Optimizing the decoding of movement goals from local field potentials in macaque cortex.
Optimizing the decoding of movement goals from local field potentials in macaque cortex.
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DOI：
10.1523/jneurosci.4165-11.2011
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发表时间：
2011-12-14
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影响因子：
--
通讯作者：
Pesaran B
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文献类型：
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作者：
Markowitz DA;Wong YT;Gray CM;Pesaran B
运动神经假体设备的成功开发取决于准确可靠地解码来自大脑的信号的能力。运动神经假体在表现非人类灵长类动物方面得到了广泛的研究，但技术限制在优化性能方面进展有限。特别是，由于固定几何结构的多电极阵列的广泛使用，对跨皮质层的运动相关神经元活动的组织仍然知之甚少。在这项研究中，我们使用长期植入的具有单独可移动电极的多电极阵列来检验运动目标的编码如何依赖于皮质深度。在一系列跨越几个月的记录中，我们在两只猴子进行记忆引导的眼球运动时，改变了它们额叶皮质弓状前回中每个电极的深度。我们从局部场电位(LFP)和记录的多单位放电活动中解码眼睛运动目标，记录的深度范围从皮质表面到3毫米。我们发现，LFP和多单元信号在皮层表面0.5 mm以内的浅层位置产生最高的解码性能，而在深度大于1 mm的位置性能显著降低。我们还通过改变带通滤波特性和模拟微电极阵列通道数和密度的变化来分析性能。结果表明，基于LFP的神经假体的性能强烈依赖于记录结构，并且记录深度是限制系统性能的关键参数。
The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically-implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the pre-arcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3 mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying bandpass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.