A systematic approach to selecting task relevant neurons.
A systematic approach to selecting task relevant neurons.
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DOI：
10.1016/j.jneumeth.2015.02.020
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发表时间：
2015-04-30
影响因子：
3
通讯作者：
Sarma SV
中科院分区：
文献类型：
--
作者：
Kahn K;Saxena S;Eskandar E;Thakor N;Schieber M;Gale JT;Averbeck B;Eden U;Sarma SV
由于任务相关的神经元在手术过程中不能被专门靶向，因此要做出的关键决定是在执行数据分析时选择哪些神经元是任务相关的。包括与任务无关的神经元会降低解码准确性并混淆神经生理结果。传统上，任务相关的神经元被选择为当施加刺激时放电率发生显著变化的神经元。然而，这假设神经元对刺激的编码由它们的放电率主导，很少考虑时间动态。本文提出了一种系统的神经元选择方法，它使用似然比测试来捕捉刺激的尖峰活动的贡献，同时考虑到任务无关的内在动态，影响放电率。这种方法被称为模型劣化排除刺激（MDES）测试。MDES相比，在四个案例研究：模拟，解码的例子，和两个神经生理学的例子，射击率的选择。模拟中的MDES排名与理想排名密切匹配，而射击率排名则因与任务无关的参数而偏斜。对于解码，使用MDES排名前8位的神经元实现95%的准确度，而需要排名前12位的神经元。在神经生理学的例子中，MDES匹配已发表的结果时，放电率编码显着的刺激信息，并揭示了任务相关的神经元中的振荡调制时，神经元选择使用放电率没有被捕获。这些案例研究说明了在选择任务相关神经元时考虑内在动力学的重要性，并遵循MDES方法实现这一点。MDES选择编码任务相关信息的神经元，而不管这些内在动力学如何，这些内在动力学可以使基于发射率的选择产生偏差。
Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrades decoding accuracy and confounds neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons’ encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.
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影响因子：
2.9
作者：
Coleman, Todd P.;Sarma, Sridevi S.
通讯作者：
Sarma, Sridevi S.
影响因子：
2.9
作者：
Gale JT;Shields DC;Jain FA;Amirnovin R;Eskandar EN
通讯作者：
Eskandar EN
影响因子：
3.5
作者：
Santaniello S;Montgomery EB Jr;Gale JT;Sarma SV
通讯作者：
Sarma SV
DOI：
10.1109/iembs.2011.6091037
发表时间：
2011
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Saxena S;Gale JT;Eskandar EN;Sarma SV
通讯作者：
Sarma SV
影响因子：
2.9
作者：
Eden, UT;Frank, LM;Brown, EN
通讯作者：
Brown, EN