Role of the medial temporal lobe memory system in regulating behaviour

这项研究的长期目标是阐明记忆和认知控制系统相互作用以调节行为的机制。人们普遍认为，与海马区/内侧颞叶脑区相关的“记忆系统”不仅仅是存储和检索记忆。它是被称为“默认模式网络”(DMN)或自传体记忆网络的大规模网络的一部分。它对于确定行为背景、调节情绪、压力和其他反应、想象未来以及根据过去的经验和预期的未来回报(情节式的未来思维)计划行动至关重要。我们最近的工作，利用计算模型、啮齿动物研究、人类神经成像和虚拟现实研究，进一步表征了海马体和DMN在形成上下文详细记忆和空间认知中的作用。我们的下一步将是同时使用两种不同的神经成像模式进行研究：功能磁共振成像，具有出色的空间分辨率，可以准确地识别大脑中哪些区域活跃；以及脑电，具有出色的时间分辨率，可以识别快速变化的动态特征，例如跨大脑不同区域神经活动的跨频率同步。我们将应用复杂的信号处理和机器学习方法来识别表征主要内在大脑网络的脑电特征。接下来，我们将在不同的人群中进行一系列研究，以检验生活方式因素如何调节固有的大脑网络。在参加橄榄球等高冲击性运动的校队运动员中，我们将调查反复亚脑震荡头部创伤对大脑网络和认知的影响。在久坐不动的大学生中，我们将比较有氧运动、基于脑电的正念练习以及两者结合对大脑网络、情绪和认知的影响。在酗酒的本科生中，我们将评估“间歇性未来思维”干预对饮酒行为的影响，以及DMN内在网络和大脑中与奖励相关的区域之间的相互作用。最后，在描述了内在大脑网络的脑电特征并在这些干预研究中进行了验证后，我们将开发脑电神经反馈和脑机接口应用程序，训练人们控制他们的大脑网络，并使用它们来控制外部设备。这项研究将带来利用脑电研究内在大脑网络的新方法，为开发便携式脑电设备在治疗大脑疾病方面的临床和商业应用打开广泛的机会。**

The long-range goal of this research is to elucidate mechanisms by which memory and cognitive control systems interact to regulate behaviour. It is widely recognized that the “memory system” associated with the hippocampal/medial temporal lobe brain regions does much more than just store and retrieve memories. It is part of a large-scale network known as the “default mode network” (DMN) or autobiographical memory network. It is crucial for contextualizing behaviour, modulating emotion, stress, and other responses, imagining the future and planning actions based on past experience and expected future rewards (episodic future thinking). Our recent work, using computational modelling, rodent studies, human neuroimaging and virtual reality studies, has further characterized the role of the hippocampus and DMN in the formation of contextually detailed memories and in spatial cognition. Our next step will be to conduct a study using two different neuroimaging modalities simultaneously: functional magnetic resonance imaging, which has excellent spatial resolution and can identify exactly which brain areas are active, and electroencephalograpy (EEG), which has excellent temporal resolution and can identify rapidly changing dynamic features such as cross-frequency synchronization of neural activity across different regions of the brain. We will apply sophisticated signal processing and machine learning methods to identify EEG features that characterize the main intrinsic brain networks. Next, we will conduct a series of studies in different populations, to examine how lifestyle factors modulate the intrinsic brain networks. In varsity athletes who compete in high impact sports such as rugby, we will investigate the impact of repeated sub-concussive head trauma on brain networks and cognition. In sedentary university students, we will compare the impact of aerobic exercise, EEG-based Mindfulness practice and a combination of the two on brain networks, mood and cognition. In undergraduates who are heavy drinkers, we will evaluate the impact of an “episodic future thinking” intervention on drinking behaviours and on the interplay between the DMN intrinsic network and reward-related regions of the brain. Finally, having delineated EEG features of intrinsic brain networks and verified them in these intervention studies, we will develop EEG neurofeedback and brain-computer-interfacing applications that train a person to control their brain networks and use them to control external devices. This research will lead to novel methods for studying intrinsic brain networks using EEG, opening up wide-ranging opportunities for developing clinical and commercial applications of portable EEG devices in treating disorders of the brain.**