ArticleProceedings of the National Academy of Sciences of the United States of America2022
Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Structural Eigenmodes of the Brain to Improve the Source Localization of EEG: Application to Epileptiform Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Sleep magnetoencephalography enhances detection and source imaging of seizures and fast oscillations in focal cortical dysplasia.Epilepsia · 2026Article
- A geometry aware framework enhances noninvasive mapping of whole human brain dynamics.Nature biomedical engineering · 2026Article
- Transcranial focused ultrasound induces source localizable cortical activation in resting state humans when applied concurrently with transcranial electric stimulation.Nature communications · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Artificial intelligence-enabled "inherited noninvasive intracellular recording" for prolonged monitoring of cardiac action potentials.Science advances · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- [Localizing target for transcranial electrical stimulation in epilepsy patients combining scalp electroencephalogram and neural computational model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Mapping epileptogenic brain using a unified spatial-temporal-spectral source imaging framework.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy.Epilepsia · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Physics-informed neural networks for physiological signal processing and modeling: a narrative review.Physiological measurement · 2025Review
- Deep learning-based EEG source imaging is robust under varying electrode configurations.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Interictal discharges spread along local recurrent networks between tubers and surrounding cortex.The Journal of physiology · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model.Network neuroscience (Cambridge, Mass.) · 2025Article
- Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.Cognitive neurodynamics · 2024Article
- Electroencephalographic source imaging of spikes with concurrent high-frequency oscillations is concordant with the clinical ground truth.Epilepsia · 2024Article
- M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model.APL bioengineering · 2024Article
- Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Abstract
Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF's capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
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