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Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

Neural Computation (2017) 29 (10): 2581–2632.
This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.
Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data
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Network Neuroscience (2024) 8 (3): 965.
Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times
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Network Neuroscience (2022) 6 (2): 614.
Dynamical differential covariance recovers directional network structure in multiscale neural systems
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Proceedings of the National Academy of Sciences (2022) 119 (24)
Inference of synaptic connectivity and external variability in neural microcircuits
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Journal of Computational Neuroscience (2020) 48 (2): 123.
Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI
  • Tiger W. Lin
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Neural Computation (2020) 32 (12): 2389.
Rethinking Measures of Functional Connectivity via Feature Extraction
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Scientific Reports (2020) 10 (1)
Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions
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Network Neuroscience (2019) 3 (4): 1009.

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