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Neural network‐based analysis of MR time series
Author(s) -
Fischer Harald,
Hennig Jürgen
Publication year - 1999
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/(sici)1522-2594(199901)41:1<124::aid-mrm17>3.0.co;2-9
Subject(s) - cluster analysis , computer science , projection (relational algebra) , visualization , series (stratigraphy) , data mining , artificial intelligence , cluster (spacecraft) , pattern recognition (psychology) , algorithm , paleontology , biology , programming language
Clustering has been introduced to analyze fMRI data by means of partitioning data into time series of similar temporal behavior. It is hoped that one of these clusters represents a dynamic effect of interest, like functional activation. Using self‐organizing maps for clustering, additional information can be obtained by ordering cluster centers on a two‐dimensional projection plane. The map's capability of data visualization is used to summarize all dynamic effects of an experiment by means of data partitioning. The map does allow differently sized and populated clusters in the data by forming “superclusters” on the map. The method is introduced as a conceptual extension to clustering. Applications to fMRI and to MR mammography are discussed. Magn Reson Med 41:124‐131, 1999. © 1999 Wiley‐Liss, Inc.