Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, Aarhus C 8000, Denmark.
Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark.
Neuroimage. 2022 May 15;252:119026. doi: 10.1016/j.neuroimage.2022.119026. Epub 2022 Feb 22.
Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model's ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters per state that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model.
大脑中的功能连接（FC）在一个会话中表现出微妙但可靠的调制。一种估计时变 FC 的方法是使用基于状态的模型，该模型将 fMRI 时间序列描述为状态的时间序列，每个状态都有一个相关的、特征性的 FC 模式。然而，这些模型从数据中的估计有时无法以有意义的方式捕捉到变化，以至于模型估计将整个会话（或其中最大的部分）分配给单个状态，从而无法有效地捕捉会话内状态调制；我们将这种现象称为模型变得静态，或模型停滞。在这里，我们旨在使用模拟 fMRI 时间序列和静息态 fMRI 数据来量化数据的性质和模型参数的选择如何影响模型检测 FC 时间变化的能力。我们表明，大的受试者间 FC 差异可能会压倒更微妙的会话内调制，导致模型变得静态。此外，分区的选择也会影响模型检测时间变化的能力。我们最后表明，当每个状态需要估计的自由参数数量高，并且可用于此估计的观察数量低时，模型通常会变得静态。基于这些发现，我们针对时变 FC 研究提出了一系列实用的预处理、分区和模型复杂度方面的建议。
