Compressed online dictionary learning for fast resting-state fMRI decomposition
Compressed online dictionary learning for fast resting-state fMRI decomposition
复制标题
用于快速静息态功能磁共振成像分解的压缩在线字典学习
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
B. Thirion
中科院分区：
文献类型：
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作者：
A. Mensch;G. Varoquaux;B. Thirion
我们提出了一种快速静息状态fMRI空间分解的方法，该方法基于在对来自受试组的串联的个体记录应用字典学习之前对时间维度进行降维。通过引入REST fMRI空间分解之间对应关系的度量，我们证明了时间缩减字典学习产生的结果与未缩减分解的结果一样可靠。我们还表明，这种减少显著提高了计算可伸缩性。
We present a method for fast resting-state fMRI spatial decompositions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability.