GEFF: Graph embedding for functional fingerprinting
GEFF: Graph embedding for functional fingerprinting
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DOI：
10.1016/j.neuroimage.2020.117181
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发表时间：
2020-11-01
期刊：
影响因子：
5.7
通讯作者：
Goni, Joaquin
中科院分区：
文献类型：
--
作者：
Abbas, Kausar;Amico, Enrico;Goni, Joaquin
根据功能性MRI（fMRI）数据估计的功能性连接体（FC）具有可用于从人群中识别个体（受试者识别）的个体指纹。虽然识别率高时，使用休息状态的FC，其他任务显示中等至低的值。此外，识别率是任务依赖性的，并且当比较不同的认知状态时，识别率较低，如通过不同的fMRI任务捕获的。在这里，我们提出了一个嵌入框架GEFF（功能指纹图嵌入），该框架基于FC到特征向量的组级分解。GEFF使用一个或多个任务FC（学习阶段）创建一组受试者的特征空间表示。在识别阶段，我们比较来自该特征空间（验证数据集）内的学习主题的FC的新实例。验证数据集包含来自与学习数据集相同的任务或来自未包含在学习中的其余任务的FC。本征空间内的验证功能块的评估结果显着增加的主题识别率的所有功能磁共振成像任务测试和潜在的任务无关的指纹识别过程。值得注意的是，GEFF学习阶段的静息状态与一个功能磁共振成像任务相结合，涵盖了主体识别的大部分认知空间。因此，在设计实验时，可以选择一个任务fMRI来问一个特定的问题，并将其与静息状态fMRI联合收割机结合起来，使用GEFF提取最大的主体可区分性。除了受试者识别之外，GEFF还用于识别认知状态，即识别与给定FC相关的任务，而不管受试者是否已经在学习数据集中（独立于受试者的任务识别）。此外，我们还表明，从学习阶段的特征向量可以被表征为任务和主题占主导地位，主题占主导地位或两者都没有，使用其相应的负载的双向方差分析，提供了更深入的了解跨个人和认知状态的功能连接的方差程度。
It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent taskidentification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.