Brain-Computer Interface (BCI) Enabled Memory Training for Schizophrenia

 描述(由申请人提供)： 脑机接口(BCI)对精神分裂症患者的记忆训练 支持人类脑细胞在成年后期的生长和适应能力或“神经可塑性”的科学进步，为旨在保护和恢复大脑功能的干预措施提供了新的希望。脑科学和计算机技术的融合为旨在训练大脑功能(如记忆和注意力)的软件创造了一个消费者市场，其原理是，大脑电路可以像肌肉一样在重复锻炼后得到加强。所谓的“基于计算机的认知训练”软件可以低成本私下购买，可以在移动设备上使用，其设计具有愉悦性和激励性，并且可以在没有临床监督的情况下自行管理。然而，虽然可访问性和便携性是基于计算机的干预的优势，但S也有一个重要且经常被忽视的缺点：不能假设在这次培训中会使用受损的大脑区域，或通常预期的进行认知训练的方法。取而代之的是，可以优先使用围绕虚弱或受损的脑组织自然形成的代偿机制。因此，随着代偿机制在训练中被学习和加强，受损组织的未充分利用可能导致其自然功能的进一步减弱，而不是加强。这项拟议的研究将试图通过开发脑机接口(BCI)支持的培训计划来解决当前认知培训软件的一个关键限制。BCI技术在康复中的应用主要集中在脊髓损伤和运动神经元疾病上，在这些疾病中，BCI使用户能够通过脑电(EEG)传输的大脑活动来控制外部设备。在脑机接口的一个新应用中，该项目将研究如何使用对培训软件功能的交互控制来监控和加强培训期间的目标大脑活动。该项目将扩展正在进行的精神分裂症基于计算机的认知训练的研究，该研究已经产生了大量患者和健康社区成员执行记忆任务的脑电记录。使用高级分类方法分析存档数据将提供与正确和失败的记忆试验相关的脑电活动模式，以及最能区分患者和健康对照对象的脑电差异。接下来，将基于相同的记忆任务建立一个原型训练计划，但通过游戏环境和BCI控制来增强。传统的基于脑电的脑机接口软件将用于在线信号处理、脑电特征分类以及与训练原型的通信。记忆训练原型将根据存档数据中确定的最佳大脑活动参数，对试验开始、难度水平和用户反应进行BCI控制。最后，在精神分裂症和健康志愿者的试点研究中，将在两种训练条件下对记忆训练原型的可用性和有效性进行评估：1)使用被选为最佳表现的脑机接口参数，2)使用被选为表现次优的脑机接口参数。“次优”条件将作为主动训练条件的控制，使用相同的启用BCI的功能，但设置为对与失败的记忆试验性能相关的脑电活动做出反应。主要结果将基于两种训练条件下的脑电记录和记忆表现的比较，确定目标大脑活动可以在BCI控制下进行，并且当大脑活动处于最佳状态时性能得到增强。培训原型的可用性和可接受性将通过问卷调查进行评估。

 DESCRIPTION (provided by applicant): Brain-Computer Interface (BCI) Enabled Memory Training for Schizophrenia Advances in science supporting the growth and adaptability, or "neuroplasticity", of human brain cells into late adulthood provide new promise for interventions designed to preserve and re habilitate brain function. The merging of brain science and computer technology has created a consumer market for software designed to train brain functions, such as memory and attention, following the rationale that brain circuitry can be strengthened like muscles in response to repetitive exercise. So called "computer-based cognitive training" software can be purchased privately at low cost, can be used on mobile devices, is designed to be enjoyable and motivating, and can be self-administered without clinical oversight. However, while accessibility and portability are advantages of computer-based interventions, there i s also an important and often overlooked shortcoming: it cannot be assumed that compromised brain areas, or normally expected approaches to performing cognitive training exercises, will be utilized during this training. Instead, compensatory mechanisms that have developed naturally around weakened or damaged brain tissue may be used preferentially. Therefore, as compensatory mechanisms are learned and reinforced during training, underutilization of the damaged tissue may lead to further weakening, rather than strengthening, of its natural function. The proposed research will attempt to address a critical limitation of current cognitive training software through the development of a brain-computer interface (BCI) enabled training program. Use of BCI technology in rehabilitation has primarily focused on spinal cord injury and motor neuron disease, where BCI enables the user to control external devices by brain activity transmitted via electroencephalogram (EEG). In a novel application of BCI, this project will examine how interactive control over training software functions could be used to monitor and reinforce targeted brain activity during training. This project will extend ongoing research on computer-based cognitive training in schizophrenia, which has produced a large pool of EEG recordings of patients and healthy community members performing a memory task. Analysis of archived data using advanced classification approaches will provide patterns of EEG activity associated with correct and failed memory trials, and differences in EEG that best distinguish patients from healthy comparison subjects. Next, a prototype training program will be built based on the same memory task but enhanced by a gaming environment and BCI control. Conventional EEG-based BCI software will be used for online signal processing, classification of EEG features, and communication with the training prototype. The memory training prototype will feature BCI control over trial start, difficulty level, and user response according to parameters for optimal brain activity identified in the archived data. Finally, usability and efficacy of the memory trainng prototype will be evaluated in a pilot stud y of schizophrenia and healthy volunteers under two training conditions: 1) using BCI parameters selected for "optimal" performance and, 2) using BCI parameters selected for "suboptimal" performance. The "suboptimal" condition will serve as a control for the active training condition, using the same BCI-enabled features but set to respond to EEG activity associated with failed memory trial performance. Primary outcomes will be based on comparison of EEG recordings and memory performance under the two training conditions, determining that targeted brain activity can be enlisted under BCI control, and that performance is enhanced when brain activity is in an optimal state. Usability and acceptability of the training prototype will be assessed by questionnaire.