Datko Michael, Gougelet Robert, Huang Ming-Xiong, Pineda Jaime A
Cognitive Science, University of California San DiegoLa Jolla, CA, USA; Neurosciences, University of California San DiegoLa Jolla, CA, USA.
Cognitive Science, University of California San Diego La Jolla, CA, USA.
Front Neurosci. 2016 Jun 9;10:258. doi: 10.3389/fnins.2016.00258. eCollection 2016.
Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10-17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low gamma (30-60 Hz), and high gamma (60-120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity.
社交和沟通障碍是自闭症谱系障碍(ASD)的核心症状之一,大量证据支持这些障碍与各种皮质网络的异常功能和连接性有关这一观点。本研究探讨了静息状态下不同频段MEG振幅来源与功能连接MRI之间的联系。结合这些模态的目的是将通过MEG测量的神经振荡活动来源用作功能相关的种子区域,用于更传统的成对fMRI连接性分析。我们对静息状态fMRI数据进行了基于种子的连接性分析,使用来自相同9名ASD患者(10 - 17岁)静息状态MEG数据中特定频率振幅来源的种子区域。然后,我们比较了自闭症患者与年龄匹配的典型发育对照参与者之间这些MEG来源衍生区域的fMRI连接性。我们使用为MEG数据设计的源建模技术来检测六个频段中的显著振幅来源:δ(2 - 4 Hz)、θ(4 - 8 Hz)、α(8 - 12 Hz)、β(12 - 30 Hz)、低γ(30 - 60 Hz)和高γ(60 - 120 Hz)。为每个参与者生成的MEG衍生源图在标准MNI空间中进行配准,并为每个频率获得组水平的源图。对于每个频段,这些t检验产生的10个最大聚类用作fMRI功能连接性分析的感兴趣区域(ROI)。对于每个频段,在这些ROI的每对之间获得成对的BOLD信号相关性。使用t检验比较ASD组和TD组之间的每个成对相关性。我们还将这些成对相关性限制在已知的网络结构上,从而得到一组针对我们考虑的每个网络的后续相关矩阵。特定频率的MEG来源在ASD组中具有不同的fMRI静息状态功能连接模式,但也许最显著的是发现许多低γ和高γ活动来源之间存在连接不足。这些新发现表明,在ASD中,如先前fMRI研究所示,功能定义的网络存在差异,并且在由脑磁图神经振荡活动定义的区域组之间也存在差异。