Local field potentials in a pre-motor region predict learned vocal sequences.
Local field potentials in a pre-motor region predict learned vocal sequences.
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运动前区的局部场电位可预测习得的发声序列。
DOI：
10.1371/journal.pcbi.1008100
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发表时间：
2021-09
影响因子：
4.3
通讯作者：
Gilja V
中科院分区：
文献类型：
--
作者：
Brown DE 2nd;Chavez JI;Nguyen DH;Kadwory A;Voytek B;Arneodo EM;Gentner TQ;Gilja V
运动前区HVC内的神经元活动与鸟类习得歌唱的发音产生密切同步，并对其至关重要。这种神经活动的特征详细描述了在HVC群体中仔细识别的小神经元子集中连续爆发的模式。这些特征很好地描述了HVC的动态，但在这个测量范围之外还没有得到验证。使用局部场势(LFP)来提取超越单个细胞贡献的有关行为的信息有着丰富的历史。这些信号具有在较长时间内保持稳定的优势，它们已被用于研究和解码人类语音和其他复杂的运动行为。在这里，我们对在歌曲产生过程中自由行为的雄性斑雀的HVC中推测的LFP信号进行特征描述，以确定种群活动是否可以对复杂的运动-发声行为的机制产生类似的见解。在初步观察到LFP的结构变化与歌曲中的所有发声都不同之后，我们表明，从多个频带中提取时变特征来解码特定发声元素(音节)的身份并预测它们在母题中的时间起点是可能的。这证明了LFP在研究鸣禽发声行为方面的实用性。令人惊讶的是，HVC LFP的时频结构定性上类似于在人类和非人类哺乳动物运动区中发现的公认的振荡。这种生理相似性，尽管有不同的解剖结构，可能会让我们深入了解学习和/或产生复杂的运动-发声行为的共同计算原理。发声，如语音和歌曲，是一个运动过程，需要从特定大脑区域接收指令的众多肌肉群的协调。在鸣禽中，HVC是歌唱所需的运动前脑区；它由一组在歌唱时稀疏放电的神经元组成。HVC是如何实现歌曲生成的，目前还不是很清楚。在这里，我们描述的网络活动假定来自HVC，在歌唱过程中每个发声元素的启动之前。这种网络活动既可以用来预测每个发声元素(音节)的身份，也可以用来预测它在歌曲中何时出现。此外，这种网络活动类似于人类、非人类灵长类和哺乳动物运动前区域中与肌肉运动有关的活动。这些相似之处增加了越来越多的文献，这些文献发现鸣禽和人类在发声器官的运动控制方面有相似之处。此外，考虑到鸣鸟和人类运动发声系统的相似性，这些结果表明鸣鸟模型可以被用来加速临床可翻译语音假体的开发。
Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors. Vocalizations, such as speech and song, are a motor process that requires the coordination of numerous muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing; it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity presumptively from HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Furthermore, given the similarities of the songbird and human motor-vocal systems, these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.
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细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
5.7
作者：
Ghez, C;Hening, W;Gordon, J
通讯作者：
Gordon, J
影响因子：
1.3
作者：
Bruno, Julia Hyland;Tchernichovski, Ofer
通讯作者：
Tchernichovski, Ofer
DOI：
10.1016/j.cub.2021.05.035
发表时间：
2021-08-09
期刊：
Current biology : CB
影响因子：
--
作者：
Arneodo EM;Chen S;Brown DE 2nd;Gilja V;Gentner TQ
通讯作者：
Gentner TQ
影响因子：
56.9
作者：
FETZ, EE
通讯作者：
FETZ, EE