ArticleAdvances in neural information processing systems2020
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings.
Article in Advances in neural information processing systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.Nature communications · 2026Article
- Stiefel Manifold Dynamical Systems for Tracking Representational Drift.bioRxiv : the preprint server for biology · 2026Article
- Cross-population amplitude coupling in high-dimensional oscillatory neural time series.Frontiers in computational neuroscience · 2026Article
- Prioritized learning of cross-population neural dynamics.Journal of neural engineering · 2025Article
- Fast Multigroup Gaussian Process Factor Models.Neural computation · 2025Article
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.bioRxiv : the preprint server for biology · 2025Article
- Oscillating neural circuits: Phase, amplitude, and the complex normal distribution.The Canadian journal of statistics = Revue canadienne de statistique · 2023Article
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
- Disentangling the flow of signals between populations of neurons.Nature computational science · 2022Article
- Directed Spectral Measures Improve Latent Network Models Of Neural Populations.Advances in neural information processing systems · 2021Article
- Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings.Advances in neural information processing systems · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent variables). Our interest is in the cross-correlations among the latent variables which, in neural recordings, may capture the flow of information from one brain region to another. Simulations show that LDFA-H outperforms existing methods in the sense that it captures target factors even when within-region correlation due to noise dominates cross-region correlation. We applied our method to local field potential (LFP) recordings from 192 electrodes in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task. The results capture time-varying lead-lag dependencies between PFC and V4, and display the associated spatial distribution of the signals.
Identifiers
36605231PMC9812282What OpenQuestion holds
Registered trials
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.