Multimodal Dynamic Imaging of Human Brain Activity

在日常生活中，我们经常面临的一个核心挑战是正确地评估他人的意图（“她想做什么？...”）和感官事件的输入（“现在可能发生什么--好的或坏的？...基于主动感知（“在我看来，她是想......“）和检索关联（"她就是那个. ').认知神经科学的相应问题是，理想情况下，从非侵入性的大脑活动记录中识别出伴随并支持活跃的人类认知和行为的分布式大脑活动模式。这个问题有两个部分：第一，-什么样的分布式大脑动力学模式遵循，伴随，并预测特定的世界事件和主体行为？ 为了充分理解主体在执行给定任务时的经验和行为，我们必须考虑每个任务事件对主体的重要性和每个行为事件的意图。这些因素不能直接知道，但在许多情况下，可以从受试者行为的详细记录以及每个记录的环境或行为事件发生的特定历史背景中准确地猜测或推断。在从人类头皮非侵入性地记录脑电图（EEG）和/或脑磁图（MEG）信号的情况下，问题的第二部分仍然存在-哪些大脑区域产生所识别的信号模式？“分析电磁头皮数据的常用方法是将记录的事件和行为分成几个简单的类别，对锁定到每个事件类别的记录的大脑动力学时间进行平均，然后将物理逆源估计方法应用于所得平均值中的峰值头皮图。该项目将探索使用新的机器学习方法，包括高级独立成分分析（伊卡）和稀疏贝叶斯学习（SBL）方法，以联合建模记录的任务事件，受试者行为和复杂学习任务中记录的大脑动态数据。该项目有两个目标：首先，识别在特定背景下可靠地伴随受试者行为的非平均EEG和/或MEG数据中的模式，其次，确定受试者的皮层地幔的精确区域，其局部同步其电磁活动以产生识别的头皮模式。如果成功，该项目将提高非侵入性电磁脑成像的价值，以高时间和空间分辨率识别和测量支持人类积极认知的局部同步皮层活动的复杂分布式模式。

he central active challenge we are constantly addressing in daily life is to correctly assess the intent of others ('What is she trying to do? ...') and the import of sensory events ('What - good or bad - may happen now? ...') based on active perception ('It looks to me like she is trying to ...') and retrieved associations (''And she was the one who ...'). The corresponding problem for cognitive neuroscience is to identify, ideally from non-invasive brain activity recordings, those patterns of distributed brain activity that accompany and support active human cognition and behavior. This problem has two parts: First, -What patterns of distributed brain dynamics follow from, accompany, and predict specific world events and subject behavior? -To fully understand the experience and behavior of subjects in performing a given task, we must take into account both the import of each task event to the subject and the intent of each of behavioral event. These factors cannot be known directly, but they may be accurately guessed or inferred, in many cases, from detailed recordings of subject behavior and from the specific historical context in which each recorded environmental or behavioral event occurs. In the case of electroencephalographic (EEG) and/or magnetoencephalographic (MEG) signals recorded non-invasively from the human scalp, a second part of the problem remains -Which brain areas generate the identified signal patterns?' The usual approach to analyzing electromagnetic scalp data has been to separate recorded events and behavior into a few simple categories, to average the recorded brain dynamics time locked to each event category, and then to apply physical inverse source estimation methods to scalp maps of peaks in the resulting averages. This project will explore using new machine learning methods, including advanced independent component analysis (ICA) and sparse Bayesian learning (SBL) methods, to jointly model the recorded task event, subject behavior, and brain dynamic data recorded in a complex learning task. The project has two goals: First, to identify patterns in unaveraged EEG and/or MEG data that reliably accompany subject behavior in specific contexts, and second to determine the exact areas of the subject's cortical mantle that locally synchonize their electromagnetic activities to produce the identified scalp patterns. If successful, the project will enhance the value of noninvasive electromagnetic brain imaging for identifying and measuring, with high temporal and spatial resolution, complex, distributed patterns of locally synchronous cortical activity that support active human cognition.