Stimulus-Independent Data Analysis for fMRI

Silke Dodel, J. Michael Herrmann, Theo Geisel

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus- independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabil- ities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.
Original languageEnglish
Title of host publicationEmergent Neural Computational Architectures Based on Neuroscience
EditorsStefan Wermter, Jim Austin, David Willshaw
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Pages39-52
Number of pages14
ISBN (Electronic)978-3-540-44597-5
ISBN (Print)978-3-540-42363-8
DOIs
Publication statusPublished - 2001

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume2036
ISSN (Print)0302-9743

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