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

许多关于人脑如何处理信息和产生动作的知识都是通过实验室环境中精心控制的实验获得的。然而，要了解大脑的运作，需要探索它在结构化任务之外的功能。目前的项目使用大规模的大脑活动记录以及视频、音频和深度相机记录来探索多天的神经处理，所有这些都同时和连续地监测一个受试者。重要的是，与现有的大多数研究不同，受试者没有接受任何指导，只是在医院房间里随心所欲地行事--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，利用对受试者的外部监测来解释自然神经活动。该项目的结果将对人类大脑的理解起到催化作用，为在实验室实验的结构化范围之外研究大脑功能打开了大门。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，使部署能够预测用户需求并提高实验室外生活质量的系统成为可能。此外，正在进行的与神经外科医生的合作重点是评估这种新的数据密集型脑行为学绘制方法，以及它如何补充现有的临床功能脑绘制。该项目将支持和支持数据科学和神经科学交叉点的学生的教育，包括在本科生、研究生和博士后职业生涯阶段培训科学家。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。这项建议侧重于数据驱动的创新，以实现从长期的、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言语行为的算法。无监督聚类将用于发现大脑活动中的连贯模式，聚类将使用从外部监控流自动解析的行为进行注释。由于数据集的大小和个体之间的巨大差异，这种可伸缩的计算方法绕过了繁琐的手动注释和参数微调。第二个目标是推断参与无任务自然主义行为的大脑皮层网络的动态因果关系网络。这一目的集中于检验这样一种假设，即自然主义行为的神经关联不同于重复的、指导的行为。功能网络和皮质区域的动态因果关系将使用非线性动力系统理论的方法来探索。这些网络将与临床脑图谱的结果进行比较。这个项目将改进自然主义背景下最先进的神经解码，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.