Taylor Hoyt Patrick, Yap Pew-Thian
Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
Department of Radiology, University of North Carolina, Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2023 Oct;14227:268-276. doi: 10.1007/978-3-031-43993-3_26. Epub 2023 Oct 1.
Functional connectivity (FC) "gradients" enable investigation of connection topography in relation to cognitive hierarchy, and yield the primary axes along which FC is organized. In this work, we employ a variant of the "gradient" approach wherein we solve for the normal modes of FC, yielding functional connectome harmonics. Until now, research in this vein has only considered static FC, neglecting the possibility that the principal axes of FC may depend on the timescale at which they are computed. Recent work suggests that momentary activation patterns, or brain states, mediate the dominant components of functional connectivity, suggesting that the principal axes may be invariant to change in timescale. In light of this, we compute functional connectome harmonics using time windows of varying lengths and demonstrate that they are stable across timescales. Our connectome harmonics correspond to meaningful brain states. The activation strength of the brain states, as well as their inter-relationships, are found to be reproducible for individuals. Further, we utilize our time-varying functional connectome harmonics to formulate a simple and elegant method for computing cortical flexibility at vertex resolution and demonstrate qualitative similarity between flexibility maps from our method and a method standard in the literature.
功能连接性（FC）“梯度”有助于研究与认知层次相关的连接拓扑结构，并产生FC组织的主要轴。在这项工作中，我们采用了“梯度”方法的一种变体，即求解FC的正常模式，从而产生功能连接组谐波。到目前为止，这方面的研究只考虑了静态FC，而忽略了FC的主轴可能取决于计算它们的时间尺度这一可能性。最近的研究表明，瞬间激活模式或脑状态介导了功能连接的主要成分，这表明主轴可能在时间尺度变化时保持不变。有鉴于此，我们使用不同长度的时间窗口计算功能连接组谐波，并证明它们在不同时间尺度上是稳定的。我们的连接组谐波对应于有意义的脑状态。发现个体的脑状态激活强度及其相互关系是可重复的。此外，我们利用随时间变化的功能连接组谐波，制定了一种简单而优雅的方法，用于在顶点分辨率下计算皮质灵活性，并证明我们的方法与文献中的标准方法得到的灵活性图谱之间存在定性相似性。
