Predicting behavior through dynamic modes in resting-state fMRI data.
Predicting behavior through dynamic modes in resting-state fMRI data.
复制标题
通过静息态功能磁共振成像数据的动态模式预测行为。
DOI：
10.1016/j.neuroimage.2021.118801
复制
发表时间：
2022
期刊：
影响因子：
5.7
通讯作者：
Kawahara,Yoshinobu
中科院分区：
文献类型：
--
作者：
Ikeda,Shigeyuki;Kawano,Koki;Watanabe,Soichi;Yamashita,Okito;Kawahara,Yoshinobu
静息态功能连接（FC）的动态特性为脑-行为关系提供了丰富的信息。动态模式分解（DMD）已被用来作为一种方法来表征FC的动态。然而，它仍然不清楚是否动态模式（DM），时空相干模式DMD计算，提供有关个人行为差异的信息。本研究建立了一个方法学的方法来预测个体差异的行为使用DM。此外，我们研究了在七个特定频带（0-0.1，...，0.6-0.7 Hz）进行预测。为了验证我们的方法，我们测试了59个行为指标中的每一个是否可以通过对Gram矩阵进行多变量模式分析来预测，该矩阵是使用从个体的静息状态功能磁共振成像（rs-fMRI）数据计算的特定于受试者的DM创建的。DMD成功地预测了行为，并优于时间和空间独立成分分析，这是传统的数据分解方法提取空间活动模式。在排列测试中预测具有显著准确性的大多数行为测量与认知有关。我们发现，<0.2 Hz频段内的DM主要有助于预测，并且具有与几种常见的静息态网络相似的空间结构。我们的研究结果表明，DMD是有效的提取时空特征的rs-fMRI数据。
Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0–0.1,...,0.6–0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.