ArticlePLoS computational biology2020
Modeling time-varying brain networks with a self-tuning optimized Kalman filter.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 51 citations in OpenAlex.
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- Functional Near-Infrared Spectroscopy Reveals Functional Rewiring between Macaque Motor Areas Following Postinfarction Recovery of Manual Dexterity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
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- Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically Pumped Magnetometer Magnetoencephalography.Bioengineering (Basel, Switzerland) · 2024Article
- A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks.Sensors (Basel, Switzerland) · 2024Article
- Investigating the effect of template head models on Event-Related Potential source localization: a simulation and real-data study.Frontiers in neuroscience · 2024Article
- Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation.Sensors (Basel, Switzerland) · 2023Article
- Editorial: Chasing brain dynamics at their speed: what can time-varying functional connectivity tell us about brain function?Frontiers in neuroscience · 2023Article
- High-density EEG sleep correlates of cognitive and affective impairment at 12-month follow-up after COVID-19.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2022Article
- Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors.Network neuroscience (Cambridge, Mass.) · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- Four concurrent feedforward and feedback networks with different roles in the visual cortical hierarchy.PLoS biology · 2022Article
- Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes.Scientific data · 2022Article
- Reduced Effective Connectivity in the Motor Cortex in Parkinson's Disease.Brain sciences · 2021Article
- Experimental Protocol to Assess Neuromuscular Plasticity Induced by an Exoskeleton Training Session.Methods and protocols · 2021Article
- Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.
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