Wen Zhenfu, Yu Tianyou, Yang Xinbin, Li Yuanqing
Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China.
Front Neurosci. 2019 Jan 29;12:1003. doi: 10.3389/fnins.2018.01003. eCollection 2018.
Humans selectively process external information according to their internal goals. Previous studies have found that cortical activity and interactions between specific cortical areas such as frontal-parietal regions are modulated by behavioral goals. However, these results are largely based on simple stimuli and task rules in laboratory settings. Here, we investigated how top-down goals modulate whole-brain functional connectivity (FC) under naturalistic conditions. Analyses were conducted on a publicly available functional magnetic resonance imaging (fMRI) dataset (OpenfMRI database, accession number: ds000233) collected on twelve participants who made either behavioral or taxonomic judgments of behaving animals containing in naturalistic video clips. The task-evoked FC patterns of the participants were extracted using a novel inter-subject functional correlation (ISFC) method that increases the signal-to-noise ratio for detecting task-induced inter-regional correlation compared with standard FC analysis. Using multivariate pattern analysis (MVPA) methods, we successfully predicted the task goals of the participants with ISFC patterns but not with standard FC patterns, suggests that the ISFC method may be an efficient tool for exploring subtle network differences between brain states. We further examined the predictive power of several canonical brain networks and found that many within-network and across-network ISFC measures supported task goals classification. Our findings suggest that goal-directed processing of naturalistic stimuli systematically modulates large-scale brain networks but is not limited to the local neural activity or connectivity of specific regions.
人类根据其内在目标选择性地处理外部信息。先前的研究发现，诸如额顶叶区域等特定皮层区域之间的皮层活动和相互作用会受到行为目标的调节。然而，这些结果很大程度上基于实验室环境中的简单刺激和任务规则。在此，我们研究了自上而下的目标在自然条件下如何调节全脑功能连接（FC）。我们对一个公开可用的功能磁共振成像（fMRI）数据集（开放fMRI数据库，登录号：ds000233）进行了分析，该数据集收集自12名参与者，他们对自然视频片段中出现的动物行为进行了行为或分类判断。与标准FC分析相比，使用一种新颖的受试者间功能相关性（ISFC）方法提取参与者的任务诱发FC模式，该方法提高了检测任务诱导的区域间相关性的信噪比。使用多变量模式分析（MVPA）方法，我们成功地用ISFC模式而非标准FC模式预测了参与者的任务目标，这表明ISFC方法可能是探索脑状态之间细微网络差异的有效工具。我们进一步检查了几个典型脑网络的预测能力，发现许多网络内和跨网络的ISFC测量支持任务目标分类。我们的研究结果表明，对自然刺激的目标导向处理会系统地调节大规模脑网络，但不仅限于特定区域的局部神经活动或连接。
