Neurodynamics of Attention: MEG, EEG, and Modeling

描述（由申请人提供）：本提案的目的是研究正常人类受试者的神经动力学和注意力之间的关系。我们的目标是结合联合收割机的见解，从人类宏观的实验措施和计算神经网络建模测试的假设，即选择性注意影响皮质活动测量的时间和频率域，这种活动是由特定的细胞水平的神经元事件介导的。这将采用双管齐下的办法来实现。首先，我们将实验性地探索使用感觉任务的注意皮层节奏的影响。具体来说，我们将使用最近在Arthinoula A开发的技术。Martinos中心在正中神经（MN）刺激期间同时测量脑磁图（MEG）和脑电图（EEG）信号。我们将在时域和频域中分析初级（SI）和次级（SII）体感系统中产生的信号。在时域中，我们将测量诱发反应的幅度和振幅，在频域中将测量频谱功率和锁相。当受试者参加或不参加MN刺激时，我们将在SI和SII内和SI和SII之间比较这些测量。其次，我们将使用神经网络建模来测试，如果乙酰胆碱水平的变化，伴随着注意力的时间和频率域活动的变化之间创建一个生物物理联系。这种方法将需要开发一个分层的皮质柱模型，该模型再现了用脑磁图/脑电图在颅外测量的振荡电流偶极子。使用该模型进行模拟，也可以对注意力对皮层活动的影响做出新的实验可检验的预测。这种双重方法可能会导致更好地理解注意力的宏观和细胞机制。这项为期五年的培训计划将联合收割机结合候选人在数学和计算神经网络建模方面的背景，以及导师在MEG/EEG和神经科学方面的专业知识，以研究注意力对大脑神经动力学的影响。广泛的长期目标是创建一个生物病理学上真实的神经网络模型，该模型可以与非侵入性临床成像技术结合使用，作为能够诊断和治疗神经注意力障碍的工具。

DESCRIPTION (provided by applicant): The purpose of this proposal is to investigate the relationship between neural dynamics and attention in normal human subjects. We aim to combine insights from human macroscopic experimental measures and computational neural network modeling to test the hypothesis that selective attention affects cortical activity measured in both the time and frequency domain, and that this activity is mediated by specific cellular level neuronal events. This will be accomplished using a two-fold approach. First, we will experimentally probe the effects of attention of cortical rhythms using a sensory task. Specifically, we will use techniques recently developed at the Arthinoula A. Martinos center to simultaneously measure magnetoencephalography (MEG) and electroencephalography (EEG) signals during median nerve (MN) stimulation. We will analyze the signals generated in the primary (SI) and secondary (SII) somatosensory system in both the time and frequency domain. In the time domain, we will measure amplitudes and latencies of evoked responses, and in the frequency domain will measure spectral power and phase-locking. We will compare these measures within and between SI and SII when the subject is attending or not attending to the MN stimulation. Second, we will use neural network modeling to test if changes in the level of acetylcholine that accompany attention create a biophysical link between changes in time and frequency domain activity. This approach will entail the development of a model of a laminated cortical column(s) that reproduces the oscillatory current dipoles that are measured extracranially with MEG/EEG. Simulations with the model can also lead to new experimentally testable predictions of the effects of attention on cortical activity. This two-fold approach may lead to a better understanding of the macroscopic and cellular mechanisms of attention. This proposed five-year training program will combine the candidate's background in mathematics and computational neural network modeling with the mentor's expertise in MEG/EEG and neuroscience to investigate the influence of attention on brain neurodynamics. The broad long term objective is to create a biophysically realistic neural network model that can be used in conjunction with non-invasive clinical imaging techniques as a tool capable of diagnosing and treating neurological attention disorders.