Non-invasive laminar electrophysiology in humans

通过经验，我们学会了从我们周围的世界期待什么。我们对特定的感官信息变得熟悉，并利用这一先前的经验来预测我们期望看到或触摸的东西。当感觉信息不像我们预期的那样时，这些信息(或预测误差)被前馈以纠正未来的预测。例如，你可能会有这样的印象，你停靠的列车要离开车站，只是因为另一列火车在你旁边移动。这是一个视觉信息对世界状态进行预测的例子--在这种情况下，这恰好是一个预测错误。我们从大脑皮层的解剖中知道，携带反馈(预测)和前馈(预测误差)信息的通路以平行的方式相互交织在一起，这些信息将大脑中处理非常低水平感觉信息的区域相互连接起来，通过多个中间级别一直到我们做出决定的大脑区域。有趣的是，这些通路有不同的起源，前馈通路和反馈通路分别起源于皮层的上层和下层(相隔约3-4 mm)。除了在解剖学上是可区分的，这些反馈和前馈数据流在不同的频率范围内工作，反馈信号的变化(大约每秒10-20次)比前馈信号(大约每秒30-60次)更慢。目前，当这些前馈和反馈信号通过大脑时，我们可以观察它们的唯一方法是在动物的大脑中植入微电极阵列。这是因为大多数人类大脑扫描仪要么可以看到这些层，但只能观察它们在几秒钟内是如何变化的(功能磁共振成像)；或者它们可以及时区分反馈和前馈信号，但无法分辨它们来自哪里(脑电图仪或EEG)。这笔赠款建立在脑磁图(MEG)技术的最新发展基础上，在这种技术中，我们测量了人脑中流动的电流在头部外部产生的磁场。与脑电一样，脑磁图可以在时间和频率上区分这些前馈和反馈信号；重要的是，我们最近表明，使用脑磁图也可以区分大脑皮层。这是因为我们有关于磁场测量系统(MEG)的不同皮质层所在位置的非常精确的模型。在这项授权中，我们将这两件事放在一起，并期望证明我们可以在频率(反馈低频，前馈高频)和空间(前馈和反馈分别起源于上层和下层大脑皮层)中无创地将反馈从前馈信息中分离出来。这是一种完全安全和非侵入性的技术，我们可以在人类身上使用。重要的是，它将使我们能够研究这种前馈和反馈信息是如何同时在人脑的多个区域传播的--这在侵入性动物研究中是不可能做到的。这不仅有助于我们了解大脑是如何工作的，而且有助于我们了解当这些反馈和前馈流在帕金森氏症和精神分裂症等情况下受到损害时会发生什么。

Through experience we learn what to expect from the world around us. We become familiar with particular sensory information and we use this previous experience to make predictions about what we expect to see or touch. When the sensory information is not as we expected, this information (or prediction error) is fed forward to correct future predictions. For example it may be that you have had the impression that your stationary train is leaving the station simply because another train moves alongside you. This is an example of visual information making a prediction about the state of the world- which in this case happens to be a prediction error. We know from the anatomy of the cortex that the pathways that carry this feedback (predictions) and feedforward (prediction errors) information intertwine in parallel streams which interconnect brain regions that process very low level sensory information through multiple intermediate levels right up to those brain regions in which we make decisions about what to do. Interestingly, these pathways have distinct origins with feedforward and feedback pathways originating in the upper and lower cortical layers respectively (separated by around 3-4mm). Besides being distinguishable anatomically, these feedback and feedforward streams operate within distinct frequency ranges, the feedback signals changing more slowly (about 10-20 times a second) than the feedforward (about 30-60 times a second). At present the only way that we can look at these feedforward and feedback signals as they pass through the brain is through implanting micro-electrode arrays in the brains of animals. This is because the majority of human brain scanners either can see the layers but can only watch how they change over many seconds (functional Magnetic Resonance Imaging); or they distinguish the feedback and feedfoward signals in time but cannot resolve where they are coming from (electroencephalography or EEG). This grant builds on recent technological developments in magnetoencephalography (MEG) in which we measure magnetic fields outside the head produced by electrical currents flowing in the human brain. MEG, like EEG, can distinguish between these feedforward and feedback signals in time and frequency; importantly we have recently shown that it is also possible to distinguish between cortical layers using MEG. This is made possible because we have very precise models of where the different cortical layers lie with respect to our magnetic field measuring system (MEG). In this grant we put these two things together and expect to show that we can non-invasively disentangle feedback from feedforward information in both frequency (feedback low frequency, feedforward high frequency) and space (feedforward and feedback origins in upper and lower cortical layers respectively). This is a completely safe and non-invasive technique we can use in humans. Importantly, it will allow us to study how this feedforward and feedback information propagates across multiple areas of the human brain simultaneously - something that cannot even be done in invasive animal studies.This will not only help us understand how the brain works, but will help us understand what happens when these feedback and feedforward streams become compromised in conditions such as Parkinson's disease of schizophrenia.