The Mind Research Network, 1101 Yale Boulevard, Albuquerque, NM 87106, USA.
MAGMA. 2010 Dec;23(5-6):351-66. doi: 10.1007/s10334-010-0197-8. Epub 2010 Feb 17.
In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.
We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections.
This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05).
The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.
本文提出了一种基于空间独立成分分析（sICA）估计的不同脑网络（成分）窗时间序列间相关性的动态功能网络连接（FNC）分析方法。我们将该方法应用于 fMRI 数据，以评估其性能并研究功能连接的任务调制。
我们研究了该方法的理论基础，进行了模拟分析，并将其应用于精神分裂症患者（SP）和健康对照（HC）的 fMRI 数据。对 fMRI 数据的分析包括：（a）组 sICA 以确定与任务相关的显著活动区域，（b）使用最大滞后相关和时频分析对这些网络进行静态和动态 FNC 分析，（c）HC-SP 组间功能网络连接和这些连接的任务调制差异。
该新方法能够评估连接的任务调制，并在执行听觉Oddball 任务（AOT）时识别出两个研究组之间有意义的组件间连接和差异。静态 FNC 结果表明，在 HC 中，涉及内侧视觉-额叶、内侧颞叶-内侧视觉、顶叶-内侧颞叶、顶叶-内侧视觉和内侧颞叶-前颞叶的连接明显更强，而仅在 SP 中右侧额顶-眶额（RLFP）-眶额连接明显更强。动态 FNC 显示，SP 中运动-额叶、RLFP-内侧颞叶和后默认模式（pDM）-顶叶连接的任务调制明显更强，而 HC 中眶额-pDM 和内侧颞叶-额叶连接的任务调制明显更强（均 P<0.05）。
动态 FNC 的任务调制提供了两组之间的发现和差异，这与现有的假设一致，即精神分裂症患者在执行任务时表现出较少的运动、感觉、认知功能分离和默认模式网络活动的分离。基于 sICA 的动态 FNC 提供了与静态 FNC 不同但互补的结果。例如，它揭示了默认模式网络与其他区域的动态连接变化，这些变化在任务调制方面在精神分裂症中存在显著差异，这是静态 FNC 无法发现的发现。
