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

脑机接口 (BCI) 支持精神分裂症记忆训练

• 批准号: 9114892
• 负责人: Jason Karl Johannesen
• 金额: --
• 依托单位: VA CONNECTICUT HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9114892
• 关键词: Address; Adult; Affect; Algorithms; Area; Attention; Base of the Brain; Brain; Brain Injuries; Characteristics; Classification; Clinical; Cognitive deficits; Communication; Communities; Computer software; Computer-Assisted Image Analysis; Computers; Data; Development; Devices; Educational Intervention; Electroencephalogram; Environment; Exercise; Feasibility Studies; Feedback; Frequencies; Growth; Hand; Human; Impaired cognition; Individual; Intention; Intervention; Lead; Learning; Machine Learning; Marketing; Memory; Memory impairment; Methods; Modeling; Monitor; Motor Neuron Disease; Muscle; Neurologic; Neuronal Plasticity; Participant; Patients; Pattern; Performance; Preparation; Process; Questionnaires; Readiness; Rehabilitation device; Rehabilitation therapy; Research; Rest; Sampling; Schizophrenia; Science; Self-Administered; Sensitivity and Specificity; Short-Term Memory; Software Design; Spinal cord injury; Staging; Stimulus; Task Performances; Technology; Tissues; Training; Training Programs; Validation; Work; active control; alertness; base; brain cell; brain circuitry; brain computer interface; brain tissue; cognitive function; cognitive training; computer science; cost; data archive; design; handheld mobile device; healthy volunteer; human subject; information processing; insight; member; neural patterning; neurofeedback; neuropathology; neurophysiology; neuropsychiatry; next generation; novel; portability; primary outcome; programs; prototype; public health relevance; relating to nervous system; remediation; response; signal processing; therapy design; translational study; usability

 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.