Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours.
Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours.
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DOI：
10.1371/journal.pone.0166598
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Mormann F
中科院分区：
文献类型：
--
作者：
Niediek J;Boström J;Elger CE;Mormann F
在活体动物脑中记录细胞外神经元是神经科学中最成熟的实验技术之一，最近在人类中变得可行。许多有趣的科学问题只有在细胞外记录持续几个小时，并且在整个记录过程中跟踪单个神经元时才能解决。例如，这些问题涉及学习和记忆巩固的神经元机制以及癫痫发作的产生。到目前为止，有几个困难限制了细胞外多小时记录在神经科学中的使用：数据集变得巨大，并且在临床记录环境中数据必然是嘈杂的。目前还没有对此类记录进行尖峰排序的方法。尖峰分选是指识别几个神经元对一个电极中记录的信号的贡献的过程。为了克服这些困难，我们开发了Combinato：一个完整的数据分析框架，用于在持续12小时或更长时间的嘈杂录音中进行尖峰排序。我们的框架包括用于伪影拒绝、自动尖峰排序、手动优化和有效可视化结果的软件。我们的完全自动化框架擅长两项任务：在模拟和真实的数据上测试时，它优于现有方法，并且它使研究人员能够分析多小时的记录。我们在短时间和多小时的模拟数据集上评估了我们的方法。为了评估我们的方法在实际神经科学实验中的性能，我们使用了来自神经外科患者的数据，记录这些数据是为了识别内侧颞叶中的视觉反应神经元。这些神经元对给定刺激的语义内容而不是视觉特征做出反应。为了用多小时的记录来测试我们的方法，我们利用了人类内侧颞叶中的神经元，这些神经元在晚上和第二天早上选择性地对相同的刺激做出反应。
Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.
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人类右侧杏仁核对动物的特定类别反应。
影响因子：
25
作者：
Mormann F;Dubois J;Kornblith S;Milosavljevic M;Cerf M;Ison M;Tsuchiya N;Kraskov A;Quiroga RQ;Adolphs R;Fried I;Koch C
通讯作者：
Koch C
影响因子：
2.9
作者：
Kadir SN;Goodman DF;Harris KD
通讯作者：
Harris KD
DOI：
10.1016/j.cub.2009.06.060
发表时间：
2009-08-11
期刊：
Current biology : CB
影响因子：
--
作者：
Quian Quiroga R;Kraskov A;Koch C;Fried I
通讯作者：
Fried I
影响因子：
3.7
作者：
Bondar IV;Leopold DA;Richmond BJ;Victor JD;Logothetis NK
通讯作者：
Logothetis NK
影响因子：
3.3
作者：
Schmitzer-Torbert, N;Jackson, J;Redish, AD
通讯作者：
Redish, AD