Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

Khalid J. Oweis; Madison M. Berl; William D. Gaillard; Elizabeth S. Duke; Kaitlin Blackstone; Murray H. Loew; Jason M. Zara

doi:10.1117/12.843927

9 March 2010 Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

Khalid J. Oweis, Madison M. Berl, William D. Gaillard, Elizabeth S. Duke, Kaitlin Blackstone, Murray H. Loew, Jason M. Zara

Author Affiliations +

Proceedings Volume 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging; 76261W (2010) https://doi.org/10.1117/12.843927
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

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Khalid J. Oweis, Madison M. Berl, William D. Gaillard, Elizabeth S. Duke, Kaitlin Blackstone, Murray H. Loew, and Jason M. Zara "Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76261W (9 March 2010); https://doi.org/10.1117/12.843927

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KEYWORDS

Epilepsy

Brain

Control systems

Functional magnetic resonance imaging

Feature extraction

Statistical analysis

Principal component analysis

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