Dansereau Christian L, Bellec Pierre, Lee Kangjoo, Pittau Francesca, Gotman Jean, Grova Christophe
Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University Montreal, QC, Canada ; Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University Montreal, QC, Canada ; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Functional Neuroimaging Unit, Université de Montréal Montreal, QC, Canada.
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Functional Neuroimaging Unit, Université de Montréal Montreal, QC, Canada ; Department of Computer Science and Operations Research, University of Montreal Montreal, Quebec, Canada.
Front Neurosci. 2014 Dec 23;8:419. doi: 10.3389/fnins.2014.00419. eCollection 2014.
The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting "abnormal" individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one "abnormal" lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.
静息状态下血氧水平依赖（BOLD）信号的自发缓慢波动的空间相干性通常用于表征潜在的静息态网络（RSN）。研究表明，这些模式在空间上是有组织的，并且在不同受试者之间具有高度可重复性。此外，有人提出在病理条件下RSN会发生重组。已经在受试者组之间进行了RSN组织的比较，但很少应用于个体水平，而这是临床应用所需的一个步骤。将模块化的概念定义为大脑活动在稳定网络中的组织方式，我们提出个体异常网络检测（DANI）方法来识别个体水平上的模块化变化。使用一种空间聚类方法：稳定聚类的自举分析（BASC）（Bellec等人，2010年）来估计每个RSN的稳定性。我们的贡献在于：（i）提供每个网络最稳定核心的功能图谱；（ii）与健康对照人群相比，检测网络组织中的“异常”个体变化。首先使用逼真的模拟数据对DANI进行评估，结果表明关注保守的核心大小（50%最稳定区域）可提高检测模块化变化的灵敏度。然后将DANI应用于六名局灶性癫痫患者的静息态功能磁共振成像（fMRI）数据，这些患者通过同步脑电图/fMRI采集随后进行手术接受了多模态评估。仅选择无癫痫发作结果的患者，并使用术后磁共振成像确定切除区域。DANI自动检测出6名患者中的5名存在异常变化，灵敏度极高，显示他们每个人至少有一个与癫痫病灶密切相关的“异常”侧化网络。对于每位患者，我们还检测到一些远处网络异常，这表明癫痫大脑存在一些远程重组。
