内容来源：https://www.geekwire.com/2025/ai-map-brain-regions/

内容总结：

近日，国际科研团队借助类ChatGPT人工智能技术，成功绘制出迄今最精细的小鼠脑图谱。这项发表于《自然-通讯》的研究由加州大学旧金山分校和艾伦脑科学研究所主导，通过名为CellTransformer的AI模型精准标记了1300个脑区与亚区结构，其中多个亚区属首次被发现。

研究团队采用与ChatGPT同源的"转换器"神经网络架构，对取自4只实验鼠脑部的200多个组织切片、涉及900万细胞的空间转录组数据进行分析。该模型不仅能复现专家已定义的脑区结构，更在目前认知有限的脑区识别出全新微观亚区，如同将地图精度从"大陆级"提升至"街区级"。

艾伦研究所分子遗传学主任博西莉卡·塔西克指出："大脑中位置决定功能。精确绘制脑区图谱不仅能深化对大脑的认知，更为靶向治疗神经系统疾病提供可能。"目前针对中脑网状核和上丘等区域的新发现，有望推动研发副作用更小的精准脑部疗法。

尽管这项技术具备扩展到人类大脑研究的潜力，但科学家表示，由于人脑结构更复杂、数据采集难度大，实现同等精度的全脑图谱绘制可能还需十年时间。未来研究将聚焦多模态数据整合，通过结合细胞连接模式与活动特征，进一步揭示大脑奥秘。

中文翻译：

科学家表示，他们运用一款可与ChatGPT相媲美的人工智能程序，绘制出了迄今最精细的小鼠大脑图谱之一，图中标记了1300个大脑区域与子区域。其中部分子区域属首次被绘制，研究人员称这项研究还将持续深入。

西雅图艾伦脑科学研究所分子遗传学主任博西莉卡·塔西奇表示："现有迹象表明，我们完全能突破当前成果。"这项由加州大学旧金山分校和艾伦研究所主导的脑图谱研究，详细成果已发表于《自然·通讯》期刊。

论文资深作者、加州大学旧金山分校神经科学家雷扎·阿巴西-阿斯尔在新闻稿中称："我们的模型与ChatGPT等AI工具基于同种强大技术，两者都采用擅长理解上下文关系的'转换器'网络。"塔西奇向GeekWire解释，这种上下文理解对治疗神经系统疾病至关重要。

"大脑中位置决定一切。"她强调，"精确绘制大脑地理图谱，界定各区域及其功能，不仅能深化认知，更能提升治疗效能。"更精细的脑细胞结构图谱将推动靶向药物研发，减少副作用。塔西奇比喻道："若没有地图，如何定位问题所在？"

传统脑图谱绘制依赖人工解部，如今科学家已能更精准识别数百万脑细胞的位置与功能。海量数据采集能力的大幅提升，使AI辅助分析成为必然。塔西奇指出："实验技术的飞跃使新一代测序技术彻底革新。通过单细胞测序数千基因、定义同类细胞，这种方法正在重塑生物学。"她认为，能处理高维数据的软件让当代成为"神经科学的黄金时代"。

最新研究的核心是名为CellTransformer的AI模型。该模型通过分析约900万个脑细胞的空间转录组数据，界定细胞所属的"功能社区"。研究团队首先让模型划定25个大脑区域，随后将分辨率提升至670个区域，每次提升结果均与专家手工绘制图谱吻合。当分辨率最终调至1300个区域时，模型不仅复现了已知区域，更在认知薄弱区发现了全新精细子区域。

塔西奇生动描述这一过程："就像从仅显示大陆的地图，逐步细化到标出国家、州县、城市乃至社区。"她解释道："我们让程序分析每个细胞的'邻居'特征，根据邻域共性划定区域——这正是CellTransformer的实现原理。"

新发现的子区域包括中脑网状核（负责感觉与运动信息整合）以及上丘（协调感官信息并引导眼、头、躯体朝向兴趣目标）。塔西奇表示，通过调整算法参数可获得更精细图谱，但关键在于"如何界定其生物学意义"。

命名新发现区域也面临挑战。塔西奇将其比作探索新大陆："发现新地貌后，需要系统命名并建立与旧图谱的关联。"更深层的问题在于，这类基于细胞类型的图谱如何与神经连接图谱、脑细胞活动模式相融合。她期待未来能建立多模态模型，综合基因表达、细胞类型、连接组和活动模式来定义大脑区域。

尽管这项适用于小鼠大脑的AI技术"完全可延伸至人类大脑"，但塔西奇预计仍需漫长时间："人类大脑体积庞大，数据采集是首要障碍。完整采集人脑精细数据可能还需十年。"

该研究题为《基于转换器的小鼠大脑数据驱动精细区域发现》，第一作者为加州大学旧金山分校研究员亚历克斯·李，合著者包括阿尔玛·迪布克、迈克尔·孔斯特、沈琴、尼古拉斯·拉斯克、莉迪亚·吴、曾红葵、博西莉卡·塔西奇和雷扎·阿巴西-阿斯尔。

英文来源：

Scientists say an artificial intelligence program that they compare to ChatGPT has helped them create one of the most detailed maps of the mouse brain to date, with 1,300 regions and subregions marked on the map.
Some of those subregions have never been charted before — and the researchers say there’s more to come. “I think there are already indications that we can go beyond what we see now,” said Bosiljka Tasic, director of molecular genetics at Seattle’s Allen Institute for Brain Science.
The mapping effort, led by researchers at the University of California at San Francisco and the Allen Institute, is detailed in a study published today in the journal Nature Communications.
“Our model is built on the same powerful technology as AI tools like ChatGPT,” senior author Reza Abbasi-Asl, a neuroscientist at UCSF, said in a news release. “Both are built on a ‘transformer’ network which excels at understanding context.”
That context could be important for treating neurological ailments, Tasic told GeekWire.
“Location is everything in the brain,” she said. “Defining the geography of the brain, and then defining all these regions and their functions, not only leads to better understanding, but also better ability to treat.”
More detailed maps of the brain’s cellular structure could lead to more targeted drug treatments that cause fewer side effects. “We always want to go toward better, more precise brain therapies, but in order to do that, you need to know where you need to interfere, what went wrong in what place, and what you need to fix,” Tasic said. “And if you don’t have the map, how are you going to know where it is?”
Mapping the brain’s neighborhoods
Brain-mapping efforts have typically relied on human interpretation of the brain’s anatomy, but scientists are getting better at identifying the location and function of millions of individual brain cells. They’re getting so much better at collecting huge masses of data that they need AI to help with the interpretation.
“We are at a point where we have amazing experimental technology, so next-generation sequencing is completely revolutionized,” Tasic said. “Our way to define cell types — the fact that you can measure thousands of genes per cell, and define cells that are similar as a cell type — has transformed biology.”
The availability of software that can deal with such high-dimensional data is making this “an amazing time for a neuroscientist,” she said.
The key to the newly published study is an AI model called CellTransformer. The model sifts through huge sets of data about the locations and functions of brain cells, known as spatial transcriptomics data sets, to determine which cells belong in the same “neighborhood” of the brain.
CellTransformer analyzed spatial transcriptomics data about 9 million cells in more than 200 tissue sections that were taken from the brains of four individual mice. At first, researchers programmed the model to define the boundaries of 25 regions in the brain. Eventually, they raised the resolution to define 670 regions and subregions. At each level of resolution, CellTransformer’s brain maps matched what had been defined previously by human experts.
Then the dial was turned up to produce 1,300 regions and subregions. At that level, CellTransformer successfully replicated maps of cataloged regions of the brain. It also identified previously uncataloged, finer-grained subregions in areas of the brain that are currently poorly understood.
Tasic said the process was like going from a map that showed only continents, or only countries, to a map that showed states, cities and even the neighborhoods within cities.
“What we’re saying is, let’s take any cell and ask, ‘Who are the neighbors?’ And then, based on the commonality of the neighbors, call it a region,” she said. “Basically, that’s what CellTransformer did.”
Some of the previously uncharted subregions are in the midbrain reticular nucleus, which plays a complex role in processing sensory and motor information. Other newly identified subregions are in the superior colliculus, a part of the midbrain that processes sensory information and initiates eye, head and body movements to focus on objects of interest.
Focusing on new neuro-frontiers
Tasic said it’s possible to turn up the dial on CellTransformer’s algorithms to produce maps of the brain that are even more detailed. “Now, the question is, which ones are meaningful, in what way, and what do they represent biologically?” she said.
Another question has to do with what to call the newly characterized subregions. “Just imagine that you came to a new land, and you are seeing there is this and there is that. But now I need to name it. Now I need to see what else is around,” Tasic said. “We want to give meaningful, systematic names and also reference how it relates to older maps.”
Perhaps the biggest questions relate to how the newly published map, which is based on cell types, will line up with maps that trace connections between cells, or patterns of brain-cell activity. “I’m just hoping for more systematic data collection, more systematic data analysis, and more multimodal models — models that will not only measure gene expression and cell type, but connectivity and productivity, and define brain regions based on all of those,” Tasic said.
Tasic said the AI-based techniques that were developed for mapping mouse brains are “absolutely extendable to the human brain,” but she doesn’t expect that to happen overnight.
“The limit is actually data collection,” she said. “The human brain is huge, so that’s one problem. … I don’t want to give any estimates, but it will take maybe a decade more to just collect [data about] the full human brain at the level of detail that we did for the mouse.”
UCSF researcher Alex Lee is the principal author of the Nature Communications study, titled “Data-Driven Fine-Grained Region Discovery in the Mouse Brain With Transformers.” Other authors include Alma Dubuc, Michael Kunst, Shenqin Lao, Nicholas Lusk, Lydia Ng, Hongkui Zeng, Bosiljka Tasic and Reza Abbasi-Asl.