Urbschat Annika, Uppenkamp Stefan, Anemüller Jörn
Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Front Neurosci. 2021 Feb 1;14:616906. doi: 10.3389/fnins.2020.616906. eCollection 2020.
The investigation of abstract cognitive tasks, e.g., semantic processing of speech, requires the simultaneous use of a carefully selected stimulus design and sensitive tools for the analysis of corresponding neural activity that are comparable across different studies investigating similar research questions. Multi-voxel pattern analysis (MVPA) methods are commonly used in neuroimaging to investigate BOLD responses corresponding to neural activation associated with specific cognitive tasks. Regions of significant activation are identified by a thresholding operation during multivariate pattern analysis, the results of which are susceptible to the applied threshold value. Investigation of analysis approaches that are robust to a large extent with respect to thresholding, is thus an important goal pursued here. The present paper contributes a novel statistical analysis method for fMRI experiments, searchlight classification informative region mixture model (SCIM), that is based on the assumption that the whole brain volume can be subdivided into two groups of voxels: spatial voxel positions around which recorded BOLD activity does convey information about the present stimulus condition and those that do not. A generative statistical model is proposed that assigns a probability of being informative to each position in the brain, based on a combination of a support vector machine searchlight analysis and Gaussian mixture models. Results from an auditory fMRI study investigating cortical regions that are engaged in the semantic processing of speech indicate that the SCIM method identifies physiologically plausible brain regions as informative, similar to those from two standard methods as reference that we compare to, with two important differences. SCIM-identified regions are very robust to the choice of the threshold for significance, i.e., less "noisy," in contrast to, e.g., the binomial test whose results in the present experiment are highly dependent on the chosen significance threshold or random permutation tests that are additionally bound to very high computational costs. In group analyses, the SCIM method identifies a physiologically plausible pre-frontal region, anterior cingulate sulcus, to be involved in semantic processing that other methods succeed to identify only in single subject analyses.
对抽象认知任务（例如语音的语义处理）的研究需要同时使用精心挑选的刺激设计和用于分析相应神经活动的灵敏工具，这些工具在调查相似研究问题的不同研究中具有可比性。多体素模式分析（MVPA）方法常用于神经成像，以研究与特定认知任务相关的神经激活对应的血氧水平依赖（BOLD）反应。在多变量模式分析期间，通过阈值操作识别显著激活区域，其结果易受应用的阈值影响。因此，研究在很大程度上对阈值具有鲁棒性的分析方法是本文追求的一个重要目标。本文提出了一种用于功能磁共振成像（fMRI）实验的新型统计分析方法——搜索光分类信息区域混合模型（SCIM），该方法基于这样的假设：整个脑体积可细分为两组体素，一组是其周围记录的BOLD活动确实传达了当前刺激条件信息的空间体素位置，另一组则不传达。基于支持向量机搜索光分析和高斯混合模型的组合，提出了一种生成统计模型，该模型为大脑中的每个位置分配一个信息性概率。一项研究参与语音语义处理的皮质区域的听觉fMRI研究结果表明，SCIM方法将生理上合理的脑区域识别为信息性区域，类似于我们与之比较的作为参考的两种标准方法所识别的区域，但有两个重要区别。与例如二项式检验相比，SCIM识别的区域对显著性阈值的选择非常鲁棒，即“噪声”较小，在本实验中，二项式检验的结果高度依赖于所选的显著性阈值，或者随机排列检验还面临非常高的计算成本。在组分析中，SCIM方法识别出一个生理上合理的前额叶区域——前扣带回沟参与语义处理，而其他方法仅在单受试者分析中才能识别该区域。
