An Ultra High-Density Virtual Array with Nonlinear Processing of Multimodal Neural Recordings

具有多模态神经记录非线性处理的超高密度虚拟阵列

• 批准号: 9766300
• 负责人: Duygu Kuzum
• 金额: $ 22.89万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766300
• 关键词: 3-Dimensional; Affect; Algorithms; Area; Behavior; Brain; Calcium; Calcium Signaling; Caliber; Cells; Chemicals; Collaborations; Data; Data Collection; Data Set; Deposition; Development; Dimensions; Disease; Dystonia; Electrical Engineering; Electrocorticogram; Electrodes; Electrophysiology (science); Epilepsy; Exploratory/Developmental Grant; Functional disorder; Geometry; Goals; Image; Individual; Lead; Learning; Measurement; Mental Depression; Methods; Modality; Modeling; Multimodal Imaging; Nervous system structure; Neurons; Neurosciences; Noise; Optics; Outcome; Parkinson Disease; Population; Research; Resolution; Scanning; Schizophrenia; Signal Transduction; Surface; Synaptic Potentials; System; Techniques; Technology; Testing; base; computer framework; computerized data processing; density; design; electric impedance; experience; experimental study; graphene; high resolution imaging; holistic approach; in vivo; innovation; multimodal data; multimodality; nanoparticle; nervous system disorder; network dysfunction; neural circuit; novel strategies; operation; optical imaging; optogenetics; relating to nervous system; sensor; signal processing; spatial integration; temporal measurement; two-photon; virtual

An Ultra High-Density Virtual Array with Nonlinear Processing of Multimodal Neural Recordings A major goal of neuroscience is to record the activity of all neurons in an area of an intact brain and understand the relationship between neural activity and behavior. However, with current technologies, it is not feasible to have a direct and simultaneous access to every neuron in a three-dimensional brain area. Here we propose a novel approach, combining an innovative signal processing method with optical and electrical recording technologies to `virtually' record from all neurons in a three dimensional volume. If successful, this approach will allow us to substantially increase the number of recorded neurons without the need for direct optical or electrical access to each neuron. The proposed Virtual Array technology has the potential to dramatically increase the number of simultaneously recorded neurons in an intact brain relatively non- invasively. The common approaches include high-density electrophysiological probes, which are highly invasive and also limited in the density of recording, and fast-scanning optical techniques that have limited temporal resolution. As an alternative approach, we propose to develop a framework to computationally increase the number of recorded neurons out of recording data from simultaneous electrophysiology and imaging. The computational framework will be developed from a dataset in which micro- electrocorticogram (µECoG) are recorded simultaneously while the activities of the underlying neurons is recorded with two-photon calcium imaging at multiple cortical depths. We will virtually reconstruct this single-cell activity by solving appropriate optimization problem involving forward models for µECoG recordings and calcium signals. This optimization problem will be solved using alternating convex algorithms.

超高密度虚拟阵列，具有多模式的非线性处理 神经记录 神经科学的主要目标是记录所有神经的活动 完整的大脑并了解神经活动与行为之间的关系。 但是，使用当前的技术，直接且简单的 进入三维脑区域中的每个神经元。在这里，我们提出了一本小说 方法，将创新的信号处理方法与光学和电气相结合 在三维中，记录所有神经元的“虚拟”记录 体积。如果成功，这种方法将使我们大大增加 记录的神经元无需直接光学或电气进入每个神经元。 提出的虚拟阵列技术有可能大幅度增加 完整大脑中简单记录的神经元的数量相对非 - 侵入性。常见方法包括高密度电生理问题， 这是高度侵入性的，并且在记录的密度和快速扫描中也有限 临时分辨率有限的光学技术。作为另一种方法，我们 提出开发一个框架以增加记录的数量的框架 从同时电生理学和成像中记录数据中的神经元。这 计算框架将从微观的数据集开发 简单地记录了电化图（µECOG），而 在多个皮质上使用两光子钙成像记录基础神经元 深度。我们将通过解决适当的 优化问题涉及µEcog记录和钙的正向模型 信号。该优化问题将使用替代凸算法解决。