Analysis of brain fMRI time-series using HRF knowledge-based correlation classifier on unsupervised self-organizing neural network map

Stephan G. Erberich; Stefan Bluml; Marvin D. Nelson M.D.

doi:10.1117/12.480667

2 May 2003 Analysis of brain fMRI time-series using HRF knowledge-based correlation classifier on unsupervised self-organizing neural network map

Stephan G. Erberich, Stefan Bluml, Marvin D. Nelson M.D.

Author Affiliations +

Proceedings Volume 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications; (2003) https://doi.org/10.1117/12.480667
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Brain imaging and particular functional MRI (fMRI), which acquires brain volumes in time, reveals new understanding of the functional/structural relation in neuroscience. During fMRI imaging physiological state changes occur in the brain regions activated from the task paradigm which the subject performs in the scanner. These state changes can be depicted in the small veins of the activated region due to the blood oxygen level dependent (BOLD) effect. For each brain voxel in the fMRI experiment one accumulates a time series vector which has to be analyzed for similarity to the original task paradigm vector and its characteristic hemodynamic response function (HRF). Various analysis methods have been discussed for fMRI analysis, model-based statistical or unsupervised data-driven techniques. The purpose of this paper is to introduce a new method which combines two different approaches. We use an unsupervised self-organizing map (SOM) neural network to reduce the time series vector space by non-linear pattern recognition into a 2D table of representative time series wave-forms. Using a-priori knowledge of the HRF, either derived from a theoretical wave-form model or estimated from a brain region of interest (ROI), one can use correlation analysis between the time series patterns of the SOM table and the HRF to depict regions of activation specific to the HRF. An optional second SOM training with a reduce number of neurons of the best-matching time series to the HRF classification refines the second neural network pattern table. The learned time series pattern of each neuron and the corresponding brain voxels are superimposed onto the subject's brain image for visual investigation.

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Stephan G. Erberich, Stefan Bluml, and Marvin D. Nelson M.D. "Analysis of brain fMRI time-series using HRF knowledge-based correlation classifier on unsupervised self-organizing neural network map", Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); https://doi.org/10.1117/12.480667

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KEYWORDS

Brain

Functional magnetic resonance imaging

Neural networks

Neurons

Neuroimaging

Statistical analysis

Brain mapping

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