Anwendungsmöglichkeiten und Praktikabilität der Independent Component Analysis (ICA) in der funktionellen Magnetresonanztomographie (fMRT)
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Philipps-Universität Marburg
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.
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This item has been published with the following license: In Copyright