Closed-loop Personalised Non-invasive Brain Stimulation for Cognitive Enhancement

用于增强认知的闭环个性化非侵入性大脑刺激

• 批准号: 2884307
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884307
• 关键词: Closed; loop; Personalised; Non; invasive

Project Description: Transcranial electric stimulation (TES) applies weak electric currents tonon-invasively stimulate the brain. While tES has shown promising potential to restore/enhance brainfunction, its clinical applications are limited due to a large inter- and intra-individual variability in thestimulation response. Current tES studies typically use same stimulation parameters for all participants inan open-loop manner where the neurostimulation parameters are fixed and do not vary with changes inthe current brain states.This project aims to improve efficacy of tES by providing individualised closed-loop stimulations, wherethe stimulation parameters (location, intensity, and timing of stimulation) will be adjusted for each userand over the time. To achieve this aim, in the largest study of its kind, using more than 40000 MRI scans,this project investigates the relationship between brain electrical-fields, demographics and head size.Subsequently, machine learning algorithms are developed to estimate personalised tES intensity andlocation using demographics and head size. To estimate the timing of stimulation, we will developadvanced algorithms capable of monitoring changes in cognitive states in real-time using relevantbehavioural and neural markers. Our findings together will build the personalised closed-loop frameworkfor tES. As a case study, we validate our framework by focusing on enhancing attention in elderly.

项目描述：经颅电刺激（TES）是利用微弱的电流对大脑进行非侵入性刺激。虽然tES已显示出恢复/增强脑功能的潜力，但由于刺激反应的个体间和个体内差异较大，其临床应用受到限制。目前的tES研究通常以开环方式对所有参与者使用相同的刺激参数，其中神经刺激参数是固定的，并且不随当前大脑状态的变化而变化。该项目旨在通过提供个性化的闭环刺激来提高tES的疗效，其中刺激参数（刺激的位置，强度和时间）将随着时间的推移针对每个用户进行调整。为了实现这一目标，在同类最大的研究中，使用超过40000次MRI扫描，该项目调查了脑电场，人口统计学和头部大小之间的关系。随后，开发了机器学习算法，以估计个性化的tES强度和位置使用人口统计学和头部大小。为了估计刺激的时间，我们将开发先进的算法，能够使用相关的行为和神经标记实时监测认知状态的变化。我们的研究结果将共同构建tES的个性化闭环框架。作为一个案例研究，我们验证了我们的框架，专注于提高老年人的注意力。