Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.
Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.
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DOI：
10.1088/1741-2552/acd3b1
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发表时间：
2023-05-25
影响因子：
4
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中科院分区：
文献类型：
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Objective.为上肢瘫痪的参与者启用单个假肢手指的神经控制。Approach.两名四肢瘫痪的参与者分别在左后顶叶皮层（PPC）植入了96通道阵列。其中一名参与者在左侧运动皮层（MC）的手柄附近额外植入了96通道阵列。在数十次会议中，我们记录了参与者试图移动右手手指时的神经活动。离线，我们使用线性判别分析与交叉验证从神经放电率分类尝试手指运动。然后，参与者在线使用神经分类器来控制脑机接口（BMI）的单个手指。最后，我们的特点是在个人手指运动的双手的神经表征几何。主要结果。两名参与者在对侧手指的BMI控制期间实现了86%和92%的在线准确性（机会= 17%）。离线，线性解码器实现了十指解码精度分别为70%和66%，使用PPC记录和75%，使用MC记录（机会= 10%）。在MC和一个PPC阵列中，一个因子分解的代码链接了对侧和同侧手的相应手指运动。意义这是第一个从PPC解码对侧和同侧手指运动的研究。对侧手指的在线BMI控制超过了以前的手指BMI。PPC和MC信号可用于控制单个假肢手指，这可能有助于四肢瘫痪患者的手部恢复策略。
Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis. Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain–machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands. Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands. Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
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影响因子：
64.8
作者：
Ethier, C.;Oby, E. R.;Bauman, M. J.;Miller, L. E.
通讯作者：
Miller, L. E.
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Krubitzer, Leah
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Salzman CD
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通讯作者：
Culham, Jody C.
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通讯作者：
Tenore, Francesco, V