Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI

DOI10.1162/neco_a_01323zbMath1453.92164arXiv1711.03000OpenAlexW3087743382WikidataQ99569111 ScholiaQ99569111MaRDI QIDQ3386440

Giri Krishnan, Qasim Bukhari, Tiger W. Lin, Yusi Chen, Maxim Bazhenov, Terrence J. Sejnowski

Publication date: 4 January 2021

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.03000

zbMATH Keywords

functional connectivitycortical networksfMRI recordingsneural network of Hodgkin-Huxley neurons

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Neural networks for/in biological studies, artificial life and related topics (92B20)

Uses Software

• GCCA
• glasso
• QUIC

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
• Rank-Sparsity Incoherence for Matrix Decomposition
• Measures of Conditional Linear Dependence and Feedback Between Time Series
• 10.1162/153244303321897717
• Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

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