Analysis of event-related fMRI using nonlinear regression self-organizing map neural network

Stephan G. Erberich; Manou Liebert; Klaus Willmes; Armin Thron; Walter Oberschelp

doi:10.1117/12.428153

21 May 2001 Analysis of event-related fMRI using nonlinear regression self-organizing map neural network

Stephan G. Erberich, Manou Liebert, Klaus Willmes, Armin Thron, Walter Oberschelp

Proceedings Volume 4321, Medical Imaging 2001: Physiology and Function from Multidimensional Images; (2001) https://doi.org/10.1117/12.428153
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

Functional magnetic resonance imaging (fMRI) becomes a common method to study task induced brain activation. Using rapid Echo Planar Imaging (EPI) sequences one can obtain a higher MR-Signal under a task condition close by activated areas as a result of susceptibility changes in blood oxygenation (BOLD effect). Beside the commonly used blocked task designs, event- related paradigms gain more importance for activation of higher cognitive functions enabling more sophisticated and complex paradigms. For the analysis of event-related fMRI data one can use statistical tests, in example t-test used by SPM Software. The introduced analysis method based on an artificial neural network algorithm, a self-organizing map (SOM), is capable to distinguish between task related activation, deactivation and baseline patterns from the time series. This is achieved by temporal sorting and projection of all events from one condition into one combined hemodynamic response sampling for each voxel. These responses, having individual patterns can be separated by their pattern features and is done by training of the neural network. After training the SOM consists of a pattern-to-voxel mapping which is superimposed onto either an anatomical or EPI image of the subject for the task evaluation.

Citation Download Citation

Stephan G. Erberich, Manou Liebert, Klaus Willmes, Armin Thron, and Walter Oberschelp "Analysis of event-related fMRI using nonlinear regression self-organizing map neural network", Proc. SPIE 4321, Medical Imaging 2001: Physiology and Function from Multidimensional Images, (21 May 2001); https://doi.org/10.1117/12.428153

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