EEG STUDIES OF ELECTROMOTIVE GENERATORS AFFECTED BY ALCOHOL

颈椎节段的电生理臂 脑成像和电生理学继续取得重大进展 刺激产生、控制和治疗的方法学进展 多感官通道内的呈现。任何感官事件， 其可以被数字存储(包括声音，视觉图像， 触觉图案等)，现在可以完全同步地呈现 随着脑电的持续采集。我们还编写了C代码 中指定刺激的标识进行动态更新 预定义序列。这种动态更新允许每个 预先指定的刺激序列将根据以下条件进行唯一更改 每个个体受试者的反应历史。所有主题 可以在以下序列中的任何点记录响应 刺激呈现；而不仅仅是以预定义的“反应间隔”。 这使我们能够观察复杂的响应模式，这些模式可能 在描述一群具有不同特征的个体时很有用 认知需求不同的任务中的反应特征。 此外，此代码正在修改以用于功能磁共振成像协议 这样，我们就可以在 电生理学和fMRI/PET设置。工作已经开始使用 上面提到的多感官心理物理任务中的代码 刺激的强度呈现在不同的感官上 正在为每个单独的受试者匹配模式。 多感官幅度匹配是必要的，以更精确 探索所提出的多感官的功能组织 注意系统。这是通过定量操作 无人注意或忽略的刺激的知觉突显 感官模式。所产生的关于量化的数据 感觉通道间加工的互易性作为 选择性的关注将使我们最终解决以下问题 选择性处理和不透视性，让人分心更多 比单独在一个感官系统内观察更普遍的方式。一个 不能过滤掉令人分心的信息被认为是 行为冲动的成分和这些多感官 技术可能使我们能够更准确地描述 冲动的人。多感官分析也将有助于 对大脑区域有更全面的了解 来自多个感官通路的输入汇聚。这些网站 包括弓状沟和颞上回。我们是 积极采取分析策略，共同登记电极 序列数据与结构磁共振和功能血流的关系 公共坐标系中的数据。通过从 磁头表面上空间致密的电极阵列， 从EEG/ERP技术获得的精细时间信息可以 结合成像模式的精细空间信息 例如PET和fMRI来构建真实的时空模型 认知现象背后的神经网络。有必要 增加我们收集的电生理数据 具有成像数据的多感觉选择性注意任务，以便 更精确地可视化和描述多感官 上述加工区。成像方式的使用 将使我们能够验证这些多感官区域的激活 大脑和EEG/ERP技术的使用使我们能够 确定这些区域何时激活并生成模型 神经网络的时空动力学 这是跨感觉通道对刺激进行选择性处理的基础。 一旦一个正常的多感官选择性加工模型 经验证，我们可以查看以下方面的不同中断模式 表现出冲动控制问题以帮助 确定这些中断的来源。

The electrophysiological arm of the Section of Brain Imaging and Electrophysiology continues to make significant methodological advances in stimulus generation, control, and presentation within multiple sense modalities. Any sensory event, which can be digitally stored (including sounds, visual images, tactile patterns, etc.), can now be presented in complete synchrony with the continuous EEG acquisition. We have also written C code for dynamically updating the identity of a specified stimulus in a predefined sequence. This dynamic updating allows each pre-specified stimulus sequence to be uniquely altered based upon the response history of each individual subject. All subject responses can be logged at any point within the sequence of stimulus presentation; not just at predefined "response intervals". This enables us to look at complex patterns of responding that may be useful in characterizing groups of individuals with different response profiles in tasks of varying cognitive demand. Additionally, this code is being modified for use in fMRI protocols so that we can run identical versions of tasks in the electrophysiology and fMRI/PET settings. Work has begun using the code mentioned above in a multisensory psychophysics task where the intensity of stimuli presented in different sensory modalities is being matched for each individual subject. Multisensory magnitude matching is necessary to more precisely probe the functional organization of the proposed multisensory attention system. This is done by quantitatively manipulating the perceptual salience of stimuli presented in nonattended or ignored sensory modalities. The resulting data regarding the quantitative reciprocity of processing across sensory modalities as a function of selective attention will allow us to ultimately address questions of selective processing and imperviousness to distraction in a more general way than looking within one sensory system alone. An inability to filter out distracting information is thought to be a component of behavioral impulsivity and these multisensory techniques may enable us to more precisely characterize subtypes of impulsive individuals. A multisensory analysis will also facilitate a more general understanding of brain areas that are the sites of convergence of inputs from multiple sensory pathways. These sites include the arcuate sulcus and the superior temporal gyrus. We are actively pursuing analytic strategies to co-register the electrode array data with that of structural MRI and functional blood flow data within a common coordinate system. By recording from spatially dense arrays of electrodes on the surface of the head, the fine temporal information obtained from EEG/ERP techniques can be combined with the fine spatial information of imaging modalities such as PET and fMRI to construct true spatio-temporal models of neural networks underlying cognitive phenomena. It is necessary to augment the electrophysiological data that we have collected using multisensory selective attention tasks with imaging data in order to more precisely visualize and characterize the multisensory processing areas mentioned above. The use of imaging modalities will allow us to verify the activation of these multisensory areas of the brain and the use of EEG/ERP techniques enables us to determine when these areas become activated and produce a model of the spatio-temporal dynamics of the neural network which underlies selective processing of stimuli across sensory modalities. Once a model of normal multisensory selective processing has been validated we can look at different patterns of disruptions in individuals who exhibit impulse control problems to aid in identifying the source of these disruptions.