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What brains do we study and why do we study them?
European Journal of Neuroscience ( IF 3.698 ) Pub Date : 2020-10-28 , DOI: 10.1111/ejn.15025
Emre Yaksi 1 , Carmen Sandi 2
Affiliation  

A recap of FENS FORUM 2020 Brain Debate: Chair: Trevor Robbins, Participants: Tracy Bale, Peter Dayan, Gilles Laurent, Christian Lüscher, Guo‐li Ming, Angela Roberts, Irene Tracey, Emre Yaksi

What animal models do we use for studying brains? Why do we use them for our specific questions? How shall we prioritize certain models over others? These were only few of the topics that were discussed during the FENS FORUM 2020 Brain Debate. But perhaps one of the main questions in the heart of these discussions was “why do we study brains?” To a great extent, our individual answers, as neuroscientists, to this ultimate question has a tremendous influence on the way we answer all of the above. Why do we study brains really?

For some of us, studying the brain is a step towards progress in our human journey and to shine light onto our most sophisticated organ, the human brain, the source of our imagination and our civilization. Some of us study brains to develop therapies for brain disorders, such as psychological or neurological diseases, a very impactful goal given the increasing challenges to mental health in our society. Yet, many of us study brains to understand the life surrounding us and its evolution, which is powered by the universal principles of neural computations driving animal behaviour. Furthermore, understanding these brain computations can also allow us to engineer hardware, algorithms and artificial intelligence systems, which can process BIG data sets to tackle challenging questions and, perhaps, one day might even evolve to think like us.

Naturally, we all have inherent biases towards prioritizing our own questions, and highlighting the advantages of our own model system to achieve these goals. However, scientific policies created by politicians, funding agencies, scientific journals and of course the science community play important roles in prioritizing the scope of scientific research and, as a natural extension, the animal models that we chose to study brains. Originally inspired by FENS president, Professor Carmen Sandi, The FENS FORUM 2020 Brain Debate (https://forum2020.fens.org/event/sie01‐the‐brain‐debate‐1530‐1700) reflected the views of the neuroscience community at large on “which brains to study”, covering a range of speakers working on small genetically tractable models (i.e. worms/flies/fish), rodents, primates, human organoids, diverse species of non‐genetic animal models, as well as artificial neural network simulations of the brain. The debate clearly illustrated that each of these models present several advantages over the others, and they all have their own short comings.

Small genetically tractable animals (i.e. worms, flies, zebrafish) provide the ability tackle challenging questions, e.g. linking genes to behaviours, or revealing holistic diagrams of neural architecture and brain function, at a relatively low cost without compromising the scientific quality. Rodent models (mice and rats) represent the model of choice for a huge community of neuroscientists because of the ever‐expanding toolbox to monitor and specifically manipulate mammalian brain while studying intricate behaviours. Non‐human primates allow scientists to investigate the primate brains that develop, organize and function in particularly similar manners to our brains. The recent rise of human brain organoids, now facilitates the study of several questions related to human brain development and diseases. For example, human three‐dimensional cell cultures, coined “mini‐brains”, present sophisticated developmental programs, including key brain‐like molecular and cellular features, and even wiring diagrams.

Despite several exciting discoveries and the mind‐blowing pace of research in the different models described above, some of the most exciting historical neuroscientific discoveries were done in animals that are not listed above. We refer here to the squid giant axon, where Hodgkin and Huxley conducted their studies on fundamental principles of action potentials (Hodgkin & Huxley, 1952), and how much we learned from a sea slug Aplysia on synaptic plasticity (Castellucci et al., 1970), from crustaceans and leeches on central pattern generators (Marder & Calabrese, 1996), or from the barn‐owls about the fundamental principles of sound localization (Knudsen & Konishi, 1978). In fact, the constant decline in the proportion of scientists using these diverse species of non‐genetic animal models raises the question how many such amazing discoveries we are missing out by focusing our attention and funding to a handful of animal models.

The recent developments of better hardware for running complex iterative algorithms and handling large data sets now allows many computational neuroscientists to build better simulations of neural networks to understand the fundamental principles of brain organization and to manipulate these computational models to test specific hypothesis that can inspire and focus experimental work to follow. Similarly, the ability to iterate and evolve convolutional neural networks allows artificial intelligence systems to perform challenging tasks to help us to deal with BIG data analysis, and even to make future predictions based on learning of past events, in someway similar to our brains.

Undoubtedly, the best model for studying human brain is the humans. The development of functional magnetic resonant imaging, brain stimulation methods (e.g. deep brain stimulation, transcranial magnetic stimulation), computational models (e.g. the Human Brain Project), extensive data banks of human brain tissue samples (e.g. the UK Brain Banks Network) and genomes (notably, the Human Genome project) allows nowadays scientists to investigate our brains and neurological diseases in ways that were unthinkable some years ago. Yet, investigations of human brains are still challenging due their limited accessibility for monitoring and for specifically manipulating its activity or genomic composition. This also restricts us to dive into a mechanistic understanding of the functioning of human brain, and its neurological diseases.

But, what are the best models? Which models should be prioritized for funding, and which ones will be particularly effective to foster our progress for a better understanding of brain function. The major take home message from the FENS FORUM Brain Debate was that “there is no “THE BEST” model that fulfil all requirements”. Our key challenge for each neuroscientist is to identify our main question first, and then look for what models allow us to make the biggest progress to tackle it. This is not always easy, especially when the funding is channelized towards one or few model systems, since this inherently limits the number of investigators and trainees with the ability to search and use the right model for the right question. It is also important that a well‐defined question would prevent evaluators to dismiss less commonly used models. We all need to provide better arguments why OUR model is the best one for OUR well‐defined and unique question.

This view implies a tremendous responsibility on the funding agencies—driven by our societal needs—and the publishers, but as well on us scientists, both when driving discoveries in the laboratory, and when acting as grant evaluators or reviewing research articles. Hence, we truly hope that the Brain Debate in the FENS FORUM 2020 prompted us to consider all these aspects when we choose our model systems for our specific questions, and when we evaluate each other's discoveries.



中文翻译:

我们学习什么大脑,为什么我们要学习它们?

FENS FORUM 2020脑辩论回顾:主席:Trevor Robbins,参加者:Tracy Bale,Peter Dayan,Gilles Laurent,ChristianLüscher,郭立明,Angela Roberts,Irene Tracey,Emre Yaksi

What animal models do we use for studying brains? Why do we use them for our specific questions? How shall we prioritize certain models over others? These were only few of the topics that were discussed during the FENS FORUM 2020 Brain Debate. But perhaps one of the main questions in the heart of these discussions was “why do we study brains?” To a great extent, our individual answers, as neuroscientists, to this ultimate question has a tremendous influence on the way we answer all of the above. Why do we study brains really?

For some of us, studying the brain is a step towards progress in our human journey and to shine light onto our most sophisticated organ, the human brain, the source of our imagination and our civilization. Some of us study brains to develop therapies for brain disorders, such as psychological or neurological diseases, a very impactful goal given the increasing challenges to mental health in our society. Yet, many of us study brains to understand the life surrounding us and its evolution, which is powered by the universal principles of neural computations driving animal behaviour. Furthermore, understanding these brain computations can also allow us to engineer hardware, algorithms and artificial intelligence systems, which can process BIG data sets to tackle challenging questions and, perhaps, one day might even evolve to think like us.

自然地,我们所有人都有内在的偏向于优先考虑我们自己的问题,并强调我们自己的模型系统在实现这些目标方面的优势。但是,由政治家,资助机构,科学杂志以及自然而然的科学界制定的科学政策在确定科学研究的范围以及作为我们自然选择的研究大脑的动物模型的优先级方面起着重要作用。受FENS总裁Carmen Sandi教授的启发,FENS FORUM 2020脑辩论(https://forum2020.fens.org/event/sie01-thebrain-debate-1530-1700)反映了整个神经科学界的观点关于“要研究的大脑”的内容,涵盖了从事小型遗传易处理模型(例如蠕虫/苍蝇/鱼),啮齿动物,灵长类动物,人类类器官的研究者,多种非遗传动物模型,以及大脑的人工神经网络模拟。辩论清楚地表明,这些模型中的每一个都具有相对于其他模型的多个优点,并且它们都有各自的缺点。

小型可遗传处理的动物(例如,蠕虫,苍蝇,斑马鱼)能够以较低的成本解决具有挑战性的问题,例如将基因与行为联系起来,或揭示神经结构和脑功能的整体图,而不会降低科学质量。啮齿动物模型(小鼠和大鼠)代表着庞大的神经科学家社区的选择模型,因为在研究复杂行为时,不断扩展的工具箱可以监视并专门操纵哺乳动物的大脑。非人类的灵长类动物使科学家能够研究与我们的大脑特别相似的方式发展,组织和运作的灵长类动物大脑。人类大脑类器官的最近兴起,现在促进了与人类大脑发育和疾病有关的几个问题的研究。例如,

尽管在上述不同模型中有多个令人兴奋的发现和令人兴奋的研究速度,但一些最令人兴奋的历史神经科学发现还是在上面未列出的动物中完成的。我们在这里指的是鱿鱼巨型轴突,霍奇金和赫x黎在那进行了关于动作电位基本原理的研究(霍奇金和赫x黎,  1952年),以及我们从海参Aplysia中学到了关于突触可塑性的知识(Castellucci等,  1970)。),中央模式发生器上的甲壳类和水((Marder&Calabrese,  1996年)或谷仓猫头鹰关于声音本地化的基本原理(Knudsen&Konishi,  1978年))。实际上,使用这些种类繁多的非遗传动物模型的科学家的比例不断下降,这提出了一个问题,即我们将注意力和资金投入到少数动物模型上,会错过多少这样的惊人发现。

用于运行复杂的迭代算法和处理大数据集的更好硬件的最新发展,现在使许多计算神经科学家能够构建更好的神经网络仿真,以理解大脑组织的基本原理并操纵这些计算模型来测试可以启发和启发具体假设的假设。着重进行实验工作。同样,迭代和演化卷积神经网络的能力使人工智能系统可以执行具有挑战性的任务,以帮助我们处理BIG数据分析,甚至可以基于对过去事件的学习做出未来的预测,这在某种程度上类似于我们的大脑。

毫无疑问,研究人脑的最佳模型是人。功能磁共振成像,脑刺激方法(例如深部脑刺激,经颅磁刺激),计算模型(例如人脑计划),人脑组织样本的广泛数据库(例如英国脑库网络)和基因组的发展(值得注意的是,人类基因组计划)如今使科学家能够以几年前无法想象的方式研究我们的大脑和神经系统疾病。但是,由于对人类大脑的监视和特定操作其活动性或基因组组成的可访问性有限,因此对人类大脑的研究仍具有挑战性。这也限制了我们对人脑功能及其神经系统疾病的机械理解。

但是,最好的型号是什么?哪些模型应该优先获得资金,哪些模型对促进我们的进步以更好地理解大脑功能特别有效。FENS论坛论坛辩论的主要内容是“没有“ THE BEST”模型可以满足所有要求”。对于每位神经科学家来说,我们面临的主要挑战是首先确定我们的主要问题,然后寻找可以使我们在解决该问题上取得最大进展的模型。这并不总是容易的,尤其是在将资金分配给一个或几个模型系统时,因为这固有地限制了能够搜索和使用正确问题的正确模型的研究人员和受训人员的数量。同样重要的是,一个明确定义的问题将阻止评估人员放弃较不常用的模型。

这种观点暗示着由我们的社会需求驱动的资助机构和出版商的巨大责任,也包括对我们科学家的巨大责任,无论是在实验室中推动发现,还是作为赠款评估者或审查研究论文。因此,我们真正希望,在FENS FORUM 2020的“脑力辩论”促使我们在针对特定问题选择模型系统以及评估彼此的发现时考虑所有这些方面。

更新日期:2020-12-31
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