ArticleJournal of the American Statistical Association2015
A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.
Article in Journal of the American Statistical Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 39 citations in OpenAlex.
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- A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.Biometrics · 2023Article
- Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error.Statistica Sinica · 2023Article
- Conditional Functional Graphical Models.Journal of the American Statistical Association · 2023Article
- A nonlinear sparse neural ordinary differential equation model for multiple functional processes.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- A Joint estimation approach to sparse additive ordinary differential equations.Statistics and computing · 2022Article
- Kernel Ordinary Differential Equations.Journal of the American Statistical Association · 2022Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- The interaction between OXTR rs2268493 and perceived maternal care is associated with amygdala-dorsolateral prefrontal effective connectivity during explicit emotion processing.European archives of psychiatry and clinical neuroscience · 2020Article
- Bayesian modeling of dependence in brain connectivity data.Biostatistics (Oxford, England) · 2020Article
- The Roles of Statistics in Human Neuroscience.Brain sciences · 2019Article
- A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI.Frontiers in neuroscience · 2019Article
- Multiple Matrix Gaussian Graphs Estimation.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2018Article
- Testing group differences in brain functional connectivity: using correlations or partial correlations?Brain connectivity · 2015Article
- Highly adaptive tests for group differences in brain functional connectivity.NeuroImage. Clinical · 2015Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.
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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.