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首页> 外文期刊>BMC Medical Informatics and Decision Making >Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia
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Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

机译：使用小波变换进行fMRI的脑图绘制和功能模式检测;在阅读障碍检测中的应用

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BackgroundFunctional 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.MethodsIn 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.ResultsWe 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.ConclusionThe results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject.
机译：背景功能磁共振成像（fMRI）已被证明对研究脑功能很有用。然而，由于噪声和失真的存在，fMRI信号与实际神经活动之间的映射很困难。由于困难，健康和患病病例的fMRI脑图像的差异模式分析被认为是重要的研究课题。通过功能磁共振成像扫描，可以将血流量增加识别为激活的大脑区域。同样，基于体数据的多切片图像，fMRI提供了检测和分析大脑不同部位的功能信息。方法本文采用一种分层方法的功能，该方法基于修改后的最大模型执行优化算法（ MCM）在我们以前的研究中进行了评估。通过采用改进的最大相关模型（MCM）来检测包含显着响应的活动区域来设计优化算法。具体来说，在这项研究中，基于两组数据集（阅读障碍和健康受试者）对优化算法进行了检查，以验证该算法增强大脑感兴趣区域中信号活动质量的能力。验证算法后，应用离散小波变换（DWT）识别健康与阅读障碍者之间的差异。结果我们成功地表明，我们的优化算法改善了健康与阅读障碍者的fMRI信号活性。此外，我们发现基于DWT的功能可以识别健康人和阅读障碍者之间的差异。结论本研究的结果提供了阅读障碍者功能异常关联的见解，这可能有助于从健康受试者中识别神经生物学。

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