An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.
An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.
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DOI：
10.1002/hbm.25742
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发表时间：
2022-04-01
影响因子：
4.8
通讯作者：
Cong F
中科院分区：
文献类型：
--
作者：
Zhao W;Li H;Hao Y;Hu G;Zhang Y;Frederick BB;Cong F
高维数据在神经影像领域已经变得很常见，尤其是群体级功能磁共振成像（fMRI）数据集。功能磁共振成像连接分析是一种广泛使用的强大技术，用于研究功能性大脑网络，以探究大脑功能和神经心理疾病的潜在机制。然而，像独立成分分析（ICA）这样的数据驱动技术可能会产生不稳定和不一致的结果，混淆了感兴趣的真实效果，并阻碍了对大脑功能和连接性的理解。造成这种不稳定的一个关键因素是功能磁共振成像数据缩减过程中发生的信息丢失。在时域中对高维 fMRI 数据进行数据缩减以识别组数据集中的重要信息对于此类分析是必要的，并且对于确保输出的准确性和稳定性至关重要。在本研究中，我们描述了一种基于自适应邻域保留嵌入（NPE）算法的功能磁共振成像数据缩减策略。模拟和真实数据结果表明，与广泛使用的数据缩减方法、主成分分析相比，基于 NPE 的数据缩减方法（a）在有效数据缩减方面表现出优越的性能，同时增强了组级信息，（b）开发了一种基于特征向量邻接图的独特策略来选择组件，（c）当 NPE 的输出用于 ICA 时，在不同模型阶下生成更可靠和可重复的大脑网络，（d）对揭示更敏感任务 fMRI 的任务诱发激活，以及（e）对于日益流行的快速 fMRI 和非常大的数据集来说非常有吸引力和强大。它在数据缩减和信息保存方面表现出优越的性能。它在生成可靠且可重复的大脑网络方面显示出卓越的优点。它避免了基于特征向量方差选择分量的限制。
High dimensionality data have become common in neuroimaging fields, especially group‐level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data‐driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE‐based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group‐level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task‐evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets. It shows superior performance on data reduction and information preservation. It shows superior merits in generating reliable and reproducible brain networks. It avoids the limitation of selecting components based on variance of eigenvectors.
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