Establishing brain states in neuroimaging data.
Establishing brain states in neuroimaging data.
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DOI：
10.1371/journal.pcbi.1011571
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发表时间：
2023-10
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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大脑状态的定义仍然难以捉摸，在神经科学的不同子领域有不同的解释——从麻醉的清醒水平到单个神经元的活动，从脑电图的电压到功能磁共振成像的血流量。这种共识的缺乏对神经动力学精确模型的发展提出了重大挑战。然而，在动力系统理论的基础上，有一个关于构成系统“状态”的定义。，系统未来的规范。在这里，我们建议采用这一定义，通过将动态因果模型（DCM）应用于静息和任务条件fMRI数据的低维嵌入，来建立神经成像时间序列中的大脑状态。我们发现，在静息条件下，约90%的被试可以用一阶模型更好地描述，而在任务条件下，约55%的被试可以用二阶模型更好地描述。我们的工作对在计算神经科学中几乎完全使用一阶方程的现状提出了质疑，并提供了一种在神经成像数据集中建立大脑状态及其相关相空间表示的新方法。在计算神经科学的核心，有一个看似简单的问题尚未被提出——“大脑状态”究竟是什么？这个问题是由各种看似无关的大脑状态定义引起的：从麻醉时的清醒水平，到单个神经元的活动，脑电图的电压，以及功能磁共振成像的血流量。然而，对于动力系统的状态有一个经常被忽视的精确定义：一些允许我们说出系统下一步做什么的信息。在这里，我们展示了同样的定义可以用来量化在神经成像时间序列中预测未来所需的信息。我们在模拟的帮助下证明，该理论框架可用于提取构成动态系统状态的一系列场景中的特征特征。然后，我们将相同的方法应用于fMRI数据集，并表明与休息条件相比，任务条件需要更多关于神经系统历史的信息来构成大脑状态。
The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the ’state’ of a system—i.e., a specification of the system’s future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets. There is a deceptively simple question that remains unasked at the heart of computational neuroscience—what exactly is a ’brain state’? This question is motivated by the various and seemingly unrelated definitions of brain states: ranging from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. There is, however, a precise definition of the state of a dynamical system that often remains overlooked: some piece of information that allow us to say what the system does next. Here, we show that this same definition can be used to quantify the information required to predict the future in neuroimaging timeseries. We demonstrate, with the aid of simulations, that this theoretical framework can be used to extract the characteristic features constituting dynamical system states in a range of scenarios. We then apply the same methodology to fMRI datasets and show that task conditions require more information about a neural system’s history to constitute a brain state, as compared with rest conditions.
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