Higher School of Economics, Moscow, Russia; LIFT, Life Improvement by Future Technologies Institute, Moscow, Russia; Artificial Intelligence Research Institute, Moscow, Russia.
Higher School of Economics, Moscow, Russia; Artificial Intelligence Research Institute, Moscow, Russia.
Neuroimage. 2024 Nov 1;301:120868. doi: 10.1016/j.neuroimage.2024.120868. Epub 2024 Sep 27.
The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization of brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation time series at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To overcome this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial-temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of "representationally relevant" sources. Applied to evoked response time series, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCA's efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to real EEG and MEG datasets, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig, 1995), Spatial spectral decomposition (Nikulin, 2011), and Source power comodulation (Dähne, 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.
代表性相似性分析（RSA）的原理假定，神经表示反映了编码信息的结构，从而可以探索大脑信息处理的空间和时间组织。传统的 RSA 在应用于 EEG 或 MEG 数据时，由于建模复杂性和几何/解剖学数据不足，在获取大脑源水平的激活时间序列方面面临挑战。为了克服这一挑战，我们引入了代表性不相似性成分分析（ReDisCA），这是一种用于估计与目标代表性不相似性矩阵（RDM）对齐的 EEG 或 MEG 响应中的空间-时间成分的方法。ReDisCA 产生信息丰富的空间滤波器和相关的地形图，提供了有关“代表性相关”源位置的见解。将 ReDisCA 应用于诱发反应时间序列，可产生具有所需 RDM 的时间源激活谱。重要的是，虽然 ReDisCA 不需要反演建模，但它的输出与 EEG 和 MEG 观测方程一致，可以用作严格的源定位程序的输入。通过模拟和与传统方法的比较，证明了 ReDisCA 的有效性，我们展示了更高的源定位准确性，并将该方法应用于真实的 EEG 和 MEG 数据集，揭示了无需反演建模的生理上合理的代表性结构。ReDisCA 增加了一系列无需反演建模的方法，例如独立成分分析（Makeig，1995）、空间谱分解（Nikulin，2011）和源功率共调制（Dähne，2014），这些方法旨在从 EEG 或 MEG 数据中提取具有所需特性的源。除了 EEG 和 MEG 分析之外，ReDisCA 还有望在 fMRI 数据分析和探索多层人工神经网络中出现的代表性结构方面得到应用。
