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近年来,得益于方法学的重大进步和从分子到整个大脑多层次的数字数据集成及建模,脑科学研究无疑已迈入一个新时代。在这一背景下,神经科学与技术、计算的交叉领域已取得重要进展。新兴的大脑科学整合了高质量的研究、多层次数据的集成、跨学科的大规模合作文化,同时促进了科研成果的应用转化。就如欧洲的“人脑计划”(Human Brain Project, HBP)所提倡的那样,采取系统化的方法对于应对未来十年内的医学与技术挑战至关重要。
本文旨在为未来十年的数字大脑研究发展一套新概念,并与广泛的研究社区展开讨论,寻找共识点,以此确立科学的共同目标。同时,提供一个科学框架,支持当前及未来的EBRAINS研究基础设施发展(EBRAINS是HBP工作产生的研究基础设施)。此外,本文还旨在向利益相关者、资助组织和研究机构传达未来数字大脑研究的信息,吸引他们的参与;探讨综合性大脑模型在人工智能,包括机器学习和深度学习方面的变革潜力;并概述一个包含反思、对话及社会参与的协作研究方法,以应对伦理与社会的机会与挑战,作为未来神经科学研究的一部分。(本文为文章上篇)
关键词:人类大脑,数字研究工具,研究路线图,大脑模型,数据共享,研究平台
▷Amunts, Katrin, et al. "The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing." Imaging Neuroscience 2 (2024): 1-35.
引言
在过去几十年里,我们对人脑的理解已取得显著进展。面对大脑复杂性的挑战,研究者已经从单一分子和基因的层面,到突触、细胞和局部神经回路,乃至于作为一个整体器官的大脑的各个层面,探索其多种功能和功能障碍。
今天,神经系统疾病已成为继心脏病之后的第二大死亡原因,造成约2.76亿伤残调整生命年(Disability-Adjusted Life-Years, 是指从发病到死亡所损失的全部健康寿命年)(Feigin et al., 2019)。[1]2010年,欧洲的大脑疾病总成本高达 7980亿欧元 (Olesen et al., 2012)。为了应对这一挑战并开发更有效的因果治疗方法,我们需要深入理解大脑的工作原理。我们不可避免地会面临器官的复杂性和其庞大的规模,同时也存在合法的伦理和方法论限制,这些限制不允许我们直接从人体材料中获取所有必要的数据集。这为实证和数字研究带来了挑战。解决这些挑战需要洞察大脑的基础结构、器官中的生理现象,以及对神经机制的理论理解。
结合结构性和功能性磁共振成像(fMRI)、脑磁图(MEG)或脑电图(EEG)等多种方法,科学家们已成功识别与感觉、运动控制和执行功能相关的生物标志。然而,要从细胞机制到系统层面的效应建立联系,我们需要多尺度神经科学的支持。还有研究指出,我们还需要理解大脑各区域之间是如何进行“语义”交流的 (Douglas & Martin, 2007) 。例如,Buzsáki (2019) 认为,全局和局部的振荡构成了大脑内部通信的“语法”。
针对多种脑疾病,遗传机制的明确已对诊断和治疗起到了直接的重要作用。此外,几种信号传导途径的分子和细胞机制也已被解码。尽管如此,我们仍需加深对大脑组织、大脑结构与功能、动力学和行为之间关系的理解,特别是其在学习和睡眠期间的重组,以及认知基础的条件。模拟以及人工智能(AI)解析意识结构的潜力已成为神经科学讨论的一部分 (见 Dehaene et al., 2017; Graziano, 2019)。具备模拟意识能力的机器的出现,可能意味着“意识的困难问题”可以通过模拟“意识的容易问题”来解决 (Chalmers, 1995)。
大脑的多尺度架构不仅赋予了它适应能力和计算能力,也促成了不同大脑区域间明显的个体差异。这些差异的程度取决于不同大脑区域和其他因素 (Croxson et al., 2018; Zilles & Amunts, 2013)。理解这些变异将有助于改进诊断和个性化治疗,同时促进对认知功能机制的阐释。从基础科学的角度看,这是理解进化和不同认知特征的必要条件 (Thiebaut de Schotten & Forkel, 2022)。
神经成像技术、微电子技术和光学方法的创新进展,极大地推动了我们对大脑功能的了解。这些技术提供了更高的空间和时间分辨率,并能持续观测更长时间,从而产生了大量的数据。目前已经有成千上万的参与者参与其中,他们提供了大量数据集,尽管这些数据集的分辨率较低。这些数据帮助我们识别了决定大脑健康与衰老的多种因素,例如生活方式、环境因素、基因构成及这些因素之间的相互作用。这种实证研究产生了大量高度结构化的数据以及元数据,也加剧了对日益增长的对数据集的需求。
那么,根据目前的数据,哪些问题已经可以得到解答,哪些地方还需要做更多工作呢?2002年诺贝尔奖得主悉尼·布伦纳 (Sydney Brenner) 在其获奖演讲“大自然给科学的礼物”(Brenner,2003)中提到:“我们正在数据的海洋中挣扎,渴望获得知识。生物科学的爆炸性增长得益于我们积累描述性事实的前所未有的能力……我们需要将数据转化为知识,并需要一个框架来实现这一转换。”尽管现有大量数据,但各实验室的研究目标和方法差异巨大,数据往往无法直接比较。此外,严格的质量控制和来源追踪的多维数据,包括具有高空间和时间分辨率的功能成像数据及组学数据等,还相对稀少。
这些数据往往来自多个不同的实验室。显然,要定义和实现宏伟的科学目标,需要不同领域的神经科学实验室之间,以及拥有互补技术专长的实验室之间的紧密合作。例如,需要图像分析、神经解剖学、数据分析、计算、生理学、生物医学、建模、理论和计算的专家共同协作。研究大脑和大脑疾病时,一些神经伦理问题和社会需求与价值的考量也非常重要,这促使神经科学家与人文科学研究者之间的互动更加紧密。总的来说,这些发展促进了跨学科合作,需要适当组织和重视。
这种横跨不同大脑研究领域的紧密合作是HBP等大型国际项目的显著特点。[2]HBP从2013年启动至2023年结束,是欧洲未来与新兴技术领域的旗舰项目之一。HBP项目的启动目标是深入理解大脑,这一目标与当时计算和数字技术的显著进步相符(Amunts et al., 2016, 2019; Markram et al., 2011)。HBP是全球首批大规模大脑研究项目之一,在将数字大脑研究转变为一个更方便协作、可复制、以及伦理和社会责任为导向的学科方面发挥了开创性作用(Amunts et al.,2022)。
HBP已经建立了科学工作流的基础,使得多尺度、多物种的实验数据与理论及数据驱动模型之间能进行FAIR(可查找的、可访问的、可互操作的、可重用的;Wilkinson et al.,2016)比较(Eriksson et al.,2016)。研究成果已经带来了关于学习机制(Bellec et al., 2020; Cramer et al., 2020; Deperrois et al., 2022; Göltz et al., 2021; Jordanet al.,2021;Masoli et al.,2021;Stöckl et al.,2021)、视觉-运动控制 (Abadía et al., 2021; Pearson et al., 2021)、视觉(Chen et al., 2020; Svanera et al., 2021; van Vugt et al., 2018)、意识(Demertzi et al.,2019;Lee et al.,2022)、睡眠(Capone et al.,2019;Le Van Quyen et al.,2016;Rosanova et al.,2018)、空间导航(Bicanski & Burgess,2018;Northoff et al.,2020;Stoianov et al.,2018;van Beest et al.,2018;et al.,2021)、预测编码和感知(Oude Lohuis et al.,2022),以及语言(Dehaene et al., 2015)等方面的新见解,同时也推动了新理论概念和分析方法的发展。Neuron杂志[3]在2015年的特刊中,特别讨论了认知架构,旨在汇聚关键研究以理解和模拟人类大脑功能,许多特色论文都是在HBP项目启动阶段的合作成果(Dehaene et al., 2015)。
神经科学社区现在已经能够利用最新的计算、模拟和人工智能技术的最新发展。项目中创建的实验数据、计算模型和工具、仪器以及如神经形态系统这样的专用硬件,旨在显著加速大脑医学和研究的发展,并为半导体行业提供低能耗解决方案(“大数据需要硬件革命”,2018)。该联盟已经开发出EBRAINS这一协作研究平台,目标是通过数字工具和计算技术将大脑研究推向新的高度,进一步发展医学和神经启发型技术的应用。EBRAINS已成为欧洲研究基础设施战略论坛(ESFRI)路线图的一部分,ESFRI的目标是支持欧洲研究基础设施政策的连贯和战略导向的方法,并促进多边倡议,以更好地使用和发展研究基础设施,这不仅涵盖欧洲,还扩展到跨大陆层面。EBRAINS正在被开发成为一个由科学家为科学家服务的可持续研究基础设施。
为应对伦理与社会问题,HBP在EBRAINS的治理和研究层面融入了负责任研究与创新(RRI)的原则和实践。其目标是预测、反思,并在网络层面采取行动,以应对当前及未来的神经伦理、哲学、社会和法律挑战,并主动解决有关EBRAINS研究及其成果的关注、滥用和商业化的双重用途研究问题(Stahl et al., 2021)。
展望未来十年,我们基于已取得的成就,确定了对大脑知识的缺口,并制定了未来的研究目标。我们相信,实现这些目标的努力将从数字大脑研究的进展以及技术与计算交叉领域的最新发展中受益。数字大脑研究利用数据科学、人工智能、计算、建模和模拟、图谱等领域的优势,推动大脑研究进展,并将其转化应用于医学和技术。这些目标也将从神经科学与神经伦理的整合以及涉及需求、可接受性和期望性的伦理与社会问题的多学科合作中获益。
本手稿通过一个参与性过程发展而来(附录1)。这项工作由HBP发起,邀请整个研究社区通过提交评论共同塑造愿景。这个过程持续超过两年,使得原始文件经历了实质性的变更,研究概念得到更广泛的代表,有时还伴随着争议性的讨论,特别是关于建模和模拟的角色。作者们就共同目标和实现这些目标的步骤达成了共识。虽然我们并不认为存在一种“万能”的方法来处理这些问题,但我们确信,围绕数字大脑研究主题的讨论将推动更广泛的神经科学领域的进展(见附录2)。
附录1 白皮书:参与流程和时间表
附录2 支持声明
Rafael Yuste: “作为在美国工作的欧洲人,我强烈支持这一倡议,它有助于将欧洲神经科学推向领导地位,并帮助欧洲国家利用共同努力实现相同目标的好处。”
Linda Richards:“总体而言,这份手稿呈现了推动该领域前进的新方法,非常令人兴奋。”
Alexandra A. de Sousa:“作为欧洲脑进化研究网络的创始人,我强烈支持这一倡议,特别是它提到了比较和进化神经科学的重要性。”
蒲慕明:“理解人脑的结构和功能,并开发有效的诊断和干预脑疾病的方法,是所有社会的长期目标。这些任务庞大,需要全球合作,以促进快速进展并共享知识与技术。中国脑科学项目现已由中国政府全额资助,为未来十年提供保障。许多中国科学家与欧美科学家有密切联系,他们希望建立国际合作项目,并设立有效的协作机制。”
George Paxinos:“观察到在多层次脑图谱的开发中取得的进展,实在令人兴奋。近年来出现的高级数字工具为研究不同物种的大脑结构提供了全新的可能性。”
神经科学:现状
为了理解我们的缺失部分并激励我们的数字大脑研究方法,回顾现代神经科学的起点至关重要。这一学科的发展经历了多个关键阶段。
现代神经科学起源于19世纪的最后二十年,大脑的认识发生了根本性变革:从被视为结构单一的整体,转变为一个由众多神经元组成的复杂网络(DeFelipe, 2009; Mazzarello, 2010; Shepherd, 2015)。20 世纪初关于大脑分区的新概念,即大脑中负责特定功能的区域,催生了微观结构的大脑图谱的诞生(例如,Brodmann, 1909; Vogt & Vogt, 1919)。系统的神经病理学研究加深了我们对健康和疾病状态下大脑的理解。1930 年代全脑电图为 1950 年代细胞内电生理记录以及对神经元和突触生理学的基本了解铺平了道路。1930 年代化学神经传递的发现及1950 年代药理学的革命对神经学和精神病学产生了重大影响(Carlsson et al., 1957; Dale et al., 1936; Vogt, 1954),同时也加深了我们对于大脑如何作为一个分布式计算网络灵活适应变化世界的基本理解(Dayan, 2012)。
1950年代引入的Hodgkin-Huxley模型用数学术语描述了动作电位的机制(Hodgkin & Huxley, 1952)。到了1960年代和1970年代,关于感觉(主要是视觉)和运动系统的生理学及解剖学研究,为我们更新对大脑的认识提供了宝贵见解。尽管我们现在认识到这种理解还过于简单(Shepherd, 2009)。1980年代我们对神经元膜生物物理学和受体及离子通道的功能有了深刻的理解(Sakmann & Neher, 1984),而1990年代全脑成像技术的兴起开启了一个快速进展期,深化了我们对大脑结构、它与基因和环境的关系以及个体差异的理解。新兴技术,包括分子生物学、遗传学、药理学、心理物理学、神经成像和计算神经科学,结合电子和计算技术,逐步丰富了大脑研究(Finger, 1994)。
21 世纪初,新工具的开发,如光遗传学,首次允许通过激活或抑制特定的神经元类型,来研究它们的功能(Deubner et al., 2019; Emiliani et al., 2022; Häusser, 2021; Südhof, 2017)。新型高分辨率成像技术,如在动物实验中使用的双光子钙成像,极大提高了我们对细胞和亚细胞生理的理解(Toi et al., 2022; Yang & Yuste, 2017)。与此同时,宽视野钙成像作为系统神经科学中的一种强大工具出现,允许同时记录多个大脑区域,具有足够的时空分辨率来解析行为相关信息(Cardin et al., 2020; Ren & Komiyama, 2021b)。最近,单细胞转录组学的发展,结合电生理表征和形态重建,为研究人员提供了关于哺乳动物大脑中神经元类型的坚实知识基础(Chartrand et al., 2023; Fuzik et al., 2016; Gouwens et al., 2020; Lee et al., 2023)。
有人提出,刺激的全局特性可以通过神经元同步来编码的假设(Brama et al., 2015),例如“同步绑定”理论曾认为,当编码这些特征的神经元在毫秒内同时放电时,诸如视觉对象的颜色和运动之类的特征就会被整合为连贯的感知(Gray et al., 1989)。然而,后来的研究表明,并不存在同步绑定(Lamme & Spekreijse, 1998; Roelfsema et al., 2004; Thiele & Stoner, 2003),而是对象的特征通过基于对象的注意力,在神经元层面上提高了放电率,从而整合成一个连贯的整体(Poort et al., 2012; Roelfsema et al., 1998)。未来几年内,我们可能将获得用于研究特定任务的动物大脑回路的毫秒级表征工具,这些工具将应用于海马-皮层网络(Klausberger & Somogyi, 2008; Lisman et al., 2017)、运动皮层(Li et al., 2015)、桶状皮层(Staiger & Petersen, 2021)、基底-皮层网络(Gombkoto et al., 2021)和一些组织性行为的下丘脑网络(Karigo et al., 2021)。
同时,我们对特定大脑功能的理论和概念理解也变得更为丰富和复杂。现在可以在多个尺度上研究解剖与功能之间的联系(Zaborszky, 2021)。微观尺度的形态学特征包括髓鞘、细胞、受体构造、细胞密度、突触、单个神经元的尖峰模式、轴突和树突的分枝模式、棘密度和基因表达,而生理特征涵盖从离子通道生物物理到突触电位或神经元尖峰模式的广泛范围。研究表明,特定区域的突触组织、受体构造和分枝模式揭示了惊人的连接复杂性,虽然这些特征如何导致皮层内部和皮层之间以及区域内的处理差异仍然是一个未解之谜(Amunts et al., 2020; Haueis, 2021; Palomero-Gallagher & Zilles, 2019; Rockland, 2022)。
在宏观层面,研究者利用MRI技术探索大脑中各个皮质区域之间的相互联系,如视觉系统的层级处理和基本连接模式(Felleman & Van Essen, 1991)。在此尺度,大脑血氧水平依赖性(BOLD)信号展现出慢速、低频的自发波动和系统性模式,这些通过静息态功能连接被捕捉到(Raichle et al., 2001)。但是,BOLD成像与电生理模式之间的精确关系尚未明确。研究假设架构类型是层级处理的决定因素 (Barbas, 2015; Bastos et al., 2015; Mejias et al., 2016; Vezoli et al., 2021)。跨模态区域之间的连接使得这些区域能够整合不同单模态感觉的表征,归入到执行分类和规则决策的脑区(Mesulam, 1998; Pandya et al., 2015)。在连接区域之间以及区域内部,神经元的复杂性方面已取得一定的研究进展。
具体来说,许多人类研究中使用的功能成像BOLD信号与局部能量消耗密切相关 (Viswanathan & Freeman, 2007),这可能反映了映射到跨层神经元和皮质层的树突活动和中间神经元活动。这些局部的微电路和树突活动对比较内部模型与自上而下的预期和自下而上的信息流的认知功能至关重要。这些局部计算对意识处理的细胞机制可能起到关键作用 (Aru et al., 2020),并可能在其他测量神经输出的电记录技术中被遗漏。理解特定层级计算将是一项重要的计算突破,结合对局部微电路活动和树突活动敏感的记录技术 (Larkum et al., 2018) 与相应的皮层计算理论模型 (Haider et al., 2021; Sacramento et al., 2018) 可实现此目标。
所谓的中观尺度是在微电路层面上定义的,研究者依此描述大脑的不同细胞类型及其连接性和涌现的动力学。然而,具体的单元仍然存在争议。尽管在1970年代,多种尺寸的皮层柱(如微柱、超柱等)被视为功能模块 (Jones, 1983; Mountcastle, 1997; Rockland, 2010; Szentágothai, 1978),但持续的讨论提出了基本电路类型的组合,包括前馈兴奋性、循环反馈兴奋性、前馈抑制性、循环反馈抑制性和抑制-抑制性电路 (Nadasdy et al., 2006)。这些电路是通过进化压力塑造的。因此,理解不断进化和成熟的皮层电路逻辑,以便识别跨物种的特定电路,这将揭示解剖特征在多大程度上具有相似或不同的功能。
理解中观尺度电路对于正确连接微观和宏观尺度的大脑组织描述至关重要,这有助于从微观特征正确推断宏观行为 (Haueis, 2021)。广域荧光成像可以桥接微观和宏观空间尺度之间的神经活动,帮助我们理解局部电路是如何与更大的神经网络相互作用的 (Cardin et al., 2020; Ren & Komiyama, 2021a)。通过结合不同记录方式,我们能够突破单一技术的局限性(Allegra Mascaro et al., 2015),例如,最近的研究结合广域钙成像与其他成像技术,如双光子钙成像和 fMRI (Barson et al., 2020; Lake et al., 2020)。现在我们能够在同一人脑切片中对分子定义的细胞类型及其细胞结构进行成像,从而详细刻画中观尺度的结构复杂性及其在跨尺度连接中的作用(Axer & Amunts, 2022; Kooijmans et al., 2020)。这种方法将更好地理解不同细胞类型在局部及全球层面上的连接方式。
最近,研究重点逐渐转向神经群体的几何形态和动力学 (Ebitz & Hayden, 2021; Saxena & Cunningham, 2019)。推动这一研究方向的是这样一个假设:(最有意义的)神经活动发生在捕捉神经变异性的显著部分的低维状态空间或流形中,这些状态空间或流形可以通过在高维神经记录上使用降维技术来识别。研究这些低维状态空间的几何形态和动力学,提出了关于大脑如何控制运动 (Churchland et al., 2012) 以及如何支持感知和认知任务的新的机制假设 (Chung & Abbott, 2021)。
为了连接不同的尺度并理解从一个尺度到另一个尺度的过渡规则,需要详细的模型来连接这些空间和时间尺度。此外,还需建立建立生物物理模型来描述生理过程如何被测量设备捕捉的。例如,此类模型可以通过结合使用侵入性电生理学与高分辨率层流功能磁共振成像,探测大脑皮层深处神经元群体的多单元活动和局部场电位Havlicek et al., 2015) :这包括描述兴奋性和抑制性神经元亚群的层特异性分布的微电路模型,然后为 fMRI 信号模型提供输入,并生成包含神经血管耦合、血液动力学响应和 BOLD 信号物理模型的 fMRI 信号生成模型。
随着心理现象计算概念化的兴起和人工神经网络的成功,人们对大脑组织复杂性的认识不断加深。Marr (1982) 指出,除了神经实现层面之外,还有两个组织层面:算法层面和计算层面。进入21世纪,随着计算能力的增强,计算神经科学的需求也日益增长,它已成为实验和临床研究中不可或缺的一部分。现在,我们不仅可以模拟具体的神经过程,还能构建更宏观、更整合的模型。这些模型将不可避免地揭示大脑的认知架构,并促进更通用人工智能的发展。
大脑理论将计算模型融入概念框架中,并根据信息理论框架(自由能原理,Friston et al., 2006; Parr et al., 2022)或动力系统理论(流形上的结构流,Jirsa & Sheheitli, 2022) 提出它们的功能原则。除了模拟生物信息处理外,计算方法还能高效分析大型复杂数据集,通过人工神经网络、理论建模和模拟等手段,将大脑的结构与功能联系起来。在细胞-分子级别和/或系统模型中的模拟有助于测试特定假设或预测大脑结构、动力学甚至行为的特性,同时也能整合各种技术获取的不同研究结果。将所有实验发现(包括模型、文本、图像和其他数据)整合到一个统一的知识框架中仍然是必要的。这反过来这对于将神经科学的发现转化为数字医学、提出新的治疗策略以及发展神经启发技术至关重要,后者利用了对感知、可塑性、学习和记忆的不断增长的见解。
目前最先进的技术被用于研究整个时空谱的过程,通常是针对特定种、属、科、目、纲或门而定制的。在系统发育树的不同分支(例如无脊椎动物)上开发的方法仅缓慢地适应其他层级的使用,例如在啮齿类动物和灵长类动物中的应用。近期,研究人员为果蝇创建了一个包含所有细胞和细胞类型的详尽图谱 (Li et al., 2020),并研究了导致宏观功能变化的电路遗传规范 (Handler et al., 2019)。这些信息对理解宏观层面状态转换与个体在连接强度上的差异之间的关系可能至关重要(Taylor et al., 2022)。通过统计整合系统发育知识,将这些知识从模型动物转译至人类,研究人员可以无创地在人脑中横跨尺度构建联系 (Felsenstein, 1985; 早期提到了这种方法的必要性)。
其他无脊椎动物研究的成功例子包括了一系列精巧的可逆扰动工具,这些工具用于解析微观和宏观电路功能,如光遗传学、化学遗传学和路径选择性扰动。这些工具最初在藻类中开发,并在无脊椎动物中得到了进一步的完善。随后,这些工具极大地革新了啮齿类动物的研究领域(Kim et al., 2017),但直到近期才开始应用于灵长类动物的研究(Gerits et al., 2012;Han et al., 2009;Klink et al., 2021)。比如,研究人员选择使用斑马鱼来理解那些在哺乳动物中难以测试的遗传或发育机制(Rastegar & Strähle, 2016)。靶向扰动也可以通过 CRISPR/Cas9 引入到诱导多能干细胞模型的神经元或脑类器官中。
目前,在比较两种或更多代表性物种的特征时,神经科学会参考系统发育(进化历史)。通过在两个远缘相关的物种中识别进化上的趋同特征,可以用来三角测量相关特征之间的关联证据(例如,某一脑结构及其相关的行为功能)。识别在近缘物种之间差异明显的进化发散特征,有助于确定物种特异性特化的起源(例如,人类而非其他灵长类动物所独有的脑部特征)。在过去几十年中,为多种物种编制的基因组序列为系统发育信息的爆炸式增长提供了基础,随之诞生了用于跨物种比较特征的全新统计工具集,称为系统发育比较方法。
系统发育比较方法伴随数字数据集的可用性及比较神经影像学的发展而兴起(Friedrich et al., 2021)。这些方法将为计算分析不断增长的比较神经科学数据提供新机会。它们可以进行同源性推断的统计测试;可以模拟一个特征在进化中的保守程度;并允许在更广泛的分类群中定量检验特征的趋同。随着更多复杂的大脑数据以数字形式变得可用,并涵盖更多物种,我们将有可能对大脑结构、神经回路和细胞生物学的进化进行建模,同时整合基因组、表观遗传和转录组机制。
例如,研究人员已在125种哺乳动物中研究了结构性大脑连接组与系统发育距离的比较(Faskowitz et al., 2022)。此外,通过古代 DNA 研究的新发现正逐渐显现,这些研究已被用来将人类特有的基因表达特征与神经解剖学关联起来,特别是研究尼安德特人对人类 DNA 的贡献(Gunz et al., 2019)。一些当前与人类神经精神疾病相关联的等位基因可能曾与这些适应相关,这些适应是智人——及我们最近与之混居的群体——适应世界各地环境变化时形成的(Benton et al., 2021)。随着关于神经解剖、基因组、生理和行为的数据及比较化石记录的持续积累,新的研究机会将持续出现。比较数据和进化模型可以被用来通过“逆向工程”方法开发人类及其他物种的思维,记录它们自然历史中的变化(Sendhoff et al.,2009)。
除此之外,神经科学家还研究各种模型物种的系统层面,以掌握大脑结构和功能的具体原理,这不仅限于传统的灵长类和啮齿类模型。虽然小鼠模型在理解人类疾病的神经生物学中发挥了重要作用,但对于许多人类疾病来说,它们并不理想(Brenowitz & Zakon, 2015)。例如,小鼠常被用来研究衰老,但它们缺少许多与人类衰老和相关疾病相似的生物特征。某些模型生物的衰老过程则与人类相似,尤其是猫和狗,它们展现了许多与人类衰老相似的特征,并且随着年龄增长表现出大脑萎缩和认知衰退(Gunn-Moore et al., 2007;Landsberg et al., 2012;Youssef et al., 2016)。一些猫和狗大脑中的神经病理学与人类阿尔茨海默病中观察到的相似(Head et al., 2000, 2005)。扩展使用各种模型系统来研究人类健康和疾病,有助于我们应对人类医学中的复杂问题。
鸟类模型,尽管其大脑结构与哺乳动物大不相同,已成为研究复杂认知基础的热门选择。这包括记忆空间路线或数百个食物藏匿点、解决问题、社会利他主义、心理理论和多任务处理等功能(Balakhonov & Rose, 2017;Emery, 2006;Güntürkün & Bugnyar, 2016)。鸟类具有卓越的认知能力,夜莺更拥有一个与人类语言系统相似的歌唱系统。这意味着鸟类是目前唯一一个用于研究大脑中语音信息开发和处理的动物模型,极大地推动了比较神经解剖学和脑皮层演化的研究(Brainard & Doupe, 2002;Brenowitz et al., 1997;Jarvis, 2004, 2019;Nottebohm, 2005)。
此外,经过超过 3.65 亿年的独立进化,鸟类演化出了与哺乳动物不同的脑皮层结构,但在相关大脑区域展示出相似的连通性、神经化学特征、神经元数量以及与认知功能相关的细胞的基因表达谱(Colquitt et al., 2021;Herold et al., 2011, 2014;Kverková et al., 2022;Shanahan et al., 2013;Ströckens et al., 2022)。这些比较研究为理解大脑结构和功能之间的基本联系提供了深刻的洞见,并可能识别出鸟类和哺乳动物大脑中高级认知不可或缺的共同神经机制(Stacho et al., 2020)。大规模的比较研究是理解认知的关键,并为解读正常和病理人类大脑功能的神经机制提供了独特的工具。
至于人类/灵长类动物是否进化出独特的结构特性,这仍是一个悬而未决的问题。例如,锥体细胞、中间神经元和胶质细胞的数量和复杂性,以及特定的大脑网络特性,可能在人类和非人类哺乳动物之间存在差异(Benavides-Piccione et al., 2020;Berg et al., 2021;Fang et al., 2022)。这些研究仅涵盖了少数哺乳动物种类,目前还无法预见这些差异是否会在增加更多物种和参数的考虑下持续存在。此外,尽管之前认为新小脑的扩展是人类独有的(Balsters et al., 2010,但现在看来它很可能在所有灵长类动物中以可预测的方式发生(Magielse et al., 2023)。目前已开发出的方法让我们能够以接近使用动物模型的详细程度来检查人类大脑的组织和功能(Eyal et al., 2018;Montero-Crespo et al., 2020)。
虽然这篇概览远非全面,但它揭示了几个重要的点:(1)神经科学的进步不仅源于概念上的提升,也与新方法和技术的出现紧密相关;(2)新技术不仅加深了我们对大脑的理解,同时也为我们打开了复杂性的新层次,引发了新的问题;(3)不断增加的跨领域、跨尺度、跨物种和跨模型的知识整合与协作需求正在日益增长。
工具
多种新工具正在深刻推动我们对大脑结构和功能的理解;同时,研究人员还拥有新的能力和强大的算力来分析数据和模拟大脑功能。这些工具来自全球不同的平台和联盟。
本文将重点介绍 EBRAINS,这是一个专为神经科学研究设计的分布式数字研究基础设施。EBRAINS[4 为用户提供了一个统一、数字化、开放、可互操作的平台,通过这个平台,可以访问以前散布在各个实验室的数据、工具、方法和理论。它是在 HBP 项目下开发的,并且遵循 FAIR 数据原则(Wilkinson et al., 2016)。
EBRAINS 提供了一系列服务,包括共享神经科学数据和模型、人类的多级大脑图谱、啮齿动物及非人类灵长类动物的大脑图谱、模拟研究、脑启发技术、医学数据分析以及专门的协作工具。此外,它还整合了创新的神经形态计算,并支持在虚拟机器人中进行实验。由欧洲顶尖的高性能计算中心的专家协调的 Fenix[5]基础设施,极大地促进了对高计算和存储需求的研究。通过 Fenix,神经科学家还可以与其他研究领域的社区合作,共同开发新的软件和解决方案,以应对数据和计算密集型研究的挑战。这一合作非常重要,因为它使得不同领域的社区在面对类似问题(例如,大数据集的可视化、数据的快速交互式访问)时能够实现资源的更有效利用。
EBRAINS研究基础设施吸引了广泛且多样化的用户群体,从经验丰富的应用/服务开发者到资深神经科学家、年轻研究人员及学生均有涉及。利益相关者和用户之间的协作工作和共同创造将成为 EBRAINS 社区的重要组成部分,并指导其发展。该平台特别注重工具的易用性,并巧妙地平衡了界面的复杂性和用户需求。这样的设计便于合作工作,通过结合不同工具构建计算工作流,解决各种问题(例如 Eriksson et al., 2022; Fothergill et al., 2019; Wagner et al., 2022)。在这一点上,EBRAINS 正在改变科学家们研究大脑的方式,无论是在大规模的神经科学研究还是个体项目中都是如此。
计算工作流的特点应包括易于访问、可共享性、自动化、可复现性、互操作性、可移植性和开放性。在此基础上,特别重要的是采用知识图谱。[6]知识图谱这一工具包含了多模态信息的表示,以及EBRAINS工作流的以下“独立性”特点:
工具和服务独立于使用它们的工作流。这些工具和服务的输入被参数化,以便它们可以根据在不同工作流程中(重新)使用它们的其他工具和服务产生不同的输出。
工作流独立于执行它们的底层基础设施:公共工作流语言(CWL)[7]被用来以一种通用和标准的方式描述工作流,确保在具有不同要求、依赖和配置的基础设施中可以透明地执行。
工作流独立于底层的工作流管理系统。有几种系统与CWL兼容,可以执行工作流步骤,监控执行过程,处理故障,自动获取日志和输出以及执行其他相关操作。
这些都为国际合作神经科学研究提供了技术基础,代表了一个大规模合作项目的接口,如国际大脑计划 (IBI)[8] 和NIH大脑计划 (Litvina et al., 2019)。同样地,欧洲EBRA联盟制定了欧洲研究共享议程,旨在增强大脑研究的影响力,推进基础、转化和临床大脑研究,改善患有大脑疾病人群的生活,促进大脑创新,并应对欧洲及全球面临的社会和经济挑战。[9]还有一些人使用了知识表征(KR)这一术语,强调对神经科学大量知识进行正确、稳定和可验证的表征的必要性(Di Maio, 2021)。
再举一个例子:鉴于数字大脑研究的重要性及其对认知、行为和心理健康潜在的益处和价值驱动的影响,马来西亚建立了马来西亚开放科学平台(MSOP)[10],该平台旨在加强国内外的科学、技术和创新。超越大脑本身,在更广泛的范围内,人类参考图谱(Borner et al., 2021)和欧盟委员会的虚拟人类孪生(VHT)计划(由EDITH 协调与支持行动推动*)致力于开发必要的基础设施,推动整个人体多尺度多器官孪生的创建。这些孪生可能从 EBRAINS 的经验和开发的工具中受益。
我们还缺少什么?
对大脑功能及其失调的深入理解现在不仅成为可能,而且迫在眉睫。神经系统疾病和精神病对患者、护理者、家属以及整个社会都构成了重大负担。推动这些领域的进展,也受到了探索自我本质、意识和认知等哲学问题的驱动。要更好地理解大脑健康的基础及生死边界,必须汇集不同的视角。同时,伦理、哲学、法律与监管、文化及政治的挑战密切相关,需要同时被解决。
大脑医学的进步与基础研究的突破密不可分,然而许多基本问题仍待解决。譬如,记忆的形成、意识知觉的基础、突触信号转导的电学与分子生化机制的相互作用、神经胶质细胞在信号转导和代谢中的角色、不同大脑状态在突触结构终身重组中的作用、动力学与认知模型之间的关系、细胞集合是如何产生具体的认知功能等,这些都是需进一步阐明的重要方面。此外,大脑结构的特定动力学变化,如细胞结构、髓鞘、化学构造和区域间连通性,尚未被充分理解,但这些因素最终影响着不同大脑区域之间兴奋性与抑制性细胞活动的比例,,从而在不同区域之间形成动态平衡(Barbero-Castillo et al., 2021; Deco et al., 2018; Demirtaş et al., 2019; Jancke et al., 2022; Kringelbach et al., 2020)。
目前对支持认知功能(如记忆或决策)的机制的了解,受到现有技术的规模和精度的限制——微观尺度的同步记录仅限于少数大脑区域,而全脑成像则缺乏必要的空间和/或时间分辨率。计算模型可以帮助填补这一空白,但目前也处于瓶颈期:几乎所有的认知功能机制模型都专注于微观尺度(Amit & Brunel, 1997; Mante et al., 2013; Wang, 2002),而全脑模型主要用于模拟大规模的神经动力学(Breakspear, 2017; Deco et al., 2011)。为了弥合这一领域的分歧,必须开发新的模型方法,这些方法可能包括在大规模大脑模型中引入简化的认知功能(Mejías & Wang, 2022),扩展诸如循环神经网络这类认知模型至多区域框架(Yang & Molano-Mazón, 2021),或提高现有认知多区域模型的生物合理性(Dora et al., 2021)。
由于临床需求而驱动的与大脑的互动需求(包括读取和刺激/操控),已开辟了如评估意识障碍(如无反应觉醒综合症、锁定综合症等)、脑机接口、认知增强、感官恢复和感官扩展技术等新兴且不断扩展的领域,这些技术的相关性已远超医疗领域,影响到社会大众。同时,也需要能够进行高时间和空间分辨率的大脑记录和活动控制,且这些技术应尽可能具备非侵入性。这些技术进展需要神经科学与微电子、纳米电子学、光学、光控药物、纳米机器人、新材料(例如石墨烯)等领域的跨学科合作。预计不久将在安全性、生物相容性、大脑反应性变化(如胶质增生、细胞死亡)、信噪比、侵入性问题(如手术、感染)以及大脑功能的闭环控制等方面取得进展,这些进展将带来法律和伦理方面的影响。
尽管这些领域的进展令人印象深刻,但要全面理解基本过程,需要将每个系统(如视觉、感觉运动)与大脑的其他部分、身体和环境进行整合。此外,还需将分子、亚细胞、细胞和系统层面整合,实现包含所有这些复杂关系的突现属性的“多尺度”理解。仅考虑系统的一部分无法充分理解这些层面。每个层面的功能失常可能引发各种神经和精神疾病。为了全面理解这一过程,我们需要了解所有单个步骤,这在今天的很多情况下是困难或不可能的。因此,必须在适当的抽象级别上处理各个步骤,发展相关理论,并通过多层次的结构和功能图谱访问不同大脑组织层面的数据。
最新的自下而上的计算模型可以整合如特定离子通道、突触受体和神经调节剂等微观特征,并评估它们在细胞亚群体水平上的影响。最近,这种方法甚至扩展到全脑层面,通过研究麻醉剂的分子靶点,如异丙酚,及其在大规模活动水平上的作用。例如,改变K+电导(Dalla Porta 等,2023 年)或抑制性(GABA-A)突触受体的动力学,可以诱导大脑活动转变为同步慢波,达到类似于麻醉剂的效果。[11]这是计算模型可以发挥作用的领域之一。
为了全面理解行为和认知是如何通过皮层计算产生的,需要结合自下而上和自上而下的方法。典型的例子是腹侧视觉流。尽管用于物体识别的深度神经网络受到视觉系统架构的启发,这些网络同样也优化了视觉系统本身的功能模型。实际上,深层网络中模型神经元的统计属性与大脑中记录的真实神经元非常相似(Yamins & DiCarlo, 2016;Zhuang et al.,2021)。用更精细的自下而上模型再现这种自上而下模型的功能仍是一大挑战。
实验测量与模型预测之间的相互作用非常强大,它们已经在理解网络级现象(如振荡、波动(Breakspear, 2017; Marder et al., 2022; Tort-Colet et al., 2021))方面取得了显著进展。将这种方法扩展到整个大脑层面更具挑战性,因为其涉及的复杂性较高,而且现有的非侵入性人类成像和记录技术的时空分辨率仍不足。将这些模型与成像技术结合,需要深入了解各种信号的生物物理特性。当使用计算模型来定量预测认知功能和衰老时,这一点尤为重要(Charvet, 2021; Charvet et al., 2022; Heckner et al., 2023; Jonsson et al., 2019),比如,基于患者和健康受试者的成像数据,并在计算模型与临床数据之间建立精确的反馈循环,这最终应有助于更好地理解神经疾病。
网络和其他模型还用于研究生理机制在病理条件下如何发生变异,例如,蛋白质水平的微观变化可能导致异常的行为或临床症状(Mäki-Marttunen et al., 2019)。其中最为人所熟知的是癫痫病例,已经确定了几个导致异常高兴奋性的微观靶标。另一个例子是一个包括神经递质受体数据的多因素因果模型,该模型能预测阿尔茨海默病症状的临床严重程度的变化幅度,从而进一步支持个性化大脑模型的价值,以及将多模态数据融入模型的重要性(Khan et al., 2022)。与此相比,许多其他病理状态如精神分裂症的组织病理学和大脑信号还不甚明了,计算模型在识别机制及预测可能具有信息量的宏观和/或行为特征方面可能起到关键作用。为了解答这些和其他研究问题,必须应对许多技术、方法和计算上的挑战(见Box1)。
Box 1:技术、方法和计算挑战
大脑研究在大脑接口、分析、机制理解、数据解释及大脑处理建模方面,面临巨大的技术和计算挑战。以下是一些例子:
(1)数据的复杂性(包括多级大脑组织、层级结构、并行信息处理、冗余、电化学处理等)。其中一个关键问题是不同尺度间的关系,这关系到找到最适合阐明这些关系的粒度(及相应的数据)。在物理学中有一个“重整化”的概念,即在不同尺度间保持定律的一致性(例如稀疏耦合、层级动力学、计算原理等)。此外,必须在所有相关尺度上进行测量,以获得如何从低级状态合成高级状态的信息,并应对神经退化问题,即不同系统配置可能支持相同或相似功能的倾向。
(2)不同硬件、软件和分析方法带来的数据格式和数据模型的多样性。不同研究者和实验室提供的数据常常存在差异,这给数据整合和互操作性带来了障碍。促进标准和协调程序的采用,包括采用标准化大脑图谱进行空间参照,是极为重要的。这些措施对于促进数据的重用及其在不同情境下的组合和应用起到了关键作用。
(3)来自人类受试者的大脑数据可以进行去标识处理,但可能无法完全匿名化(即无法追溯到个体)。因此,有必要提供安全的数据存储服务,这些服务需要提供受控或限制的访问权限,以便于数据的再利用。在这些受保护的存储系统中,数据的发现性涉及到公开共享匿名元数据,这是目前 EBRAINS 正在采用的做法。
(4)很多行为和某些机制是人类特有的,但大量数据不可直接获取且仍然未知(例如,无法在活人的大脑中测量到细胞层面的化学反应动力学)。研究动物大脑的比较方法以及模拟和建模是解决这一问题的策略。
(5)跨个体的变异性和多样性。将来自不同人群的信息整合到个性化医学的图谱、数据库和研究中是必要的。
(6)鉴于大脑的空间和时间活动具有多尺度性质,数据集的特定空间和时间分辨率极为关键。从微观和纳米尺度到中观和宏观尺度的尺度整合是一大挑战,捕捉大脑动力学同样充满挑战。这需要根据发现的拓扑特征,在一个共同的框架中表示不同的尺度,即在多级和多尺度的图谱和模型中加入时间维度。
(7)“子系统”的大尺寸问题(如神经递质受体这类大分子含有许多原子,具有复杂的动力学结构,大型网络,整个大脑的视角与特定兴趣区域相比,大型人群等)。
(8)系统在病理状态下的反应模式、动力学、可塑性和行为表现出广泛的多样性。
(9)系统性质的变化,表现为不同空间尺度的可塑性(从树突棘到大型网络;尖峰适应、长期增强、长期抑制等过程)或病变后的神经退化。
(10)预测和分析的准确性和可靠性,尤其是在将其应用于大脑医学方面,对于个别受试者而言至关重要。
(11)缺乏一个全面的大脑理论,或选择几种相互竞争的理论。
(12)缺乏将广泛的大脑样本通过现代实验方法(包括欧洲和世界各地已有 100 多年历史的实验方法)进行整合和记录的能力,更好地利用历史大脑样本和数据显得尤为重要。这些样本数量可能达到数十万甚至数百万,大多数还未进行数字化或通过网络工具提供。其中一些包括稀有物种或在无法再现的条件下获得的大脑(例如,未治疗的脑病患者)。让这些资料在全球范围内数字化,供研究人员使用,将极大地促进进化、比较以及临床研究;然而,这一目标涉及到数据交换、存储和安全方面的重大挑战。目前已经开始尝试将尸检大脑解剖与数字框架中的体内成像结合,例如https://bradipho.eu/。
伦理和社会问题作为负责任数字大脑研究的动力
数字大脑研究应由科学好奇心和推动社会利益的愿望所驱动;同时,它应反映社会的优先关切,这包括更深入地理解大脑功能、开发更优秀的诊断工具,以及治疗大脑疾病的更有效方法。本节中,我们将简要提出如何确保社会关切得到妥善处理并体现在研究及其成果中,并探讨如何确保研究和创新过程的负责任执行。未来的研究项目必须融入前瞻性实践、神经伦理反思、多方利益相关者及公民的参与,并持续遵守现行的法律、规章和良好研究惯例。这包括仔细考虑数据生成和研究管理中性别与多样性的作用,关注可能的双用途研究或误用神经科学发现的风险,以及对研究的伦理可持续性、人权影响及其长远的社会政治影响进行深思。
数字大脑研究需要考虑的其他社会法律问题还包括数据保护问题、欧洲委员会的通用数据保护条例(GDPR)下的数据管理、社会期待、接受度以及数字大脑模型的可持续性问题,还有先进人工智能、脑启发型计算和神经机器人研究可能带来的问题等。例如,神经科学与技术的融合很可能引领AI的新方向。在数字大脑研究中,应重视不仅仅是数据的大量积累,更要确保数据的多样性代表性,包括性别、年龄和种族等因素。这种包容性扩展到所有参与者,包括研究人员、实践者和利益相关者。通过倡导多样性,该领域能够有效地解决AI中的偏见问题,并主动应对创新方法、技术和应用带来的新问题。
负责任的研究与创新(RRI)框架提出了一种多学科策略,用于解决未来数字大脑研究愿景所伴随的伦理、哲学、社会和监管挑战。此外,RRI激发的研究和实践有助于建设一个积极主动识别现存和新兴社会及伦理挑战的负责任数字大脑研究未来。
数字大脑模型是未来大脑研究的核心概念和模式。它们提出了一些重要的哲学问题(例如,脑机接口访问其他大脑的权限应该有什么限制?)(Evers & Sigman, 2013) 和伦理社会问题(例如,这项技术是否有潜在的应用问题?谁负责潜在应用的分析和决策?我们希望如何在社会中利用这些模型?)(Evers & Salles, 2021)。清晰的概念定义是就数字大脑研究引发的伦理问题进行知情讨论的前提。通过 RRI 框架审视这些问题,包括对涉及概念的意义和适当性进行反思,以及神经科学研究不同学科之间的交流和对话,包括哲学家、伦理学家、社会科学家与政策制定者、利益组织和公众等社会利益相关者的互动(Box 2)。
Box 2:伦理问题
数字大脑研究所引发的伦理问题需被认识到,特别是与数字孪生技术相关的问题。
(1)隐私。数字孪生系统会持续地用现实世界的数据进行更新。当成像、遗传及临床数据结合使用时,这些数据往往容易被识别。即便是数量庞大的“孤立”信息源也可能被识别,特别是在涉及罕见病例时。随着大数据时代的来临,有效的去识别化变得越来越困难(Choudhury et al.,2014)。在同意过程中,告知个体关于隐私的考虑极为重要,他们需要明白随时间推移,识别风险可能会增加(White et al.,2022)。作为一个社区,我们需要与政府机构合作,未来制定相关政策。
(2)“读心术”。数字大脑研究往往涉及情感、感知、记忆及心理状态等领域,这些领域通常被视为内心生活的神圣部分,因此对隐私的担忧更为严重。已有研究表明,通过大脑成像及其他生理测量,可以在人群中预测行为(Bell et al.,2019;Talozzi et al.,2023)。数字大脑模型的潜力更大,不仅可以对特定的大脑状态进行分类,还能提出增强这些状态的方法(Ligthart et al.,2021)。
(3)恶意行为。数字大脑研究的“双重用途”性质逐渐被人们认识到,它可能带来的危害与益处同样重大。
未完待续……
参考文献:
- 1 https://www.thelancet.com/gbd/about
- 2 https://www.humanbrainproject.eu/en/
- 3 https://www.cell.com/neuron/issue?pii=S0896-6273%2814%29X0043-7
- 4 EBRAINS: https://ebrains.eu/
- 5 Fenix: https://fenix-ri.eu/
- 6 https://search.kg.ebrains.eu/
- 7 https://www.commonwl.org/
- 8 International Brain Initiative: https://www.internationalbraininitiative.org/
- 9 https://www.ebra.eu/sebra/
- 10 https://www.akademisains.gov.my/mosp/
- 11 work in progress in showcase 3 of the HBP: https://www.humanbrainproject.eu/en/follow-hbp/news/2022/06/20/how-ebrains-used-investigate-disorders-consciousness/
- 12 https://www.theatlantic.com/science/archive/2019/07/ten-years-human-brain-project-simulation-markram-ted-talk/594493/
- 13 https://www.neurorobotics.net/
- (2018). Big data needs a hardware revolution. Nature, 554(7691), 145–146. https://doi.org/10.1038/d41586-018-01683-1
- (2023). How we promote data sharing. Nat Neurosci, 26(12), 2038. https://doi.org/10.1038/s41593-023-01529-8
- Abadía, I., Naveros, F., Ros, E., Carrillo, R. R., & Luque, N. R. (2021). A cerebellar-based solution to the nondeterministic time delay problem in robotic control. Sci Robot, 6(58), eabf2756. https://doi.org/10.1126/scirobotics.abf2756
- Allegra Mascaro, A. L., Silvestri, L., Sacconi, L., & Pavone, F. S. (2015). Towards a comprehensive understanding of brain machinery by correlative microscopy. J Biomed Opt, 20(6), 61105. https://doi.org/10.1117/1.Jbo.20.6.061105
- Amit, D. J., & Brunel, N. (1997). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb Cortex, 7(3), 237–252. https://doi.org/10.1093/cercor/7.3.237
- Amunts, K., DeFelipe, J., Pennartz, C., Destexhe, A., Migliore, M., Ryvlin, P., Furber, S., Knoll, A., Bitsch, L., Bjaalie, J. G., Ioannidis, Y., Lippert, T., Sanchez-Vives, M. V., Goebel, R., & Jirsa, V. (2022). Linking brain structure, activity and cognitive function through computation. eNeuro, 9(2), ENEURO.0316-21.2022. https://doi.org/10.1523/eneuro.0316-21.2022
- Amunts, K., Ebell, C., Muller, J., Telefont, M., Knoll, A., & Lippert, T. (2016). The Human Brain Project: Creating a European research infrastructure to decode the human brain. Neuron, 92(3), 574–581. https://doi.org/10.1016/j.neuron.2016.10.046
- Amunts, K., Knoll, A. C., Lippert, T., Pennartz, C. M. A., Ryvlin, P., Destexhe, A., Jirsa, V. K., D’Angelo, E., & Bjaalie, J. G. (2019). The Human Brain Project-synergy between neuroscience, computing, informatics, and brain-inspired technologies. PLoS Biol, 17(7), e3000344. https://doi.org/10.1371/journal.pbio.3000344
- Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M., Bludau, S., Bazin, P. L., Lewis, L. B., Oros-Peusquens, A. M., Shah, N. J., Lippert, T., Zilles, K., & Evans, A. C. (2013). BigBrain: An ultrahigh-resolution 3D human brain model. Science, 340(6139), 1472–1475. https://doi.org/10.1126/science.1235381
- Amunts, K., Mohlberg, H., Bludau, S., & Zilles, K. (2020). Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science, 369(6506), 988–992. https://doi.org/10.1126/science.abb4588
- Aru, J., Suzuki, M., & Larkum, M. E. (2020). Cellular mechanisms of conscious processing. Trends Cogn Sci, 24(10), 814–825. https://doi.org/10.1016/j.tics.2020.07.006
- Axer, M., & Amunts, K. (2022). Scale matters: The nested human connectome. Science, 378(6619), 500–504. https://doi.org/10.1126/science.abq2599
- Balakhonov, D., & Rose, J. (2017). Crows rival monkeys in cognitive capacity. Sci Rep, 7(1), 8809. https://doi.org/10.1038/s41598-017-09400-0
- Balsters, J. H., Cussans, E., Diedrichsen, J., Phillips, K. A., Preuss, T. M., Rilling, J. K., & Ramnani, N. (2010). Evolution of the cerebellar cortex: The selective expansion of prefrontal-projecting cerebellar lobules. Neuroimage, 49(3), 2045–2052. https://doi.org/10.1016/j.neuroimage.2009.10.045
- Barbas, H. (2015). General cortical and special prefrontal connections: Principles from structure to function. Annu Rev Neurosci, 38(1), 269–289. https://doi.org/10.1146/annurev-neuro-071714-033936
- Barbero-Castillo, A., Mateos-Aparicio, P., Dalla Porta, L., Camassa, A., Perez-Mendez, L., & Sanchez-Vives, M. V. (2021). Impact of GABA(A) and GABA(B) inhibition on cortical dynamics and perturbational complexity during synchronous and desynchronized states. J Neurosci, 41(23), 5029–5044. https://doi.org/10.1523/jneurosci.1837-20.2021
- Barson, D., Hamodi, A. S., Shen, X., Lur, G., Constable, R. T., Cardin, J. A., Crair, M. C., & Higley, M. J. (2020). Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat Methods, 17(1), 107–113. https://doi.org/10.1038/s41592-019-0625-2
- Bassetti, C. L. A. (2022). European Academy of Neurology 2019–2022. Eur J Neurol, 29(9), 2567–2571. https://doi.org/10.1111/ene.15421
- Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J. M., Oostenveld, R., Dowdall, J. R., De Weerd, P., Kennedy, H., & Fries, P. (2015). Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron, 85(2), 390–401. https://doi.org/10.1016/j.neuron.2014.12.018
- Becker, F., Bibow, P., Dalibor, M., Gannouni, A., Hahn, V., Hopmann, C., Jarke, M., Koren, I., Kröger, M., Lipp, J., Maibaum, J., Michael, J., Rumpe, B., Sapel, P., Schäfer, N., Schmitz, G. J., Schuh, G., & Wortmann, A. (2021). A conceptual model for digital shadows in industry and its application. In A. Ghose, J. Horkoff, V. E. Silva Souza, J. Parsons, & J. Evermann (Eds.), Conceptual modeling. Springer. https://doi.org/10.1007/978-3-030-89022-3_22
- Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: Making an informed choice. Qual Quant, 53(2), 1051–1074. https://doi.org/10.1007/s11135-018-0802-x
- Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R., & Maass, W. (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nat Commun, 11(1), 3625. https://doi.org/10.1038/s41467-020-17236-y
- Benavides-Piccione, R., Regalado-Reyes, M., Fernaud-Espinosa, I., Kastanauskaite, A., Tapia-González, S., León-Espinosa, G., Rojo, C., Insausti, R., Segev, I., & DeFelipe, J. (2020). Differential structure of hippocampal CA1 pyramidal neurons in the human and mouse. Cereb Cortex, 30(2), 730–752. https://doi.org/10.1093/cercor/bhz122
- Benton, M. L., Abraham, A., LaBella, A. L., Abbot, P., Rokas, A., & Capra, J. A. (2021). The influence of evolutionary history on human health and disease. Nat Rev Genet, 22(5), 269–283. https://doi.org/10.1038/s41576-020-00305-9
- Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S. L., Gouwens, N. W., Hodge, R. D., Kalmbach, B., Lee, C., Lee, B. R., Alfiler, L., Baker, K., Barkan, E., Beller, A., … Lein, E. S. (2021). Human neocortical expansion involves glutamatergic neuron diversification. Nature, 598(7879), 151–158. https://doi.org/10.1038/s41586-021-03813-8
- Bicanski, A., & Burgess, N. (2018). A neural-level model of spatial memory and imagery. eLife, 7, e33752. https://doi.org/10.7554/eLife.33752
- Booklet | Brain-inspired intelligent robotics: The intersection of robotics and neuroscience sciences. (2016). Science, 354(6318), 1445–1445. https://doi.org/10.1126/science.354.6318.1445-b
- Borner, T., Geisler, C. E., Fortin, S. M., Cosgrove, R., Alsina-Fernandez, J., Dogra, M., Doebley, S., Sanchez-Navarro, M. J., Leon, R. M., Gaisinsky, J., White, A., Bamezai, A., Ghidewon, M. Y., Grill, H. J., Crist, R. C., Reiner, B. C., Ai, M., Samms, R. J., De Jonghe, B. C., & Hayes, M. R. (2021). GIP receptor agonism attenuates GLP-1 receptor agonist–induced nausea and emesis in preclinical models. Diabetes, 70(11), 2545–2553. https://doi.org/10.2337/db21-0459
- Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., … Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810), 84–88. https://doi.org/10.1038/s41586-020-2314-9
- Brainard, M. S., & Doupe, A. J. (2002). What songbirds teach us about learning. Nature, 417(6886), 351–358. https://doi.org/10.1038/417351a
- Brama, H., Guberman, S., Abeles, M., Stern, E., & Kanter, I. (2015). Synchronization among neuronal pools without common inputs: In vivo study. Brain Struct Funct, 220(6), 3721–3731. https://doi.org/10.1007/s00429-014-0886-6
- Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., Liebenberg, M., Michael, J., Pennekamp, J., Quix, C., Rumpe, B., Aalst, W. v. d., Wehrle, K., Wortmann, A., & Ziefle, M. (2022). A computer science perspective on digital transformation in production. ACM Trans Internet Things, 3(2), Article 15. https://doi.org/10.1145/3502265
- Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nat Neurosci, 20(3), 340–352. https://doi.org/10.1038/nn.4497
- Brenner, S. (2003). Nobel lecture. Nature’s gift to science. Biosci Rep, 23(5–6), 225–237. https://doi.org/10.1023/b:bire.0000019186.48208.f3
- Brenowitz, E. A., Margoliash, D., & Nordeen, K. W. (1997). An introduction to birdsong and the avian song system. J Neurobiol, 33(5), 495–500. https://pubmed.ncbi.nlm.nih.gov/9369455/
- Brenowitz, E. A., & Zakon, H. H. (2015). Emerging from the bottleneck: Benefits of the comparative approach to modern neuroscience. Trends Neurosci, 38(5), 273–278. https://doi.org/10.1016/j.tins.2015.02.008
- Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Barth. https://wellcomecollection.org/works/vrnkkxtj
- Buzsáki, G. (2019). The brain from inside out. Oxford Academic. https://doi.org/10.1093/oso/9780190905385.001.0001
- Callaway, E., Dong, H.-W., Ecker, J., Hawrylycz, M., Huang, J., Lein, E., Ngai, J., Osten, P., Ren, B., Tolias, A., White, O., Zeng, H., Zhuang, X., Ascoli, G., Behrens, M., Chun, J., Feng, G., Gee, J., Ghosh, S., & Sunkin, S. (2021). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature, 598, 86–102. https://doi.org/10.1038/s41586-021-03950-0
- Capone, C., Pastorelli, E., Golosio, B., & Paolucci, P. S. (2019). Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model. Sci Rep, 9(1), 8990. https://doi.org/10.1038/s41598-019-45525-0
- Cardin, J. A., Crair, M. C., & Higley, M. J. (2020). Mesoscopic imaging: Shining a wide light on large-scale neural dynamics. Neuron, 108(1), 33–43. https://doi.org/10.1016/j.neuron.2020.09.031
- Carlsson, A., Hillarp, N.-Å., & Hoükfelt, B. (1957). The concomitant release of adenosine triphosphate and catechol amines from the adrenal medulla. J Biol Chem, 227, 243–252. https://doi.org/10.1016/S0021-9258(18)70811-9
- Chalmers, D. (1995). Facing up to the problem of consciousness. J Conscious Stud, 2(3), 200–219. https://consc.net/papers/facing.pdf
- Chartrand, T., Dalley, R., Close, J., Goriounova, N. A., Lee, B. R., Mann, R., Miller, J. A., Molnar, G., Mukora, A., Alfiler, L., Baker, K., Bakken, T. E., Berg, J., Bertagnolli, D., Braun, T., Brouner, K., Casper, T., Csajbok, E. A., Dee, N., … Lein, E. S. (2023). Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex. Science, 382(6667), eadf0805. https://doi.org/10.1126/science.adf0805
- Charvet, C., Ofori, K., Falcone, C., & Rigby-Dames, B. (2022). Transcription, structure, and organoids translate time across the lifespan of humans and great apes. bioRxiv. https://doi.org/10.1101/2022.10.28.513899
- Charvet, C. J. (2021). Cutting across structural and transcriptomic scales translates time across the lifespan in humans and chimpanzees. Proc R Soc B Biol Sci, 288(1944), 20202987. https://doi.org/10.1098/rspb.2020.2987
- Chen, X., Wang, F., Fernandez, E., & Roelfsema, P. R. (2020). Shape perception via a high-channel-count neuroprosthesis in monkey visual cortex. Science, 370(6521), 1191–1196. https://doi.org/10.1126/science.abd7435
- Choudhury, S., Fishman, J. R., McGowan, M. L., & Juengst, E. T. (2014). Big data, open science and the brain: Lessons learned from genomics. Front Hum Neurosci, 8, 239. https://doi.org/10.3389/fnhum.2014.00239
- Chung, S., & Abbott, L. F. (2021). Neural population geometry: An approach for understanding biological and artificial neural networks. Curr Opin Neurobiol, 70, 137–144. https://doi.org/10.1016/j.conb.2021.10.010
- Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 487(7405), 51–56. https://doi.org/10.1038/nature11129
- Colquitt, B. M., Merullo, D. P., Konopka, G., Roberts, T. F., & Brainard, M. S. (2021). Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits. Science, 371(6530). https://doi.org/10.1126/science.abd9704
- Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., Schemmel, J., & Zenke, F. (2022). Surrogate gradients for analog neuromorphic computing. Proc Natl Acad Sci U S A, 119(4), e2109194119. https://doi.org/doi:10.1073/pnas.2109194119
- Cramer, B., Stöckel, D., Kreft, M., Wibral, M., Schemmel, J., Meier, K., & Priesemann, V. (2020). Control of criticality and computation in spiking neuromorphic networks with plasticity. Nat Commun, 11(1), 2853. https://doi.org/10.1038/s41467-020-16548-3
- Croxson, P. L., Forkel, S. J., Cerliani, L., & Thiebaut de Schotten, M. (2018). Structural variability across the primate brain: A cross-species comparison. Cereb Cortex, 28(11), 3829–3841. https://doi.org/10.1093/cercor/bhx244
- Dale, H. H., Feldberg, W., & Vogt, M. (1936). Release of acetylcholine at voluntary motor nerve endings. J Physiol, 86(4), 353–380. https://doi.org/10.1113/jphysiol.1936.sp003371
- Dalla Porta, L., Barbero-Castillo, A., Sanchez-Sanchez, J. M., & Sanchez-Vives, M. V. (2023). M-current modulation of cortical slow oscillations: Network dynamics and computational modeling. PLoS Comput Biol, 19(7), e1011246. https://doi.org/10.1371/journal.pcbi.1011246
- Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C. S., Wang, C. H., Hsu, C. N., Lee, C. K., Ruan, P., Xu, D., Wu, D., Huang, E., Kitamura, F. C., Lacey, G., … Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med, 27(10), 1735–1743. https://doi.org/10.1038/s41591-021-01506-3
- Dayan, P. (2012). Twenty-five lessons from computational neuromodulation. Neuron, 76(1), 240–256. https://doi.org/10.1016/j.neuron.2012.09.027
- Deco, G., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Kringelbach, M. L. (2018). Whole-brain multimodal neuroimaging model using serotonin receptor maps explains non-linear functional effects of LSD. Curr Biol, 28(19), 3065–3074.e3066. https://doi.org/10.1016/j.cub.2018.07.083
- Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci, 12(1), 43–56. https://doi.org/10.1038/nrn2961
- DeFelipe, J. (2009). Cajal’s butterflies of the soul: Science and art. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195392708.001.0001
- Dehaene, S., Lau, H., & Kouider, S. (2017). What is consciousness, and could machines have it? Science, 358(6362), 486–492. https://doi.org/10.1126/science.aan8871
- Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., & Pallier, C. (2015). The neural representation of sequences: From transition probabilities to algebraic patterns and linguistic trees. Neuron, 88(1), 2–19. https://doi.org/10.1016/j.neuron.2015.09.019
- Demertzi, A., Tagliazucchi, E., Dehaene, S., Deco, G., Barttfeld, P., Raimondo, F., Martial, C., Fernández-Espejo, D., Rohaut, B., Voss, H. U., Schiff, N. D., Owen, A. M., Laureys, S., Naccache, L., & Sitt, J. D. (2019). Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv, 5(2), eaat7603. https://doi.org/10.1126/sciadv.aat7603
- Demirtaş, M., Burt, J. B., Helmer, M., Ji, J. L., Adkinson, B. D., Glasser, M. F., Van Essen, D. C., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2019). Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron, 101(6), 1181–1194.e1113. https://doi.org/10.1016/j.neuron.2019.01.017
- Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022). Learning cortical representations through perturbed and adversarial dreaming. eLife, 11, e76384. https://doi.org/10.7554/eLife.76384
- Deubner, J., Coulon, P., & Diester, I. (2019). Optogenetic approaches to study the mammalian brain. Curr Opin Struct Biol, 57, 157–163. https://doi.org/10.1016/j.sbi.2019.04.003
- Devinsky, O., Patel, A. D., Cross, J. H., Villanueva, V., Wirrell, E. C., Privitera, M., Greenwood, S. M., Roberts, C., Checketts, D., VanLandingham, K. E., & Zuberi, S. M. (2018). Effect of cannabidiol on drop seizures in the lennox–gastaut syndrome. N Engl J Med, 378(20), 1888–1897. https://doi.org/10.1056/NEJMoa1714631
- Di Maio, P. (2021). System level knowledge representation for metacognition in neuroscience. In Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. https://doi.org/10.1007/978-3-030-86993-9_8
- Diamond, A., Schmuker, M., & Nowotny, T. (2019). An unsupervised neuromorphic clustering algorithm. Biol Cybern, 113(4), 423–437. https://doi.org/10.1007/s00422-019-00797-7
- Donaldson, D. R., & Koepke, J. W. (2022). A focus groups study on data sharing and research data management. Sci Data, 9(1), 345. https://doi.org/10.1038/s41597-022-01428-w
- Dora, S., Bohte, S. M., & Pennartz, C. M. A. (2021). Deep gated hebbian predictive coding accounts for emergence of complex neural response properties along the visual cortical hierarchy. Front Comput Neurosci, 15, 666131. https://doi.org/10.3389/fncom.2021.666131
- Dotson, V. M., & Duarte, A. (2020). The importance of diversity in cognitive neuroscience. Ann N Y Acad Sci, 1464(1), 181–191. https://doi.org/10.1111/nyas.14268
- Douglas, R. J., & Martin, K. A. (2007). Recurrent neuronal circuits in the neocortex. Curr Biol, 17(13), R496–500. https://doi.org/10.1016/j.cub.2007.04.024
- Ebitz, R. B., & Hayden, B. Y. (2021). The population doctrine in cognitive neuroscience. Neuron, 109(19), 3055–3068. https://doi.org/10.1016/j.neuron.2021.07.011
- Einevoll, G. T., Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., de Kamps, M., & Schürmann, F. (2019). The scientific case for brain simulations. Neuron, 102(4), 735–744. https://doi.org/10.1016/j.neuron.2019.03.027
- Eke, D. O., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., Hill, S. L., Martone, M. E., McMahon, A., Ruebel, O., Crook, S., Thiels, E., & Pestilli, F. (2022). International data governance for neuroscience. Neuron, 110(4), 600–612. https://doi.org/10.1016/j.neuron.2021.11.017
- El Houssaini, K., Bernard, C., & Jirsa, V. K. (2020). The epileptor model: A systematic mathematical analysis linked to the dynamics of seizures, refractory status epilepticus, and depolarization block. eNeuro, 7(2). https://doi.org/10.1523/eneuro.0485-18.2019
- Emery, N. J. (2006). Cognitive ornithology: The evolution of avian intelligence. Philos Trans R Soc Lond B Biol Sci, 361(1465), 23–43. https://doi.org/10.1098/rstb.2005.1736
- Emiliani, V., Entcheva, E., Hedrich, R., Hegemann, P., Konrad, K. R., Lüscher, C., Mahn, M., Pan, Z.-H., Sims, R. R., Vierock, J., & Yizhar, O. (2022). Optogenetics for light control of biological systems. Nat Rev Methods Primers, 2(1), 55. https://doi.org/10.1038/s43586-022-00136-4
- Eriksson, O., Bhalla, U. S., Blackwell, K. T., Crook, S. M., Keller, D., Kramer, A., Linne, M.-L., Saudargienė, A., Wade, R. C., & Hellgren Kotaleski, J. (2022). Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife, 11, e69013. https://doi.org/10.7554/eLife.69013
- Evers, K., & Salles, A. (2021). Epistemic challenges of digital twins & virtual brains: Perspectives from fundamental neuroethics. SCIO J Philos, 21, 27–53. https://doi.org/10.46583/scio_2021.21.846
- Evers, K., & Sigman, M. (2013). Possibilities and limits of mind-reading: A neurophilosophical perspective. Conscious Cogn, 22(3), 887–897. https://doi.org/10.1016/j.concog.2013.05.011
- Eyal, G., Verhoog, M. B., Testa-Silva, G., Deitcher, Y., Benavides-Piccione, R., DeFelipe, J., de Kock, C. P. J., Mansvelder, H. D., & Segev, I. (2018). Human cortical pyramidal neurons: From spines to spikes via models. Front Cell Neurosci, 12, 181. https://doi.org/10.3389/fncel.2018.00181
- Fang, R., Xia, C., Close, J. L., Zhang, M., He, J., Huang, Z., Halpern, A. R., Long, B., Miller, J. A., Lein, E. S., & Zhuang, X. (2022). Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH. Science, 377(6601), 56–62. https://doi.org/10.1126/science.abm1741
- Faskowitz, J., Betzel, R. F., & Sporns, O. (2022). Edges in brain networks: Contributions to models of structure and function. Network Neurosci, 6(1), 1–28. https://doi.org/10.1162/netn_a_00204
- Feigin, V. L., Nichols, E., Alam, T., Bannick, M. S., Beghi, E., Blake, N., Culpepper, W. J., Dorsey, E. R., Elbaz, A., Ellenbogen, R. G., Fisher, J. L., Fitzmaurice, C., Giussani, G., Glennie, L., James, S. L., Johnson, C. O., Kassebaum, N. J., Logroscino, G., Marin, B., … Vos, T. (2019). Global, regional, and national burden of neurological disorders, 1990–2016: A systematic analysis for the global burden of disease study 2016. Lancet Neurol, 18(5), 459–480. https://doi.org/10.1016/S1474-4422(18)30499-X
- Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex, 1(1), 1–47. https://doi.org/10.1093/cercor/1.1.1-a
- Felsenstein, J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution, 39(4), 783–791. https://doi.org/10.1111/j.1558-5646.1985.tb00420.x
- Finger, S. (1994). Origins of neuroscience: A history of explorations into brain function. Oxford University Press. https://doi.org/10.1097/00005072-199409000-00015
- Fothergill, B. T., Knight, W., Stahl, B. C., & Ulnicane, I. (2019). Responsible data governance of neuroscience big data. Front Neuroinform, 13, 28. https://doi.org/10.3389/fninf.2019.00028
- Frank, M. J., Samanta, J., Moustafa, A. A., & Sherman, S. J. (2007). Hold your horses: Impulsivity, deep brain stimulation, and medication in parkinsonism. Science, 318(5854), 1309–1312. https://doi.org/10.1126/science.1146157
- Frank, M. J., Seeberger, L. C., & O’Reilly, R.C. (2004). By carrot or by stick: Cognitive reinforcement learning in parkinsonism. Science, 306(5703), 1940–1943. https://doi.org/10.1126/science.1102941
- Friedrich, P., Forkel, S. J., Amiez, C., Balsters, J. H., Coulon, O., Fan, L., Goulas, A., Hadj-Bouziane, F., Hecht, E. E., Heuer, K., Jiang, T., Latzman, R. D., Liu, X., Loh, K. K., Patil, K. R., Lopez-Persem, A., Procyk, E., Sallet, J., Toro, R., … Thiebaut de Schotten, M. (2021). Imaging evolution of the primate brain: The next frontier? Neuroimage, 228, 117685. https://doi.org/10.1016/j.neuroimage.2020.117685
- Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. J Physiol Paris, 100(1), 70–87. https://doi.org/10.1016/j.jphysparis.2006.10.001
- Furber, S. B., & Bogdan, P. A. (2020). SpiNNaker: A spiking neural network architecture. http://dx.doi.org/10.1561/9781680836523
- Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016). Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat Biotechnol, 34(2), 175–183. https://doi.org/10.1038/nbt.3443
- Gerits, A., Farivar, R., Rosen, B. R., Wald, L. L., Boyden, E. S., & Vanduffel, W. (2012). Optogenetically induced behavioral and functional network changes in primates. Curr Biol, 22(18), 1722–1726. https://doi.org/10.1016/j.cub.2012.07.023
- Gillette, K., Gsell, M. A. F., Prassl, A. J., Karabelas, E., Reiter, U., Reiter, G., Grandits, T., Payer, C., Štern, D., Urschler, M., Bayer, J. D., Augustin, C. M., Neic, A., Pock, T., Vigmond, E. J., & Plank, G. (2021). A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med Image Anal, 71, 102080. https://doi.org/10.1016/j.media.2021.102080
- Goldfarb, M. G., & Brown, D. R. (2022). Diversifying participation: The rarity of reporting racial demographics in neuroimaging research. Neuroimage, 254, 119122. https://doi.org/10.1016/j.neuroimage.2022.119122
- Göltz, J., Kriener, L., Baumbach, A., Billaudelle, S., Breitwieser, O., Cramer, B., Dold, D., Kungl, A. F., Senn, W., Schemmel, J., Meier, K., & Petrovici, M. A. (2021). Fast and energy-efficient neuromorphic deep learning with first-spike times. Nat Mach Intell, 3(9), 823–835. https://doi.org/10.1038/s42256-021-00388-x
- Gombkoto, P., Gielow, M., Varsanyi, P., Chavez, C., & Zaborszky, L. (2021). Contribution of the basal forebrain to corticocortical network interactions. Brain Struct Funct, 226(6), 1803–1821. https://doi.org/10.1007/s00429-021-02290-z
- Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., Bertagnolli, D., Bickley, K., Bomben, J., Braun, T., Brouner, K., Casper, T., Crichton, K., Daigle, T. L., Dalley, R., … Zeng, H. (2020). Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell, 183(4), 935–953.e919. https://doi.org/10.1016/j.cell.2020.09.057
- Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989). Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338(6213), 334–337. https://doi.org/10.1038/338334a0
- Graziano, M. S. (2019). Rethinking consciousness: A scientific theory of subjective experience. W. W. Norton ISBN: 978-0393652611.
- Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. (pp. 85–113). ISBN : 978-3-319-38754-3
- Grieves, M. W. (2019). Virtually intelligent product systems: Digital and physical twins. In Complex systems engineering: theory and practice. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/5.9781624105654.0175.0200
- Gunn-Moore, D., Moffat, K., Christie, L. A., & Head, E. (2007). Cognitive dysfunction and the neurobiology of ageing in cats. J Small Anim Pract, 48(10), 546–553. https://doi.org/10.1111/j.1748-5827.2007.00386.x
- Güntürkün, O., & Bugnyar, T. (2016). Cognition without cortex. Trends Cogn Sci, 20(4), 291–303. https://doi.org/10.1016/j.tics.2016.02.001
- Gunz, P., Tilot, A. K., Wittfeld, K., Teumer, A., Shapland, C. Y., van Erp, T. G. M., Dannemann, M., Vernot, B., Neubauer, S., Guadalupe, T., Fernández, G., Brunner, H. G., Enard, W., Fallon, J., Hosten, N., Völker, U., Profico, A., Di Vincenzo, F., Manzi, G., … Fisher, S. E. (2019). Neandertal introgression sheds light on modern human endocranial globularity. Curr Biol, 29(1), 120–127.e125. https://doi.org/10.1016/j.cub.2018.10.065
- Haider, P., Ellenberger, B., Kriener, L., Jordan, J., Senn, W., & Petrovici, M. (2021). Latent equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. https://doi.org/10.48550/arXiv.2110.14549
- Han, X., Qian, X., Bernstein, J. G., Zhou, H. H., Franzesi, G. T., Stern, P., Bronson, R. T., Graybiel, A. M., Desimone, R., & Boyden, E. S. (2009). Millisecond-timescale optical control of neural dynamics in the nonhuman primate brain. Neuron, 62(2), 191–198. https://doi.org/10.1016/j.neuron.2009.03.011
- Handler, A., Graham, T. G. W., Cohn, R., Morantte, I., Siliciano, A. F., Zeng, J., Li, Y., & Ruta, V. (2019). Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning. Cell, 178(1), 60–75.e19. https://doi.org/10.1016/j.cell.2019.05.040
- Haueis, P. (2021). Multiscale modeling of cortical gradients: The role of mesoscale circuits for linking macro- and microscale gradients of cortical organization and hierarchical information processing. Neuroimage, 232, 117846. https:/doi.org/10.1016/j.neuroimage.2021.117846
- Häusser, M. (2021). Optogenetics—The might of light. N Engl J Med, 385(17), 1623–1626. https://doi.org/10.1056/NEJMcibr2111915
- Havlicek, M., Roebroeck, A., Friston, K., Gardumi, A., Ivanov, D., & Uludag, K. (2015). Physiologically informed dynamic causal modeling of fMRI data. Neuroimage, 122, 355–372. https://doi.org/10.1016/j.neuroimage.2015.07.078
- Head, E., McCleary, R., Hahn, F. F., Milgram, N. W., & Cotman, C. W. (2000). Region-specific age at onset of beta-amyloid in dogs. Neurobiol Aging, 21(1), 89–96. https://doi.org/10.1016/s0197-4580(00)00093-2
- Head, E., Moffat, K., Das, P., Sarsoza, F., Poon, W. W., Landsberg, G., Cotman, C. W., & Murphy, M. P. (2005). Beta-amyloid deposition and tau phosphorylation in clinically characterized aged cats. Neurobiol Aging, 26(5), 749–763. https://doi.org/10.1016/j.neurobiolaging.2004.06.015
- Heckner, M. K., Cieslik, E. C., Patil, K. R., Gell, M., Eickhoff, S. B., Hoffstädter, F., & Langner, R. (2023). Predicting executive functioning from functional brain connectivity: Network specificity and age effects. Cereb Cortex. https://doi.org/10.1093/cercor/bhac520
- Herold, C., Bingman, V. P., Ströckens, F., Letzner, S., Sauvage, M., Palomero-Gallagher, N., Zilles, K., & Güntürkün, O. (2014). Distribution of neurotransmitter receptors and zinc in the pigeon (Columba livia) hippocampal formation: A basis for further comparison with the mammalian hippocampus. J Comp Neurol, 522(11), 2553–2575. https://doi.org/10.1002/cne.23549
- Herold, C., Palomero-Gallagher, N., Hellmann, B., Kröner, S., Theiss, C., Güntürkün, O., & Zilles, K. (2011). The receptor architecture of the pigeons’ nidopallium caudolaterale: An avian analogue to the mammalian prefrontal cortex. Brain Struct Funct, 216(3), 239–254. https://doi.org/10.1007/s00429-011-0301-5
- Hodgkin, A. L., & Huxley, A. F. (1952). Currents carried by sodium and potassium ions through the membrane of the giant axon of loligo. J Physiol, 116(4), 449–472. https://doi.org/10.1113/jphysiol.1952.sp004717
- Iturria-Medina, Y., Carbonell, F. M., & Evans, A. C. (2018). Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration. Neuroimage, 179, 40–50. https://doi.org/10.1016/j.neuroimage.2018.06.028
- Jancke, D., Herlitze, S., Kringelbach, M. L., & Deco, G. (2022). Bridging the gap between single receptor type activity and whole-brain dynamics. FEBS J, 289(8), 2067–2084. https://doi.org/10.1111/febs.15855
- Jarvis, E. D. (2004). Learned birdsong and the neurobiology of human language. Ann N Y Acad Sci, 1016, 749–777. https://doi.org/10.1196/annals.1298.038
- Jarvis, E. D. (2019). Evolution of vocal learning and spoken language. Science, 366(6461), 50–54. https://doi.org/10.1126/science.aax0287
- Jirsa, V., & Sheheitli, H. (2022). Entropy, free energy, symmetry and dynamics in the brain. J Physics Complexity, 3(1), 015007. https://doi.org/10.1088/2632-072X/ac4bec
- Jirsa, V., Wang, H., Triebkorn, P., Hashemi, M., Jha, J., Gonzalez-Martinez, J., Guye, M., Makhalova, J., & Bartolomei, F. (2023). Personalised virtual brain models in epilepsy. Lancet Neurol, 22(5), 443–454. https://doi.org/10.1016/s1474-4422(23)00008-x
- Jirsa, V. K., Proix, T., Perdikis, D., Woodman, M. M., Wang, H., Gonzalez-Martinez, J., Bernard, C., Bénar, C., Guye, M., Chauvel, P., & Bartolomei, F. (2017). The virtual epileptic patient: Individualized whole-brain models of epilepsy spread. Neuroimage, 145(Pt B), 377–388. https://doi.org/10.1016/j.neuroimage.2016.04.049
- PubMed
- Jones, E. (1983). The columnar basis of cortical circuitry. Clin Neurosci, 5, 257–383.
- Jonsson, B. A., Bjornsdottir, G., Thorgeirsson, T. E., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D. F., Stefansson, H., Stefansson, K., & Ulfarsson, M. O. (2019). Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun, 10(1), 5409. https://doi.org/10.1038/s41467-019-13163-9
- Jordan, J., Schmidt, M., Senn, W., & Petrovici, M. A. (2021). Evolving interpretable plasticity for spiking networks. eLife, 10, e66273. https://doi.org/10.7554/eLife.66273
- Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
- Karigo, T., Kennedy, A., Yang, B., Liu, M., Tai, D., Wahle, I. A., & Anderson, D. J. (2021). Distinct hypothalamic control of same- and opposite-sex mounting behaviour in mice. Nature, 589(7841), 258–263. https://doi.org/10.1038/s41586-020-2995-0
- Khan, A. F., Adewale, Q., Baumeister, T. R., Carbonell, F., Zilles, K., Palomero-Gallagher, N., & Iturria-Medina, Y. (2022). Personalized brain models identify neurotransmitter receptor changes in Alzheimer’s disease. Brain, 145(5), 1785–1804. https://doi.org/10.1093/brain/awab375
- Kim, C. K., Adhikari, A., & Deisseroth, K. (2017). Integration of optogenetics with complementary methodologies in systems neuroscience. Nat Rev Neurosci, 18(4), 222–235. https://doi.org/10.1038/nrn.2017.15
- Klausberger, T., & Somogyi, P. (2008). Neuronal diversity and temporal dynamics: The unity of hippocampal circuit operations. Science, 321(5885), 53–57. https://doi.org/10.1126/science.1149381
- Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Barrett, L. F. (2017). Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nat Hum Behav, 1. https://doi.org/10.1038/s41562-017-0069
- Klink, P. C., Aubry, J.-F., Ferrera, V. P., Fox, A. S., Froudist-Walsh, S., Jarraya, B., Konofagou, E. E., Krauzlis, R. J., Messinger, A., Mitchell, A. S., Ortiz-Rios, M., Oya, H., Roberts, A. C., Roe, A. W., Rushworth, M. F. S., Sallet, J., Schmid, M. C., Schroeder, C. E., Tasserie, J., … Petkov, C. I. (2021). Combining brain perturbation and neuroimaging in non-human primates. Neuroimage, 235, 118017. https://doi.org/10.1016/j.neuroimage.2021.118017
- Kooijmans, R. N., Sierhuis, W., Self, M. W., & Roelfsema, P. R. (2020). A quantitative comparison of inhibitory interneuron size and distribution between mouse and macaque V1, using calcium-binding proteins. Cereb Cortex Commun, 1(1), tgaa068. https://doi.org/10.1093/texcom/tgaa068
- Kreutzer, E., Senn, W., & Petrovici, M. A. (2022). Natural-gradient learning for spiking neurons. eLife, 11, e66526. https://doi.org/10.7554/eLife.66526
- Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R., Whybrow, P. C., Logothetis, N. K., & Deco, G. (2020). Dynamic coupling of whole-brain neuronal and neurotransmitter systems. Proc Natl Acad Sci U S A, 117(17), 9566–9576. https://doi.org/10.1073/pnas.1921475117
- Kverková, K., Marhounová, L., Polonyiová, A., Kocourek, M., Zhang, Y., Olkowicz, S., Straková, B., Pavelková, Z., Vodička, R., Frynta, D., & Němec, P. (2022). The evolution of brain neuron numbers in amniotes. Proc Natl Acad Sci U S A, 119(11), e2121624119. https://doi.org/10.1073/pnas.2121624119
- Lake, E. M. R., Ge, X., Shen, X., Herman, P., Hyder, F., Cardin, J. A., Higley, M. J., Scheinost, D., Papademetris, X., Crair, M. C., & Constable, R. T. (2020). Simultaneous cortex-wide fluorescence Ca(2+) imaging and whole-brain fMRI. Nat Methods, 17(12), 1262–1271. https://doi.org/10.1038/s41592-020-00984-6
- Lamme, V. A., & Spekreijse, H. (1998). Neuronal synchrony does not represent texture segregation. Nature, 396(6709), 362–366. https://doi.org/10.1038/24608
- Landsberg, G. M., Nichol, J., & Araujo, J. A. (2012). Cognitive dysfunction syndrome: A disease of canine and feline brain aging. Vet Clin North Am Small Anim Pract, 42(4), 749–768, vii. https://doi.org/10.1016/j.cvsm.2012.04.003
- Larkum, M. E., Petro, L. S., Sachdev, R. N. S., & Muckli, L. (2018). A perspective on cortical layering and layer-spanning neuronal elements. Front Neuroanat, 12. https://doi.org/10.3389/fnana.2018.00056
- Le Van Quyen, M., Muller, L. E., 2nd, Telenczuk, B., Halgren, E., Cash, S., Hatsopoulos, N. G., Dehghani, N., & Destexhe, A. (2016). High-frequency oscillations in human and monkey neocortex during the wake-sleep cycle. Proc Natl Acad Sci U S A, 113(33), 9363–9368. https://doi.org/10.1073/pnas.1523583113
- Lee, B. R., Dalley, R., Miller, J. A., Chartrand, T., Close, J., Mann, R., Mukora, A., Ng, L., Alfiler, L., Baker, K., Bertagnolli, D., Brouner, K., Casper, T., Csajbok, E., Donadio, N., Driessens, S. L. W., Egdorf, T., Enstrom, R., Galakhova, A. A., … Ting, J. T. (2023). Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science, 382(6667), eadf6484. https://doi.org/10.1126/science.adf6484
- Lee, M., Sanz, L. R. D., Barra, A., Wolff, A., Nieminen, J. O., Boly, M., Rosanova, M., Casarotto, S., Bodart, O., Annen, J., Thibaut, A., Panda, R., Bonhomme, V., Massimini, M., Tononi, G., Laureys, S., Gosseries, O., & Lee, S. W. (2022). Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning. Nat Commun, 13(1), 1064. https://doi.org/10.1038/s41467-022-28451-0
- Lehtimäki, M., Paunonen, L., & Linne, M. L. (2019). Projection-based order reduction of a nonlinear biophysical neuronal network model. In 2019 IEEE 58th Conference on Decision and Control (CDC). https://urn.fi/URN:NBN:fi:tuni-202001091136
- Lehtimäki, M., Paunonen, L., Pohjolainen, S., & Linne, M.-L. (2017). Order reduction for a signaling pathway model of neuronal synaptic plasticity. IFAC-PapersOnLine, 50(1), 7687–7692. https:/doi.org/10.1016/j.ifacol.2017.08.1143
- Lehtimäki, M., Seppälä, I., Paunonen, L., & Linne, M.-L. (2020). Accelerated simulation of a neuronal population via mathematical model order reduction. In 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) (pp. 118–122). IEEE. https://doi.org/10.1109/AICAS48895.2020.9073844
- Li, F., Lindsey, J. W., Marin, E. C., Otto, N., Dreher, M., Dempsey, G., Stark, I., Bates, A. S., Pleijzier, M. W., Schlegel, P., Nern, A., Takemura, S. Y., Eckstein, N., Yang, T., Francis, A., Braun, A., Parekh, R., Costa, M., Scheffer, L. K., … Rubin, G. M. (2020). The connectome of the adult Drosophila mushroom body provides insights into function. eLife, 9, e62576. https://doi.org/10.7554/eLife.62576
- Li, N., Chen, T. W., Guo, Z. V., Gerfen, C. R., & Svoboda, K. (2015). A motor cortex circuit for motor planning and movement. Nature, 519(7541), 51–56. https://doi.org/10.1038/nature14178
- Ligthart, S., Douglas, T., Bublitz, C., Kooijmans, T., & Meynen, G. (2021). Forensic brain-reading and mental privacy in European human rights law: Foundations and challenges. Neuroethics, 14(2), 191–203. https://doi.org/10.1007/s12152-020-09438-4
- Lisman, J., Buzsáki, G., Eichenbaum, H., Nadel, L., Ranganath, C., & Redish, A. D. (2017). Viewpoints: How the hippocampus contributes to memory, navigation and cognition. Nat Neurosci, 20(11), 1434–1447. https://doi.org/10.1038/nn.4661
- Litvina, E., Adams, A., Barth, A., Bruchez, M., Carson, J., Chung, J. E., Dupre, K. B., Frank, L. M., Gates, K. M., Harris, K. M., Joo, H., William Lichtman, J., Ramos, K. M., Sejnowski, T., Trimmer, J. S., White, S., & Koroshetz, W. (2019). BRAIN initiative: Cutting-edge tools and resources for the community. J Neurosci, 39(42), 8275–8284. https://doi.org/10.1523/jneurosci.1169-19.2019
- Maestú, F., de Haan, W., Busche, M. A., & DeFelipe, J. (2021). Neuronal excitation/inhibition imbalance: Core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev, 69, 101372. https://doi.org/10.1016/j.arr.2021.101372
- Magielse, N., Toro, R., Steigauf, V., Abbaspour, M., Eickhoff, S. B., Heuer, K., & Valk, S. L. (2023). Primate cerebellar scaling in connection to the cerebrum: A 34-species phylogenetic comparative analysis. bioRxiv, 2023.2003.2015.532597. https://doi.org/10.1101/2023.03.15.532597
- Mäki-Marttunen, T., Kaufmann, T., Elvsåshagen, T., Devor, A., Djurovic, S., Westlye, L. T., Linne, M.-L., Rietschel, M., Schubert, D., Borgwardt, S., Efrim-Budisteanu, M., Bettella, F., Halnes, G., Hagen, E., Næss, S., Ness, T. V., Moberget, T., Metzner, C., Edwards, A. G., … Andreassen, O. A. (2019). Biophysical psychiatry—How computational neuroscience can help to understand the complex mechanisms of mental disorders. Front Psychiatry, 10, 534. https://doi.org/10.3389/fpsyt.2019.00534
- Manninen, T., Saudargiene, A., & Linne, M.-L. (2020). Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex. PLoS Comput Biol, 16(11), e1008360. https://doi.org/10.1371/journal.pcbi.1008360
- Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 78–84. https://doi.org/10.1038/nature12742
- Marceglia, S., Guidetti, M., Harmsen, I. E., Loh, A., Meoni, S., Foffani, G., Lozano, A. M., Volkmann, J., Moro, E., & Priori, A. (2021). Deep brain stimulation: Is it time to change gears by closing the loop? J Neural Eng, 18(6). https://doi.org/10.1088/1741-2552/ac3267
- Marder, E., Kedia, S., & Morozova, E. O. (2022). New insights from small rhythmic circuits. Curr Opin Neurobiol, 76, 102610. https://doi.org/10.1016/j.conb.2022.102610
- Markram, H., Meier, K., Lippert, T., Grillner, S., Frackowiak, R., Dehaene, S., Knoll, A., Sompolinsky, H., Verstreken, K., DeFelipe, J., Grant, S., Changeux, J.-P., & Saria, A. (2011). Introducing the human brain project. Procedia Computer Sci, 7, 39–42. https://doi.org/10.1016/j.procs.2011.12.015
- Marr, D. (1982). Vision. Freeman.
- Masoli, S., Ottaviani, A., Casali, S., & D’Angelo, E. (2021). Cerebellar golgi cell models predict dendritic processing and mechanisms of synaptic plasticity. PLoS Computat Biol, 16(12), e1007937. https://doi.org/10.1371/journal.pcbi.1007937
- Mazzarello, P. (2010). Golgi: A biography of the founder of modern neuroscience. Oxford University Press, USA. ISBN: 978-0195337846
- Mejias, J. F., Murray, J. D., Kennedy, H., & Wang, X. J. (2016). Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Sci Adv, 2(11), e1601335. https://doi.org/10.1126/sciadv.1601335
- Mejías, J. F., & Wang, X.-J. (2022). Mechanisms of distributed working memory in a large-scale network of macaque neocortex. eLife, 11, e72136. https://doi.org/10.7554/eLife.72136
- Mesulam, M. M. (1998). From sensation to cognition. Brain, 121(6), 1013–1052. https://doi.org/10.1093/brain/121.6.1013
- Montero-Crespo, M., Dominguez-Alvaro, M., Rondon-Carrillo, P., Alonso-Nanclares, L., DeFelipe, J., & Blazquez-Llorca, L. (2020). Three-dimensional synaptic organization of the human hippocampal CA1 field. eLife, 9. https://doi.org/10.7554/eLife.57013
- Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120 (Pt 4), 701–722. https://doi.org/10.1093/brain/120.4.701
- Nadasdy, Z., Buzsaki, G., & Zaborszky, L. (2006). Functional connectivity of the brain: Reconstruction from static and dynamic data. In L. Zaborszky, F. G. Wouterlood, & J. L. Lanciego (Eds.), Neuroanatomical tract-tracing 3: Molecules, neurons, and systems (pp. 631–681). Springer US. https://doi.org/10.1007/0-387-28942-9_20
- Northoff, G., Wainio-Theberge, S., & Evers, K. (2020). Is temporo-spatial dynamics the “common currency” of brain and mind? In quest of “spatiotemporal neuroscience”. Phys Life Rev, 33, 34–54. https://doi.org/10.1016/j.plrev.2019.05.002
- Nottebohm, F. (2005). The neural basis of birdsong. PLoS Biol, 3(5), e164. https://doi.org/10.1371/journal.pbio.0030164
- Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U., & Jönsson, B. (2012). The economic cost of brain disorders in Europe. Eur J Neurol, 19(1), 155–162. https://doi.org/10.1111/j.1468-1331.2011.03590.x
- Oude Lohuis, M. N., Pie, J. L., Marchesi, P., Montijn, J. S., de Kock, C. P. J., Pennartz, C. M. A., & Olcese, U. (2022). Multisensory task demands temporally extend the causal requirement for visual cortex in perception. Nat Commun, 13(1), 2864. https://doi.org/10.1038/s41467-022-30600-4
- Palomero-Gallagher, N., & Zilles, K. (2019). Cortical layers: Cyto-, myelo-, receptor- and synaptic architecture in human cortical areas. Neuroimage, 197, 716–741. https://doi.org/10.1016/j.neuroimage.2017.08.035
- Pandya, D., Seltzer, B., Petrides, M., & Cipolloni, P. B. (2015). 377Bibliography. In Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept (pp. 0). Oxford University Press. ISBN: 978-0195385151
- Parr, T., Pezzulo, G., & Friston, K. (2022). Active inference: The free energy principle in mind, brain, and behavior. The MIT Press. https://doi.org/10.7551/mitpress/12441.001.0001
- Parr, T., Rees, G., & Friston, K. J. (2018). Computational neuropsychology and Bayesian inference. Front Hum Neurosci, 12, 61. https://doi.org/10.3389/fnhum.2018.00061
- Pearson, M. J., Dora, S., Struckmeier, O., Knowles, T. C., Mitchinson, B., Tiwari, K., Kyrki, V., Bohte, S., & Pennartz, C. M. A. (2021). Multimodal representation learning for place recognition using deep hebbian predictive coding. Front Robot AI, 8, 732023. https://doi.org/10.3389/frobt.2021.732023
- Poort, J., Raudies, F., Wannig, A., Lamme, V. A., Neumann, H., & Roelfsema, P. R. (2012). The role of attention in figure-ground segregation in areas V1 and V4 of the visual cortex. Neuron, 75(1), 143–156. https://doi.org/10.1016/j.neuron.2012.04.032
- Proix, T., Bartolomei, F., Guye, M., & Jirsa, V. K. (2017). Individual brain structure and modelling predict seizure propagation. Brain, 140, 641–654. https://doi.org/10.1093/brain/awx004
- Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proc Natl Acad Sci U S A, 98(2), 676–682. https://doi.org/doi:10.1073/pnas.98.2.676
- Rastegar, S., & Strähle, U. (2016). The zebrafish as model for deciphering the regulatory architecture of vertebrate genomes. Adv Genet, 95, 195–216. https://doi.org/10.1016/bs.adgen.2016.04.003
- Ren, C., & Komiyama, T. (2021a). Characterizing cortex-wide dynamics with wide-field calcium imaging. J Neurosci, 41(19), 4160–4168. https://doi.org/10.1523/jneurosci.3003-20.2021
- Ren, C., & Komiyama, T. (2021b). Wide-field calcium imaging of cortex-wide activity in awake, head-fixed mice. STAR Protoc, 2(4), 100973. https://doi.org/10.1016/j.xpro.2021.100973
- Rockland, K. S. (2010). Five points on columns. Front Neuroanat, 4, 22. https://doi.org/10.3389/fnana.2010.00022
- Rockland, K. S. (2022). Clustered intrinsic connections: Not a single system. Front Syst Neurosci, 16, 910845. https://doi.org/10.3389/fnsys.2022.910845
- Roelfsema, P. R., Lamme, V. A., & Spekreijse, H. (1998). Object-based attention in the primary visual cortex of the macaque monkey. Nature, 395(6700), 376–381. https://doi.org/10.1038/26475
- Roelfsema, P. R., Lamme, V. A., & Spekreijse, H. (2004). Synchrony and covariation of firing rates in the primary visual cortex during contour grouping. Nat Neurosci, 7(9), 982–991. https://doi.org/10.1038/nn1304
- Rosanova, M., Fecchio, M., Casarotto, S., Sarasso, S., Casali, A. G., Pigorini, A., Comanducci, A., Seregni, F., Devalle, G., Citerio, G., Bodart, O., Boly, M., Gosseries, O., Laureys, S., & Massimini, M. (2018). Sleep-like cortical OFF-periods disrupt causality and complexity in the brain of unresponsive wakefulness syndrome patients. Nat Commun, 9(1), 4427. https://doi.org/10.1038/s41467-018-06871-1
- Rowald, A., & Amft, O. (2022). A computational roadmap to electronic drugs. Front Neurorobot, 16. https://doi.org/10.3389/fnbot.2022.983072
- Rowald, A., Komi, S., Demesmaeker, R., Baaklini, E., Hernandez-Charpak, S. D., Paoles, E., Montanaro, H., Cassara, A., Becce, F., Lloyd, B., Newton, T., Ravier, J., Kinany, N., D’Ercole, M., Paley, A., Hankov, N., Varescon, C., McCracken, L., Vat, M., … Courtine, G. (2022). Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med, 28(2), 260–271. https://doi.org/10.1038/s41591-021-01663-5
- Sacramento, J., Costa, R., Bengio, Y., & Senn, W. (2018). Dendritic cortical microcircuits approximate the backpropagation algorithm. https://doi.org/10.48550/arXiv.1810.11393
- Sakmann, B., & Neher, E. (1984). Patch clamp techniques for studying ionic channels in excitable membranes. Annu Rev Physiol, 46, 455–472. https://doi.org/10.1146/annurev.ph.46.030184.002323
- Saxena, S., & Cunningham, J. P. (2019). Towards the neural population doctrine. Curr Opin Neurobiol, 55, 103–111. https://doi.org/10.1016/j.conb.2019.02.002
- Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R., Prodan, P., Valean, B., Palmer, J., Langford, C., Blickensdörfer, A., van der Vlag, M., Diaz-Pier, S., Peyser, A., Klijn, W., Pleiter, D., Nahm, A., Schmid, O., Woodman, M., … Ritter, P. (2022). Brain simulation as a cloud service: The virtual brain on EBRAINS. Neuroimage, 251, 118973. https://doi.org/10.1016/j.neuroimage.2022.118973
- Sendhoff, B., Körner, E., Sporns, O., Ritter, H., & Doya, K. (2009). Creating brain-like intelligence: From basic principles to complex intelligent systems (Vol. 5436). Springer. https://doi.org/10.1007/978-3-642-00616-6_1
- Shanahan, M., Bingman, V. P., Shimizu, T., Wild, M., & Güntürkün, O. (2013). Large-scale network organization in the avian forebrain: A connectivity matrix and theoretical analysis. Front Comput Neurosci, 7, 89. https://doi.org/10.3389/fncom.2013.00089
- Shepherd, G. M. (2009). Creating modern neuroscience: The revolutionary 1950s. Oxford University Press. ISBN: 978-0195391503
- Shepherd, G. M. (2015). Foundations of the neuron doctrine. Oxford University Press. ISBN: 978-0190259389
- Stacho, M., Herold, C., Rook, N., Wagner, H., Axer, M., Amunts, K., & Güntürkün, O. (2020). A cortex-like canonical circuit in the avian forebrain. Science, 369(6511), eabc5534. https://doi.org/doi:10.1126/science.abc5534
- Stahl, B. C., Akintoye, S., Bitsch, L., Bringedal, B., Eke, D., Farisco, M., Grasenick, K., Guerrero, M., Knight, W., Leach, T., Nyholm, S., Ogoh, G., Rosemann, A., Salles, A., Trattnig, J., & Ulnicane, I. (2021). From responsible research and innovation to responsibility by design. J Responsible Innov, 8(2), 175–198. https://doi.org/10.1080/23299460.2021.1955613
- Staiger, J. F., & Petersen, C. C. H. (2021). Neuronal circuits in barrel cortex for whisker sensory perception. Physiol Rev, 101(1), 353–415. https://doi.org/10.1152/physrev.00019.2019
- Stefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T., Martin, L., Bülau, K., Hofmann-Apitius, M., Solodkin, A., McIntosh, A. R., & Ritter, P. (2021). Bridging scales in Alzheimer’s disease: Biological framework for brain simulation with the virtual brain. Front Neuroinform, 15, 630172. https://doi.org/10.3389/fninf.2021.630172
- Sterling, E., Pearl, H., Liu, Z., Allen, J. W., & Fleischer, C. C. (2022). Demographic reporting across a decade of neuroimaging: A systematic review. Brain Imaging Behav, 16(6), 2785–2796. https://doi.org/10.1007/s11682-022-00724-8
- Stöckl, C., & Maass, W. (2021). Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. Nat Mach Intell, 3(3), 230–238. https://doi.org/10.1038/s42256-021-00311-4
- Stoianov, I. P., Pennartz, C. M. A., Lansink, C. S., & Pezzulo, G. (2018). Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis. PLoS Comput Biol, 14(9), e1006316. https://doi.org/10.1371/journal.pcbi.1006316
- Ströckens, F., Neves, K., Kirchem, S., Schwab, C., Herculano-Houzel, S., & Güntürkün, O. (2022). High associative neuron numbers could drive cognitive performance in corvid species. J Comp Neurol, 530(10), 1588–1605. https://doi.org/10.1002/cne.25298
- Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv, 1906.02243. https://doi.org/10.48550/arXiv.1906.02243
- Südhof, T. C. (2017). Molecular neuroscience in the 21(st) century: A personal perspective. Neuron, 96(3), 536–541. https://doi.org/10.1016/j.neuron.2017.10.005
- Svanera, M., Morgan, A. T., Petro, L. S., & Muckli, L. (2021). A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes. J Vis, 21(7), 5. https://doi.org/10.1167/jov.21.7.5
- Szentágothai, J. (1978). The Ferrier Lecture, 1977 The neuron network of the cerebral cortex: A functional interpretation. Proc R Soc Lond B Biol Sci, 201(1144), 219–248. https://doi.org/10.1098/rspb.1978.0043
- Talozzi, L., Forkel, S. J., Pacella, V., Nozais, V., Allart, E., Piscicelli, C., Pérennou, D., Tranel, D., Boes, A., Corbetta, M., Nachev, P., & Thiebaut de Schotten, M. (2023). Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke. Brain, 146(5), 1963–1978. https://doi.org/10.1093/brain/awad013
- Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. Int J Prod Res, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
- Taylor, N. L., D’Souza, A., Munn, B. R., Lv, J., Zaborszky, L., Müller, E. J., Wainstein, G., Calamante, F., & Shine, J. M. (2022). Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. Neuroimage, 260, 119455. https://doi.org/10.1016/j.neuroimage.2022.119455
- Thiebaut de Schotten, M., & Forkel, S. J. (2022). The emergent properties of the connected brain. Science, 378(6619), 505–510. https://doi.org/10.1126/science.abq2591
- Thiele, A., & Stoner, G. (2003). Neuronal synchrony does not correlate with motion coherence in cortical area MT. Nature, 421(6921), 366–370. https://doi.org/10.1038/nature01285
- Thompson, W. H., Wright, J., Bissett, P. G., & Poldrack, R. A. (2020). Dataset decay and the problem of sequential analyses on open datasets. eLife, 9. https://doi.org/10.7554/eLife.53498
- Toi, P. T., Jang, H. J., Min, K., Kim, S.-P., Lee, S.-K., Lee, J., Kwag, J., & Park, J.-Y. (2022). In vivo direct imaging of neuronal activity at high temporospatial resolution. Science, 378(6616), 160–168. https://doi.org/doi:10.1126/science.abh4340
- Tort, A. B. L., Brankačk, J., & Draguhn, A. (2018). Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci, 41(4), 186–197. https://doi.org/10.1016/j.tins.2018.01.007
- Tort-Colet, N., Capone, C., Sanchez-Vives, M. V., & Mattia, M. (2021). Attractor competition enriches cortical dynamics during awakening from anesthesia. Cell Rep, 35(12), 109270. https://doi.org/10.1016/j.celrep.2021.109270
- van Beest, E. H., Mukherjee, S., Kirchberger, L., Schnabel, U. H., van der Togt, C., Teeuwen, R. R. M., Barsegyan, A., Meyer, A. F., Poort, J., Roelfsema, P. R., & Self, M. W. (2021). Mouse visual cortex contains a region of enhanced spatial resolution. Nat Commun, 12(1), 4029. https://doi.org/10.1038/s41467-021-24311-5
- van den Bosch, R., Lambregts, B., Määttä, J., Hofmans, L., Papadopetraki, D., Westbrook, A., Verkes, R. J., Booij, J., & Cools, R. (2022). Striatal dopamine dissociates methylphenidate effects on value-based versus surprise-based reversal learning. Nat Commun, 13(1), 4962. https://doi.org/10.1038/s41467-022-32679-1
- van Vugt, B., Dagnino, B., Vartak, D., Safaai, H., Panzeri, S., Dehaene, S., & Roelfsema, P. R. (2018). The threshold for conscious report: Signal loss and response bias in visual and frontal cortex. Science, 360(6388), 537–542. https://doi.org/10.1126/science.aar7186
- Vezoli, J., Magrou, L., Goebel, R., Wang, X.-J., Knoblauch, K., Vinck, M., & Kennedy, H. (2021). Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks. Neuroimage, 225, 117479. https://doi.org/10.1016/j.neuroimage.2020.117479
- Viswanathan, A., & Freeman, R. D. (2007). Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat Neurosci, 10(10), 1308–1312. https://doi.org/10.1038/nn1977
- Vogel, J. W., Young, A. L., Oxtoby, N. P., Smith, R., Ossenkoppele, R., Strandberg, O. T., La Joie, R., Aksman, L. M., Grothe, M. J., Iturria-Medina, Y., Alzheimer’s Disease Neuroimaging, I., Pontecorvo, M. J., Devous, M. D., Rabinovici, G. D., Alexander, D. C., Lyoo, C. H., Evans, A. C., & Hansson, O. (2021). Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat Med, 27(5), 871–881. https://doi.org/10.1038/s41591-021-01309-6
- Vogt, C., & Vogt, O. (1919). Allgemeine ergebnisse unserer hirnforschung (Vol. 25). JA Barth.
- Vogt, M. (1954). The concentration of sympathin in different parts of the central nervous system under normal conditions and after the administration of drugs. J Physiol, 123(3), 451–481. https://doi.org/10.1113/jphysiol.1954.sp005064
- Wagner, A. S., Waite, L. K., Wierzba, M., Hoffstaedter, F., Waite, A. Q., Poldrack, B., Eickhoff, S. B., & Hanke, M. (2022). FAIRly big: A framework for computationally reproducible processing of large-scale data. Sci Data, 9(1), 80. https://doi.org/10.1038/s41597-022-01163-2
- Wang, H. E., Woodman, M., Triebkorn, P., Lemarechal, J. D., Jha, J., Dollomaja, B., Vattikonda, A. N., Sip, V., Medina Villalon, S., Hashemi, M., Guye, M., Makhalova, J., Bartolomei, F., & Jirsa, V. (2023). Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. Sci Transl Med, 15(680), eabp8982. https://doi.org/10.1126/scitranslmed.abp8982
- Wang, X. J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955–968. https://doi.org/10.1016/s0896-6273(02)01092-9
- Wendling, F. (2008). Computational models of epileptic activity: A bridge between observation and pathophysiological interpretation. Expert Rev Neurother, 8(6), 889–896. https://doi.org/10.1586/14737175.8.6.889
- White, T., Blok, E., & Calhoun, V. D. (2022). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp, 43(1), 278–291. https://doi.org/10.1002/hbm.25120
- Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci Data, 3, 160018–160018. https://doi.org/10.1038/sdata.2016.18
- Yamins, D. L. K., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nat Neurosci, 19(3), 356–365. https://doi.org/10.1038/nn.4244
- Yang, G. R., & Molano-Mazón, M. (2021). Towards the next generation of recurrent network models for cognitive neuroscience. Curr Opin Neurobiol, 70, 182–192. https://doi.org/10.1016/j.conb.2021.10.015
- Yang, W., & Yuste, R. (2017). In vivo imaging of neural activity. Nat Methods, 14(4), 349–359. https://doi.org/10.1038/nmeth.4230
- Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., Cash, D. M., Thomas, D. L., Dick, K. M., Cardoso, J., van Swieten, J., Borroni, B., Galimberti, D., Masellis, M., Tartaglia, M. C., Rowe, J. B., Graff, C., Tagliavini, F., Frisoni, G. B., … Alexander, D. C. (2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference. Nat Commun, 9(1), 4273. https://doi.org/10.1038/s41467-018-05892-0
- Yong, E. (2019). The Atlantic. https://www.theatlantic.com/science/archive/2019/07/ten-years-human-brain-project-simulation-markram-ted-talk/594493/
- Youssef, S. A., Capucchio, M. T., Rofina, J. E., Chambers, J. K., Uchida, K., Nakayama, H., & Head, E. (2016). Pathology of the aging brain in domestic and laboratory animals, and animal models of human neurodegenerative diseases. Vet Pathol, 53(2), 327–348. https://doi.org/10.1177/0300985815623997
- Zaborszky, L. (2021). Brain structure and function: The first 15 years—A retrospective. Brain Struct Funct, 226(8), 2467–2475. https://doi.org/10.1007/s00429-021-02362-0
- Zahodne, L. B., Manly, J. J., Narkhede, A., Griffith, E. Y., DeCarli, C., Schupf, N. S., Mayeux, R., & Brickman, A. M. (2015). Structural MRI predictors of late-life cognition differ across African Americans, Hispanics, and Whites. Curr Alzheimer Res, 12(7), 632–639. https://doi.org/10.2174/1567205012666150530203214
- Zhuang, C., Yan, S., Nayebi, A., Schrimpf, M., Frank, M. C., DiCarlo, J. J., & Yamins, D. L. K. (2021). Unsupervised neural network models of the ventral visual stream. Proc Natl Acad Sci U S A, 118(3). https://doi.org/10.1073/pnas.2014196118
- Zilles, K., & Amunts, K. (2013). Individual variability is not noise. Trends Cogn Sci, 17(4), 153–155. https://doi.org/10.1016/j.tics.2013.02.003
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