Decoding mental concept identities using electrocorticography

项目摘要/摘要 失语症是中风后常见的致残结局。虽然有些治疗方法可以在 在急性期，患有慢性严重赤字的人很少会有有意义的复苏。通常，这些患者 有语音或发音规划缺陷，但其语义功能得到保留。因为. 在这些患者中，一种新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 在这样的设备中使用的概念身份。我们将首先在没有语言的患者中开发该模型 使用侵入性电子录音的缺陷。在清醒的脑部手术中，我们会放置高密度的 与高级语义区相对应的预先指定的大脑位置上的皮层脑电图仪(ECoG)网格。 患者将执行语义决策任务，神经网络模型将被训练以预测概念 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。到时候我们会的 演示如何将此模型应用于失语症患者，方法是使用 重度失语症患者的无创性脑磁图检查。证明了这个模型 可以用来从大脑活动中破译概念认同，它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。

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.