Centre for Neuroscience, Indian Institute of Science, Bangalore 560012, India.
Centre for Neuroscience, Indian Institute of Science, Bangalore 560012, India
eNeuro. 2020 Jul 13;7(4). doi: 10.1523/ENEURO.0512-19.2019. Print 2020 Jul/Aug.
Flexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured using functional magnetic resonance imaging (fMRI) data either with instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity. Because the fMRI hemodynamic response is slow, and is sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds), simulation studies have shown that lag-based fMRI functional connectivity, measured with approaches like Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim is challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. Here we demonstrate that, despite these widely held caveats, GC networks estimated from fMRI recordings contain useful information for classifying task-specific cognitive states. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants (Human Connectome Project database). A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with >80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified complementary, task-core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, may provide useful markers of behaviorally relevant cognitive states.
大脑区域之间的灵活功能相互作用介导着关键的认知功能。这种相互作用可以使用功能磁共振成像 (fMRI) 数据来测量，无论是使用即时（零滞后）还是基于滞后（时滞）的功能连接。由于 fMRI 血流动力学响应较慢，并且以比潜在神经动力学慢几个数量级的时间尺度（秒）进行采样（毫秒），模拟研究表明，基于滞后的 fMRI 功能连接，例如 Granger-Geweke 因果关系 (GC) 测量方法，提供了潜在神经相互作用的虚假和不可靠估计。由于神经真实连接通常与 fMRI 记录同时不可用，因此对这一说法进行实验验证具有挑战性。在这里，我们证明，尽管存在这些广泛的警告，但从 fMRI 记录中估计的 GC 网络包含有用的信息，可用于对特定任务的认知状态进行分类。我们使用来自 1000 名参与者的 fMRI 数据（人类连接组计划数据库）估计了即时和基于滞后的 GC 功能连接网络。基于即时或基于滞后的 GC 的线性分类器可靠地区分了七种不同的任务和静息脑状态，交叉验证准确率>80%。通过网络模拟，我们证明即时和基于滞后的 GC 分别利用快速和慢速时间尺度上的相互作用来实现稳健的分类。通过人类 fMRI 数据，即时和基于滞后的 GC 确定了互补的、任务核心网络。最后，GC 连接的变化解释了各种认知评分的个体间差异。我们的发现表明，即时和基于滞后的方法揭示了大脑功能连接的互补方面，并表明使用 fMRI 估计的缓慢、有方向的功能相互作用可能为与行为相关的认知状态提供有用的标志物。
