MIT衰老大脑倡议研讨会展示多领域创新：AI匹配阿尔茨海默病干预、麻醉闭环调控等

一场跨学科研讨会想解决什么问题

MIT衰老大脑倡议自2015年成立起就持一个判断：阿尔茨海默病等神经退行性疾病不是单点故障，而是“系统级”的复杂崩解，需要多来源、多类型的进展才能应对。9月25日的“衰老大脑创新”研讨会用六场报告展示了这一思路在MIT的落地方式，涵盖神经科学、工程、化学等领域。

用AI把个体差异纳入预防

机械工程教授Giovanni Traverso与ABI主任Li-Huei Tsai等合作，开发了名为FINGERS-7B的人工智能系统，目标是改善阿尔茨海默病的个性化照护，把患者匹配到最适合的预防干预。该系统整合临床试验数据、患者生活方式史和多组学数据（如基因组学、蛋白质组学），用于预测个体疾病进展和干预反应。项目成员Adrián Noriega de la Colina指出，每个人的生物学高度独特，这与其对干预的反应相关；预测除用于个体化预防外，也可能帮助临床试验更精准地选定目标人群。

麻醉与脑波：另一个可预防的风险

Picower研究所教授Earl K. Miller关注老年大脑的另一类问题：全身麻醉暴露带来的认知副作用。他提到老年人大型手术后术后谵妄发生率较高，并介绍了与Institute Professor Emery N. Brown合作开发的闭环系统，通过基于脑波的 unconsciousness 测量来优化麻醉给药。两个实验室已显示，多种麻醉药物尽管分子作用方式不同，都通过让脑波彼此相位偏移、破坏大脑皮层通信协调来诱导无意识。

从肌肉模型到化学探针

机械工程副教授Ritu Raman用整合神经与血管的先进肌肉组织实验室模型研究神经肌肉疾病，包括肌萎缩侧索硬化（ALS）和肌营养不良症；针对ALS，其实验室已能用患者来源的诱导多能干细胞制作培养物，以观察神经肌肉接头并模拟药物反应。化学助理教授Oleta Johnson的实验室则尝试在蛋白质聚集发生前加以阻止，重点是识别相关蛋白、检测其进入问题状态并控制其构象。Alan Jasanoff介绍了用于磁共振成像的化学探针，使胆碱酯酶和脂质等分子可见；Laura Kiessling则讨论细胞表面糖类（聚糖），尤其是与阿尔茨海默病相关的硫酸乙酰肝素。

From its beginning in 2015, the premise of MIT’s Aging Brain Initiative (ABI) has been that neurodegenerative diseases such as Alzheimer’s are complex, “system-wide” breakdowns that will require advances of many kinds from many sources to address. The six talks at the ABI’s symposium “Innovations for the Aging Brain” on Sept. 25 illustrated what that’s come to mean at MIT, as scientists and engineers described their latest work to improve health for the aging brain.

“The ABI is unique because it brings together researchers from a diverse array of fields,” said ABI Director Li-Huei Tsai, Picower Professor in the the Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences (BCS). “Today’s symposium showcases MIT’s collective brilliance across neuroscience, engineering, chemistry, and beyond.”

One of the talks, for instance, featured a team effort including Giovanni Traverso, the Karl Van Tassel (1925) Professor of Mechanical Engineering and a physician at Brigham and Women’s Hospital, who has joined forces with Tsai and other collaborators to develop an artificial intelligence system that could improve personalized Alzheimer’s care by matching patients to the best prevention interventions for them. The system integrates data from clinical trials, patient lifestyle histories, and multi-omics data (e.g., genomics, proteomics) to predict an individual’s Alzheimer’s progression and likely response to interventions. 

Speaking alongside project leads Arvid Gollwitzer, an engineer-scientist in the Broad Institute of MIT and Harvard, and MIT-Novo Nordisk AI Fellow Adrián Noriega de la Colina, Traverso described how the team engineered an AI model, called FINGERS-7B, to integrate these multiple highly diverse data sources into its training, thereby amassing 8 trillion “tokens” to inform its predictions.

“Every individual is very unique in their biology, and that links to how we respond to those interventions,” said Noriega. Not only could AI predictions help tailor individual prevention measures, he added, but also it could inform clinical trial designs by better pinpointing the most appropriate populations to target with a prospective drug or other treatment.

Picower Professor Earl K. Miller focused on prevention of a different problem for the aging brain: cognitive side effects from exposure to too much general anesthesia. Noting high rates of postoperative delirium in older adults after major surgery, Miller described his research with ABI founding member and Institute Professor Emery N. Brown, a Picower Institute and BCS colleague, to develop a closed-loop system that uses brain wave-based measurements of unconsciousness to optimize anesthesia dosing. Miller and Brown’s labs have shown that regardless of their molecular means of action, multiple anesthesia drugs induce unconsciousness in the same way: by shifting brainwaves out of phase with each other, disrupting the coordination of communication across the brain’s cortex that sustains cognition and consciousness. Informed by such brain wave “signatures” of unconsciousness, the system could help anesthesiologists to safely tailor dosing during surgery.

In her talk, associate professor of mechanical engineering Ritu Raman, the Eugene Bell Career Development Chair of Tissue Engineering, described how she’s approaching the study of neuromuscular diseases, including amyotrophic lateral sclerosis (ALS) and muscular dytrophies, by developing advanced lab models of muscle tissue that also integrate nerves and vasculature. For instance, for ALS, her lab has been able to make the cultures from induced pluripotent stem cells derived from patients. The lab models give her team an opportunity to examine neuromuscular junctions in detail and to model how they change in response to potentially therapeutic drugs.

In many neurodegenerative diseases associated with aging, such as Alzheimer’s and Parkinson’s, a major pathology is misfolding and aggregation of intrinsically disordered proteins, such as amyloid-beta or alpha-synuclein. In her talk, William R (1964) and Daniel L. (1995) Young Career Development Assistant Professor of Chemistry Oleta Johnson described her lab’s work to prevent aggregation before it starts. In pursuit of that strategy, she said her lab’s research is focused on developing the chemical knowledge and means to identify proteins at issue, detect that they are moving into a problematic state, and then control their conformation.

Eugene McDermott Professor in the Brain Sciences and Human Behavior Alan Jasanoff, a member of the BCS and Biological Engineering departments as well as the McGovern Institute for Brain Research, described a very different application of chemistry to aging brain research. He described how his lab develops novel chemical probes to make molecules in the brain visible in magnetic resonance imaging. These include the neuromodulatory enzyme cholinesterase, which is the target of many of the few drugs approved to treat Alzheimer’s, and also lipids, which several brain cell types mishandle in Alzheimer’s, Tsai and others have shown. His lab has also developed technology to genetically encode a novel probe in brain cells to improve MRI visualization of brain activity.

And while many researchers focus on the prominent problems that proteins or lipids present in neurodegenerative disease, Novartis Professor of Chemistry Laura Kiessling discussed a less-appreciated but still important component of cellular function: sugars called glycans that line the surface of cells. In particular, researchers have linked a glycan called heparan sulfate to Alzheimer’s disease. Kiessling’s lab, working with ABI founding member Ed Boyden, Y. Eva Tan Professor of Neurotechnology, is working to characterize and understand glycans. Her lab, for instance, has shown that glycans of different sequences organize on cell membranes so that they can bind and recruit specific proteins to distinct regions on cells.

“We are really excited about the fact that we’ve observed this now for the first time and have a toolkit to study other diseases,” she said. Understanding glycan binding could even give scientists a means to target different cells and cellular processes.

Following the six talks, more than a dozen trainees at a poster session presented even more aging brain-related projects from around the Institute, further highlighting MIT’s diversity of research in the area, something the ABI exists to encourage.

From massive AI datasets to the sugars on individual cells, the symposium highlighted how MIT scientists are pushing the frontiers of science and engineering to improve understanding of, and interventions for, the health of the aging brain.