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Fast Whole Brain Geometry Aware Functional Alignment for Cross Subject Decoding","Decoding brain activity supports characterization of brain processes and reveals the functional architecture behind cognition. Inter-individual variability in brain response patterns limits decoders that generalize across people. Functional alignment addresses this by aligning functional data across individuals before training population-level decoders, while balancing feature alignment with preservation of anatomical structure and ensuring efficient computation. SpectralOT introduces a new fMRI alignment approach embedding cortical geometry via Laplace-Beltrami eigenmodes to regularize alignment across subjects.","Fast Whole-Brain, Geometry-Aware Functional Alignment for  \nCross-Subject Decoding  \nPierre-Louis Barbarant1,2,3 Florent Meyniel2, 3 Bertrand Thirion1  \n1 Université Paris-Saclay, Inria, CEA, Palaiseau 91120, France  \n2 Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, Commissariat à l’Energie Atomique et aux énergies alternatives, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France  \n3 Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle  \nhospitalo-universitaire 15, Université Paris Cité, Paris, France  \n[pierre-louis.barbarant@inria.fr](pierre-louis.barbarant@inria.fr) , [bertrand.thirion@inria.fr](bertrand.thirion@inria.fr)  \narXiv :2607 . 10931v1 [ q-bio .NC] 12 Jul 2026  \nAbstract  \nDecoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional alignment: aligning functional data across individuals before training populationlevel decoders. The core issue is to strike the balance between aligning functional features and preserving the anatomical structure, while maintaining computational efficiency. We introduce a new functional alignment method for fMRI, SpectralOT, that embeds cortical geometry into Laplace-Beltrami eigenmodes along functional data to regularize the alignment.  \nIntroduction  \nAccurate brain activity decoders have now become ubiquitous in analyzing functional Magnetic Resonance Imaging (fMRI) data (Haynes, 2015; Liu et al. , 2025 ; Naselaris et al. , 2011) . The development of such decoders have enabled the study of increasingly complex cognitive processes by relating the activity of various brain regions to a rich set of stimuli. However, the predictive power of population-level decoders trained through anatomically normalized data is hindered by the inter-individual variability of brain responses. Building a robust population-level decoders requires to overcome not only the anatomical variability, but also the function variability between individuals. More precisely, the two main issues related to inter-individual variability are: firstly, the discrepancies in anatomy, yielding different spatial support for the functional signal, and secondly, the variability in the functional response location and magnitude (Haxby et al. , 2001; Sabuncu et al. , 2009) . It is important to note that because both issues are entangled, solutions have to take both spatial and functional information into account.  \nIn order to deal with anatomical variability across subjects, researchers have first developed and adopted  \naccurate diffeomorphic matching to the MNI collection (Fonov et al. , 2009) . Surface-based registration has further improved alignment (Fischl et al. , 1999a; Robinson et al. , 2014) . However, these anatomical templates cannot match all details of the brain folding, and besides, functional architecture is not strictly tied to anatomical organization. Given that the understanding of functional architecture involves accounting for spatial relationships (Lu et al. , 2025), the next frontier for the community is to create functional atlases that describe accurately the spatial organization of cognitive functions: functional templates, and the related registration procedures. While Optimal Transport (OT) solvers have shown promising performance in that respect, state-of-the-art approaches suffer from key limitations. Joint minimization of inhomogeneous criteria (functional matching, squared geodesic distance pairwise matching, marginal relationships of the coupling), makes solvers extremely costly and complex to run, and the relative weighting of these inhomogeneous terms remains a practical and computational hurdle. This computational burden preclu","cbCail6ckBbpw86e","https://ap.wps.com/l/cbCail6ckBbpw86e","pdf",4680524,1,16,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"Why do population-level fMRI decoders struggle to generalize across individuals?\",\"answer\":\"Inter-individual variability affects both anatomy and function. Anatomical discrepancies change where the functional signal is supported, and functional responses vary in location and magnitude across subjects.\"},{\"question\":\"What is the key idea behind functional alignment in this work?\",\"answer\":\"Functional alignment aligns functional data across individuals prior to training population-level decoders. It aims to balance aligning functional features with preserving anatomical structure while keeping computation efficient.\"},{\"question\":\"What does SpectralOT contribute compared with prior functional alignment approaches?\",\"answer\":\"SpectralOT introduces a functional alignment method for fMRI that embeds cortical geometry using Laplace-Beltrami eigenmodes along functional data. It is easier to parametrize and is described as orders of magnitude faster than state-of-the-art solutions.\"}]",1784208393,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"fast-whole-brain-geometry-aware-functional-alignment-for-cross-subject-decoding","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/fast-whole-brain-geometry-aware-functional-alignment-for-cross-subject-decoding/86081/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do population-level fMRI decoders struggle to generalize across individuals?","Question",{"text":74,"@type":75},"Inter-individual variability affects both anatomy and function. Anatomical discrepancies change where the functional signal is supported, and functional responses vary in location and magnitude across subjects.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is the key idea behind functional alignment in this work?",{"text":79,"@type":75},"Functional alignment aligns functional data across individuals prior to training population-level decoders. It aims to balance aligning functional features with preserving anatomical structure while keeping computation efficient.",{"name":81,"@type":72,"acceptedAnswer":82},"What does SpectralOT contribute compared with prior functional alignment approaches?",{"text":83,"@type":75},"SpectralOT introduces a functional alignment method for fMRI that embeds cortical geometry using Laplace-Beltrami eigenmodes along functional data. It is easier to parametrize and is described as orders of magnitude faster than state-of-the-art solutions.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":28,"slug":117},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]