Soch Joram
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin Center for Advanced Neuroimaging, Berlin, Germany.
Berlin Center for Computational Neuroscience, Berlin, Germany.
Imaging Neurosci (Camb). 2025 Sep 2;3. doi: 10.1162/IMAG.a.131. eCollection 2025.
In multivariate pattern analysis (MVPA) for functional magnetic resonance imaging (fMRI) signals, trial-wise response amplitudes are sometimes estimated using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates can be highly correlated due to the temporally smoothed shape of the hemodynamic response function. In previous work (Soch et al., 2020), we have proposed inverse transformed encoding modeling (ITEM), a principled approach for trial-wise decoding from fMRI signals in the presence of trial-by-trial correlations. Here, we (i) perform simulation studies addressing its performance for multivariate signals and (ii) present searchlight-based ITEM analysis-which allows to predict a variable of interest from the vicinity of each voxel in the brain. We empirically validate the approach by confirming plausible hypotheses about the well-understood visual system.
在针对功能磁共振成像（fMRI）信号的多变量模式分析（MVPA）中，有时会使用一般线性模型（GLM）来估计每个试次的响应幅度，该模型为每个试次设置一个起始回归变量。当使用快速事件相关设计且试次在时间上紧密间隔时，由于血液动力学响应函数的时间平滑形状，这些估计值可能会高度相关。在之前的工作中（Soch等人，2020年），我们提出了逆变换编码建模（ITEM），这是一种在存在逐试次相关性的情况下从fMRI信号中进行逐试次解码的原则性方法。在这里，我们（i）进行模拟研究以探讨其对多变量信号的性能，（ii）展示基于搜索light的ITEM分析——它允许从大脑中每个体素的附近预测感兴趣的变量。我们通过证实关于充分理解的视觉系统的合理假设来实证验证该方法。
