Academic Journal
Rapid cortical mapping with cross-participant encoding models.
| Τίτλος: | Rapid cortical mapping with cross-participant encoding models. |
|---|---|
| Συγγραφείς: | Tang J; Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, United States., Huth AG; Department of Statistics, University of California, Berkeley, CA, United States.; Department of Neuroscience, University of California, Berkeley, CA, United States. |
| Πηγή: | Imaging neuroscience (Cambridge, Mass.) [Imaging Neurosci (Camb)] 2026 Sep 03; Vol. 4. Date of Electronic Publication: 2026 Sep 03 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: The MIT Press Country of Publication: United States NLM ID: 9918663686606676 Publication Model: eCollection Cited Medium: Internet ISSN: 2837-6056 (Electronic) Linking ISSN: 28376056 NLM ISO Abbreviation: Imaging Neurosci (Camb) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cambridge, Massachusetts : The MIT Press, [2023]- |
| Ιατρικοί όροι (MeSH): | Brain Mapping*/methods , Cerebral Cortex*/physiology , Cerebral Cortex*/diagnostic imaging , Magnetic Resonance Imaging*/methods , Models, Neurological*, Image Processing, Computer-Assisted/methods ; Humans ; Language ; Semantics |
| Περίληψη: | Voxelwise encoding models trained on functional MRI data can produce detailed maps of cortical organization. However, voxelwise encoding models must be trained on many hours of brain responses from each participant, limiting clinical applications. In this study, we introduce a cross-participant modeling framework for rapid cortical mapping. In this framework, voxelwise encoding models are trained on many hours of brain responses from previously scanned reference participants, and then transferred to a new participant by aligning brain responses using a small set of stimuli. We evaluated cross-participant encoding models on linguistic semantic mapping, non-linguistic semantic mapping, and auditory mapping. In each case, we found that cross-participant encoding models had more accurate selectivity estimates and prediction performance than within-participant encoding models trained on the same amount of data from the new participant. We also found that cross-participant encoding models improved with the amount of data from each reference participant and the number of reference participants. These results demonstrate that cross-participant modeling can substantially reduce the amount of data required for detailed cortical mapping, which may facilitate new clinical applications of functional neuroimaging. (© 2026 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.) |
| Competing Interests: | A.G.H. and J.T. are inventors on a provisional patent application (the applicant is The University of Texas System Board of Regents) that is directly relevant to the cross-participant mapping approach used in this work. The patent application will not affect the open-source availability of the data and code for research purposes. |
| Σχόλια: | Update of: bioRxiv. 2026 Mar 27:2026.03.26.714320. doi: 10.64898/2026.03.26.714320.. (PMID: 41929225) |
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| Grant Information: | F32 DC022178 United States DC NIDCD NIH HHS; R01 DC020088 United States DC NIDCD NIH HHS |
| Contributed Indexing: | Keywords: fMRI; functional alignment; language; language mapping; semantic mapping; vision |
| Entry Date(s): | Date Created: 20260905 Date Completed: 20260905 Latest Revision: 20260910 |
| Update Code: | 20260910 |
| PubMed Central ID: | PMC13543446 |
| DOI: | 10.1162/IMAG.a.1349 |
| PMID: | 42699512 |
| Βάση Δεδομένων: | MEDLINE |
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