Data and model considerations for estimating time-varying functional connectivity in fMRI.
Data and model considerations for estimating time-varying functional connectivity in fMRI.
复制标题
功能磁共振成像中时变功能连接估计的数据和模型考虑。
DOI：
10.1016/j.neuroimage.2022.119026
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发表时间：
2022-05-15
期刊：
影响因子：
5.7
通讯作者：
Vidaurre, D.
中科院分区：
文献类型：
--
作者：
Ahrends, C.;Stevner, A.;Pervaiz, U.;Kringelbach, M. L.;Vuust, P.;Woolrich, M. W.;Vidaurre, D.
时变FC模型有时无法检测到fMRI数据中的时间变化。受试者之间和会话内的FC变异性影响模型停滞。分割的选择会影响实际fMRI数据中的模型停滞。观测次数和每个状态的自由参数对模型停滞有重要影响。大脑中的功能连接(FC)已被证明在一次会议中表现出微妙但可靠的调制。估计时变FC的一种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，每个状态都具有关联的FC的特征模式。然而，从数据估计这些模型有时无法以有意义的方式捕获变化，以至于模型估计将整个会话(或其中最大部分)分配给单个状态，因此无法有效地捕获会话内状态调制；我们将这种现象称为模型变得静态，或模型停滞。在这里，我们的目标是量化数据的性质和模型参数的选择如何影响模型使用模拟的fMRI时间进程和静止状态的fMRI数据检测FC的时间变化的能力。我们表明，主题之间巨大的FC差异可能会压倒会话内微妙的调制，导致模型变得静态。此外，分割的选择也会影响模型检测时间变化的能力。最后，我们证明，当需要估计的每个状态的自由参数的数量较多，而可用于该估计的观测值的数量较少时，模型通常变得静态。基于这些发现，我们从模型的前处理、分割和复杂性方面得出了一套适用于时变功能分类研究的实用建议。
Time-varying FC models sometimes fail to detect temporal changes in fMRI data. Between-subject and within-session FC variability affect model stasis. The choice of parcellation affects model stasis in real fMRI data. The number of observations and free parameters per state critically affect model stasis. 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.
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29.9
作者：
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