Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain.
Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain.
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DOI：
10.3389/fnhum.2013.00015
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发表时间：
2013
影响因子：
2.9
通讯作者：
Thompson-Schill SL
中科院分区：
文献类型：
--
作者：
Coutanche MN;Thompson-Schill SL
在功能磁共振成像（fMRI）时间过程中，大脑区域激活水平的波动被用于功能连接（FC），以识别具有同步反应的网络。人们越来越认识到，多体素活动模式包含不能从单变量激活水平提取的信息。在这里，我们提出了一种新的分析方法，量化区域在多体素活动模式可辨别性中的同步性，而不是跨时间序列的单变量激活。我们在每个时间点引入了多体素模式可判别性的度量，然后使用它来识别共享特定条件的多体素信息的同步时间过程的区域。该方法具有多体素模式分析所具有的对分布式信息的敏感性和可访问性，使其能够应用于单变量响应不可分离的条件下的数据。我们通过分析人们使用FC和信息连接（IC）方法观看四种不同类型的人造物体（通常不能通过单变量分析分离）时收集的数据来证明这一点。IC揭示了使用FC无法检测到的对象处理区域网络。IC的结果支持先前的发现和假设的对象处理。这种新方法允许研究人员提出无法通过典型FC寻址的问题，正如多体素模式分析（MVPA）为可通过一般线性模型（GLM）寻址的问题增加了新的研究途径一样。
The fluctuations in a brain region's activation levels over a functional magnetic resonance imaging (fMRI) time-course are used in functional connectivity (FC) to identify networks with synchronous responses. It is increasingly recognized that multi-voxel activity patterns contain information that cannot be extracted from univariate activation levels. Here we present a novel analysis method that quantifies regions' synchrony in multi-voxel activity pattern discriminability, rather than univariate activation, across a timeseries. We introduce a measure of multi-voxel pattern discriminability at each time-point, which is then used to identify regions that share synchronous time-courses of condition-specific multi-voxel information. This method has the sensitivity and access to distributed information that multi-voxel pattern analysis enjoys, allowing it to be applied to data from conditions not separable by univariate responses. We demonstrate this by analyzing data collected while people viewed four different types of man-made objects (typically not separable by univariate analyses) using both FC and informational connectivity (IC) methods. IC reveals networks of object-processing regions that are not detectable using FC. The IC results support prior findings and hypotheses about object processing. This new method allows investigators to ask questions that are not addressable through typical FC, just as multi-voxel pattern analysis (MVPA) has added new research avenues to those addressable with the general linear model (GLM).
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影响因子：
5.7
作者：
Coutanche, Marc N.;Thompson-Schill, Sharon L.
通讯作者：
Thompson-Schill, Sharon L.
影响因子：
5.7
作者：
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通讯作者：
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通讯作者：
Prokopenko, Mikhail
影响因子：
3.2
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通讯作者：
Connolly, Andrew C.