Stratification and Augmentation of EEG-Neurofeedback in MDD by Monitoring of Dynamic Brain States via Simultaneous Electroencephalography and Magnetic Resonance (EEG-fMRI)

• Applicant: Professor Dr. Martin Walter
• Subject Area: Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry ; Cognitive, Systems and Behavioural Neurobiology
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 388570319

• Final Report Year: 2024

Final Report Abstract

The overarching aim of this project was to elucidate neurobiological mechanisms of the neurofeedback (NF) training, since it has been applied for several decades, without a thorough understanding of the underlying processes in the brain. By combining electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) information during the NF training, we aimed to obtain important markers that underlie a successful training outcome and to use this information in a simultaneous EEG/fMRI NF to enhance learning performance. Achieving this aim required several technical milestones to be reached. ● First, it was imperative to establish the framework for NF training itself, in a manner that matches common best practices in the NF field. Since EEG is the most commonly used modality, the capability to successfully use EEG NF was our starting point. We have developed specific software to be able to do so. ● Second, it was necessary to administer EEG NF during fMRI, which requires removing MR-specific artifacts from the EEG signals in real-time. ● Third, it was essential to establish correspondence between a feature of the EEG signal, targeted during NF training, and the dynamic brain states derived from the concurrent fMRI. ● Fourth, it was necessary to establish a framework for real-time processing and analysis of the fMRI data. ● Finally, we needed to differentiate between successful and unsuccessful regulation attempts. We have introduced a novel approach to measure individual NF performance using fMRI signals. The results of our work show the potential of being exploited in further investigations. In particular, implementing the concept of the EEG microstates in the EEG NF paradigms, with their parameters as targets for conscious regulation is a promising methodological optimization.

Publications

• 7 Tesla Real-time fMRI using a real-time distortion correction algorithm. In 23rd Annual Meeting of the Organization for Human Brain Mapping. 2017
  van der Meer, J., Hellrung, L., In, M.H., Götting, F., Borchardt, V., Möller, H. & Walter, M.
  
• ICA-based approach for ROI detection in rtfMRI Neurofeedback experiment. In 18th Conference of Junior Neuroscientists of Tübingen. 2017
  Izyurov, I., Krylova, M., Jamalabadi, H., Walter, M. & Shetsova, O.
  
• Self-regulation of dACC in real-time fMRI neurofeedback with simultaneous EEG. In 18th Conference of Junior Neuroscientists of Tübingen. 2017
  Shetsova, O., Izyurov, I., Jamalabadi, H., Krylova, M., Alizadeh, S. & Veit, R.
  
• Motion and physiological noise effects on amygdala real-time fMRI neurofeedback learning. Cold Spring Harbor Laboratory.
  Hellrung, Lydia; Borchardt, Viola; Götting, Florian N.; Stadler, Jörg; Tempelmann, Claus; Tobler, Philippe N.; Walter, Martin & van, der Meer Johan N.
  
• Understanding the electro-metabolic dynamics of brain spontaneous activity. In 11th FENS Forum of Neuroscience. 2018
  Krylova, M., Jamalabadi, H., Shevtsova, A., Alizadeh, S. & Walter, M.
  
• Algorithm for Automatic Real- Time Electrooculographic Artifact Correction. Neurofeedback, 2019, Paris
  Sinnigen, N., Izyurov, I., Krylova, M., Jamalabadi, H., Alizadeh, S. & Walter, M.
  
• Implementing dynamic time warping algorithm for estimation training performance in fMRI neurofeedback. In 20th Conference of Junior Neuroscientists. 2019
  Izyurov, I., Krylova, M., Alizadeh, S., Jamalabadi, H., Li, M. & Walter, M.
  
• Real-Time, python-based and amplifier-agnostic EEG-fMRI artifact correction. In 25th Annual Meeting of the Organization for Human Brain Mapping. 2019
  van der Meer, J.N., Stevenson, N., Matysiak, A., Hellrung, L. & Breakspear, M.
  
• Self-Regulation of dorsal Anterior Cingulate Cortex Activity in fMRI Neurofeedback with Concurrent Passive EEG. Neurofeedback, 2019, Paris
  Izyurov, I., Krylova, M., Jamalabadi, H., Alizadeh, S., van der Meer, J. & Walter, M.
  
• Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks. Human Brain Mapping, 41(9), 2334-2346.
  Sikka, Apoorva; Jamalabadi, Hamidreza; Krylova, Marina; Alizadeh, Sarah; van der Meer, Johan N.; Danyeli, Lena; Deliano, Matthias; Vicheva, Petya; Hahn, Tim; Koenig, Thomas; Bathula, Deepti R. & Walter, Martin
  
• Evidence for modulation of EEG microstate sequence by vigilance level. NeuroImage, 224, 117393.
  Krylova, Marina; Alizadeh, Sarah; Izyurov, Igor; Teckentrup, Vanessa; Chang, Catie; van der Meer, Johan; Erb, Michael; Kroemer, Nils; Koenig, Thomas; Walter, Martin & Jamalabadi, Hamidreza
  
• Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks. Scientific Reports, 11(1).
  Krylova, Marina; Skouras, Stavros; Razi, Adeel; Nicholson, Andrew A.; Karner, Alexander; Steyrl, David; Boukrina, Olga; Rees, Geraint; Scharnowski, Frank & Koush, Yury
  
• The missing role of gray matter in studying brain controllability. Network Neuroscience, 5(1), 198-210.
  Jamalabadi, Hamidreza; Zuberer, Agnieszka; Kumar, Vinod Jangir; Li, Meng; Alizadeh, Sarah; Amani, Ali Moradi; Gaser, Christian; Esterman, Michael & Walter, Martin