Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

• Published in: BMC medical informatics and decision making 2009-11, Vol.9 Suppl 1 (Suppl 1), p.S6-S6, Article S6
• Main Authors: Ji, Soo-Yeon, Ward, Kevin, Najarian, Kayvan
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Brain Mapping; Computer Simulation; Dyslexia; Dyslexia - diagnosis; Dyslexia - metabolism; Humans; Magnetic Resonance Imaging - methods; Models, Theoretical; NMR; Nuclear magnetic resonance; Signal processing; Signal Processing, Computer-Assisted; Studies; Wavelet transforms
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-9-S1-S6

Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain. In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects. We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects. The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject.