An information-theoretic quantification of the content of communication between brain regions.
An information-theoretic quantification of the content of communication between brain regions.
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大脑区域之间通信内容的信息论量化。
DOI：
10.1101/2023.06.14.544903
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Panzeri,Stefano
中科院分区：
文献类型：
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作者：
Celotto,Marco;Bím,Jan;Tlaie,Alejandro;DeFeo,Vito;Lemke,Stefan;Chicharro,Daniel;Nili,Hamed;Bieler,Malte;Hanganu-Opatz,IleanaL;Donner,TobiasH;Brovelli,Andrea;Panzeri,Stefano
量化大脑区域之间交流的数量、内容和方向是理解大脑功能的关键。传统的基于维纳-格兰杰因果关系原理分析大脑活动的方法量化了同时记录的大脑区域之间的神经活动传播的整体信息，但没有揭示有关感兴趣的特定特征（如感官刺激）的信息流。在这里，我们开发了一个新的信息理论措施，称为特定于空间的信息传输（FIT），量化了多少信息的特定功能之间的流动两个区域。FIT融合了维纳-格兰杰因果关系原理和信息内容特异性。我们首先推导出FIT，并证明其关键属性分析。然后，我们用神经活动的模拟来说明和测试它们，证明FIT在区域之间传播的总信息中识别关于特定特征的信息。然后，我们分析了用不同记录方法获得的三个神经数据集，磁和脑电图，以及尖峰活动，以证明FIT能够揭示大脑区域之间信息流的内容和方向，超出了传统分析方法所能识别的范围。FIT可以通过揭示以前未解决的特定特征信息流来提高我们对大脑区域如何交流的理解。
Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information propagated between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto-and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional analytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously unaddressed feature-specific information flow.