用独立成分分析法提取可重现的特定受试者脑电诱发反应

Silvia Federica Cotroneo, Heidi Ala-Salomäki, L. Parkkonen, Mia Liljeström, R. Salmelin
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摘要

摘要 可靠的神经活动个体水平测量对于捕捉脑磁图(MEG)记录的大脑活动的个体间变异性至关重要。虽然传统的群体水平分析突出了数据中的共同特征,但个体水平的特异性往往会丢失。目前评估大脑反应可重复性的方法侧重于群体层面的统计,而忽略了受试者特定的时间和空间特征。本研究提出了一种组合 ICA 算法(comICA),旨在提取个体内部一致的 MEG 诱发反应。comICA 背后的假设基于诱发反应的时间轮廓、相应的空间信息以及独立性和线性。ComICA 是通过模拟数据和高级认知任务(图片命名)的重复测试记录来呈现和测试的。结果表明,在模拟数据中提取共享激活的可靠性很高(成功率大于 93%),并能成功再现测试-重复测试 MEG 记录的组级再现结果。我们的模型为降低噪音、有针对性地提取实验设计中的特定激活成分以及跨不同记录的潜在整合提供了方法。
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Extracting reproducible subject-specific MEG evoked responses with independent component analysis
Abstract Reliable individual-level measures of neural activity are essential for capturing interindividual variability in brain activity recorded by magnetoencephalography (MEG). While conventional group-level analyses highlight shared features in the data, individual-level specificity is often lost. Current methods for assessing reproducibility of brain responses focus on group-level statistics and neglect subject-specific temporal and spatial characteristics. This study proposes a combined ICA algorithm (comICA), aimed at extracting within-individual consistent MEG evoked responses. The proposed hypotheses behind comICA are based on the temporal profiles of the evoked responses, the corresponding spatial information, as well as independence and linearity. ComICA is presented and tested against simulated data and test–retest recordings of a high-level cognitive task (picture naming). The results show high reliability in extracting the shared activations in the simulations (success rate >93%) and the ability to successfully reproduce group-level results on reproducibility for the test–retest MEG recordings. Our model offers means for noise reduction, targeted extraction of specific activation components in experimental designs, and potential integration across different recordings.
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