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Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity

机译:通过内部和网络间连接的精神分裂症中磁性肺和功能性MRI Connectomics

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摘要

Examination of intrinsic functional connectivity using functional MRI (fMRI) has provided important findings regarding dysconnectivity in schizophrenia. Extending these results using a complementary neuroimaging modality, magnetoencephalography (MEG), we present the first direct comparison of functional connectivity between schizophrenia patients and controls, using these two modalities combined. We developed a novel MEG approach for estimation of networks using MEG that incorporates spatial independent component analysis (ICA) and pairwise correlations between independent component timecourses, to estimate intra- and intern network connectivity. This analysis enables group-level inference and testing of between-group differences. Resting state MEG and fMRI data were acquired from a large sample of healthy controls (n=45) and schizophrenia patients (n=46). Group spatial ICA was performed on fMRI and MEG data to extract intrinsic fMRI and MEG networks and to compensate for signal leakage in MEG. Similar, but not identical spatial independent components were detected for MEG and fMRI. Analysis of functional network connectivity (FNC; i.e., pairwise correlations in network (ICA component) timecourses) revealed a differential between-modalities pattern, with greater connectivity among occipital networks in fMRI and among frontal networks in MEG. Most importantly, significant differences between controls and patients were observed in both modalities. MEG FNC results in particular indicated dysfunctional hyperconnectivity within frontal and temporal networks in patients, while in fMRI FNC was always greater for controls than for patients. This is the first study to apply group spatial ICA as an approach to leakage correction, and as such our results may be biased by spatial leakage effects. Results suggest that combining these two neuroimaging modalities reveals additional disease-relevant patterns of connectivity that were not detectable with fMRI or MEG alone.
机译:使用功能性MRI(FMRI)检查内在功能连通性,提供了有关精神分裂症中脱蛋白的重要研究结果。使用互补的神经影像模态,磁性脑图(MEG)扩展这些结果,我们介绍了精神分裂症患者和对照之间的功能性连通性的第一次直接比较,使用这两种方式组合。我们开发了一种新颖的MEG方法,用于使用MEG估算网络,该网络包含空间独立分量分析(ICA)和独立组件时间案之间的成对相关性,以估计内部网络连接和实习网络连接。此分析使组级推断和对组之间的差异进行测试。从大型健康对照样品(n = 45)和精神分裂症患者(n = 46)中获得休息状态MEG和FMRI数据。小组空间ICA在FMRI和MEG数据上进行,以提取内在的FMRI和MEG网络,并补偿MEG中的信号泄漏。为MEG和FMRI检测到类似,但不相同的空间独立组分。功能网络连接分析(FNC;即网络(ICA组件)时刻的成对相关性地显示了模差模式模式,在FMRI和MEG中的正面网络中具有更大的枕部网络连接。最重要的是,在两种方式中观察到对照和患者之间的显着差异。 MEG FNC结果特别指出患者前部和时间网络内的功能障碍高速连接,而在FMRI FNC中的控制始终比对患者更大。这是第一项申请组空间ICA作为泄漏校正的方法的研究,因此我们的结果可能被空间泄漏效果偏置。结果表明,组合这两个神经影像衰减方式揭示了额外的疾病相关的连通模式,这些连通性不可检测到FMRI或MEG。

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