ICA for fusion of brain imaging data

Vince D. Calhoun, Tülay Adali

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Many studies are currently collecting multiple types of imaging data and information from the same participants. Each imaging method reports on a limited domain and is likely to provide some common information and some unique information. This motivates the need for a joint analysis of these data. Most commonly, each type of image is analyzed independently and then perhaps overlaid to demonstrate its relationship with other data types (e.g., structural and functional images). A second approach, called data fusion, utilizes multiple image-types together in order to take advantage of the "cross" information. In the former approach, any cross information is "thrown" away, hence such an approach, for example, would not detect a change in functional magnetic resonance imaging (fMRI) activation maps that are associated with a change in the brain structure while the second approach would be expected to detect such changes.

Original languageEnglish (US)
Title of host publicationSignal Processing Techniques for Knowledge Extraction and Information Fusion
PublisherSpringer US
Pages221-240
Number of pages20
ISBN (Print)9780387743660
DOIs
StatePublished - Dec 1 2008
Externally publishedYes

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Calhoun, V. D., & Adali, T. (2008). ICA for fusion of brain imaging data. In Signal Processing Techniques for Knowledge Extraction and Information Fusion (pp. 221-240). Springer US. https://doi.org/10.1007/978-0-387-74367-7_12