脑源定位技术的精度评估及其在实际中的应用

脑源定位技术旨在通过头皮表面的脑电、脑磁信号来识别大脑内的神经活动源,是研究大脑皮层神经活动、认知过程和病理功能的基础。其毫秒级的时间分辨率可以有效弥补功能核磁共振在低时间分辨率方面的不足。然而,理论分析层面中逆问题的不适定性,以及实践操作层面上不同的记录方式、电极数量和头模型构建等过程带来的误差,给脑源定位的准确性带来极大挑战,也在一定程度上限制了脑源定位方法在神经科学和心理学研究以及临床诊断治疗中的实际应用。因此,理论分析和实践操作层面中的精度评估在脑源定位方法的实际使用中至关重要。针对以上问题,本文在对现有脑源定位方法介绍的基础上,着重分析了脑源定位技术的精度评估方法以及其在基础研究和临床诊断治疗中的实际应用。具体地,本文在理论分析中总结了基于空间分辨率、基于点扩散以及串扰函数的评估方法对于不同脑源定位方法中源的重叠程度和其他源对目标源的影响;在实践操作中介绍了记录方式、电极数量和密度、头部容积传导模型等因素对源定位精度的影响;进一步介绍了脑源定位技术在时频分析、连通性分析中的应用,以及其在临床中的应用,包括癫痫、注意缺陷与多动障碍等脑部疾病。

Accuracy Evaluation of Brain Source Localization Technology and Its Application in Practice

Brain source localization technology aims to identify the source of neural activity in the brain through the EEG and MEG signals on the scalp surface, which is the basis of studying the neural activity, cognitive process, and pathological function of the cerebral cortex. Its millisecond temporal resolution can effectively make up for the shortcomings of fMRI in low temporal resolution. Brain source localization contains two processes, forward problem, and inverse problem. The forward problem is to simulate the electric potential of the head surface generated by the neural source of brain activity, which is calculated by the volume conduction model, and the model is mainly built by the boundary element method, finite element method, and finite difference method. The inverse problem aims to reconstruct the distribution of current sources in the brain. The main solutions include the distributed source model and the equivalent current dipole model. But the solution to the inverse problem is not unique, and the regularization method is the classical means to resolve it, including the minimum L1 norm and the minimum L2 norm methods. Nonlinear optimization, beamforming, the Bayes approach, deep learning, and other technologies have been created in recent years to increase the accuracy of the brain source localization technique. However, due to the ill pose of the inverse problem and the errors caused by different recording methods, the number of electrodes, and head model construction in practice, the accuracy evaluation is still challenging in brain source localization, which greatly limits the practical application of brain source localization methods in neuroscience and psychology research, clinical diagnosis, and treatment. In this work, the existing brain source localization methods and analysis of the accuracy evaluation methods of brain source localization technology and its practical application in basic research and clinical diagnosis and treatment are introduced. Specifically, different recording methods, the number and density of electrodes, and the head volume conduction model all have a certain influence on the source positioning accuracy. In practice, because different inverse problem algorithms produce different source location results, this study summarizes the evaluation methods based on spatial resolution, point diffusion, and crosstalk function on the degree of source overlap among different brain source localization methods and the influence of other sources on target sources. In addition, the application of brain source localization technology in time-frequency analysis and connectivity analysis is introduced, which can help researchers better understand the connections and functions of various brain regions in cognitive activities. Currently, brain source localization technology has been used clinically in epilepsy, attention deficit, hyperactivity disorder, and other brain abnormalities or diseases. The main progresses of brain source localization technology about the abovementioned five aspects which include the process of brain source localization, the method of inverse solution, influencing factors of positioning accuracy, accuracy evaluation method, and the research and clinical application are reviewed. Furthermore, some scientific problems concerning accuracy evaluation are discussed in this paper. We hope to provide certain references and help with the development and application of brain source localization.