ProjectLOOP-TMS – Closing the loop on non-invasive brain stimulation with deep neural networks
Basic data
Acronym:
LOOP-TMS
Title:
Closing the loop on non-invasive brain stimulation with deep neural networks
Duration:
01/11/2025 to 30/04/2027
Abstract / short description:
We aim to develop and validate adaptive closed-loop brain stimulation using advanced real-time electroencephalography-transcranial magnetic stimulation (EEG–TMS) technology guided by probabilistic deep artificial neural networks. We plan to explore the innovation potential of this disruptive neurotechnology by collaboration with key opinion leaders and industry.
TMS is performed routinely worldwide in tens of thousands of healthcare institutions for treating a wide variety of brain disorders. However, current TMS protocols are limited by small therapeutic effect sizes and large proportions of non-responders. A significant problem is that one size does not fit all: existing TMS protocols are non-personalized, operate without information of instantaneous brain states, function in an open-loop manner, and lack adaptability.
To address this, we have developed brain state-dependent stimulation in the ongoing ERC Synergy project ConnectToBrain. Real-time EEG–TMS has enabled reliable prediction of high- vs. low-excitability brain states and coupling of TMS to a prespecified state. While this is major progress, brain state-dependent stimulation is still open-loop and non-adaptive.
The key innovation in our current proposal is implementing truly adaptive closed-loop algorithms that continuously monitor TMS effects and optimize stimulation parameters in real-time to maximize beneficial long-term changes in targeted brain networks. Recent advances in machine learning now make this sophisticated approach possible.
This project represents a significant leap beyond ConnectToBrain by creating a user-independent, fully automated, adaptive closed-loop real-time EEG-TMS system ready for further testing in clinical trials. Our technology has breakthrough potential as a paradigm-shifting innovation in non-invasive therapeutic brain stimulation, addressing an urgent societal and medical need for more effective, individualized treatment of brain disorders.
TMS is performed routinely worldwide in tens of thousands of healthcare institutions for treating a wide variety of brain disorders. However, current TMS protocols are limited by small therapeutic effect sizes and large proportions of non-responders. A significant problem is that one size does not fit all: existing TMS protocols are non-personalized, operate without information of instantaneous brain states, function in an open-loop manner, and lack adaptability.
To address this, we have developed brain state-dependent stimulation in the ongoing ERC Synergy project ConnectToBrain. Real-time EEG–TMS has enabled reliable prediction of high- vs. low-excitability brain states and coupling of TMS to a prespecified state. While this is major progress, brain state-dependent stimulation is still open-loop and non-adaptive.
The key innovation in our current proposal is implementing truly adaptive closed-loop algorithms that continuously monitor TMS effects and optimize stimulation parameters in real-time to maximize beneficial long-term changes in targeted brain networks. Recent advances in machine learning now make this sophisticated approach possible.
This project represents a significant leap beyond ConnectToBrain by creating a user-independent, fully automated, adaptive closed-loop real-time EEG-TMS system ready for further testing in clinical trials. Our technology has breakthrough potential as a paradigm-shifting innovation in non-invasive therapeutic brain stimulation, addressing an urgent societal and medical need for more effective, individualized treatment of brain disorders.
Involved staff
Managers
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
Contact persons
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Department of Informatics, Faculty of Science
Bernstein Center for Computational Neuroscience Tübingen (BCCN)
Interfaculty Institutes
Interfaculty Institutes
Cluster of Excellence: Machine Learning: New Perspectives for Science (CML)
Centers or interfaculty scientific institutions
Centers or interfaculty scientific institutions
Tübingen AI Center
Central cross-faculty facilities
Central cross-faculty facilities
Other staff
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
Local organizational units
Department of Neurology with Focus on Neurovascular Diseases
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
Hertie Institute for Clinical Brain Research (HIH)
Non-clinical institutes
Faculty of Medicine
Faculty of Medicine
Funders
Brüssel, Belgium