论文标题
识别刺激驱动的神经活动模式在多人颅内记录中
Identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings
论文作者
论文摘要
识别刺激驱动的神经活动模式对于研究认知的神经基础至关重要。这在颅内数据集中尤其具有挑战性，那里的电极位置在患者的情况下通常会有所不同。本章首先概述了在一般情况下确定刺激驱动的神经活动模式的主要挑战。接下来，我们将回顾几种特定方式的考虑因素和方法，并讨论有关颅内记录的几个问题。在此背景下，我们将考虑多种受试者和跨主体的方法来识别和建模刺激驱动的神经活动模式。这些方法包括广义线性模型，多变量模式分析，表示性相似性分析，关节刺激 - 活性模型，分层矩阵分解模型，高斯过程模型，几何比喻模型，对象间相关性和对象间功能相关性。最近的文献中的例子说明了主要概念并为每种方法提供概念性直觉。
Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition. This can be particularly challenging in intracranial datasets, where electrode locations typically vary across patients. This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case. Next, we will review several modality-specific considerations and approaches, along with a discussion of several issues that are particular to intracranial recordings. Against this backdrop, we will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings. These approaches include generalized linear models, multivariate pattern analysis, representational similarity analysis, joint stimulus-activity models, hierarchical matrix factorization models, Gaussian process models, geometric alignment models, inter-subject correlations, and inter-subject functional correlations. Examples from the recent literature serve to illustrate the major concepts and provide the conceptual intuitions for each approach.
