Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Learning to Decode Cognitive States from Brain Images
Machine Learning
IR principles for content-based indexing and retrieval of functional brain images
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Toward content-based indexing and retrieval of brain images
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
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The thresholded t-map produced by the General Linear Model (GLM) gives an effective summary of activation patterns in functional brain images and is widely used for feature selection in fMRI related classification tasks. As part of a project to build content-based retrieval systems for fMRI images, we have investigated ways to make GLM more adaptive and more robust in dealing with fMRI data from widely differing experiments. In this paper we report on exploration of the Finite Impulse Response model, combined with multiple linear regression, to identify the "locally best Hemodynamic Response Function (HRF) for each voxel" and to simultaneously estimate activation levels corresponding to several stimulus conditions. The goal is to develop a procedure for processing datasets of varying natures. Our experiments show that Finite Impulse Response (FIR) models with a smoothing factor produce better retrieval performance than does the canonical double gamma HRF in terms of retrieval accuracy.