A CCA and ICA-based Mixture Model for Identifying Major Depression Disorder.
A CCA and ICA-based Mixture Model for Identifying Major Depression Disorder.
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基于 CCA 和 ICA 的混合模型，用于识别重度抑郁症。
DOI：
10.1109/tmi.2016.2631001
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发表时间：
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期刊：
影响因子：
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通讯作者：
Yang Lihua
中科院分区：
文献类型：
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作者：
Lin Wuhong;Wu Huawang;Liu Yishu;Lv Dongsheng;Yang Lihua
fMRI 信号通常在处理和分析之前进行过滤。这个过程会导致低频波动中较高频率所携带的信息丢失。 ICA 和 CCA 是功能磁共振成像中的两种经典方法。 ICA 找到观察数据的统计独立成分，但是如果没有辅助程序，这些成分通常在生理上无法解释。 CCA 按某种顺序将两组数据分解为分量对，但是这些分量可能是真实信号和噪声的混合。为了获得统计上独立的分量并避免过滤过程中的信息丢失，我们提出了一种基于ICA和CCA的混合模型，该模型不需要对数据进行过滤。实验表明，新模型与经典ICA和CCA相比具有一定的优势。新模型获得的成分在统计上是独立的。可以保留低频波动中包含的有用信息。综合数据实验显示出令人满意的结果。作为一种应用，这个新模型被用来设计一种算法来区分严重抑郁症和正常对照，并取得了令人鼓舞的实验结果。
The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.