Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.
Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.
Cereb Cortex. 2023 Feb 20;33(5):2375-2394. doi: 10.1093/cercor/bhac214.
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
功能连接（FC）谱包含特定于个体的特征，这些特征在时间上是保守的，并且有可能捕捉到大脑-行为关系。大多数先前的研究都集中在这些 FC 指纹的空间特征（节点和系统）上，这些特征是在整个成像过程中计算出来的。我们提出了一种对 FC 进行时间滤波的方法，该方法允许在保持基于节点的活动的空间模式的同时选择特定的时间点。为此，我们利用了最近提出的将 FC 分解为边时间序列（eTS）的方法。我们系统地分析功能磁共振成像帧，以定义在多个指纹指标、相似性指标和数据集上增强可识别性的特征。结果表明，这些指标的特征随 eTS 共波动幅度、运行内帧的相似性、转换速度以及功能系统的表达而变化。我们进一步表明，对能够最大化指纹指标的特征进行数据驱动的优化可以在特定时间点隔离出多个系统表达的空间模式。选择仅 10%的数据就可以产生比从整个数据集获得的更强的指纹。我们的研究结果支持 FC 指纹在时间上具有差异表达的观点，并表明当同时考虑空间和时间特征时，可以识别出多个不同的指纹。
