Predicting task performance based on electrophysiological resting state networks

基于电生理静息态网络预测任务表现

• 批准号: 429710162
• 负责人: Professorin Dr. Esther Florin
• 金额: --
• 依托单位: Institut für Klinische Neurowissenschaften und Medizinische Psychologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/429710162?language=en
• 关键词: Predicting; task; performance; based; electrophysiological

Human performance differs between individuals, but also within individuals between repetitions of the same task. The proposed project investigates whether it is possible to identify intrinsic brain features responsible for performance differences in tasks and that correspondingly allow to predict performance. This requires analyzing the dynamics of brain activity and the interaction between brain regions before the actual task. The brain networks that emerge while we are not performing a task are called resting state networks (RSNs). To study the dynamics of these RSNs, participants will be measured in the magnetoencephalogram (MEG), which is a non-invasive measurement technique that allows recording electrophysiological neural activity of the whole brain with a temporal resolution commensurate with actual neural activity. Within the MEG participants will perform a somatosensory paradigm where they have to discriminate whether one or two stimuli were applied to their index finger. The project will focus on the somatosensory domain, but its results should transfer to other sensory domains. The time interval between the two stimuli will be adjusted to ensure that participants correctly discern the two stimuli in 50% of the trials. An inter-trial period of at least 10 seconds will allow investigating the fast changing network activity by employing a Hidden Markov Model. The pattern of networks and the networks active immediately preceding the stimuli are expected to be predictive for the behavioral performance of the subjects. Identification and characterization of such a task-rest nexus will allow for a better understanding of the functional relevance of resting state brain activity. Because changes in resting state activity have been identified in many neurological and psychiatric diseases, such findings will be instrumental to better diagnose certain diseases through the recording of spontaneous brain activity.

人的表现在个体之间是不同的，而且在个体内部重复同样的任务也是不同的。该项目旨在调查是否有可能确定导致任务表现差异的内在大脑特征，并相应地预测表现。这需要在实际任务之前分析大脑活动的动态和大脑区域之间的相互作用。当我们不执行任务时出现的大脑网络被称为静息状态网络（RSNs）。为了研究这些rsn的动态，参与者将在脑磁图（MEG）中进行测量，这是一种非侵入性测量技术，可以记录整个大脑的电生理神经活动，其时间分辨率与实际神经活动相当。在脑磁图测试中，参与者将进行体感测试，他们必须区分一个或两个刺激是施加在他们的食指上。该项目将侧重于体感领域，但其结果应转移到其他感官领域。两个刺激之间的时间间隔将被调整，以确保参与者在50%的试验中正确识别两个刺激。至少10秒的试验间隔将允许使用隐马尔可夫模型来调查快速变化的网络活动。网络的模式和在刺激之前立即活跃的网络被认为可以预测受试者的行为表现。这种任务-休息关系的识别和表征将有助于更好地理解静息状态大脑活动的功能相关性。由于在许多神经和精神疾病中已经发现了静息状态活动的变化，这些发现将有助于通过记录自发的大脑活动来更好地诊断某些疾病。