How AI Is Changing the Pace of Discovery for Millions Living With Dementia

NVIDIA Blog 10/27/26, 03:00 PM News

healthcare-ai alzheimers-research nvidia federated-learning bioinformatics open-source drug-discovery

Summary

The Consortium for Biomedical Research and AI in Neurodegeneration (C-BRAIN) is working with NVIDIA to use AI, federated learning, and GPU infrastructure to unlock untapped Alzheimer's research data and accelerate the pace of scientific discovery for the 57 million people living with dementia.

<div id="bsf_rt_marker"></div>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. <h2>Speeding Research From Weeks to Hours</h2> 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 <a target="_blank" href="https://www.nvidia.com/en-us/data-center/">NVIDIA data center GPUs</a> on premises alongside cloud-based nodes on Microsoft Azure, with additional NVIDIA GPU capacity, including <a target="_blank" href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">NVIDIA Blackwell systems</a>, 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 <a target="_blank" href="https://github.com/NVIDIA-BioNeMo">NVIDIA BioNeMo</a> 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. <h2>From Correlation to Cause</h2> 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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# How AI Is Changing the Pace of Discovery for Millions Living With Dementia Source: [https://blogs.nvidia.com/blog/open-source-ai-tools-cbrain-dementia/](https://blogs.nvidia.com/blog/open-source-ai-tools-cbrain-dementia/) 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](https://www.nvidia.com/en-us/data-center/)on premises alongside cloud\-based nodes onMicrosoft Azure,with additional NVIDIA GPU capacity, including[NVIDIA Blackwell systems](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/), 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](https://github.com/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 withLawrence 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\.”

How AI Is Changing the Pace of Discovery for Millions Living With Dementia

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