Decoding mental concept identities using electrocorticography

使用皮层电图解码心理概念身份

• 批准号: 10652023
• 负责人: William L. Gross
• 金额: $ 19.5万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-05 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652023
• 关键词: Acute; Aphasia; Area; Articulation; Brain; Brain Neoplasms; Characteristics; Chronic; Clinical; Clinical Trials; Code; Communication; Computer Models; Data; Development; Devices; Electrocorticogram; Electrophysiology (science); Eligibility Determination; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Image; Imaging technology; Impairment; Implant; Individual; Institution; Knowledge; Language; Language Disorders; Left; Lesion; Location; Machine Learning; Magnetoencephalography; Maps; Methods; Modality; Modeling; Motor; Neural Network Simulation; Non-aphasic; Operative Surgical Procedures; Outcome; Patient Care; Patients; Pattern; Performance; Persons; Phase; Population; Production; Psyche structure; Recovery; Research; Retrieval; Semantic memory; Semantics; Signal Transduction; Solid; Specific qualifier value; Specificity; Speech; Stroke; System; Technology; Testing; Training; Translating; awake; brain based; brain computer interface; brain electrical activity; brain surgery; cohort; deep neural network; density; design; experimental study; innovation; machine learning model; model development; neural; neuroprosthesis; novel; operation; phonology; portability; post stroke; preservation; stroke outcome; stroke-induced aphasia; temporal measurement

PROJECT SUMMARY/ABSTRACT Aphasia is a common and disabling outcome following stroke. Although some treatments are available in the acute phase, people with chronic, severe deficits rarely have meaningful recovery. Frequently, these patients have phonological or articulatory planning deficits, while their semantic functions are preserved. Because of this, a novel treatment modality in these patients is a speech brain-computer interface (BCI) designed to decode semantic activity. In this project we are developing a machine learning model to decode brain activity to concept identities, to be used in such a device. We will first develop the model in patients with no language deficits using invasive electrical recordings. During awake brain surgeries, we will place high-density electrocorticography (ECoG) grids on prespecified brain locations corresponding to high-level semantic areas. Patients will perform a semantic decision task, and the neural network model will be trained to predict concept identities from the recorded ECoG activity using a semantic model developed by our lab. We will then demonstrate the application of this model to people with aphasia by performing the same task using the noninvasive magnetoencephalography (MEG) in people with severe aphasia. Demonstrating that this model can be used to decode concept identities from brain activity, and that it is applicable to people with severe aphasia, will open up a new avenue of treatment for this population.

项目概要/摘要 失语是中风后常见的致残后果。尽管一些治疗方法可以在 在急性期，患有慢性严重缺陷的人很少有有意义的康复。这些患者常常 具有语音或发音规划缺陷，但其语义功能得以保留。由于 针对这些患者的一种新颖的治疗方式是语音脑机接口（BCI），旨在 解码语义活动。在这个项目中，我们正在开发一个机器学习模型来解码大脑活动 概念标识，用于此类设备。我们将首先在没有语言的患者中开发模型 使用侵入性电记录来弥补缺陷。在清醒的脑部手术期间，我们将放置高密度 与高级语义区域相对应的预先指定的大脑位置上的皮质电图（ECoG）网格。 患者将执行语义决策任务，神经网络模型将被训练来预测概念 使用我们实验室开发的语义模型从记录的 ECoG 活动中识别身份。我们随后将 通过使用以下命令执行相同的任务来演示该模型对失语症患者的应用： 严重失语症患者的无创脑磁图（MEG）。证明这个模型 可用于从大脑活动中解码概念身份，并且适用于患有严重疾病的人 失语症将为这一人群开辟一条新的治疗途径。