论文标题
通过两阶段的广义规范相关分析，与任务相关的多个任务相关的fMRI数据处理
Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis
论文作者
论文摘要
功能磁共振成像（fMRI）是研究人脑的最流行方法之一。与任务相关的FMRI数据处理旨在确定执行特定任务时激活哪些大脑区域，并且通常基于血氧水平依赖性（BOLD）信号。背景大胆信号还反映了区域大脑活动的系统波动，这归因于静止状态脑网络的存在。我们提出了一个新的fMRI数据生成模型，该模型考虑了常见的任务相关和静止状态组件的存在。我们首先通过广义规范相关分析的两个连续阶段估算了与任务相关的常见时间组件，然后我们估算了与任务相关的常见空间组件，从而导致与任务相关的激活图。使用合成数据的方法的实验测试表明，即使在非常低的信号与噪声比（SNR）下，我们也能够获得非常准确的时间和空间估计，这在fMRI数据处理中通常就是这种情况。现实世界中FMRI数据的测试比基于一般线性模型（GLM）的标准程序显示出显着的优势。
Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).
