Regression DCM for fMRI
Regression DCM for fMRI
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DOI：
10.1016/j.neuroimage.2017.02.090
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发表时间：
2017-07-15
期刊：
影响因子：
5.7
通讯作者：
Stephan, Klaas E.
中科院分区：
文献类型：
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作者：
Frassle, Stefan;Lomakina, Ekaterina I.;Stephan, Klaas E.
从神经影像数据推断神经元群体之间有效（定向）连接的大规模网络模型的开发是计算神经科学的一个关键挑战。神经影像和电生理学数据的动态因果模型 (DCM) 经常用于推断有效连接，但目前仅限于小图（通常最多 10 个区域），以便保持模型反演计算上的可行性。在这里，我们提出了一种用于功能磁共振成像 (fMRI) 数据的 DCM 的新变体，适合评估大型（全脑）网络中的有效连接。该方法依赖于将线性 DCM 转换到频域并将其重新表述为贝叶斯线性回归的特例。本文详细推导了回归 DCM (rDCM)，并提出了一种变分贝叶斯反演方法，与经典 DCM 相比，该方法能够实现极快的推理并将模型反演加速几个数量级。使用模拟和经验数据，我们证明了 rDCM 在不同的 fMRI 数据信噪比 (SNR) 和重复时间 (TR) 设置下的表面有效性。特别是，我们通过挑战 rDCM 在包含 66 个区域和 300 个自由参数的模拟全脑网络中推断有效连接强度，评估了 rDCM 作为全脑连接组学工具的潜在效用。我们的结果表明，rDCM 代表了一种计算高效的方法，具有从单个 fMRI 数据推断全脑连接的巨大潜力。
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.