Regression dynamic causal modeling for resting-state fMRI.
Regression dynamic causal modeling for resting-state fMRI.
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DOI：
10.1002/hbm.25357
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发表时间：
2021-05
影响因子：
4.8
通讯作者：
Stephan KE
中科院分区：
文献类型：
--
作者：
Frässle S;Harrison SJ;Heinzle J;Clementz BA;Tamminga CA;Sweeney JA;Gershon ES;Keshavan MS;Pearlson GD;Powers A;Stephan KE
“静息态”功能磁共振成像（rs-fMRI）广泛用于研究大脑连接性。到目前为止，研究人员仅限于计算高效但无向的功能连接性测量，或有针对性但仅限于小型网络的有效连接性估计。在这里，我们展示了最近为任务 fMRI 开发的一种方法——回归动态因果模型（rDCM）——扩展到 rs-fMRI，并为全脑网络提供方向估计和可扩展性。首先，仿真表明 rDCM 在较宽的信噪比和重复次数范围内忠实地恢复参数值。其次，我们测试了 rDCM 相对于已建立的有效连接模型（频谱 DCM）的构造有效性。使用来自近 200 名健康参与者的 rs-fMRI 数据，rDCM 产生了与光谱 DCM 估计一致的生物学合理结果。重要的是，rDCM 计算效率很高，可以在标准硬件上在几分钟内重建全脑网络（> 200 个区域）。这为连接组学开辟了充满希望的新途径。 “静息态”功能磁共振成像（rs-fMRI）广泛用于研究大脑连接性。到目前为止，研究人员仅限于计算高效但无向的功能连接性测量，或有针对性但仅限于小型网络的有效连接性估计。在这里，我们展示了最近为任务 fMRI 开发的一种方法——回归动态因果模型（rDCM）——扩展到 rs-fMRI，并为全脑网络提供方向估计和可扩展性。
“Resting‐state” functional magnetic resonance imaging (rs‐fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task‐fMRI—regression dynamic causal modeling (rDCM)—extends to rs‐fMRI and offers both directional estimates and scalability to whole‐brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal‐to‐noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs‐fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole‐brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics. “Resting‐state” functional magnetic resonance imaging (rs‐fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task‐fMRI—regression dynamic causal modeling (rDCM)—extends to rs‐fMRI and offers both directional estimates and scalability to whole‐brain networks.
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DOI：
10.1016/j.physd.2009.08.002
发表时间：
2009-11-01
期刊：
Physica D. Nonlinear phenomena
影响因子：
--
作者：
Daunizeau J;Friston KJ;Kiebel SJ
通讯作者：
Kiebel SJ
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DOI：
10.1093/cercor/bhw157
发表时间：
2016-08
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Fan L;Li H;Zhuo J;Zhang Y;Wang J;Chen L;Yang Z;Chu C;Xie S;Laird AR;Fox PT;Eickhoff SB;Yu C;Jiang T
通讯作者：
Jiang T
影响因子：
48
作者：
Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
通讯作者：
Milham, Michael P.
影响因子：
2.7
作者：
Di X;Biswal BB
通讯作者：
Biswal BB
DOI：
10.1073/pnas.1820780116
发表时间：
2019-04-30
影响因子：
11.1
作者：
Baker, Justin T.;Dillon, Daniel G.;Holmes, Avram J.
通讯作者：
Holmes, Avram J.