Predicting task performance based on electrophysiological resting state networks

人类的表现在个体之间是不同的，但在个体内部，在同一任务的重复之间也是不同的。拟议的项目调查是否有可能确定内在的大脑功能，负责在任务中的性能差异，并相应地允许预测性能。 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（RSN）。为了研究这些RSN的动力学，将在脑磁图（MEG）中测量参与者，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称。在MEG中，参与者将执行体感范例，其中他们必须区分是否有一个或两个刺激施加到他们的食指。该项目将集中在躯体感觉领域，但其结果应该转移到其他感觉领域。将调整两个刺激之间的时间间隔，以确保参与者在50%的试验中正确辨别两个刺激。至少10秒的试验间隔期将允许通过采用隐马尔可夫模型来调查快速变化的网络活动。网络的模式和网络活动之前的刺激预计是预测的行为表现的主题。识别和表征这样一个任务休息的关系，将允许更好地理解休息状态下的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，因此这些发现将有助于通过记录自发脑活动更好地诊断某些疾病。

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.