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Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

机译:fMRI数据的模式识别分析揭示了心理旋转网络的动态变化

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We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0° vs. 20°, 0° vs. 60°, and 0° vs. 100°), and the discrimination accuracy is correlated with the difference in angular disparity between the con-rnditions. For the comparison with highest accuracy (0° vs. 100°), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100° condition in relation to the 0° condition (posterior cingulate, frontal, and superior temporal gyms). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100° condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100° condition.
机译:我们通过时空支持向量机(SVM)的应用调查了心理旋转网络中的时间动态和连通性的变化。时空支持向量机[Mourao-Miranda,J.,Friston,K. J.,et al。 (2007)。动态判别分析:时空支持向量机。 Neuroimage,36,88-99]是一种模式识别方法,适用于研究复杂的心理任务期间大脑网络的动态变化。它不需要模型来描述任务的每个组成部分以及BOLD脉冲响应的精确形状。通过定义一个包括认知事件的时间窗口,可以使用来自两个认知状态的时空fMRI观察值来训练SVM。在训练期间,SVM找到两个状态之间的区分模式,并产生一个包含体素和时间的区分权重向量(即时空图)。我们表明,通过将时空SVM应用于与事件相关的心理旋转实验,可以区分不同角度的差异(0°vs. 20°,0°vs. 60°和0°vs. 100 °),并且辨别精度与条件之间的角度视差之差相关。为了以最高准确度(0°与100°)进行比较,我们评估了最有区别的区域(视觉区域,顶叶区域,辅助区域和运动前区域)如何随时间改变其行为。额叶前运动区比上顶叶皮层更早地开始区分。顶壁区域似乎被消融,与上顶壁相比,在大脑旋转中较早地识别了顶壁下叶。 SVM还确定了一个区域网络,相对于0°条件(后扣带,额骨和颞上运动),在100°条件下BOLD响应降低。该网络还高度区分这两种情况。此外,我们调查了由时空SVM识别的最区分区域之间的功能连通性变化。我们观察到在100°条件下激活的几乎所有区域(双侧下,上顶叶,双侧运动前区和SMA)之间的功能连通性均增加,但在100°条件下显示BOLD响应降低的区域之间则没有。

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