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.

摘要 患有致残性运动障碍的人许多最基本的东西都依赖于辅助设备和照顾者 需要。目前的辅助设备存在固有的局限性，因为它们依赖(并阻碍)残留的运动功能 作为一个命令界面。脑机接口(BMI)提供了一条通向更强大辅助选项的途径 通过直接监测大脑活动，并使用它来实时破译运动意图。然而，BMI已经 然而，要实现值得临床广泛采用的性能和健壮性。一个关键的障碍是 到目前为止，几乎所有的BMI都使用直接解码，即它们试图绘制大脑区域的活动图，如运动 皮层(MC)直接影响到外部运动参数，如速度。这导致了BMI 脆性：它们经常在新的环境中失败，并且对神经接口的不稳定性高度敏感。相反，我设想 一种完全不同的方法，有可能影响几乎所有现有的BMI应用程序。中环 元素是动力系统解码(DSD)，这是我开发的一个框架，融合了马达的进步 神经科学以尖端的AI方法实现前所未有的解码精度。DSD使用神经 网络，以准确地揭示MC复杂的内部活动模式，称为动态，在一瞬间- 以矩为基准。这使得与内部动态相关的活动和与活动相关的活动能够完全分开 设置为外部运动参数。在离线分析中，我证明了DSD在解码方面取得了突破， 预测毫秒时间尺度上的运动的精度比当前的状态高得多- 艺术。这项提议的一个关键重点是开发通用的、独立于学科的BMI，以利用显著的 在不同受试者中观察到MC动力学的相似性。使用新的人工智能方法模拟十多年来 之前收集的猴子数据，我们将测试独立于主体的模型是否可以使BMI发挥作用 近乎开箱即用，其性能只能通过海量数据集实现，同时仍 避免繁重的、特定于对象的校准。在线下研究的同时，我们将直接与人合作 他们瘫痪了，无法开发具有无与伦比的性能和健壮性的在线BMI。性能 将通过利用高级运动的混合解码范例来实现改进 通过DSD唯一发现的信息。虽然BMI的健壮性在直接解码中通常是有限的- 由于被监测的特定神经元的逐渐变化-DSD将通过利用 MC动力学，它是多年稳定的，独立于任何特定的神经元被监测 在给定的时间内。这两项创新将使BMI获得前所未有的性能，并在 需求，多年来全天候可靠性。如果成功，这些研究将为显著改善 几乎所有BMI应用程序的性能、健壮性和临床实用性。

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