Precise measurement of correlations between frequency coupling and visual task performance.
Precise measurement of correlations between frequency coupling and visual task performance.
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DOI：
10.1038/s41598-020-74057-1
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发表时间：
2020-10-15
影响因子：
4.6
通讯作者：
Aazhang B
中科院分区：
文献类型：
--
作者：
Young J;Dragoi V;Aazhang B
功能连接分析侧重于频域关系，即频率耦合，有力地揭示了神经生理学。相干性很常用，但神经活动并不遵循其高斯假设。最近推出的频率互信息 (MIF) 技术不做任何模型假设，而是测量非高斯和非线性关系。我们开发了一个强大的 MIF 估计器，针对将频率耦合与任务性能和其他相关任务现象相关联进行了优化。鉴于多锥谱估计提供的方差减少对于精确测量此类相关性至关重要，我们提出了一种 MIF 多锥方法，并将其性能与模拟中的一致性进行了比较。此外，还计算了猕猴视觉皮层记录之间的多锥 MIF 和一致性，并分析了它们与任务表现的相关性。我们的多锥 MIF 估计器产生低方差，并且在模拟相关分析中比所有其他估计器表现更好。模拟进一步表明，多锥 MIF 捕获的信息比相干性多。对于猕猴数据集，一致性和我们新的 MIF 估计器基本一致。总的来说，我们提供了一种精确估计频率耦合的新方法，可以揭示任务表现，并帮助神经科学家准确捕获耦合与任务现象之间的相关性。此外，我们还首次提供 MIF 工具箱。
Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. Coherence is commonly used but neural activity does not follow its Gaussian assumption. The recently introduced mutual information in frequency (MIF) technique makes no model assumptions and measures non-Gaussian and nonlinear relationships. We develop a powerful MIF estimator optimized for correlating frequency coupling with task performance and other relevant task phenomena. In light of variance reduction afforded by multitaper spectral estimation, which is critical to precisely measuring such correlations, we propose a multitaper approach for MIF and compare its performance with coherence in simulations. Additionally, multitaper MIF and coherence are computed between macaque visual cortical recordings and their correlation with task performance is analyzed. Our multitaper MIF estimator produces low variance and performs better than all other estimators in simulated correlation analyses. Simulations further suggest that multitaper MIF captures more information than coherence. For the macaque data set, coherence and our new MIF estimator largely agree. Overall, we provide a new way to precisely estimate frequency coupling that sheds light on task performance and helps neuroscientists accurately capture correlations between coupling and task phenomena in general. Additionally, we make an MIF toolbox available for the first time.
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影响因子：
3
作者：
Bastos AM;Schoffelen JM
通讯作者：
Schoffelen JM
DOI：
10.1073/pnas.94.23.12699
发表时间：
1997-11-11
影响因子：
11.1
作者：
Fries, P;Roelfsema, PR;Singer, W
通讯作者：
Singer, W
影响因子：
0.9
作者：
Brillinger, David R.;Guha, Apratim
通讯作者：
Guha, Apratim
DOI：
10.1109/tasl.2012.2191960
发表时间：
2012-09-01
影响因子：
--
作者：
Kinnunen, Tomi;Saeidi, Rahim;Li, Haizhou
通讯作者：
Li, Haizhou
影响因子：
2.3
作者：
BARTKO, JJ
通讯作者：
BARTKO, JJ