Luo Zhongqiang, Long Qunfang, Bhinge Suchita, Akhonda M A B S, Adali Tulay
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1683-1686. doi: 10.1109/EMBC44109.2020.9175837.
In application to functional magnetic resonance imaging (fMRI) data analysis, a number of data fusion algorithms have shown success in extracting interpretable brain networks that can distinguish two groups such two populations-patients with mental disorder and the healthy controls. However, there are situations where more than two groups exist such as the fusion of multi-task fMRI data. Therefore, in this work we propose the use of IVA to effectively extract information that is able to distinguish across multiple groups when applied to data fusion. The performance of IVA is investigated using a simulated fMRI-like data. The simulation results illustrate that IVA with multivariate Laplacian distribution and second-order statistics (IVA-L-SOS) yields better performance compared to joint independent component analysis and IVA with multivariate Gaussian distribution in terms of both estimation accuracy and robustness. When applied to real multi-task fMRI data, IVA-L-SOS successfully extract task-related brain networks that are able to distinguish three tasks.
在功能磁共振成像（fMRI）数据分析的应用中，许多数据融合算法已成功提取出可解释的脑网络，这些脑网络能够区分两组人群，如患有精神障碍的患者和健康对照。然而，存在多于两组的情况，例如多任务fMRI数据的融合。因此，在这项工作中，我们建议使用独立向量分析（IVA）来有效地提取在应用于数据融合时能够区分多个组的信息。使用类似fMRI的模拟数据研究了IVA的性能。模拟结果表明，与联合独立成分分析和具有多元高斯分布的IVA相比，具有多元拉普拉斯分布和二阶统计量的IVA（IVA-L-SOS）在估计准确性和鲁棒性方面都具有更好的性能。当应用于真实的多任务fMRI数据时，IVA-L-SOS成功提取了能够区分三个任务的与任务相关的脑网络。
