Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.
Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.
复制标题
DOI：
10.1016/j.neuroimage.2017.09.035
复制
发表时间：
2018-10-15
期刊：
影响因子：
5.7
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Fu Z;Tu Y;Di X;Du Y;Pearlson GD;Turner JA;Biswal BB;Zhang Z;Calhoun VD
人脑是一个高度动态的系统，具有非平稳的神经活动和快速变化的神经相互作用。静息态动态功能连接（dFC）近年来得到了广泛的研究，出现的异常dFC模式被认为是精神分裂症（SZ）等精神疾病的重要特征。然而，仅仅关注FC中的时变模式是不够的，因为使用高时间分辨率成像技术的研究也发现局部神经活动本身（与互连性相反）具有高度波动性。探索脑活动的时变模式及其与时变脑连接的关系，对于深入理解脑网络的协同进化特性和脑动力学的内在机制具有重要意义。在这项研究中，我们引入了一个框架，用于表征随时间变化的大脑活动，并探索其与随时间变化的大脑连接的关联，并将此框架应用于静息状态fMRI数据集，包括151名SZ患者和163名年龄和性别匹配的健康对照（HC）。在这个框架中，48个大脑区域首先被确定为内在连接网络（ICN）使用组独立成分分析（GICA）。然后采用滑动窗口方法估计低频波动的动态幅度（dALFF）和dFC，分别用于测量时变脑活动和时变脑连接。通过k-均值聚类方法将dALFF进一步聚类为6个重复发生的状态，并探讨了dALFF状态发生的组差异。最后，计算dALFF和dFC之间的相关系数，并探讨这些dALFF-dFC相关性的组间差异。结果表明：（1）静息状态下，脑区ALFF呈高度波动性，SZ改变了这种动态模式;（2）dALFF和dFC在时间上相关，SZ改变了它们的相关性。总体结果支持和扩展了以前的工作异常的大脑活动，静态FC（sFC）和dFC在SZ，并提供了新的证据异常的随时间变化的大脑活动及其与脑连接在SZ，这可能会强调中断的大脑认知功能在这种精神疾病。
The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.
登录
查看更多内容
影响因子：
5.7
作者：
Bassett, Danielle S.;Nelson, Brent G.;Mueller, Bryon A.;Camchong, Jazmin;Lim, Kelvin O.
通讯作者：
Lim, Kelvin O.
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
动态功能网络连接状态的EEG签名。
影响因子：
2.7
作者：
Allen EA;Damaraju E;Eichele T;Wu L;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
2.9
作者：
Di X;Fu Z;Chan SC;Hung YS;Biswal BB;Zhang Z
通讯作者：
Zhang Z
影响因子：
17.6
作者：
Calhoun VD;Adalı T
通讯作者：
Adalı T