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）：工作记忆是一种核心的心理结构，在日常活动中起着核心作用，从相对简单的任务，如任务相关信息的暂时保留，到在复杂的认知任务中对这些信息的操纵和使用，以及对适应性行为的控制和指导。此外，在广泛的实验和现实世界测量中，个体在WM中保留和操作信息的能力已被证明是个体差异的重要因素，WM功能障碍与精神疾病（尤其是精神分裂症）中观察到的认知缺陷密切相关。当前认知神经科学研究的一个突出主题是，WM是由连接在一起形成功能网络的专门大脑区域之间的因果相互作用所支持的。然而，支持这一建议的大部分证据来自神经数据的功能连通性分析（fMRI， PET， EEG），它给出了特定大脑区域功能特性的相关性而不是因果性指示，以及网络相互作用如何支持认知。此外，缺乏因果网络相互作用如何产生WM功能的因果和机制描述。本研究采用非侵入性脑刺激（TMS）和神经记录（EEG）来解决这些问题，直接探测WM保留期间区域间的因果相互作用，并通过神经似是而非的建模来提供这种相互作用如何随着认知状态变化而变化的机制描述。具体目标1使用联合TMS-EEG来阐明大脑额叶和后脑区之间的因果相互作用在视觉信息短期保留中的作用。先前的研究表明，前额皮质（PFC）在自上而下控制后感觉区域的活动中起着普遍的作用。尽管PFC已被证明通过过滤掉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的激活和额后连接的增加表明，这种相互作用也可能涉及纯粹的存储功能。这种可能性将通过在记忆保留期间刺激额叶皮质来检验

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.