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Validation of periodic fMRI signals in response to wearable tactile stimulation

机译:验证周期性fMRI信号响应可穿戴式触觉刺激

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摘要

To map cortical representations of the body, we recently developed a wearable technology for automatic tactile stimulation in human functional magnetic resonance imaging (fMRI) experiments. In a two-condition block design experiment, air puffs were delivered to the face and hands periodically. Surface-based regions of interest (S-ROIs) were initially identified by thresholding a linear statistical measure of signal-to-noise ratio of periodic response. Across subjects, S-ROIs were found in the frontal, primary sensorimotor, posterior parietal, insular, temporal, cingulate, and occipital cortices. To validate and differentiate these S-ROIs, we develop a measure of temporal stability of response based on the assumption that a periodic stimulation evokes stable (low-variance) periodic fMRI signals throughout the entire scan. Toward this end, we apply time-frequency analysis to fMRI time series and use circular statistics to characterize the distribution of phase angles for data selection. We then assess the temporal variability of a periodic signal by measuring the path length of its trajectory in the complex plane. Both within and outside the primary sensorimotor cortex, S-ROIs with high temporal variability and deviant phase angles are rejected. A surface-based probabilistic group-average map is constructed for spatial screening of S-ROIs with low to moderate temporal variability in non-sensorimotor regions. Areas commonly activated across subjects are also summarized in the group-average map. In summary, this study demonstrates that analyzing temporal characteristics of the entire fMRI time series is essential for second-level selection and interpretation of S-ROIs initially defined by an overall linear statistical measure.
机译:为了绘制人体的皮层图示,我们最近开发了一种可穿戴技术,用于人体功能磁共振成像(fMRI)实验中的自动触觉刺激。在两个条件的块体设计实验中,定期向脸和手吹气。最初通过对周期性响应的信噪比进行线性统计测量来确定阈值,从而确定基于表面的关注区域(S-ROI)。在整个受试者中,在额叶,主要感觉运动,顶叶后,岛状,颞叶,扣带状和枕叶皮层中发现了S-ROI。为了验证和区分这些S-ROI,我们在整个扫描过程中周期性刺激引起稳定(低方差)周期性fMRI信号的假设下,开发了一种响应时间稳定性的度量。为此,我们将时频分析应用于fMRI时间序列,并使用循环统计来表征相角分布以进行数据选择。然后,我们通过测量复杂平面中其轨迹的路径长度来评估周期信号的时间变异性。在初级感觉运动皮层之内和之外,具有高时间变异性和相角偏差的S-ROIs均被拒绝。构建了基于表面的概率群平均图,用于空间筛选S-ROI的非感觉运动区域中低至中等的时间变化。小组平均分布图还总结了跨学科普遍激活的区域。总而言之,这项研究表明分析整个fMRI时间序列的时间特征对于二级选择和解释最初由总体线性统计量度定义的S-ROI至关重要。

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