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

项目简介：经颅电刺激(TES)应用微弱电流对大脑进行非侵入性刺激。虽然TES在恢复/增强脑功能方面显示出很大的潜力，但由于个体间和个体内估计反应的巨大变异性，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，并且不会随着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的效率，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，这项研究是同类研究中规模最大的，该项目使用了超过40000次核磁共振扫描，调查了大脑电场、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，利用人口统计学和头部大小来估计个性化TES强度和位置。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物实时监测认知状态的变化。我们的发现将共同构建TES的个性化闭环框架。作为一个案例研究，我们通过关注提高老年人的注意力来验证我们的框架。

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.