TrueBrainConnect (ERC)

TrueBrainConnect: Advancing the non-invasive assessment of brain communication in neurological disease

Advancing the non-invasive assessment of brain communication in neurological disease

Funding ID 758985 | ERC Starting Grant | 1st January 2019 to 31st Dec 2023

Pathological communication between different brain regions has been implicated in various neurological disorders. However, the computational tools for assessing such communication from neuroimaging data are not sufficiently developed. The goal of TrueBrainConnect is to establish brain connectivity analysis using non-invasive electrophysiology as a practical and reliable neuroscience tool. To achieve this, we will develop novel signal processing and machine learning techniques that address shortcomings in state-of-the-art reconstruction and localization of neural activity from sensor data, the estimation of genuine neural interactions, the prediction of external (eg, clinical ) variables from estimated neural interactions, and the interpretation of the resulting models. These techniques will be thoroughly validated and then made publicly available. We will use the TrueBrainConnect methodology to characterize the neural bases underlying dementia and Parkinson’s disease (PD), two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of ‘freezing’ episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time, to reliably estimate and anatomically localize important types of interactions between different brain structures in humans within known confidence bounds. two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of ‘freezing’ episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time, to reliably estimate and anatomically localize important types of interactions between different brain structures in humans within known confidence bounds. two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of ‘freezing’ episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time.

Find the project on CORDIS.

Project staff members

NamePosition in Lab
Prof. Dr. Stefan HaufeResearch Group Leader
Cecilia Lopez SteinmetzPostdoctoral Researcher
Franziska PellegriniPhD Candidate
Veronika ShamovaPhD Candidate
Ali HashemiPhD Candidate
Rick WilmingPhD Candidate
Nikita AgarwalPhD Candidate
Margarita SisonStudent Assistant
Tien Dung NguyenStudent Assistant
Mohammad OrabeStudent Assistant
Leo KieslichStudent Assistant
Subhi ArafatStudent Assistant

Publications and Preprints

  • Anzolin, A., Presti, P., Van De Steen, F., Astolfi, L., Haufe, S., & Marinazzo, D. (2019). Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources. Brain Topography, 32(4), 655–674. https://doi.org/10.1007/s10548-019-00705-z
  • Astalosch, M., Mousavi, M., Ribeiro, L. M., Schneider, G.-H., Stuke, H., Haufe, S., Borchers, F., Spies, C., Von Hofen-Hohloch, J., Al-Fatly, B., Ebersbach, G., Franke, C., Kühn, A. A., & Kübler-Weller, D. (2024). Risk factors for postoperative delirium severity after deep brain stimulation surgery in parkinson’s disease. Journal of Parkinson’s Disease, 14(6), 1175–1192. https://doi.org/10.3233/JPD-230276
  • Cai, C., Hashemi, A., Diwakar, M., Haufe, S., Sekihara, K., & Nagarajan, S. S. (2021). Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm. Neuroimage, 225, 117411. https://doi.org/10.1016/j.neuroimage.2020.117411
  • Cai, C., Hinkley, L., Gao, Y., Hashemi, A., Haufe, S., Sekihara, K., & Nagarajan, S. S. (2022). Empirical bayesian localization of event-related time-frequency neural activity dynamics. Neuroimage, 258, 119369. https://doi.org/10.1016/j.neuroimage.2022.119369
  • Clark, B., Wilming, R., Dox, A., Eschenbach, P., Hached, S., Wodke, D. J., Zewdie, M. T., Bruila, U., Oliveira, M., Schulz, H., Cornils, L. M., Panknin, D., Boubekki, A., & Haufe, S. (2025). EXACT: Towards a platform for empirically benchmarking machine learning model explanation methods. Measurement: Sensors, 101794. https://doi.org/10.1016/j.measen.2024.101794
  • Clark, B., Wilming, R., & Haufe, S. (2024). XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance. Machine Learning, 113(9), 6871–6910. https://doi.org/10.1007/s10994-024-06574-3
  • Daniel Boie, S., Meyer-Eschenbach, F., Schreiber, F., Giesa, N., Barrenetxea, J., Guinemer, C., Haufe, S., Krämer, M., Brunecker, P., Prasser, F., & Balzer, F. (2024). A scalable approach for critical care data extraction and analysis in an academic medical center. International Journal of Medical Informatics, 192, 105611. https://doi.org/10.1016/j.ijmedinf.2024.105611
  • Del Vecchio Del Vecchio, J., Hanafi, I., Pozzi, N. G., Capetian, P., Isaias, I. U., Haufe, S., & Palmisano, C. (2023). Pallidal recordings in chronically implanted dystonic patients: Mitigation of tremor-related artifacts. Bioengineering, 10(4), 476. https://doi.org/10.3390/bioengineering10040476
  • Giesa, N., Haufe, S., Menk, M., Weiß, B., Spies, C. D., Piper, S. K., Balzer, F., & Boie, S. D. (2024). Predicting postoperative delirium assessed by the nursing screening delirium scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach. PLOS Digital Health, 3(8), e0000414. https://doi.org/10.1371/journal.pdig.0000414
  • Giesa, N., Sekutowicz, M., Rubarth, K., Spies, C. D., Balzer, F., Haufe, S., & Boie, S. D. (2024). Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium. Communications Medicine, 4(1), 251. https://doi.org/10.1038/s43856-024-00681-x
  • Hashemi, A., Cai, C., Gao, Y., Ghosh, S., Müller, K.-R., Nagarajan, S. S., & Haufe, S. (2024). Joint learning of full-structure noise in hierarchical bayesian regression models. IEEE Transactions on Medical Imaging, 43(2), 610–624. https://doi.org/10.1109/TMI.2022.3224085
  • Hashemi, A., Cai, C., Kutyniok, G., Müller, K.-R., Nagarajan, S. S., & Haufe, S. (2021). Unification of sparse bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework. Neuroimage, 239, 118309. https://doi.org/10.1016/j.neuroimage.2021.118309
  • Hashemi, A., Gao, Y., Cai, C., Ghosh, S., Muller, K. R., Nagarajan, S., & Haufe, S. (2021, November 9). Efficient hierarchical bayesian inference for spatio-temporal regression models in neuroimaging. Advances in Neural Information Processing Systems. https://openreview.net/forum?id=kO3l8oz8EVP
  • Haufe, S., Isaias, I. U., Pellegrini, F., & Palmisano, C. (2023). Gait event prediction using surface electromyography in parkinsonian patients. Bioengineering, 10(2), 212. https://doi.org/10.3390/bioengineering10020212
  • Köhler, R. M., Binns, T. S., Merk, T., Zhu, G., Yin, Z., Zhao, B., Chikermane, M., Vanhoecke, J., Busch, J. L., Habets, J. G. V., Faust, K., Schneider, G.-H., Cavallo, A., Haufe, S., Zhang, J., Kühn, A. A., Haynes, J.-D., & Neumann, W.-J. (2024). Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in parkinson’s disease. Brain, 147(10), 3358–3369. https://doi.org/10.1093/brain/awae219
  • Kumral, D., Cesnaite, E., Beyer, F., Hofmann, S. M., Hensch, T., Sander, C., Hegerl, U., Haufe, S., Villringer, A., Witte, A. V., & Nikulin, V. V. (2022). Relationship between regional white matter hyperintensities and alpha oscillations in older adults. Neurobiology of Aging, 112, 1–11. https://doi.org/10.1016/j.neurobiolaging.2021.10.006
  • Langer, N., Plomecka, M. B., Tröndle, M., Negi, A., Popov, T., Milham, M., & Haufe, S. (2022). A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample. Neuroimage, 258, 119348. https://doi.org/10.1016/j.neuroimage.2022.119348
  • López Steinmetz, L. C., Sison, M., Zhumagambetov, R., Godoy, J. C., & Haufe, S. (2024). Machine learning models predict the emergence of depression in argentinean college students during periods of COVID-19 quarantine. Frontiers in Psychiatry, 15, 1376784. https://doi.org/10.3389/fpsyt.2024.1376784
  • Oliveira, M., Wilming, R., Clark, B., Budding, C., Eitel, F., Ritter, K., & Haufe, S. (2024). Benchmarking the influence of pre-training on explanation performance in MR image classification. Frontiers in Artificial Intelligence, 7, 1330919. https://doi.org/10.3389/frai.2024.1330919
  • Pellegrini, F., Delorme, A., Nikulin, V., & Haufe, S. (2022). Identifying good practices for detecting inter-regional linear functional connectivity from EEG. Neuroscience. https://doi.org/10.1016/j.neuroimage.2023.120218
  • Pellegrini, F., Pozzi, N. G., Palmisano, C., Marotta, G., Buck, A., Haufe, S., & Isaias, I. U. (2023). Cortical networks of parkinsonian gait: A metabolic and functional connectivity study. Neurology. https://doi.org/10.1101/2023.10.09.23296653
  • Popov, T., Tröndle, M., Baranczuk‐Turska, Z., Pfeiffer, C., Haufe, S., & Langer, N. (2023). Test–retest reliability of resting‐state EEG in young and older adults. Psychophysiology, 60(7), e14268. https://doi.org/10.1111/psyp.14268
  • Rabus, H., Agarwal, N., Boubekki, A., Bozkurt Varolgünes, Y., Clark, B., Elster, C., Faller, J., Hegemann, N., Haufe, S., Heidenreich, S., Kapsch, R.-P., Kofler, A., Kolbitsch, C., Märtens, A., Martin, J., O’Connor, G., Panknin, D., Pfeffer, O., Pojtinger, S., … Zimmermann, F. (2025). Metrology for artificial intelligence in medicine. Measurement: Sensors, 101789. https://doi.org/10.1016/j.measen.2024.101789
  • Schulz, M.-A., Bzdok, D., Haufe, S., Haynes, J.-D., & Ritter, K. (2024). Performance reserves in brain-imaging-based phenotype prediction. Cell Reports, 43(1), 113597. https://doi.org/10.1016/j.celrep.2023.113597
  • Stephani, T., Waterstraat, G., Haufe, S., Curio, G., Villringer, A., & Nikulin, V. V. (2020). Temporal signatures of criticality in human cortical excitability as probed by early somatosensory responses. Journal of Neuroscience, 40(34), 6572–6583. https://doi.org/10.1523/JNEUROSCI.0241-20.2020
  • Tu, T., Paisley, J., Haufe, S., & Sajda, P. (2019). A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. https://openreview.net/forum?id=SkeWFVreLr
  • Vidaurre, C., Haufe, S., Jorajuría, T., Müller, K.-R., & Nikulin, V. V. (2020). Sensorimotor functional connectivity: A neurophysiological factor related to BCI performance. Technische Universität Berlin. https://doi.org/10.3389/fnins.2020.575081
  • Wilming, R., Budding, C., Müller, K.-R., & Haufe, S. (2022). Scrutinizing XAI using linear ground-truth data with suppressor variables. Machine Learning, 111(5), 1903–1923. https://doi.org/10.1007/s10994-022-06167-y
  • Wilming, R., Kieslich, L., Clark, B., & Haufe, S. (2023). Theoretical behavior of XAI methods in the presence of suppressor variables (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2306.01464