Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.
Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.
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DOI：
10.3389/fninf.2018.00033
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发表时间：
2018
影响因子：
3.5
通讯作者：
Ishiguro H
中科院分区：
文献类型：
--
作者：
Keshmiri S;Sumioka H;Yamazaki R;Ishiguro H
近红外光谱（NIRS）在分析和解码人类大脑活动方面的应用越来越受到神经科学研究的关注。考虑到功能性磁共振成像（fMRI）时间序列数据显示的血氧依赖水平（BOLD）反应与近红外光谱（NIRS）捕获的血红蛋白氧/脱氧反应之间的相关性，线性模型在这些应用中发挥了核心作用。这反过来又导致了特征提取策略的适应，这些策略非常适合于表现出高度线性的数据的离散化，即斜率和平均值及其组合，以总结NIRS时间序列的信息内容。在本文中，我们证明了这些特征在捕获近红外光谱数据的变分信息方面效率低下，限制了其结果结论的可靠性和充分性。或者，我们提出这些时间序列的微分熵的线性估计作为这些信息的自然表示。我们通过比较分析这些特征在与几个工作记忆任务以及自然会话刺激相关的近红外光谱数据上的应用，为我们的说法提供了证据。
Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.
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影响因子：
25
作者：
Cohen JD;Daw N;Engelhardt B;Hasson U;Li K;Niv Y;Norman KA;Pillow J;Ramadge PJ;Turk-Browne NB;Willke TL
通讯作者：
Willke TL
影响因子：
2.5
作者：
Dieler, A. C.;Tupak, S. V.;Fallgatter, A. J.
通讯作者：
Fallgatter, A. J.
影响因子：
3
作者：
Eden, Uri T.;Kramer, Mark A.
通讯作者：
Kramer, Mark A.
影响因子：
5.7
作者：
Aguirre, GK;Zarahn, E;D'Esposito, M
通讯作者：
D'Esposito, M
影响因子：
5.3
作者：
Boynton, GM;Engel, SA;Heeger, DJ
通讯作者：
Heeger, DJ