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Article(id=1301137660517572773, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2025.07.00064, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1752163200000, receivedDateStr=2025-07-11, revisedDate=1778601600000, revisedDateStr=2026-05-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1788144992236, onlineDateStr=2026-08-31, pubDate=1786550400000, pubDateStr=2026-08-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788144992236, onlineIssueDateStr=2026-08-31, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788144992236, creator=13701087609, updateTime=1788144992236, updator=13701087609, issue=Issue{id=1301137643908128824, tenantId=1146029695717560320, journalId=1146031591421210625, year='2026', volume='44', issue='15', pageStart='1', pageEnd='164', issueExtLink='null', onlineDate='null', pubDate='1786550400000', pubDateStr='2026-08-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1788144988276, creator='13701087609', updateTime=1788145858754, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1301141295053959998, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1301141295053959999, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=16, endPage=24, ext={EN=ArticleExt(id=1301137660748259494, articleId=1301137660517572773, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Brain organoid−based multimodal brain−computer interface platform and integration of key technologies, columnId=1150494641880182997, journalTitle=Science & Technology Review, columnName=Commentary, runingTitle=null, highlight=null, articleAbstract=

Brain−computer interface (BCI) is a frontier technology that enables bidirectional information exchange between neural tissues and external devices. Broadly speaking, the term "neural tissue" includes not only the human brain itself but also artificially constructed neural tissue models, such as in vitro brain organoids. Through precise sensing and dynamic feedback of neural structural and functional states, BCI is becoming an important technological pathway for advancing experimental paradigms in neuroscience and exploring brain−inspired intelligence. However, current in vitro brain organoid culture still relies on manual operations and therefore suffers from limited stability and consistency; meanwhile, mainstream BCI technologies are still largely restricted to single−modality data acquisition and processing, making it difficult to achieve multidimensional analysis of complex neural network activity. To address these issues, this paper proposes a high−throughput multimodal BCI platform integrating four core modules: closed−loop fully automated brain organoid culture, neuroelectrophysiological interaction, in situ gene detection, and optical imaging. We systematically describe the key technologies and system−integration strategies of this platform, analyze the strengths and bottlenecks of each module, propose feasible paths for technical fusion, and further discuss its potential applications in brain−inspired computing, mechanistic studies of brain diseases, drug screening, and precision medicine.

, authors=Min DAI1, Shiping LIU2, Quanxin YUN3, Jiahong DING4, authorsList=Min DAI, Shiping LIU, Quanxin YUN, Jiahong DING, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1301137662153351345, articleId=1301137660517572773, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于类脑器官的多模态脑机交互平台与关键技术整合, columnId=1150494642043760854, journalTitle=科技导报, columnName=科技评论, runingTitle=null, highlight=null, articleAbstract=

脑机交互（brain−computer interface，BCI）是一种实现脑组织与外部设备之间信息双向传输的前沿技术。广义而言，“脑组织”不仅包括人脑本体，也涵盖体外培养的类脑器官等人工构建的神经组织模型。通过对脑组织结构与功能状态的精准感知与动态反馈，脑机交互正逐步成为推动神经科学实验范式革新与类脑智能探索的重要技术路径。然而，当前类脑器官的体外培养仍依赖人工操作，存在稳定性与一致性不足等问题；同时，主流脑机交互技术多局限于单一模态的数据采集与处理，难以实现复杂神经网络活动的多维度解析。针对上述问题，提出一种集成了闭环全自动类脑器官培养、神经电生理交互、原位基因检测与光学成像4大模块的高通量多模态脑机交互平台。系统阐述了该平台涉及的关键技术与系统整合策略，分析各模块的优势与瓶颈，提出可行的技术融合路径，并展望该平台在类脑计算以及脑疾病的机制解析、药物筛选、精准医疗等领域的潜在应用前景。

, authors=代民1, 刘石平2, 云全新3, 丁佳虹4, authorsList=代民, 刘石平, 云全新, 丁佳虹, authorCompany=null, correspAuthors=null, authorNote=

代民，副研究员，研究方向为脑机接口、神经信息处理，电子信箱：

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代民，副研究员，研究方向为脑机接口、神经信息处理，电子信箱：

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代民，副研究员，研究方向为脑机接口、神经信息处理，电子信箱：

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图片来源：Wikimedia Commons（https://commons.wikimedia.org）

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图片来源：Multichannel Systems（https://www.multichannelsystems.com）

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图片来源（本文有修改）：Advanced Science（https://doi.org/10.1002/advs.202308594）

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基于类脑器官的多模态脑机交互平台与关键技术整合
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代民 1 , 刘石平 2 , 云全新 3 , 丁佳虹 4
科技导报 | 科技评论 2026,44(15): 16-24
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科技导报 |科技评论 2026 , 44 (15) : 16 -24
基于类脑器官的多模态脑机交互平台与关键技术整合
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代民1 , 刘石平2, 云全新3, 丁佳虹4
作者信息
  • 1杭州华大生命科学研究院，杭州 310030
  • 2基因组多维解析技术全国重点实验室，杭州华大生命科学研究院，杭州 310030
  • 3基因组多维解析技术全国重点实验室，深圳华大生命科学研究院，深圳 518083
  • 4深圳华大基因科技有限公司，深圳 518083
作者简介:

代民，副研究员，研究方向为脑机接口、神经信息处理，电子信箱：

Brain organoid−based multimodal brain−computer interface platform and integration of key technologies
Min DAI1 , Shiping LIU2, Quanxin YUN3, Jiahong DING4
Affiliations
  • 1BGI Research, Hangzhou 310030, China
  • 2State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Hangzhou 310030, China
  • 3State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China
  • 4BGI, Shenzhen 518083, China
出版时间: 2026-08-13 doi: 10.3981/j.issn.1000-7857.2025.07.00064
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摘要
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脑机交互（brain−computer interface，BCI）是一种实现脑组织与外部设备之间信息双向传输的前沿技术。广义而言，“脑组织”不仅包括人脑本体，也涵盖体外培养的类脑器官等人工构建的神经组织模型。通过对脑组织结构与功能状态的精准感知与动态反馈，脑机交互正逐步成为推动神经科学实验范式革新与类脑智能探索的重要技术路径。然而，当前类脑器官的体外培养仍依赖人工操作，存在稳定性与一致性不足等问题；同时，主流脑机交互技术多局限于单一模态的数据采集与处理，难以实现复杂神经网络活动的多维度解析。针对上述问题，提出一种集成了闭环全自动类脑器官培养、神经电生理交互、原位基因检测与光学成像4大模块的高通量多模态脑机交互平台。系统阐述了该平台涉及的关键技术与系统整合策略，分析各模块的优势与瓶颈，提出可行的技术融合路径，并展望该平台在类脑计算以及脑疾病的机制解析、药物筛选、精准医疗等领域的潜在应用前景。

脑机交互  /  类脑器官  /  基因检测  /  电生理交互  /  光学成像

Brain−computer interface (BCI) is a frontier technology that enables bidirectional information exchange between neural tissues and external devices. Broadly speaking, the term "neural tissue" includes not only the human brain itself but also artificially constructed neural tissue models, such as in vitro brain organoids. Through precise sensing and dynamic feedback of neural structural and functional states, BCI is becoming an important technological pathway for advancing experimental paradigms in neuroscience and exploring brain−inspired intelligence. However, current in vitro brain organoid culture still relies on manual operations and therefore suffers from limited stability and consistency; meanwhile, mainstream BCI technologies are still largely restricted to single−modality data acquisition and processing, making it difficult to achieve multidimensional analysis of complex neural network activity. To address these issues, this paper proposes a high−throughput multimodal BCI platform integrating four core modules: closed−loop fully automated brain organoid culture, neuroelectrophysiological interaction, in situ gene detection, and optical imaging. We systematically describe the key technologies and system−integration strategies of this platform, analyze the strengths and bottlenecks of each module, propose feasible paths for technical fusion, and further discuss its potential applications in brain−inspired computing, mechanistic studies of brain diseases, drug screening, and precision medicine.

brain−computer interface  /  brain organoid  /  gene detection  /  electrophysiological interaction  /  optical imaging
代民, 刘石平, 云全新, 丁佳虹. 基于类脑器官的多模态脑机交互平台与关键技术整合. 科技导报, 2026 , 44 (15) : 16 -24 . DOI: 10.3981/j.issn.1000-7857.2025.07.00064
Min DAI, Shiping LIU, Quanxin YUN, Jiahong DING. Brain organoid−based multimodal brain−computer interface platform and integration of key technologies[J]. Science & Technology Review, 2026 , 44 (15) : 16 -24 . DOI: 10.3981/j.issn.1000-7857.2025.07.00064
正文
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1 脑机交互的复杂性呼唤多模态整合
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人类大脑结构与功能极其复杂，由约860亿个神经元和数量大致相当的胶质细胞构成。神经元之间通过突触建立庞大而动态的功能连接网络，突触数量高达100万亿级，赋予大脑强大的信息整合与处理能力[1]。从宏观层面看，这些连接构成层级分明、区域协作的神经网络系统（图1（a））；从微观层面看，单个神经元内部亦呈现出精密的分子调控机制，其膜电特性、离子通道分布和突触可塑性等共同决定神经功能状态（图1（b））。
与此同时，活体人脑极难获取；在体大脑研究（无论是人脑还是动物脑）亦受到日益严格的伦理审查和操作限制。基于此，体外培养的类脑器官正逐渐成为脑科学研究中常用且可靠的实验模型。该模型不仅在结构和功能上能够部分模拟人脑复杂性，同时具备获取便捷、来源稳定、操作可控以及伦理负担较小等优势。体外类脑器官培养技术作为一种前沿的生物医学研究手段，已经成为推动人类对大脑工作机制深入认知的关键工具。
目前为止，针对不同尺度与模态下的大脑信息，科研人员开发了多种检测/交互技术： 基因组和转录组层面依赖高通量测序技术[2]，结构层面借助光学成像手段[3−4]，功能层面则多使用电生理交互技术[5]。 然而，这些技术在标本制备、操作方案、时间和空间分辨率上的差异，使得难以同步获取一个标本的上述信息并进行联合分析。与之对应的，大脑作为一个高度复杂的动态系统，恰恰需要在统一框架中实现多模态、高通量、实时并行的信号检测与调控。割裂的技术体系已难以满足当前神经科学对系统性和动态性的要求，显著限制了脑功能解析与脑机交互技术的发展。因此，亟须构建统一、高度整合的多模态平台，以支撑对脑组织“基因—结构—功能”关系的系统性认知。
多模态脑信息整合不仅对基础研究意义重大，也将持续推动脑机交互技术的演进。在脑组织发育和功能执行过程中，基因表达、网络形态与电活动三者彼此耦合、动态调节[6]。通过同步获取神经元的分子状态、形态特征与电生理活动，研究者得以重建更全面的大脑功能图谱。 例如，在基础脑科学研究中，整合电生理记录与形态学和转录组分析，不仅揭示高放电活性区域，还可进一步溯源其分子背景与神经元亚型[7]。又如，将光学成像与电信号进行时空配准，可揭示神经活动如何塑造结构连接，并预测功能可塑性可能的起点与通路。
综上所述，在体外类脑器官培养的基础上，构建一个兼具高通量、强兼容性与多模态反馈能力的脑机交互平台，是推进神经科学纵深发展与类脑智能系统落地的关键步骤，平台结构简图如图2所示。它将为解析脑组织功能网络、构建疾病模型、筛选靶点和开展精准干预提供统一技术基础。
本文将从闭环培养、神经电生理交互、原位基因检测与光学成像4大功能模块入手，分别阐述其技术现状、关键瓶颈与发展方向，最后从系统整合角度讨论多模块协同与闭环实现路径。
2 闭环全自动类脑器官培养技术的优势与挑战
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类脑器官是高度简化的人脑三维体外模型，在实验室环境下为研究大脑发育与功能提供了可控平台。它们具备一定的结构复杂性和电活动能力，可用于疾病建模、药物筛选和发育机制研究等领域[8−9]（图3）。近2年，类脑器官空间组织与多脑区集成能力出现了可验证进展：Tsai等[10]通过多层微流控芯片构建形态发生因子梯度，获得了具有地形化分区特征的前脑多域类器官；Zhu等[11]报道了在不外加信号诱导条件下生成LGE/CGE类器官的方法，提示内源性自组织可用于构建区域特异性抑制性神经元模型；Kshirsagar等[12]构建了融合大脑、脑干/后脑与内皮系统的多区域类脑器官（MRBOs），提升了跨脑区与类血管成分协同建模能力。在这些研究基础上，未来若能实现对类脑器官生长环境的动态调控，将为建立高通量、标准化的类脑模型体系奠定技术基础。闭环全自动培养技术的核心优势在于可通过“在线感知—算法决策—执行调控—结果校验”持续迭代，同步提升培养稳定性、批间一致性与实验通量；若进一步突破低损伤长期供养、跨批次参数迁移与标准化质控，将有望把类脑器官研究推进到“长期成熟—规模复现—闭环干预”的工程化新阶段。
闭环全自动培养技术正是在此背景下应运而生。本文所称“闭环全自动”是指“感知—决策—执行—评估”4级控制链条。（1） 感知层：在线采集pH、溶氧、葡萄糖/乳酸、温湿度、渗透压及类脑器官形态学特征（直径、圆度、坏死核心比例）。（2） 决策层：依据预设工艺窗口和机器学习预测模型，实时计算补液、换液、供氧、搅拌和给药策略。（3） 执行层：由微流控阀阵、蠕动泵、气体混配和温控单元完成参数闭环调节。（4） 评估层：以电生理活性、存活率与批间变异度作为质量指标，反向更新控制参数。该定义强调“自动化”不仅是流程替代人工，更是面向生物状态漂移的自适应控制。
目前的体外类脑器官培养技术仍存在明显不足，大大限制了类脑器官的长期培养与脑功能成熟度提升[13]。当前主要挑战可归纳为4点：（1） 缺乏功能性血管网络，组织内部易缺氧并出现中心坏死；（2） 神经元空间分布与真实脑区分化仍有差距，难以稳定形成功能分区；（3） 流程高度依赖手工操作，污染风险高、批间一致性差；（4） 长期运行时传感器漂移、微流控堵塞与成本控制问题并存。针对上述挑战，近年的工程化进展开始出现：在网络拓扑方面，Duenki等[14]构建了3~4个类脑器官经轴突束闭环连接的connectoid系统，观察到更复杂网络活动并向近临界动力学迁移；在培养硬件方面，Rosen等[15]提出脱离传统培养箱的密闭循环培养方案（预印本），实现了封闭回路中的环境稳定维持与持续监测。总体而言，这些工作为闭环全自动培养提供了可行路径，但距离高稳定性、高一致性、低成本的工程化平台仍有明显距离。
3 神经电生理交互技术的发展方向与挑战
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神经电生理技术如同高灵敏度的信号监测和控制设备，能精确记录神经元电活动并可按需施加刺激调控，被广泛用于脑组织网络功能研究。尤其在神经元兴奋性、突触传递和网络同步性研究中，电生理技术具备毫秒级时间分辨率，是解析神经信息编码机制的关键工具。近年来，高密度微电极阵列（microelectrode array，MEA）的发展，使对类脑器官中大量神经元活动的并行记录成为可能（一款常用的商业MEA系统，如图4）；已有工作基于高通量MEA实现了体外神经元与类脑器官的任务耦合和初步解码验证[16−17]。代表性工作可简要概括为：（1） Kagan等[16]报道体外神经元与虚拟Pong环境闭环耦合后可表现出任务相关适应性行为；（2） Cai等[17]提出脑类器官储备池计算框架，证明类器官电活动可用于时序信息处理与智能任务解码；（3） Osaki等[18]构建轴突互连的双器官网络，观察到更复杂群体活动和短时可塑性；（4） Chow等[19]进一步显示重复刺激可重塑类器官网络统计特征。上述研究说明，电生理平台正从“被动记录”走向“可训练、可干预、可评估”的闭环形态。
然而，MEA技术在高密度条件下面临一系列工程与应用瓶颈[20]。随着通道数的增加，电极间干扰增强、信噪比下降，导致有效信号提取困难；同时，高通量采集系统的数据处理压力急剧上升，增加了实时分析的复杂性。此外，芯片制造和系统集成的成本也随之提升，限制了其在大规模筛选实验中的普及。长期的生物兼容性问题也不容忽视，金属电极可能在长期培养中引发细胞应激反应或局部组织损伤。目前已有研究探索通过碳纳米管、导电高分子（如PEDOT:PSS）等新材料进行电极表面修饰，以提升电极信号质量并降低界面阻抗，为神经电生理平台的长期稳定运行提供可行路径。
4 原位基因检测技术的集成潜力与实现瓶颈
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原位基因检测技术强调在保持空间结构的前提下，直接检测脑组织中不同区域的基因表达状态，是解析神经功能与分子机制之间关系的重要手段[21]。与传统单细胞测序技术不同，原位检测保留了细胞间拓扑关系，可进一步实现神经网络中细胞的基因表达信息与其形态/功能信息的整合。近年来，空间转录组和原位测序技术持续迭代，如MERFISH、seqFISH和Stereo−seq等平台，已在小鼠大脑、类脑器官等模型中实现高通量、单细胞分辨率的基因表达图谱绘制。例如，Stereo−seq技术通过在芯片上预设纳米探针，实现大范围组织切片中数万基因的同时定位和定量，为脑功能分区与神经网络调控提供了宝贵的空间分子信息[2]。此外，近年的跨模态配准流程已开始将空间分子信息与电活动读出联合分析，为后续闭环干预提供更可解释的证据链[22]。
尽管原位基因检测展示出广阔应用前景，但将其集成进实时、多模态脑机交互平台仍面临技术壁垒。当前主要矛盾可归结为同一问题：多数原位测序流程依赖固定/裂解及多步反应处理，既可能损伤组织结构并干扰神经电活动、打断同一样本的连续观测，又通常需要数小时至数天才能获得结果，难以满足闭环系统对低时延、实时反馈的要求。与此同时，空间转录组设备往往体积庞大、操作复杂，且数据处理依赖计算资源密集的图像识别算法，不利于嵌入式或实时平台部署。面向上述瓶颈，活细胞测序（live−seq）可被视为具有突破潜力的方向：其通过微量胞质取样在保留细胞存活的条件下获取转录信息，为兼顾连续监测与阶段分子校准提供了未来可期待的技术路径[23]。但该技术目前仍存在通量偏低、操作复杂度高、检测时效与标准化不足及跨平台复现证据有限等问题，尚难支撑实时、规模化与工程化应用。因此，在现阶段多数基因检测仍须终止样本的约束下，系统只能采用“连续监测+定点校准”的工程性折中方案。具体而言，可先持续记录MEA/钙成像捕捉快速网络变化，再按固定节律或关键事件触发进行分子取样并完成同位映射，最终以融合模型将功能异常追溯至分子背景。从长期看，仍需要无损或低损分子监测技术进一步成熟，才能真正实现分子信息与电活动的同步连续监测。
5 光学成像技术的作用拓展与技术融合
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光学成像技术在脑组织结构与功能研究中发挥着不可替代的作用，尤其在实现类脑器官内部的单细胞结构重建与功能活动可视化方面展现出显著优势[24]。借助双光子、三光子显微成像及光片荧光显微技术，研究者能够在组织透明或较浅层区域内实现亚细胞分辨率的神经形态追踪、突触连接识别和网络活动监测。例如，结合钙离子荧光探针或电压敏感分子探针，光学成像可以同步获取单个神经元的电活动变化及其在神经网络中的功能地位，为理解神经元间的信息流动和状态调控机制提供了时空匹配的观测手段。未来，随着新型荧光探针、自适应光学元件和基于深度学习的图像重建技术的发展，光学成像有望突破组织穿透深度与成像速度的限制，在更复杂组织背景中实现长时程、稳定、高分辨率的成像反馈，并与电生理记录、光遗传调控等技术实现一体化整合。
尽管当前光学成像系统在空间精度和功能解析方面具备显著优势，但其在深层组织成像能力、成像持久性和系统通量方面仍存在多重技术瓶颈[25]。首先，组织散射和吸收限制了激发光与发射信号的传导路径，深部结构成像质量易受影响，且往往需要借助侵入性手段获取目标区域信息。其次，长期成像过程中荧光探针存在光漂白与光毒性风险，易对神经元功能与存活状态造成干扰。此外，当前成像系统在高分辨率、宽视场与高速采集之间存在硬件结构上的矛盾，难以兼顾多尺度的神经网络整体观测与局部精细分析。未来需重点突破低光毒性、高信噪比的新型探针材料，开发具备多模式切换与深层成像能力的复合光学系统，引入智能化图像处理算法以提升数据提取与功能判读的自动化水平，从而实现光学成像在类脑器官脑机交互平台中的全面整合。
6 系统整合策略与多模态平台的未来展望
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高通量多模态脑机交互平台的构建预示着神经科学实验范式的转变，其在多个关键研究与应用领域具有深远的推动潜力。以神经退行性疾病为例，通过该平台可实现对类脑器官在发育、衰老及病理演变过程中电活动、分子表达与形态结构的全程监测，为个体化干预策略的制定提供生物学依据。在类脑计算研究中，借助电—光—分子多通道交互手段，可模拟真实神经网络在外部刺激下的动态响应模式，为构建具备可塑性与自适应能力的类脑芯片系统提供训练基础。此外，在高通量药物筛选与毒理学评估中，该平台可在微尺度范围内同步采集药物处理前后神经功能与基因表达等多维数据，提升实验效率与解析深度。这些应用图景充分凸显出多模态整合在推进类脑智能体系建设中的核心价值。
从系统运行机理看，该平台可被描述为“刺激—响应—解码—控制—反馈”闭环：首先以MEA、光学成像和原位分子检测联合感知类脑器官状态；随后在统一时空坐标完成特征提取与状态估计；再由解码模型输出外部设备控制指令（如机械臂轨迹、虚拟环境交互或药液输注节律）；最后依据执行结果反向调节脑组织刺激参数（电刺激脉宽/频率、光刺激时序、化学因子剂量），并以网络稳定性、任务正确率和细胞活性作为闭环评价指标。具体而言，刺激环节常用电刺激脉宽/频率调制、光遗传时序激活与微流控定量给药；响应环节通过MEA放电记录、钙/电压成像与阶段性分子读出获取多尺度信号；解码环节采用状态空间模型、时序深度网络或储备池计算完成状态估计与指令生成；控制环节以模型预测控制结合规则约束驱动外设执行；反馈环节依据性能指标在线更新刺激参数，实现闭环自适应优化。
平台应用可进一步细化为4类机制。（1） 类脑计算：通过可控刺激—响应范式训练器官网络，提取短时与长时可塑性参数，用于储备池计算与神经形态算法验证；并可通过多轮任务训练评估其记忆容量、非线性映射能力与抗噪稳定性。（2） 疾病机制解析：在患者来源类脑器官中联合跟踪“分子异常—网络失同步—结构退化”的因果链，并通过定向闭环干预验证关键病理节点是否可逆。（3） 药物筛选：建立多终点联合评分（电活动恢复、毒性、转录修复程度、结构完整性），同时引入剂量−时间反应曲线与作用持续性评估，提高候选药物优选效率。（4） 精准医疗：以患者特异性模型预测药物反应窗口与不良反应阈值，并结合病程阶段与联合用药情景，支持个体化给药策略。
在系统软硬件层面，平台可按“硬件分层—软件闭环—智能决策边界”3条主线展开，并采用“边缘采集+中心训练”的架构。硬件侧可分为4层：感知层（MEA、成像、环境与代谢传感器）、执行层（微流控阀泵、刺激器、光路调制器）、同步层（统一时钟与触发总线）、保障层（电磁屏蔽、无菌维持与温控稳态）。当前瓶颈主要在于：多设备时钟漂移导致跨模态错配、长周期培养下微流控堵塞与材料老化、强电刺激对弱光信号链的串扰，以及高并发条件下设备校准和维护成本高。软件侧需建立统一数据总线、元数据标准与质量控制流水线，形成“采集—质检—融合—建模—决策—追溯”的全流程闭环。人工智能特别是多模态大模型可承担3类任务：跨模态语义对齐、异常检测与早期预警、实验策略推荐；其部署应采用“模型建议—人工确认—安全阈值拦截”机制，并满足可解释性、可审计性与生物安全约束，避免黑箱决策直接作用于生物样本。进一步地，生物—人工混合智能系统可作为重要方向，通过类脑器官与可编程电子系统闭环耦合研究“生物可塑性+算法可优化”的协同学习机制，未来突破点包括长期稳定接口材料、在线学习与遗忘抑制机制、跨批次迁移校准及伦理边界治理。工程实现上还需落实统一的数据同步机制与实时通信协议，并在单芯片内协同集成微流控供养、高密度电极阵列与透光窗口，保证多源数据在同一时空基准下稳定采集并支撑闭环反馈。
但多模态整合也带来了显著的系统复杂性挑战。不同模块在信号采集频率、稳定性要求及工作环境方面差异显著，电生理系统需高度屏蔽外部干扰、响应速度快，而光学成像则对热稳定性和空间布置敏感，二者在集成过程中的相互干扰问题不容忽视。此外，当前多模态数据融合技术仍处发展初期，尤其是在类脑器官这类非均质、动态变化的复杂体系中，缺乏通用的特征匹配与跨模态关联分析框架。为克服这些问题，未来亟须在材料工程、系统设计、智能感知与算法建模等维度展开深度协同。构建从微观接口到宏观系统的闭环集成链条，是实现平台通用化、模块化、可拓展的重要基础，也将为推动类脑智能和神经信息处理研究迈向实用化提供有力支撑。
在上述系统瓶颈背景下，已有若干典型多模态整合技术与工程尝试，为平台化实现提供了可借鉴路径。2016年，美国、德国和瑞典的学者发表了Patch−seq技术，这是目前为止唯一一种将电生理记录、光学成像和单细胞RNA测序3个功能结合的成熟技术（图5（a））。然而其极低的通量（每次1个细胞）大大限制了它的广泛应用。令人兴奋的是，Emery等[22]发表的MEA−seqX技术为高通量平台的雏形提供了重要范例。该系统利用光学成像与空间配准算法，成功将高密度电生理记录、空间转录组测序和组织结构图像进行匹配，实现了神经组织在功能（电活动）、分子（基因表达）与形态（组织结构）3个维度上的联合分析（图5（b））。
尽管该方法仍依赖商用设备与离线处理流程，但其策略展示了信息融合在解析复杂神经系统中的巨大潜力。具体而言，MEA−seqX在技术链路上可分为3个层次：采集端以高密度MEA提取放电频率、爆发传播与网络同步等功能特征；空间端通过组织图像与空间转录组坐标配准，将电极邻域信号映射到细胞/区域分子背景；分析端以联合建模关联“电活动模式—细胞类型组成—局部分子状态”，从而提升跨模态解释能力。与此同时，该方法仍主要依赖离线流程，难以满足实时闭环干预对低时延计算与在线质控的要求；长期培养下组织形变、电极漂移与批间差异也会降低配准稳定性，限制跨实验室复现。未来平台需要在在线化处理、标准芯片接口与自动质控方面继续突破。
7 总结与展望
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类脑器官的快速发展为理解人脑结构与功能提供了前所未有的新路径，也对实验系统的复杂性提出了更高要求。多模态脑机交互平台作为承载类脑研究的关键载体，不仅在技术集成上需要跨越多重挑战，更在应用场景中展现出广阔前景。随着微结构制造、实时计算、生物传感与人工智能等多学科技术的不断融合，未来的类脑系统将不仅是被动观察对象，更将转化为可调控、可学习、可进化的智能体。我们有理由相信，在新一代高通量多模态平台支撑下，类脑研究将在脑疾病机制解析、个性化医疗、类脑计算架构等方向持续突破，为揭示大脑本质与构建生物启发式智能系统提供坚实基础。

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2026年第44卷第15期
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doi: 10.3981/j.issn.1000-7857.2025.07.00064
  • 接收时间：2025-07-11
  • 首发时间：2026-08-31
  • 出版时间：2026-08-13
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  • 收稿日期：2025-07-11
  • 修回日期：2026-05-13
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    1杭州华大生命科学研究院，杭州 310030
    2基因组多维解析技术全国重点实验室，杭州华大生命科学研究院，杭州 310030
    3基因组多维解析技术全国重点实验室，深圳华大生命科学研究院，深圳 518083
    4深圳华大基因科技有限公司，深圳 518083
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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