ArticleNetwork neuroscience (Cambridge, Mass.)2018
Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data.
Article in Network neuroscience (Cambridge, Mass.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Multiscale Spatiotemporal Causal Mapping Algorithm for Revealing Neural Network Mechanisms of Transcutaneous Auricular Vagus Nerve Stimulation.Human brain mapping · 2026Article
- Thalamocortical interactions shape hierarchical neural variability during stimulus perception.iScience · 2024Article
- Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data.Brain sciences · 2023Article
- Network inference from short, noisy, low time-resolution, partial measurements: Application toProceedings of the National Academy of Sciences of the United States of America · 2023Article
- The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.NeuroImage · 2022Article
- Sleep Spindles Preferentially Consolidate Weakly Encoded Memories.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Feed-forward information and zero-lag synchronization in the sensory thalamocortical circuit are modulated during stimulus perception.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods.Network neuroscience (Cambridge, Mass.) · 2019Article
- Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.PLoS biology · 2018Article
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Authors and funding
5 authors.
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Abstract
Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.
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