2012/09/07 by Yannick Schwartz, Gaël Varoquaux, Schwartz, Yannick +3
Computer Science · Mathematics · Neuroscience · #FOS: Computer and information sciences #Face Recognition and Perception #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Behavioral Psychology Studies #Visual perception and processing mechanisms #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1209.1450
PRNI 2012 : 2nd International Workshop on Pattern Recognition in NeuroImaging, London : United Kingdom (2012)
arxiv created 2012/09/07 · openalex publication_date 2012/09/07 · arxiv updated 2012/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their ability to identify the common patterns between brain activation maps related to two functional tasks. We provide some preliminary quantification of these similarities, and show that selection transfer makes it possible to set a spatial scale yielding ROIs that are more specific to the context of interest than with transfer learning. In particular, selection transfer outlines well known regions such as the Visual Word Form Area when discriminating between different visual tasks.