ArticleAdvances in neural information processing systems2019
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes.
Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Contrastive Latent Functional Model.Statistics in biosciences · 2026Article
- Time-varying functional connectivity as Wishart processes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.PloS one · 2024Article
- Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals.Bioprocess and biosystems engineering · 2023Article
- Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data.Statistics and its interface · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Dynamic functional connectivity, as measured by the time-varying covariance of neurological signals, is believed to play an important role in many aspects of cognition. While many methods have been proposed, reliably establishing the presence and characteristics of brain connectivity is challenging due to the high dimensionality and noisiness of neuroimaging data. We present a latent factor Gaussian process model which addresses these challenges by learning a parsimonious representation of connectivity dynamics. The proposed model naturally allows for inference and visualization of connectivity dynamics. As an illustration of the scientific utility of the model, application to a data set of rat local field potential activity recorded during a complex non-spatial memory task provides evidence of stimuli differentiation.
Identifiers
33041607PMC7540610What OpenQuestion holds
Registered trials
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