Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.
Brain Connect. 2022 Feb;12(1):61-73. doi: 10.1089/brain.2020.0896. Epub 2021 Jul 30.
Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run -means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the -means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.
从个体中收集的脑成像数据具有高度复杂性和独特的变异性；然而，在关注组平均值甚至监督预测的方法中，通常会忽略这种变异性。用于分析动态功能网络连接（dFNC）的最先进方法将整个时程分解为几个（可能重叠的）连接状态（即滑动窗口聚类）。然而，这种方法没有考虑到基础数据的同质性，并且可能导致数据集的分组意义不大。动态 N 路三聚类（dNTiC）包含一个同质性基准，以近似聚类，从而在类似的时间 - 空间和主体子集中的组之间提供更“苹果对苹果”的比较。dNTiC 通过最大化个体之间的相似性和最小化状态内组件对之间的方差来对 dFNC 状态进行排序。结果三聚类显示精神分裂症（SZ）和健康对照组（HC）在不同脑区之间存在显著差异。与 HC 受试者相比，SZ 在皮质下、默认模式、认知控制中表现出连接不足（正低），但在大多数三聚类中感觉网络之间表现出连接过度（正高）。在三聚类 3 中，HC 受试者在感觉网络之间的连接明显强于 SZ，并且皮质下和感觉网络之间的相关性也明显强于 SZ。结果还提供了 SZ 和 HC 受试者两种不同的 dFNC 状态的再出现时间的统计学显著差异。研究结果强调了所提出的方法在高维数据中描述和利用变异性的效用，以提高对异质疾病（如 SZ）和不受约束的实验条件的测量的可解释性和敏感性，例如静息功能磁共振成像。影响声明当前用于分析动态功能网络连接（dFNC）的方法在一系列 dFNC 窗口上运行均值，每个窗口都包含所有独立成分分析网络对。因此，它描绘了整个大脑的短时间连接模式，并且均值聚类固定长度的特征贯穿整个神经系统。因此，有可能错过跨越较小的网络对子集的连接特征。动态 N 路三聚类进一步通过最大化个体之间的相似性、最小化状态内组件对之间的方差来对 dFNC 状态进行排序，并报告更复杂和瞬态的模式。
