Rainer Goebel's core research interest addresses the question which neuronal representations are created and stored in the brain and how they are used to enable specific perceptual and cognitive functions such as mental imagery. These questions are investigated by integrating neuroimaging with deep neural network modeling and the development of advanced analysis tools. Progress in understanding brain mechanisms are translateed to (clinical) software applications to benefit society, such as fMRI neurofeedback and hemodynamic brain computer interfaces (BCIs).
Goebel's research has been disseminated in more than 300 peer-reviewed publications (h-index = 99; citations > 32.000, Google Scholar).
Major innovative contributions to science
Goebel’s PhD research resulted in an influential publication on the binding problem (Goebel, 1990, In Touretzky, Elman, Sejnowski, Hinton (Eds.), Connectionist Models. Proceedings of the 1990 Summer School) that provided a solution how our brain integrates visual features in different regions of the brain into a coherent perceptual experience. This innovative work received the Heinz Maier Leibnitz Advancement Award in cognitive science sponsored by the German minister of science and education (1993). In the following years, he unified cognitive theories and modelling to build one of the first biologically inspired neural models of vision (Goebel, 1993, NIPS). He contributed to testing predictions of the 'binding-by-synchronous-oscillations' hypothesis with animal electro-physiological recordings (Castelo-Branco et al., 2000, Nature).
His first fMRI studies at the Max Planck Institute for Brain Research (Frankfurt/Main, Germany) on the neural correlates of visual illusions and imagery resulted in highly cited papers (e.g. Goebel et al., 1999, EJN) that were described in several textbooks. Goebel continued his neuroimaging research at Maastricht University (2000) where his work provided new insights in our understanding of the role of the left vs. right parietal cortex in mental imagery (Sack et al., 2005, Science) and in our understanding how auditory cortex represents speech content and speaker information (Formisano et al., 2008, Science) His work on fMRI mental chronometry (Formisano et al., 2002, Neuron) provided new ways to reveal the temporal order of activation over brain areas during cognitive tasks, which later led to the development of Granger causality mapping in fMRI (Goebel et al., 2003, MRI, Roebroeck et al., 2005, NeuroImage). This and other developed methods such as the multivariate searchlight (Kriegeskorte et al., 2006, PNAS) are now used worldwide.
In 2010 Goebel received an ERC Advanced Investigators grant pushing the field towards sub-millimetre fMRI using ultra-high field scanners. The conducted work has already transcended conventional brain imaging in going from descriptions of average activity levels in brain areas to a new level of detail describing coding principles inside brain areas. This ground-breaking work has revealed for the first time the neural basis of conscious human perception at the level of cortical columns and layers (Schneider et al., 2019, PNAS). He also constributed to seminal laminar fMRI studies enabling separation of bottom-up and top-down processing (Muckli et al., 2015, Current Biology; De Martino et al., 2015, PNAS).
In the years 2000-2005 Goebel laid the foundation for fMRI neurofeedback developing the first real-time fMRI brain-computer interface (BCI) software allowing to analyse whole-brain activity during ongoing functional measurements (Goebel 2001, OHBM; Weiskopf et al., 2004, IEEE). He developed the software for the first fMRI letter-speller brain-computer interface (BCI) for patients with severe motor impairments (Sorger et al., 2012, Current Biiology) and extended this work recently to 7 Tesla allowing direct read-out of letter shapes from the 'mind’s eye' (Senden et al., 2019, Brain Structure and Function). Over the last 10 years he developed methods and software for clinical fMRI neurofeedback with successful treatment in Depression (Linden et al., 2012, PLoS; Mehler et al., 2018, Neuropsychopharmacology) and other psychiatric and neurological diseases.
Goebel's basic and applied research has obtained generous funding from national and international soruces in the order of €12 million plus €3 million via his company Brain Innovation BV. The list below provides a short overview of obtained grants:
2000: USA Grant, McDonnell-Pew Program in Cognitive Neuroscience
2001: Human Frontier Science Program (HFSP) Grant $ 180.000
2002: NWO (Netherlands Organization for Scientific Research) grant; PhD position
2003: "Alzheimer Forschung Initiative" (project grants for fMRI in AD research)
2004: Breedtestrategie (“Widening Strategy”) of Maastricht University; € 1.000.000
2005: NWO Grant "Open Competition"; PhD position
2006: EU Grant “NeuroDys”; € 250.000
2007: Dutch Grant, “SmartMix” program; € 800.000
2008: EU Marie Curie ITN “CODDE” (co-applicant via Brain Innovation BV); € 250.000
2009: NWO Graduate School programme (co-author); FPN Maastricht University; € 800.000
2009: EU Marie Curie Initial Training Network “C7”, via Brain Innovation BV; € 250.000
2009: EU Marie Curie Initial Training Network: “NeuroPhysics” (co-applicant); € 3.000.000
2010: NWO Middle-size investment; € 640.000
2010: EU Grant “DECODER”; € 480.000
2011: ERC Advanced investigator Grant; € 2.473.481
2012: EU Marie Curie ITN “ABC” (via Brain Innovation BV); € 500.000
2013: NWO-MaGW Medium-sized Investments € 500.000
2013: EU FET Flagship Human Brain Project (ramp-up phase); ca. € 200.000
2013: EU FP7 Health “IMAGEMEND” (co-applicant via Brain Innovation BV); € 800.000
2013: EU FP7 Health “BRAINTRAIN” (co-applicant via Brain Innovation BV); € 1.800.000
2014, 2015, 2019: NWO-MaGW Research Talent; 4 PhD positions; ca. € 800.000
2015: EU Horizon 2020 ITN “NextGenVis” (co-applicant via Brain Innovation BV); € 250.000
2016: EU FET Flagship Human Brain Project (SGA1 phase); ca. € 200.000
2017: STW Grant “NESTOR” (co-applicant: visual prosthesis); € 200.000
2017: ERC Proof-Of-Concept Grant “Mind’s Eye BCI”; € 150.000
2018: EU FET Flagship Human Brain Project (SGA2 phase); ca. € 500.000
2018: “Neurofeedback for Performance” grant from Dutch police (special forces); € 280.000
2019: EU H2020-MSCA-ITN “euSNN” (co-applicant via Brain Innovation BV); € 266.000
2020: EU FET Flagship Human Brain Project (SGA3 phase); ca. € 1.800.000
The following three sections describe the research topics currently studied most intensively.
Cracking laminar and columnar-level functional coding principles
With standard functional brain imaging (i.e. fMRI at 3 Tesla), we can routinely see specialised brain areas in the human brain, including “experts” for colour, visual motion, faces, words, language, planning, memory and emotions. This level of resolution reveals an amazing organisation of the brain that is similar, but not identical, across individuals. We still, however, know little about the representations coded inside specialised brain areas and how complex features emerge from combinations of simpler features when we move from one area to the next. With high-field MRI scanners (7 Tesla and beyond), the achievable functional resolution reaches to the sub-millimetre level (500–1000 microns). This is important since neurons with similar response properties seem to spatially cluster into functional units or cortical columns with a lateral extent of hundreds of microns. Studying the brain at the cortical columnar level seems to be the right level to reveal the principles that the brain uses to code information. I believe that this columnar-level code can indeed be "cracked" by adequately combining clever experimental designs (psychology), sub-millimetre fMRI (neuroimaging), sophisticated data analysis tools (signal analysis) and large-scale neuronal network modelling (computational neuroscience). Goebel believes that a massive attempt to crack the columnar-level code in as many areas as possible will ultimately lead to a deeper understanding how mind emerges from simpler units in the brain.
Large-scale neuronal network modeling of cognitive architectures
Rainer Goebel invented the “Common Brain Space” (CBS) modeling approach allowing for the first time to simulate and predict individual topographic neuroimaging data at different levels of organization reaching from networks to areas to columnar features; the CBS approach has been applied to explain visual illusions, selective visual attention and invariant object recognition and it will be used to model columnar-level feature representations measured with ultra-high field fMRI (see section above).
As the scientific lead of the co-design project (CDP4, phase SGA2) "Visuo-Motor Integration" and work package 3 (phase SGA3) "Adaptive networks for cognitive architectures: from advanced learning to neurorobotics and neuromorphic applications" his team collaborates with modellers and robotics experts in the Human Brain Project (HBP) to build sophisticated embodied large-scale neural architecture of visuo-motor integration relating AI and neuroscience. The developed deep modular neural network solves non-trivial eye movement and grasping tasks controlling virtual and real robotic systems. The resulting embodied platform helps to test theories about brain functions underlying perception and action.
Neurofeedback and brain computer interfaces
In collaboration with Niels Birbaumer and Nikolaus Weiskopf, Goebel co-pioneered fMRI-based moment-to-moment neurofeedback with the development of the "Turbo-BrainVoyager" software package. After advancing real-time fMRI methods further, Goebel currently contributes to applications of fMRI neurofeedback as a novel therapeutic tool, e.g. for patients with Parkinson’s disease and depression. Using fMRI and functional near infrared spectroscopy (fNIRS), Goebel and colleagues use real-time fMRI also to develop communication BCIs for patients with severe motor impairments. Goebel also introduced the first interactive fMRI hyper scanning experiment where measured brain activity was used online to play “Brain Pong”. He currently extents real-time fMRI to the laminar and columnar level to better understand underlying neural mechanisms and to enable content-specific neurofeedback studies and better communication BCIs for clinical applications.
Besides these major research lines, Rainer Goebel's research activities includes:
- High-resolution functional imaging of the visual system at the levels of cortical layers and cortical columns
- Neural correlates of visual attention, mental imagery, perceptual switches and decision making
- Cross-modal integration of visual-auditory and visual-haptic information
- Topographic mapping of visual, auditory and somatotopic features
- Development of new analysis methods for structural and functional MRI data
- Real-time fMRI, BCI, hyperscanning and clinical applications of neurofeedback
- Portable brain-computer interfaces (BCIs) based on functional near infrared spectroscopy (fNIRS)
- Integration of spatially (fMRI) and temporally resolved (EEG/MEG) imaging methods
- Data-driven brain imaging analysis tools to reveal functional and effective connectivity
- Advanced methods for cortex-based statistics and brain normalization
- Reconstruction of white and grey matter connectivity using diffusion-weighted MRI
- Investigating effects of temporary, virtual, lesions using Transcranial Magnetic Stimulation (TMS)
- Applying neurofeedback and brain stimulation techniques to enhance cognitive functions
Schneider, M., Kemper, V. G., Emmerling, T. C., De Martino, F., & Goebel, R. (2019). Columnar clusters in the human motion complex reflect consciously perceived motion axis. Proceedings of the National Academy of Sciences of the United States of America, 116(11), 5096-5101. https://doi.org/10.1073/pnas.1814504116
Senden, M., Emmerling, T., van Hoof, R., Frost, M., & Goebel, R. (2019). Reconstructing imagined letters from early visual cortex reveals tight topographic correspondence between visual mental imagery and perception. Brain Structure & Function, 224(3), 1167-1183. https://doi.org/10.1007/s00429-019-01828-6
Svanera, M., Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L., Goebel, R., & Valente, G. (2019). Transfer learning of deep neural network representations for fMRI decoding. Journal of Neuroscience Methods, 328, . https://doi.org/10.1016/j.jneumeth.2019.108319
Kemper, V. G., De Martino, F., Emmerling, T. C., Yacoub, E., & Goebel, R. (2018). High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4T. Neuroimage, 164, 48-58. https://doi.org/10.1016/j.neuroimage.2017.03.058
Mehler, D. M. A., Sokunbi, M. O., Habes, I., Barawi, K., Subramanian, L., Range, M., Evans, J., Hood, K., Lührs, M., Keedwell, P., Goebel, R., & Linden, D. E. J. (2018). Targeting the affective brain: a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology, 43(13), 2578–2585. https://doi.org/10.1038/s41386-018-0126-5
de Martino, F., Moerel, M., Ugurbil, K., Goebel, R., Yacoub, E., & Formisano, E. (2015). Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Proceedings of the National Academy of Sciences of the United States of America, 112(52), 16036-16041. https://doi.org/10.1073/pnas.1507552112
Muckli, L., de Martino, F., Vizioli, L., Petro, L. S., Smith, F. W., Ugurbil, K., Goebel, R., & Yacoub, E. (2015). Contextual feedback to superficial layers of V1. Current Biology, 25(20), 2690-2695. https://doi.org/10.1016/j.cub.2015.08.057
Sorger, B., Reithler, J., Dahmen, B., & Goebel, R. (2012). A real-time fMRI-based spelling device immediately enabling robust motor-independent communication. Current Biology, 22(14), 1333-1338. https://doi.org/10.1016/j.cub.2012.05.022
Goebel, R. (2012). Brainvoyager - past, present, future. Neuroimage, 62, 748-756. https://doi.org/10.1016/j.neuroimage.2012.01.083
Kriegeskorte, N., Goebel, R. W., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3863-3868. https://doi.org/10.1073/pnas.0600244103
Most recent publications:
Gulban, O. F., Bollmann, S., Huber, R., Wagstyl, K., Goebel, R., Poser, B. A., Kay, K., & Ivanov, D. (2022). Mesoscopic in vivo human T*2 dataset acquired using quantitative MRI at 7 Tesla. Neuroimage, 264, . https://doi.org/10.1016/j.neuroimage.2022.119733
Sanders, Z. B., Fleming, M. K., Smejka, T., Marzolla, M. C., Zich, C., Rieger, S. W., Lührs, M., Goebel, R., Sampaio-Baptista, C., & Johansen-Berg, H. (2022). Self-modulation of motor cortex activity after stroke: a randomized controlled trial. Brain, 145(10), 3391-3404. https://doi.org/10.1093/brain/awac239
Qubad, M., Barnes-Scheufler, C. V., Schaum, M., Raspor, E., Rösler, L., Peters, B., Schiweck, C., Goebel, R., Reif, A., & Bittner, R. A. (2022). Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment. Scientific Reports, 12(1), . https://doi.org/10.1038/s41598-022-17909-2
Tursic, A., Eck, J., Lührs, M., Linden, D., & Goebel, R. (2022). Response to Commentary of Dr. Robert T. Thibault and Dr. Hugo Pedder entitled: "Excess significance and power miscalculations in neurofeedback research". NeuroImage: Clinical, 35, . https://doi.org/10.1016/j.nicl.2022.103088
Ciarlo, A., Russo, A. G., Ponticorvo, S., Di Salle, F., Lührs, M., Goebel, R., & Esposito, F. (2022). Semantic fMRI neurofeedback: A Multi-Subject Study at 3 Tesla. Journal of neural engineering, 19(3), 1-27. . https://doi.org/10.1088/1741-2552/ac6f81
Amunts, K., DeFelipe, J., Pennartz, C., Destexhe, A., Migliore, M., Ryvlin, P., Furber, S., Knoll, A., Bitsch, L., Bjaalie, J. G., Ioannidis, Y., Lippert, T., Sanchez-Vives, M. V., Goebel, R., & Jirsa, V. (2022). Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro, 9(2), [ENEURO.0316-21.2022]. https://doi.org/10.1523/ENEURO.0316-21.2022
Goebel, R., van Hoof, R., Bhat, S., Luhrs, M., & Senden, M. (2022). Reading Imagined Letter Shapes from the Mind's Eye Using Real-time 7 Tesla fMRI. In 10th International Winter Conference On Brain-computer Interface (Bci2022) IEEE. International Winter Conference on Brain-Computer Interface BCI https://doi.org/10.1109/BCI53720.2022.9735031
Bhat, S., Lührs, M., Goebel, R., & Senden, M. (2021). Extremely fast pRF mapping for real-time applications. Neuroimage, 245, . https://doi.org/10.1016/j.neuroimage.2021.118671
Qubad, M., Barnes-Scheufler, C. V., Schaum, M., Raspor, E., Roesler, L., Benjamin, P., Goebel, R., Reif, A., & Bittner, R. (2021). Improved correspondence of fMRI visual field localizer data after macroanatomical alignment. European Neuropsychopharmacology, 53(S1), S332-S333. https://doi.org/10.1016/j.euroneuro.2021.10.427
Huber, L., Finn, E. S., Chai, Y., Goebel, R., Stirnberg, R., Stöcker, T., Marrett, S., Uludag, K., Kim, S. G., Han, S., Bandettini, P. A., & Poser, B. A. (2021). Layer-dependent functional connectivity methods. Progress in Neurobiology, 207, . https://doi.org/10.1016/j.pneurobio.2020.101835
Publications targeted at general public and medical professionals
- Article co-authored with Judith Peters (UM) “BrainVoyager – een veelzijdig, multimodaal neuroimaging softwarepakket voor data-analyse en visualisatie” in MemoRad magazine “Thema: Neuro-fMRI”
- Article co-authored with student team “Brein in Beeld” (NPO Labyrinth publieksprijs 2011): “Een hersenscanner voor iedereen!” in MemoRad magazine “Thema: Neuro-fMRI” of the Radiological Society of the Netherlands (see link above)
- Article “Reis door het brein - in steeds meer detail” in NWO publication Breinproducten - Toepassingen van het hersen- en cognitieonderzoek
- Sorger, B & Goebel, R (2020). “Real-time fMRI for brain-computer interfacing” in: Nick Ramsey and Jose Millan (Eds). “Brain-Computer Interfaces: Volume 168 - Handbook of Clinical Neurology” targeted at medical professionals. Elsevier Science & Technology
- Goebel, R. (2014) “Zooming into the Brain – Cognitive neuroscientist Rainer Goebel hopes to crack our mind’s code” in Siemens journal “Medical Solutions – The Magazine for Healthcare Leadership”
- Goebel, R. & Linden, D. (2014). Neurofeedback with Real-Time Functional MRI. In: C. Mulert & M.E. Shenton (Eds.), MRI in Psychiatry. Springer.
- Guger, G et al (2014). Brain-Computer Interfaces for Assessment and Communication in Disorders of Consciousness. In: Naik, G.R. (Ed.), Emerging Theory and Practice in Neuroprosthetics, IGIGLOBAL.
- Mathiak, K., Goebel, R. & Weiskopf N. (2013). Echtzeit-fMRT. In: Schneider, F. & Fink, G.R. (Eds.) Funktionelle MRT in Psychiatrie und Neurologie, Springer.
- Goebel (2011). Position paper neurotechnology “Modulating brain activity by fMRI Neurofeedback and Transcranial Magnetic Stimulation (TMS)” in “Kenniskamer Human Enhancement” initiated and published by Ministerie van Binnenlandse Zaken en Koninkrijksrelaties, Ministerie van Justitie, Rathenau Instituut
- Sorger, B., Reithler, J. & Goebel, R. (2010). Einsatzmöglichkeiten hämodynamisch basierter Brain-Computer-Interfaces für Kommunikation und Umgebungskontrolle. In K.H. Pantke (Ed.), Mensch und Maschine: Wie Brain-ComputerInterfaces und andere Innovationen gelähmten Menschen kommunizieren helfen, Frankfurt am Main: Mabuse-Verlag.
- Goebel, R. (2005). Chapters 2 (fMRI technical basics) and 3 (fMRI analysis basics) in: Walter H (Ed.) Funktionelle Bildgebung in Psychiatrie und Psychotherapie: Methodische Grundlagen und klinische Anwendungen. Schattauer.
- Articles about tractography and neurofeedback in popular German magazine “Gehirn und Geist”, publisher “Spektrum der Wissenschaft”.
- Interview article over 9.4 Tesla scanner in de Limburger (2011) “Ontrafeling van het menselijk brein”
- Contribution to newspaper article in de Limburger: “De Computer krijgt Verstand”
- Contribution to newspaper articles in De Volkskrant, e.g. “Valkuilen in het brein” on best practices of fMRI statistical analysis, and “Wat maakt Max Verstappen zo bijzonder?” to discuss the extraordinary talents for racing of Max Verstappen (based on Goebel’s co-authored scholarly publications on the brain of Formula 1 racers).