Item type:Thesis, Open Access

Anwendungsmöglichkeiten und Praktikabilität der Independent Component Analysis (ICA) in der funktionellen Magnetresonanztomographie (fMRT)

Thumbnail Image

Files

dcw.pdf (2.52 MB)

Date

2011-04-29

Authors

Publisher

Philipps-Universität Marburg
DOI

Supervisors

Abstract

This work gives an overview of the benefit and forms of application of the ICA via computer based analyse methods. FMRI offers an opportunity to achieve information of cerebral structure and function without the disadvantage of x-ray exposure. Accustomed, the recorded fMRI-Data is evaluated with an approach like the General Linear Model (GLM). Albeit, new methods to analyze fMRI data like the ICA excite more interest in the scientific community. ICA permits to measure statistically independent components from a given set of data. The advantage of an ICA driven method compared with the GLM approach is the opportunity to elide a priori data and to work as an explorative tool which needs no further data input to find statistically independent components. This advantage enqueues the ICA with a group of other methods, which are well known as Blind Source Separation (BSS). The approach of this work uses the Probabilistic Independent component Analysis (PICA) and the belonging MELODIC program-suite. The method was introduced by Christian F. Beckmann and steadily established. The possibilities of the PICA were examined using different forms of visual and motorical stimuli of the cortex. Thus we used a simple finger tapping paradigm, the projection of a checkerboard task and the arbitrary changed forms of ventilation like normo-, hypo- and hyperventilation to validate the PICA model. Furthermore, we used the ventilation data for our method of data adjustment, which offers a solution to improve the evaluation of GLM based data in prospective studies.

Review

Metadata

Contributors
Supervisor:
Dates
Created: 2011Issued: 2011-04-29Updated: 2011-08-08
DOI
https://doi.org/10.17192/z2011.0345
Faculty
Medizin
Publisher
Philipps-Universität Marburg
Language
ger
Data types
DoctoralThesis
Keywords
ICAIndependent Component AnalysisfMRIIndependent Component AnalysisICA
DFG-subjects
Funktionelle MR-TomographieUnabhängige Komponentenanalyse
DDC-Numbers
610
License
https://rightsstatements.org/vocab/InC-NC/1.0/
URI
https://open.uni-marburg.de/handle/10.17192/z2011.0345
show more
Wiedemeyer, Christian: Anwendungsmöglichkeiten und Praktikabilität der Independent Component Analysis (ICA) in der funktionellen Magnetresonanztomographie (fMRT). : Philipps-Universität Marburg 2011-04-29. DOI: https://doi.org/10.17192/z2011.0345.

License

item.page.license.under-copyright
This item has been published with the following license: In Copyright