Near-real-time feature-selective modulations in human cortex.
Near-real-time feature-selective modulations in human cortex.
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DOI：
10.1016/j.cub.2013.02.013
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发表时间：
2013-03-18
期刊：
影响因子：
9.2
通讯作者：
Serences, John T.
中科院分区：
文献类型：
--
作者：
Garcia, Javier O.;Srinivasan, Ramesh;Serences, John T.
为了将神经活动与认知功能联系起来，需要有关时间动态和神经代码内容的信息。传统上，记录动物中的单个神经元一直是获得高时间分辨率以及有关神经调节特性（例如对不同感觉特征的选择性）的精确信息的主要手段。最近的人类功能磁共振成像研究已经能够测量感觉皮层特定子区域内的特征选择性（例如初级视觉皮层或 V1 中的方向选择性）。然而，使用功能磁共振成像等对时间不敏感的方法来研究支持认知处理的神经机制（通常在亚秒级范围内快速发生）严重限制了可以得出的推论类型。在这里，我们描述了一种通过脑电图头皮记录来跟踪特征选择性信息处理的快速时间演化的新方法。我们根据稳态视觉诱发电位（SSVEP）对闪烁视觉刺激的反应的空间分布模式生成方向选择性反应曲线。使用这种方法，我们报告了这些特征选择性响应曲线的乘法注意力调制，其时间分辨率为 24ms-120ms，这比使用 fMRI 实现的速度要快得多。最后，我们表明可以根据这些时间精确的特征选择性响应曲线的幅度来预测辨别任务的行为表现。因此，该方法提供了高时间分辨率度量，可用于跟踪认知操作对人类皮层特征选择性信息处理的影响。
In order to link neural activity with cognitive function, information is needed about both the temporal dynamics and the content of neural codes. Traditionally, recording single neurons in animals has been the primary means of obtaining high temporal resolution as well as precise information about neural tuning properties such as selectivity for different sensory features. Recent fMRI studies in humans have been able to measure feature selectivity within specific sub-regions of sensory cortex (e.g., orientation selectivity in primary visual cortex, or V1). However, investigating the neural mechanisms that support cognitive processing – which often occur rapidly on a sub-second scale – using a temporally insensitive method such as fMRI severely limits the types of inferences that can be drawn. Here, we describe a new method for tracking the rapid temporal evolution of feature-selective information processing with scalp recordings of EEG. We generate orientation-selective response profiles based on the spatially distributed pattern of steady-state visual evoked potential (SSVEP) responses to flickering visual stimuli. Using this approach, we report a multiplicative attentional modulation of these feature-selective response profiles with a temporal resolution of 24ms–120 ms, which is far faster than that achieved using fMRI. Finally, we show that behavioral performance on a discrimination task can be predicted based on the amplitude of these temporally precise feature-selective response profiles. This method thus provides a high temporal resolution metric that can be used to track the influence of cognitive manipulations on feature-selective information processing in human cortex.
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