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

Preprints

  • Mitrovska, A., & Haufe, S. (2025). Effect of MRI defacing on EEG forward and inverse modeling. Neuroscience. https://doi.org/10.1101/2025.03.11.642561
  • Negi, A., Haufe, S., Gramfort, A., & Hashemi, A. (2025). How forgiving are M/EEG inverse solutions to noise level misspecification? An excursion into the BSI-zoo. Neuroscience. https://doi.org/10.1101/2025.03.12.642831
  • Jurhar, T., Pellegrini, F., Toro, A. I. N. del, Stephani, T., Nolte, G., & Haufe, S. (2025). Estimating time delays between signals under mixed noise influence with novel cross- and bispectral methods (arXiv:2502.17474). arXiv. https://doi.org/10.48550/arXiv.2502.17474
  • Morik, M., Hashemi, A., Müller, K.-R., Haufe, S., & Nakajima, S. (2024). Enhancing brain source reconstruction through physics-informed 3D neural networks (arXiv:2411.00143). arXiv. https://doi.org/10.48550/arXiv.2411.00143

Peer reviewed

  • Hofman, K., Chen, J. Z., Sil, T., Pellegrini, F., Haufe, S., Pozzi, N., Palmisano, C., Isaias, I., Koprich, J. B., Brotchie, J. M., Kühn, A. A., Matthies, C., Reich, M. M., Muthuraman, M., Volkmann, J., & Ip, C. W. (2025). Low β predicts motor output and cell degeneration in the A53T parkinson’s disease rat model. Brain, awaf063. https://doi.org/10.1093/brain/awaf063
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hashemi, A., Cai, C., Gao, Y., Ghosh, S., Müller, K.-R., Nagarajan, S. S., & Haufe, S. (2024a). 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
  • 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
  • Agarwal, N., Popov, T., Tröndle, M., Langer, N., & Haufe, S. (2024). Understanding brain function development and heterogeneity using normative modeling in a pediatric rs-EEG dataset. Neural Traces 2024, 95.
  • Binns, T. S., Köhler, R. M., Vanhoecke, J., Chikermane, M., Gerster, M., Merk, T., & Neumann, W. J. (2024). Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of parkinson’s disease. Nature Communications.
  • Clark, B., Karastoyanov, S., Wilming, R., & Haufe, S. (2024). The effect of whitening on explanation performance. Interpretable AI: Past, Present and Future. https://openreview.net/forum?id=0EUYnGIp4F
  • Hashemi, A., Cai, C., Gao, Y., Ghosh, S., Müller, K. R., Nagarajan, S. S., & Haufe, S. (2024b). Joint hierarchical bayesian learning of sources and noise for M/EEG brain source imaging. Neural Traces 2024, 24.
  • Haufe, S., Wilming, R., Clark, B., Zhumagambetov, R., Panknin, D., & Boubekki, A. (2024). Position: XAI needs formal notions of explanation correctness. Interpretable AI: Past, Present and Future. https://openreview.net/forum?id=g0I1h8JmtE
  • Kapralov, N., Studenova, A., Nolte, G., Haufe, S., Villringer, A., & Nikulin, V. V. (2024). Optimization of M/EEG spatial filters for extraction of ROI time series based on cross-talk function. Neural Traces 2024, 46.
  • Morik, M., Hashemi, A., Müller, K. R., Haufe, S., & Nakajima, S. (2024). Enhancing brain source reconstruction through pseudo-inverse initialization and 3D neural networks. Neural Traces 2024, 28.
  • Negi, A., Haufe, S., Gramfort, A., & Hashemi, A. (2024). Investigating the effect of noise-level misspecification in M/EEG inverse solutions. Neural Traces 2024, 64.
  • Nguyen, T. D., Pellegrini, F., Liu, Z., Delorme, A., & Haufe, S. (2024). ROIconnect: an open-source EEGLAB plugin for linear and non-linear functional connectivity analysis between brain source regions of interest. Neural Traces 2024, 54.
  • Shamova, V., Arafat, S., & Haufe, S. (2024). Evaluating the reconstruction of complex network measures of functional brain connectivity from M/EEG data. Neural Traces 2024, 12.
  • Sison, M., Götz, K., Jurhar, T., Holtkamp, M., Vorderwülbecke, B., & Haufe, S. (2024). Integrating subdural EEG-derived spectral and functional connectivity features of focal seizures to improve delineation of the epileptogenic zone. Neural Traces 2024, 113.
  • 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. https://doi.org/10.1101/2023.10.09.23296653
  • Pellegrini, F., Delorme, A., Nikulin, V., & Haufe, S. (2023). Identifying good practices for detecting inter-regional linear functional connectivity from EEG. Neuroimage, 277, 120218. https://doi.org/10.1016/j.neuroimage.2023.120218
  • Wilming, R., Kieslich, L., Clark, B., & Haufe, S. (2023a). Theoretical behavior of XAI methods in the presence of suppressor variables. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 37091–37107). PMLR. https://proceedings.mlr.press/v202/wilming23a.html
  • 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
  • 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
  • Cesnaite, E., Steinfath, P., Jamshidi Idaji, M., Stephani, T., Kumral, D., Haufe, S., Sander, C., Hensch, T., Hegerl, U., Riedel-Heller, S., Röhr, S., Schroeter, M. L., Witte, A., Villringer, A., & Nikulin, V. V. (2023). Alterations in rhythmic and non‐rhythmic resting‐state EEG activity and their link to cognition in older age. Neuroimage, 268, 119810. https://doi.org/10.1016/j.neuroimage.2022.119810
  • 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
  • Ghosh, S., Cai, C., Gao, Y., Hashemi, A., Haufe, S., Sekihara, K., Raj, A., & Nagarajan, S. S. (2023). Bayesian inference for brain source imaging with joint estimation of structured low-rank noise. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI53787.2023.10230330
  • Wilming, R., Kieslich, L., Clark, B., & Haufe, S. (2023b). XAI methods in the presence of suppressor variables: a theoretical consideration. KI 2023: Advances in Artificial Intelligence, 265.
  • 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
  • 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
  • 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
  • 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
  • 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://proceedings.neurips.cc/paper_files/paper/2021/hash/d03a857a23b5285736c4d55e0bb067c8-Abstract.html
  • 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
  • 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
  • Vidaurre, C., Haufe, S., Jorajuría, T., Müller, K.-R., & Nikulin, V. V. (2020). Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance. Frontiers in Neuroscience, 14, 575081. https://doi.org/10.3389/fnins.2020.575081
  • 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
  • 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
  • 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. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 32). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2019/file/6aed000af86a084f9cb0264161e29dd3-Paper.pdf