Uncovering individual differences in fine-scale dynamics of functional connectivity
Uncovering individual differences in fine-scale dynamics of functional connectivity
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DOI：
10.1093/cercor/bhac214
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发表时间：
2022-06-12
期刊：
影响因子：
3.7
通讯作者：
Sporns, Olaf
中科院分区：
文献类型：
--
作者：
Cutts, Sarah A.;Faskowitz, Joshua;Sporns, Olaf
功能连接（FC）配置文件包含跨时间保存的特定于受试者的功能，并有可能捕捉大脑行为的关系。大多数先前的工作都集中在这些FC指纹的空间特征（节点和系统）上，在整个成像会话中计算。我们提出了一种时间过滤FC的方法，它允许选择特定的时刻，同时还保持基于节点的活动的空间模式。为此，我们利用最近提出的分解FC到边缘时间序列（eTS）。我们系统地分析功能磁共振成像帧，以定义功能，提高跨多个指纹识别指标，相似性指标和数据集的可识别性。结果表明，这些指标的特征与eTS协同波动幅度，帧内运行的相似性，过渡速度，和功能系统的表达。我们进一步表明，数据驱动的优化功能，最大限度地提高指纹识别指标隔离系统表达在特定时刻的多个空间模式。只选择10%的数据可以产生比从完整数据集获得的更强的指纹。我们的研究结果支持的想法，FC指纹不同的时间表达，并建议，多个不同的指纹时，可以同时考虑空间和时间特征进行识别。
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.