Probabilistic independent component analysis for functional magnetic resonance imaging
Probabilistic independent component analysis for functional magnetic resonance imaging
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DOI：
10.1109/tmi.2003.822821
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发表时间：
2004-02-01
影响因子：
10.6
通讯作者：
Smith, SA
中科院分区：
文献类型：
--
作者：
Beckmann, CF;Smith, SA
我们提出了一个集成的方法，概率独立成分分析（伊卡）的功能性磁共振成像（FMRI）数据，允许在高斯噪声的存在下，非平方混合。为了避免过拟合，我们通过对数据的真实维度进行贝叶斯分析来客观估计高斯噪声的量，即，激活和非高斯噪声源的数量。这使我们能够进行概率建模，并实现数据的渐近唯一分解。它减少了解释的问题，因为每个最终的独立成分现在更有可能是由于只有一个物理或生理过程。我们还描述了其他标准伊卡的改进，如时间序列的时间预白化和方差归一化，后者是特别有用的背景下，当弱激活的降维。我们讨论了使用先验信息的时空性质的源过程，和一个替代假设检验的推理方法，使用高斯混合模型。我们的方法的性能说明和评价真实的和人工的功能磁共振成像数据，并从经典的伊卡和GLM分析得到的结果的时空精度相比。
We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.