Dartmouth College, Hanover, NH, United States.
Intel Corporation, Hillsboro, OR, United States.
Neuroimage. 2018 Oct 15;180(Pt A):243-252. doi: 10.1016/j.neuroimage.2018.01.071. Epub 2018 Feb 12.
Recent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant's cognitive state (for review see Turk-Browne, 2013). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject's network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.
最近的研究表明，功能磁共振成像（fMRI）数据的协方差结构 - 通常称为功能连接 - 可以随参与者的认知状态而变化（综述见 Turk-Browne，2013）。在这里，我们提出了一种贝叶斯层次矩阵分解模型，称为层次地形因子分析（HTFA），用于在大型多主体神经影像学数据集高效发现全脑网络。HTFA 通过首先重新表示每个大脑图像的活动来逼近每个主体的网络，这些活动由一组局部节点的活动表示，然后计算这些节点的活动时间序列的协方差。节点的数量，以及它们的位置，大小和活动（随时间变化）是从数据中学习的。由于节点的数量通常远远小于 fMRI 体素的数量，因此 HTFA 可以比传统的基于体素的功能连接方法高效几个数量级。在一个案例研究中，我们展示了 HTFA 如何恢复一组合成数据集的已知连接模式。在第二个案例研究中，我们说明了 HTFA 如何用于在参与者听故事时的真实 fMRI 数据中发现动态全脑活动和连接模式。在第三个案例研究中，我们对参与者观看电视剧集时收集的 fMRI 数据进行了类似的分析系列。在后两个案例研究中，我们发现 HTFA 得出的活动和连接模式可用于可靠地解码参与者正在经历的故事或节目的哪些时刻。此外，我们发现这些两类模式包含部分非重叠的信息，因此，基于活动和动态连接特征的组合训练的解码器的性能优于仅基于活动或连接模式训练的解码器。我们使用两种用于高效表征全脑活动和连接模式的其他（以前开发的）方法复制了这一结果。
