http://www.nitrc.org/forum/forum.php?forum_id=8914 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=30120378">Related Articles</a></td></tr></table> <p><b>Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging.</b></p> <p>Sci Rep. 2018 Aug 17;8(1):12342</p> <p>Authors: Fuchigami T, Shikauchi Y, Nakae K, Shikauchi M, Ogawa T, Ishii S</p> <p>Abstract<br/> Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding. Our registration method based on diffusion tensor imaging (DTI) transferred functional maps of each individual to a common anatomical space, where a decoding analysis of multi-voxel patterns was performed. The decoder trained on functional maps from other individuals in the common space showed a transfer decoding accuracy comparable to that of an individual decoder trained on single-subject functional maps. The DTI-based registration allowed more precise transformation of gray matter boundaries than a well-established T1-based method. These results suggest that the DTI-based registration is a promising tool for standardization of the brain functions, and moreover, will allow us to perform 'zero-shot' learning of decoders which is profitable in brain machine interface scenes.<br/> </p><p>PMID: 30120378 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 26 Apr 2026 13:03:41 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator