Neurodynamics of Attention: MEG, EEG, and Modeling

描述(申请人提供)：这项建议的目的是调查神经动力学和注意力之间的关系在正常人类受试者。我们的目标是将人类宏观实验测量的洞察力与计算神经网络建模相结合，以检验选择性注意影响在时间域和频率域测量的皮质活动的假设，以及这种活动是由特定细胞水平的神经元事件介导的。这将使用两种方法来实现。首先，我们将使用一项感觉任务，通过实验来探索大脑皮层节律注意力的影响。具体地说，我们将使用Arsinoula A.Martinos中心最近开发的技术来同时测量正中神经(MN)刺激过程中的脑磁图(MEG)和脑电(EEG)信号。我们将在时间域和频域分析在初级(SI)和次级(SII)体感系统中产生的信号。在时间域，我们将测量诱发反应的幅度和潜伏期，在频率域，我们将测量频谱功率和锁相。当受试者接受或不接受MN刺激时，我们将比较SI和SII之间的这些测量。其次，我们将使用神经网络建模来测试伴随注意力的乙酰胆碱水平的变化是否在时间域和频域活动的变化之间创建了生物物理联系。这种方法将需要开发一个层状皮质柱(S)的模型，它再现了用脑磁图/脑电图在颅外测量的振荡电流偶极子。用该模型进行模拟也可以得出注意力对大脑皮层活动的影响的新的实验可检验的预测。这种两方面的方法可能有助于更好地理解注意力的宏观和细胞机制。这项拟议的为期五年的培训计划将把候选人在数学和计算神经网络建模方面的背景与导师在脑磁图/脑电和神经科学方面的专业知识结合起来，以调查注意力对脑神经动力学的影响。广泛的长期目标是创建一种生物物理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.