Decoding mental concept identities using electrocorticography

项目总结/摘要 失语症是卒中后常见的致残性结局。虽然有些治疗方法在 在急性期，患有慢性严重缺陷的人很少有意义的恢复。通常，这些患者 语音或发音规划缺陷，而他们的语义功能被保留。因为 在这些患者中，一种新的治疗方式是语音脑机接口（BCI）， 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 to concept概念identities身份，to be used使用in such这样a device设备.我们将首先在没有语言的患者中开发模型 使用侵入性电记录的缺陷。在清醒状态下的脑部手术中，我们会将高密度的 皮层电图（ECoG）网格上预先指定的大脑位置对应于高层次的语义区。 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。然后我们将 通过使用相同的任务，演示该模型在失语症患者中的应用。 非侵入性脑磁图（MEG）在严重失语症的人。证明了这个模型 可以用来从大脑活动中解码概念身份，并且它适用于患有严重抑郁症的人。 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.