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Matter of context: Revealing the circuit architecture of internal brain state influence on behaviour

背景问题：揭示大脑内部状态对行为影响的回路架构

基本信息

批准号：
BB/S010564/1
负责人：
Asaph Zylbertal
金额：
$ 39.26万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FS010564%2F1
关键词：
Matter context Revealing circuit architecture

项目摘要

My aim is to understand how ongoing internal activity patterns within the brain shape the way it processes information and controls behaviour. Human and animal behaviour is not merely a set of 'automatic' reflexes. Rather, the way we respond to sensory inputs such as the sight of food or the sound of a phone ringing depends on multiple contextual factors such as emotional state, time of day and how satiated or alert we are. Modern neuroscience has made important progress towards understanding the brain systems that report these factors. For instance, the dopaminergic and serotonergic systems that signal reward have been extensively studied due to their importance in shaping normal behaviour as well as psychiatric disorders. However, major challenges remain in terms of understanding how multiple brain pathways act together to modulate sensory processing and behaviour. To a large extent this is due to the size and complexity of the brain which precludes simultaneous measurement of the many brain cells involved. By establishing a new research programme in the Department of Neuroscience, Physiology & Pharmacology at UCL, I plan to take a novel approach to tackle this problem. My strategy combines state-of-the-art imaging in an experimentally advantageous model organism - the larval zebrafish - with data-driven biology and computational modelling: key research avenues identified by the BBSRC.Zebrafish larvae are particularly well suited for simultaneously tracking activity in multiple brain structures. This tiny animal (3.5 mm long) is almost perfectly transparent, allowing its small brain to be monitored non-invasively using fluorescent microscopy while the fish performs a range of recognizable behaviours such as hunting and avoidance. Importantly, many of these behaviours are influenced by contextual factors such as hunger or alertness, by virtue of brain circuits fish share with all other vertebrates, including humans. To study how distributed brain networks work together to shape behaviour, I will use cutting-edge "light-sheet microscopy" to individually track the activity of each of the zebrafish's 80,000 neurons. While doing so, I will alter environmental factors to manipulate satiety, alertness and other contextual elements. Deciphering the resulting dataset will be a complex endeavour, comparable to extracting insights into market dynamics by simultaneously listening to each and every one of the 100,000 finance employees in the City of London. The potential for valuable insights is enormous, but so is the challenge in making sense of the massive amount of data and finding the most informative sources. To meet this challenge, I will use recurrent neural networks - a modern machine-learning algorithm akin to the one that powers automated speech recognition. It will enable me to identify neurons that can predict if the animal is likely to respond to a specific visual cue, even before the stimulus is presented. Such cells are good candidates for signalling contextual information and my computational modelling will resolve how they work together to collectively influence behaviour. To test my hypotheses, I will use advanced "optogenetic methods" to directly control brain activity using light and examine the resulting effects on activity elsewhere in the brain and on the behaviour of the fish.Ultimately, these findings will shed new light on how neural activity related to context and experience combine to influence fundamental brain function. Because all vertebrates possess the same basic brain plan, my experimental findings are likely to reveal principles that apply to many species, including humans. Thus, in line with the BBSRC's priority of supporting world-class basic bioscience for health, this project will provide a major advance in our understanding of how the healthy brain produces behaviour. In the longer term, this could underpin greater understanding of how brain function is disrupted during disease.

我的目标是了解大脑中正在进行的内部活动模式如何塑造它处理信息和控制行为的方式。人类和动物的行为不仅仅是一组“自动”的反应。相反，我们对食物的视觉或电话铃声等感官输入的反应取决于多种背景因素，如情绪状态、一天中的时间以及我们有多饱或多警觉。现代神经科学在理解报告这些因素的大脑系统方面取得了重要进展。例如，发出奖赏信号的多巴胺和5-羟色胺能系统由于在塑造正常行为和精神障碍方面的重要性而被广泛研究。然而，在理解多条大脑通路如何共同作用以调节感觉处理和行为方面，主要挑战仍然存在。这在很大程度上是由于大脑的大小和复杂性，这排除了同时测量涉及的许多脑细胞的可能性。通过在伦敦大学学院神经科学、生理学和药理学系建立一个新的研究项目，我计划采取一种新的方法来解决这个问题。我的策略将一种具有实验优势的模式生物--斑马鱼幼体--的最先进成像与数据驱动生物学和计算建模相结合：BBSRC确定的关键研究途径斑马鱼幼体特别适合同时跟踪多个大脑结构中的活动。这种微小的动物(3.5毫米长)几乎是完全透明的，允许使用荧光显微镜非侵入性地监视它的小大脑，而鱼则执行一系列可识别的行为，如狩猎和回避。重要的是，由于鱼类与包括人类在内的所有其他脊椎动物共享大脑回路，这些行为中的许多都受到饥饿或警觉等环境因素的影响。为了研究分布式大脑网络如何共同作用来塑造行为，我将使用尖端的“光片显微镜”来单独跟踪斑马鱼8万个神经元中的每一个的活动。在这样做的同时，我会改变环境因素，以操纵饱腹感、警觉性和其他背景因素。破译由此产生的数据集将是一项复杂的工作，相当于通过同时听取伦敦金融城10万名金融从业人员的每一个人的意见来提取对市场动态的洞察。有价值的见解的潜力是巨大的，但在理解海量数据和寻找最具信息量的来源方面的挑战也是巨大的。为了迎接这一挑战，我将使用递归神经网络--一种类似于支持自动语音识别的现代机器学习算法。这将使我能够识别神经元，这些神经元可以预测动物是否可能对特定的视觉提示做出反应，甚至在刺激出现之前。这样的细胞是发出背景信息信号的很好候选者，我的计算模型将解决它们如何协同工作，共同影响行为。为了验证我的假设，我将使用先进的“光遗传学方法”直接利用光控制大脑活动，并研究由此对大脑其他部位的活动和鱼的行为产生的影响。最终，这些发现将为与环境和经验相关的神经活动如何结合起来影响基本大脑功能提供新的线索。因为所有脊椎动物都有相同的基本大脑计划，我的实验结果可能会揭示适用于包括人类在内的许多物种的原理。因此，按照BBSRC支持世界级基础生物科学促进健康的优先事项，该项目将在我们理解健康大脑如何产生行为方面取得重大进展。从长远来看，这可能会为更好地理解疾病期间大脑功能是如何中断的奠定基础。