Resting state network estimation in individual subjects.
Resting state network estimation in individual subjects.
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DOI：
10.1016/j.neuroimage.2013.05.108
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发表时间：
2013-11-15
期刊：
影响因子：
5.7
通讯作者：
Corbetta M
中科院分区：
文献类型：
--
作者：
Hacker CD;Laumann TO;Szrama NP;Baldassarre A;Snyder AZ;Leuthardt EC;Corbetta M
静息态功能磁共振成像（fMRI）已被用于研究与正常和病理认知功能相关的脑网络。这项工作的目的是可靠地计算静息状态网络（RSN）的地形在单一的参与者。我们训练了一个监督分类器（多层感知器; MLP），将对应于预定义种子的血氧水平依赖（BOLD）相关图与特定的RSN身份相关联。从先验种子获得的地图的硬分类对于新参与者来说非常可靠。有趣的是，RSN成员的连续估计保留了大量的残差。这一结果与RSN是分层组织的观点是一致的，因此不能完全分离成空间独立的组件。在基于先验种子的映射上进行训练后，我们通过MLP传播体素相关映射，以产生整个大脑的RSN成员的估计。MLP生成的RSN地形估计与以前的研究一致，即使在训练数据中没有代表的大脑区域。这种方法可以在未来的研究中用于将RSN地形图与功能性脑组织的其他测量（例如，任务诱发的反应、刺激映射和与损伤相关的缺陷）。多层感知器直接比较两种替代体素分类程序，具体地说，二元回归和线性判别分析;感知器产生更多的空间特定的RSN地图比任何替代方案。
Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
5.7
作者：
Binder JR;Gross WL;Allendorfer JB;Bonilha L;Chapin J;Edwards JC;Grabowski TJ;Langfitt JT;Loring DW;Lowe MJ;Koenig K;Morgan PS;Ojemann JG;Rorden C;Szaflarski JP;Tivarus ME;Weaver KE
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Weaver KE
影响因子：
8.5
作者：
Erkmen, Burcu;Yildirim, Tuelay
通讯作者：
Yildirim, Tuelay
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.