今天全世界有5 700万人患有痴呆症。 对于努力帮助他们的研究人员来说，障碍不是缺乏努力，而是数据过多、专家太少无法综合数据，以及无法跟上疾病的发现速度。
神经脱氧核糖核酸生物医学研究联合会(C-BRAIN)与NVIDIA合作，正利用AI来改变这种情况。
阿尔茨海默病是当今全球主要的健康挑战之一，每年诊断出超过一千万个新的痴呆症病例。 尽管在过去15年中，国家卫生研究所的资金增加了7倍——每年达39亿美元——但还没有发现任何治疗能够阻止、预防或扭转这种疾病。
瓶颈不够高的影响科学发现，过多的数据生活在机构仓中，合成速度也太慢，无法跟上所发表的研究。
C-BRAIN由兰德尔·贝特曼博士和由学术，慈善，药典和研究机构等18个成员机构组成的财团所领导，于2025年11月发布，通过加速研究，使新治疗成为可能来帮助科学家更好，更快的发现。
“我们有机会利用人工智能进行与以往完全不同的研究，以加速发现来帮助病人，他描述他们的工作是“科学家为科学家建造的AI工具”。
财团正在建立一个利用以前尚未开发的阿尔茨海默研究数据的系统，以支持科学家的工作。
将研究从周到小时的速度
阿尔茨海默研究中的基本资产是AI工具或人类研究人员从未公开访问过的数据集。 这包括未公布的调查结果、负面结果和成员机构受管制的专有数据。
许多生命科学数据并没有进入前沿模型，“结果就是我们把许多情报放在桌面上。”
C-BRAIN将此称为"黑暗数据"
一些暗相数据被直接存入安全的计算机系统，以便AI工具访问。 为了在不损害病人隐私或机构治理的情况下使用这些数据，C-BRAIN采用混合集中和联合的方法——即AI模型从每个机构的数据中学习，而不必离开数据。 这种基础设施在技术上要求建设和规模化，这是NVIDIA 带来合作的一部分。
C-BRAIN目前运行着64个NVIDIA数据中心GPU，与Microsoft Azure上以云为主的节点相并列，并新增了NVIDIA GPU容量，包括NVIDIA Blackwell系统，作为财团的雄心规模。
为了探索AI和加速计算如何在导航其技术需要的同时推进阿尔茨海默的研究，C-BRAIN正与NVIDIA合作将技术包括NVIDIA BioNeMo开放开发商平台和C-BRAIN研究管道的MONAI框架。
“C-BRAIN与NVIDIA的合作旨在解锁这些模型，使其能提供影响相当大和重要的东西。” 称 Bateman。
Ojewole 称。 校对：Soup “NVIDIA软件肯定会在那里发挥作用。”
C-BRAIN的研究人员已经建造了两种工具：OpenScientist——与劳伦斯·伯克利国家实验室一起建造，由研究科学家贾斯汀·里斯(Justin Reese)领导；InsightEngine，由387个实验室建造。
OpenScientist是一位开源代理AI联合科学家，他接受科学家的研究问题和数据集，然后半自主地产生假说，进行分析并产生可核查，可审计的见解。 InsightEngine是一个开源研究平台，它利用代理AI来测试与科学文献相对抗的假说，分析用户提供的数据集，探索生物医学知识图并对照证据检查每一项声称，在研究人员承诺进行昂贵的实验之前捕捉幻觉。
在公布的案例研究中，开放科学家在几分钟内完成了分析，估计领域科学家需要几周到几个月的时间，每跑10美元以下。 在C-BRAIN目前拥有200多个研究小组的情况下，这种能力可以释放出大量的研究能力，每年可能超过100万小时。
从关联到原因
展望未来，C-BRAIN旨在建立一个多模式生物基础模型，对其数据全宽——神经成像、多相生理学、流体生物标记和纵向临床措施——进行培训。
到第三年，它旨在发现脑衰老和阿尔茨海默氏病的新因果机制，利用现有研究和数据的全部范围转向可检验的科学原则。
Bateman 称，“能够思考和理性的AI有可能加速发现，从而导致对全体人口的有力和可实施的预防和治疗。”
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There are 57 million people worldwide living with dementia today. For the researchers working to help them, the obstacle isn’t a lack of effort — it’s too much data, too few experts to synthesize it and a pace of discovery that can’t keep up with the disease.
The Consortium for Biomedical Research and AI in Neurodegeneration (C-BRAIN), working with NVIDIA, is using AI to change that.
Alzheimer’s disease is one of today’s leading global health challenges, with over 10 million new dementia cases diagnosed each year. Despite a 7x increase in National Institutes of Health funding in the last 15 years — reaching $3.9 billion annually — no treatment has been found to stop, prevent or reverse the disease yet.
The bottlenecks are not enough high impact scientific findings, too much data living in institutional silos and too slow a pace of synthesis to keep up with the research that is published.
C-BRAIN, led by Dr. Randall Bateman and a consortium of 18 member institutions across academic, philanthropic, pharma and research organizations, launched in November 2025 to help scientists make better, faster discoveries by accelerating the research that makes new treatments possible.
“We have a window of opportunity with artificial intelligence to do research fundamentally different than we’ve ever done before, in a way that accelerates discoveries to help patients,” said Bateman, founder and director of C-BRAIN and the Charles F. and Joanne Knight Distinguished Professor of Neurology at the Washington University School of Medicine in St. Louis. He described their work as “AI tools built by scientists, for scientists.”
The consortium is building a system that harnesses previously untapped Alzheimer’s research data to support scientists’ work.
Speeding Research From Weeks to Hours
Essential assets in Alzheimer’s research are datasets that have never been publicly accessible to AI tools or human researchers. This includes unpublished findings, negative results and governed proprietary data from member institutions.
“A lot of life sciences data doesn’t make its way into the frontier models,” said Dr. Ade Ojewole, chief technology officer of C-BRAIN. “The consequence of that is we’re leaving a lot of intelligence on the table.”
C-BRAIN refers to this as “dark data,” which slows down Alzheimer’s research.
Some dark data is directly deposited into secure computer systems for AI tools to access. To use this data without compromising patient privacy or institutional governance, C-BRAIN uses a hybrid centralized and federated approach — where AI models learn from data at each institution without that data ever having to leave its walls. This kind of infrastructure is technically demanding to build and scale, which is part of what NVIDIA brings to the collaboration.
C-BRAIN currently runs 64 NVIDIA data center GPUs on premises alongside cloud-based nodes on Microsoft Azure, with additional NVIDIA GPU capacity, including NVIDIA Blackwell systems, on the horizon as the consortium’s ambitions scale.
To explore how AI and accelerated computing can advance Alzheimer’s research while navigating its technical demands, C-BRAIN is working with NVIDIA to incorporate technologies including the NVIDIA BioNeMo open developer platform and the MONAI framework for C-BRAIN’s research pipelines.
“The C-BRAIN collaboration with NVIDIA aims to unlock those models in a way that they can deliver something quite impactful and important,” said Bateman.
“A great deal of optimization is going to be needed as we push the boundaries of what today’s spatial omics machine learning architectures can model,” Ojewole said. “NVIDIA software is definitely going to come into play there.”
Researchers at C-BRAIN have already built two tools: OpenScientist — built with Lawrence Berkeley National Laboratory in an effort led by research scientist Justin Reese — and InsightEngine, built with 387 Labs.
OpenScientist is an open source agentic AI co-scientist that takes a scientist’s research question and dataset, then semi-autonomously generates hypotheses, runs analyses and produces verifiable, auditable insights. InsightEngine is an open source research platform that uses agentic AI to test hypotheses against the scientific literature, analyzing user-supplied datasets, exploring a biomedical knowledge graph and checking every claim against evidence to catch hallucinations before researchers commit to costly experiments.
In published case studies, OpenScientist completed analyses in minutes that domain scientists estimated would take weeks to months, at a cost of under $10 per run. At C-BRAIN’s current reach of over 200 research groups, this capability could release substantial research capacity, potentially exceeding one million hours annually at scale.
From Correlation to Cause
Looking ahead, C-BRAIN aims to build a multimodal biological foundation model trained on the full breadth of its data — neuroimaging, multi-omics, fluid biomarkers and longitudinal clinical measures.
By year three, it aims to discover new causal mechanisms of brain aging and Alzheimer’s disease, moving to testable scientific principles using the full scope of available research and data.
Bateman said, “AI that can think and reason has the potential to accelerate discoveries that will lead to powerful and implementable preventions and treatments for the entire population.”
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