ArticleFrontiers in neuroinformatics2018
Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy.Frontiers in human neuroscience · 2022Article
- Conditional Entropy: A Potential Digital Marker for Stress.Entropy (Basel, Switzerland) · 2021Article
- High density optical neuroimaging predicts surgeons's subjective experience and skill levels.PloS one · 2021Article
- Entropy and the Brain: An Overview.Entropy (Basel, Switzerland) · 2020Review
- fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks.Entropy (Basel, Switzerland) · 2020Article
- Higher hypnotic suggestibility is associated with the lower EEG signal variability in theta, alpha, and beta frequency bands.PloS one · 2020Article
- Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.