Visual Feature Extraction From Voxel-Weighted Averaging of Stimulus Images in 2 fMRI Studies

被引：1

作者：

Hart, Corey B. ^{[1
]}

Rose, William J. ^{[2
]}

机构：

[1] Lockheed Martin, Adv Technol & Innovat, King Of Prussia, PA 19406 USA

[2] Lockheed Martin, Informat Syst & Global Solut, King Of Prussia, PA 19406 USA

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2013年 / 60卷 / 11期

关键词：

Bayesian estimation; component analysis; fMRI; generalized linear models; imaging; voxel; HUMAN EXTRASTRIATE CORTEX; VENTRAL TEMPORAL CORTEX; HUMAN BRAIN; CLASSIFICATION; PERCEPTION; FACES; AREA;

D O I：

10.1109/TBME.2013.2268538

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

引用

页码：3124 / 3130

页数：7