NCS-FO: Understanding Neural Processing in Long-Term, Naturalistic Human Brain Recordings Using Data-Intensive Approaches

关于人类大脑如何处理信息和产生行动的许多知识都是通过实验室环境中精心控制的实验获得的。然而，理解大脑的运作需要探索其在结构化任务之外的功能。目前的项目通过对大脑活动进行大规模记录，并辅以视频、音频和深度摄像机记录，在许多天内探索神经处理过程，所有这些都是同时持续监控一个对象的。重要的是，与大多数现有的研究不同，这里的受试者没有接受任何指示，只是在病房里按照自己的意愿行事——包括吃饭、睡觉和与家人交谈。该项目将推进数据密集型科学和人类神经科学，利用受试者的外部监测来解释自然的神经活动。这个项目的结果将在理解人类大脑方面起到催化作用，为在实验室实验的结构范围之外研究大脑功能打开大门。开发的神经解码算法将直接适用于当前的脑机接口（BCI）技术，使系统的部署能够预测用户的需求并提高实验室外的生活质量。此外，与神经外科医生正在进行的合作重点是评估这种新的数据密集型方法用于行为学脑制图，以及它如何补充现有的临床功能脑制图。该项目将支持和促进数据科学和神经科学交叉领域学生的教育，包括在本科、研究生和博士后职业阶段培训科学家。研究结果将作为开放获取出版物和代码库分发，以支持对可再生科学的承诺。该提案侧重于数据驱动的创新，以便从长期、自然的神经记录中更准确地解码和推断动作。第一个目标是开发自动解码自然运动和语言行为的算法。无监督聚类将用于发现大脑活动中的连贯模式，并且聚类将使用从外部监控流中自动解析的行为来注释。由于数据集的大小和个体之间的巨大差异，这种可扩展的计算方法避免了繁琐的手动注释和参数微调。第二个目的是推断参与无任务、自然行为的皮层网络的动态因果关系网络。这一目标着重于测试自然行为的神经关联不同于那些重复的、指示的行为的假设。功能网络和皮层区域的动态因果关系将使用非线性动力系统理论的方法进行探索。这些网络将与临床脑制图的结果进行比较。该项目将在自然环境中改进最先进的神经解码，并揭示人类无任务行为的神经关联。

Much knowledge about how human brains process information and generate actions has been informed by carefully controlled experiments in laboratory settings. However, understanding the brain in action requires exploration of its functions outside structured tasks. The current project explores neural processing over many days using large-scale recordings of brain activity augmented with video, audio and depth camera recordings, all simultaneously and continuously monitoring a subject. Importantly, unlike the majority of existing studies, here the subjects receive no instructions but are simply behaving as they wish in their hospital room-including eating, sleeping, and conversing with family. The project will advance data-intensive science and human neuroscience, leveraging external monitoring of the subjects to interpret naturalistic neural activity. The results of this project will be catalytic in understanding of the human brain, opening the door to study of brain function outside the structured confines of laboratory experiments. The neural decoding algorithms developed will be directly applicable to current Brain-Computer Interfacing (BCI) technologies, enabling the deployment of systems that can predict the user's needs and improve quality of life outside the laboratory. Further, ongoing collaborations with neurosurgeons focus on evaluating this novel data-intensive approach to ethological brain mapping and how it may complement existing clinical functional brain mapping. The project will support and enable the education of students at the intersection of data science and neuroscience, including training scientists at the undergraduate, graduate, and post-doctoral career stages. Results from the research will be distributed as open access publications and code repositories, supporting a commitment to reproducible science. This proposal focuses on data-driven innovations to enable more accurate decoding and inference of actions from long-term, naturalistic neural recordings. The first aim proposes to develop algorithms for automated decoding of natural motor and speech behaviors. Unsupervised clustering will be used to discover coherent patterns in brain activity, and clusters will be annotated with behaviors automatically parsed from external monitoring streams. Motivated by the size of the dataset and substantial variety between individuals, this scalable computational approach circumvents tedious manual annotation and fine-tuning of parameters. The second aim proposes to infer networks of dynamic causality of cortical networks engaged in task-free, naturalistic behaviors. This aim focuses on testing the hypothesis that neural correlates of naturalistic behaviors differ from those of repeated, instructed behaviors. Functional networks and the dynamic causality of cortical areas will be explored using methods from nonlinear dynamical systems theory. These networks will be compared to results from clinical brain mapping. This project will improve state-of-the-art neural decoding in naturalistic contexts and uncover neural correlates of task-free behaviors in humans.