Fusing motor neuroscience and artificial intelligence to create next-generation neural prostheses.

融合运动神经科学和人工智能来创造下一代神经假体。

• 批准号: 10246037
• 负责人: Chethan Pandarinath
• 金额: $ 145.12万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246037
• 关键词: Adoption; Amyotrophic Lateral Sclerosis; Area; Artificial Intelligence; Brain; Calibration; Caregivers; Clinical; Complex; Data; Data Set; Development; Elements; Goals; Hybrids; Intention; Maps; Methods; Modeling; Monitor; Monkeys; Motor; Motor Cortex; Movement; Neurons; Neurosciences; Paralysed; Pathway interactions; Pattern; Performance; Public Health; Quality of life; Residual state; Self-Help Devices; Spinal cord injury; Testing; Time; Work; brain machine interface; disability; dynamic system; functional restoration; improved; innovation; millisecond; motor disorder; neural network; neural prosthesis; next generation; relating to nervous system; virtual

ABSTRACT People with disabling motor disorders rely on assistive devices and caregivers for many of their most basic needs. Current assistive devices are inherently limited, as they rely on (and encumber) residual motor function as a command interface. Brain-machine interfaces (BMIs) provide a pathway to more powerful assistive options by directly monitoring brain activity and using it to decipher movement intention in real-time. However, BMIs have yet to achieve performance and robustness that would warrant widespread clinical adoption. A key obstacle is that nearly all BMIs to date use direct decoding, i.e., they attempt to map the activity of brain areas like motor cortex (MC) directly onto external movement parameters such as velocity. This has resulted in BMIs that are brittle: they often fail in new contexts, and are highly sensitive to neural interface instabilities. Instead, I envision a radically different approach with the potential to impact virtually every existing BMI application. The central element is dynamical systems decoding (DSD), a framework I developed that fuses advances in motor neuroscience with cutting-edge AI methods to achieve unprecedented decoding accuracy. DSD uses neural networks to precisely reveal MC's complex internal activity patterns, known as dynamics, on a moment-by- moment basis. This enables a clean separation between activity related to internal dynamics and activity related to external movement parameters. In offline analyses, I showed that DSD enables a breakthrough in decoding, predicting movements on millisecond timescales with substantially higher accuracy than the current state-of-the- art. A key focus of this proposal is developing universal, subject-independent BMIs that harness the remarkable similarities in MC dynamics observed across subjects. Using new AI methods to model more than a decade of previously-collected monkey data, we will test whether subject-independent models can enable BMIs that work nearly `out of the box', with performance that could only be achieved through massive datasets, while still avoiding burdensome, subject-specific calibration. In parallel with offline studies, we will work directly with people who are paralyzed to develop online BMIs with unparalleled performance and robustness. Performance improvements will be achieved through hybrid decoding paradigms that capitalize on high-level movement information that is uniquely uncovered via DSD. While BMI robustness is typically limited in direct decoding – due to gradual changes in the specific neurons being monitored – DSD will enable robust BMIs by leveraging MC dynamics, which are stable for years and independent of whichever specific neurons are being monitored at a given time. These two innovations would enable BMIs that achieve unprecedented performance and on- demand, 24/7 reliability for years. If successful, these studies will pave the way to dramatically improving the performance, robustness, and clinical utility of nearly every BMI application.

抽象的 致残运动障碍的人依靠辅助设备和护理人员来获得许多最基本的 需要。当前的辅助设备本质上是有限的，因为它们依靠（和限制）残留运动功能 作为命令接口。脑机界面（BMI）为更强大的辅助选择提供了途径 通过直接监测大脑活动并使用它实时破译运动意图。但是，BMI有 然而，要实现绩效和鲁棒性，可以保证临床采用宽度。一个关键的障碍是 迄今为止，几乎所有的BMI都使用直接解码，即，他们试图绘制大脑区域的活动 皮质（MC）直接进入外部运动参数，例如速度。这导致了BMI 脆弱：它们通常在新的环境中失败，并且对神经界面不稳定性高度敏感。相反，我设想 一种根本不同的方法，可能会影响几乎所有现有的BMI应用程序。中央 元素是动态系统解码（DSD），这是我开发的框架，它融合了电机的进步 具有前沿AI方法的神经科学，以实现前所未有的解码精度。 DSD使用神经科学 网络可以精确地揭示MC在一刻的一刻 瞬间。这使与内部动力学与活动相关的活动之间的活动可以进行干净的分离 到外部运动参数。在离线分析中，我表明DSD在解码方面具有突破性， 预测毫秒时尺度上的运动，其精度大大高于当前的最新运动 艺术。该提案的一个重点是发展普遍的，独立于主题的BMI，以利用非凡的BMI 跨受试者观察到的MC动力学的相似性。使用新的AI方法来建模十多年 先前收集的猴子数据，我们将测试独立于主题的模型是否可以启用可行的BMI 几乎是“开箱即用”，其性能只能通过大量数据集实现 避免伯恩斯，特定于主题的校准。与离线研究并行，我们将直接与人合作 他们瘫痪了以无与伦比的性能和鲁棒性开发在线BMI。表现 通过大利用高级运动的混合解码范例来实现改进 通过DSD独特发现的信息。虽然BMI鲁棒性通常在直接解码时受到限制 - 但 由于监测的特定神经元的等级变化，DSD将通过利用来实现强大的BMI MC动力学，多年来稳定，并且与正在监测的任何特定神经元无关 给定的时间。这两项创新将使BMI能够实现前所未有的绩效和on- 需求，多年24/7可靠性。如果成功，这些研究将为大幅度改善 几乎所有BMI应用的性能，鲁棒性和临床实用性。

项目成果

High-performance neural population dynamics modelingenabled by scalable computational infrastructure

通过可扩展的计算基础设施实现高性能神经群体动力学建模

• DOI: 10.21105/joss.05023
• 发表时间: 2023
• 期刊: Journal of Open Source Software
• 作者: Patel, Aashish N.;Sedler, Andrew R.;Huang, Jingya;Pandarinath, Chethan;Gilja, Vikash
• 通讯作者: Gilja, Vikash