The role of causal network interactions in the retention of information in working memory: An empirical and theoretical investigation

描述（由申请人提供）：工作记忆（WM）是一种核心心理结构，在日常活动中起着核心作用，从相对简单的任务（如暂时保留任务相关信息）到复杂认知任务中操纵和使用此信息以及控制和指导适应性行为。此外，在实验和真实的世界的措施，一个人的能力，以保留和操纵WM的信息已被证明是一个重要的因素，在广泛的个体差异的基础上，WM功能障碍已强烈牵连在精神疾病，最显着的精神分裂症中观察到的认知缺陷。当前认知神经科学研究的一个突出主题是，工作记忆是由连接在一起的功能网络的专门大脑区域之间的因果相互作用支持的。然而，支持这一观点的大部分证据都来自神经数据的功能连接分析（fMRI、PET、EEG），这些数据提供了特定大脑区域功能特性的相关性而非因果性指示，以及网络交互如何支持认知。此外，因果网络相互作用如何引起WM功能的因果和机制的描述是缺乏的。拟议的研究使用非侵入性脑刺激（TMS）和神经记录（EEG）来解决这些问题，以直接探测WM保留期间的因果区域间相互作用，以及神经上合理的建模，以提供这种相互作用如何随认知状态变化而变化的机制描述。具体目标1使用组合TMS-EEG来阐明额叶和后部脑区之间的因果相互作用在视觉信息的短期保留中的作用。先前的研究表明，前额叶皮层（PFC）在自上而下控制后部感觉区的活动中发挥着普遍作用。虽然PFC已被证明可以通过过滤分散注意力的信息来调节WM的维持，但在没有分散注意力的情况下PFC激活和增加的前后连接表明，这种相互作用也可能涉及纯粹的存储功能。这种可能性将通过刺激保持期间的额叶皮层来检验 的WM的信息，并记录所产生的反应，无论是在刺激区和在远端皮层网站，使用EEG。如果前后功能相互作用在维持中发挥直接作用，我们预计连接的强度将随着WM中保存的信息量的增加而增加。同样，我们希望观察到TMS诱发激活的空间扩散到后脑区的系统性变化时，PFC被刺激在延迟期间的任务，需要存储的信息，是在不同的后脑区编码。具体目标2将使用WM和变化检测的现有神经模型来实现系统级连接模型，该模型可以捕获WM维护的EEG特征，并解释目标1中预期的TMS/EEG的观察到的状态依赖性。建立神经建模和TMS/EEG之间的这种联系将为未来的工作奠定基础，旨在正式测试关于因果网络相互作用如何支持WM和其他认知功能的不同预测，并将为检查可能的异常网络相互作用提供基础，这些异常网络相互作用是精神分裂症等衰弱性精神疾病症状的基础。

DESCRIPTION (provided by applicant): Working memory (WM) is a core psychological construct that plays a central role in everyday activities, from relatively simple tasks such as th temporary retention of task-relevant information, to the manipulation and use of this information in complex cognitive tasks and in the control and guidance of adaptive behaviors. Additionally, an individual's ability to retain and manipulate information in WM has been shown to be an important factor underlying individual differences across a broad spectrum of experimental and real world measures, and WM dysfunction has been strongly implicated in the cognitive deficits observed in psychiatric illnesses, most notably schizophrenia. A prominent theme in current cognitive neuroscience research is the idea that WM is supported by causal interactions between specialized brain areas linked together into functional networks. However, much of the evidence supporting this proposal has come from functional connectivity analysis of neural data (fMRI, PET, EEG), which gives a correlational rather than causal indication of the functional properties of particular brain areas, and of how network interactions might support cognition. Furthermore, a causal and mechanistic description of how causal network interactions give rise to WM functions is lacking. The proposed research addresses these issues using non-invasive brain stimulation (TMS), and neural recording (EEG) to directly probe causal interregional interactions during retention in WM, and neurally plausible modeling to provide a mechanistic description of how such interactions vary as a function of cognitive state changes. Specific Aim 1 uses combined TMS-EEG to clarify the role of causal interactions between frontal and posterior brain areas in the short-term retention of visual information. Prior work has suggested a general role for the prefrontal cortex (PFC) in the top-down control of activity in posterior sensory areas. Although the PFC has been shown to regulate WM maintenance by filtering out distracting information, PFC activation and increased frontal-posterior connectivity in the absence of distraction suggests that such interactions might also be involved in pure storage functions. This possibility will be examined by stimulating the frontal cortex during the retention of information in WM and recording the resulting response, both within the stimulated area and at distal cortical sites, using EEG. If frontal-posterior functional interactions play a direct rol in maintenance, we expect the strength of connectivity to increase with the amount of information being held in WM. Similarly, we expect to observe systematic changes in the spatial spread of TMS-evoked activations to posterior brain areas when the PFC is stimulated during the delay period of tasks requiring the storage of information that is coded in distinct posterior brain area. Specific Aim 2 will use an existing neural model of WM and change detection to implement a systems-level model of connectivity that can capture EEG signatures of WM maintenance and account for the observed state-dependence of TMS/EEG expected in Aim 1. Establishing this link between neural modeling and TMS/EEG will lay the groundwork for future work aimed at formally testing different predictions regarding how causal network interactions support WM and other cognitive functions, and will provide a basis for examining possible aberrant network interactions underlying the symptoms of debilitating mental illnesses, such as schizophrenia.