The Florey Institute of Neuroscience and Mental Health, Austin Hospital, The University of Melbourne Melbourne, VIC, Australia ; Department of Medicine, The University of Melbourne Melbourne, VIC, Australia.
The Florey Institute of Neuroscience and Mental Health, Austin Hospital, The University of Melbourne Melbourne, VIC, Australia ; Department of Medicine, The University of Melbourne Melbourne, VIC, Australia ; Department of Radiology, The University of Melbourne Melbourne, VIC, Australia.
Front Neurosci. 2014 Sep 19;8:285. doi: 10.3389/fnins.2014.00285. eCollection 2014.
Event-related ICA (eICA) is a partially data-driven analysis method for event-related fMRI that is particularly suited to analysis of simultaneous EEG-fMRI of patients with epilepsy. EEG-fMRI studies in epileptic patients are typically analyzed using the general linear model (GLM), often with assumption that the onset and offset of neuronal activity match EEG event onset and offset, the neuronal activation is sustained at a constant level throughout the epileptiform event and that associated fMRI signal changes follow the canonical HRF. The eICA method allows for less constrained analyses capable of detecting early, non-canonical responses. A key step of eICA is the initial deconvolution which can be confounded by various sources of structured noise present in the fMRI signal. To help overcome this, we have extend the eICA procedure by utilizing a fully standalone and automated fMRI de-noising procedure to process the fMRI data from an EEG-fMRI acquisition prior to running eICA. Specifically we first apply ICA to the entire fMRI time-series and use a classifier to remove noise-related components. The automated objective de-noiser, "Spatially Organized Component Klassificator" (SOCK) is used; it has previously been shown to distinguish a substantial fraction of noise from true activation, without rejecting the latter, in resting-state fMRI. A second ICA is then performed, this time on the event-related response estimates derived from the denoised data (according to the usual eICA procedure). We hypothesize that SOCK + eICA has the potential to be more sensitive than eICA alone. We test the effectiveness of SOCK by comparing activation obtained in an eICA analysis of EEG-fMRI data with and without the use of SOCK for 14 patients with rolandic epilepsy who exhibited stereotypical IEDs arising from a focus in the rolandic fissure.
事件相关独立成分分析（eICA）是一种部分基于数据的事件相关 fMRI 分析方法，特别适合分析癫痫患者的同时 EEG-fMRI。癫痫患者的 EEG-fMRI 研究通常使用广义线性模型（GLM）进行分析，通常假设神经元活动的起始和结束与 EEG 事件的起始和结束相匹配，神经元激活在整个癫痫事件中保持在恒定水平，并且相关的 fMRI 信号变化遵循典型的 HRF。eICA 方法允许进行更具约束性的分析，能够检测早期的非典型反应。eICA 的一个关键步骤是初始解卷积，这可能会受到 fMRI 信号中存在的各种结构噪声源的干扰。为了克服这个问题，我们通过利用完全独立和自动化的 fMRI 去噪程序来处理 EEG-fMRI 采集的 fMRI 数据，从而扩展了 eICA 程序。具体来说，我们首先将 ICA 应用于整个 fMRI 时间序列，并使用分类器去除与噪声相关的成分。使用自动客观去噪器“空间组织成分分类器”（SOCK）；它以前已经被证明可以在静息状态 fMRI 中区分大量的噪声和真实激活，而不会拒绝后者。然后进行第二次 ICA，这次是对去噪后的数据进行的（根据通常的 eICA 程序）。我们假设 SOCK+eICA 比单独使用 eICA 更敏感。我们通过比较 14 例 Rolandic 癫痫患者的 EEG-fMRI 数据的 eICA 分析中使用和不使用 SOCK 获得的激活来测试 SOCK 的有效性，这些患者表现出源自 Rolandic 裂的典型 IED。
