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TitleFirstLastJournalYearMore
Self-orthogonalizing attractor neural networks emerging from the free energy principle.T. SpisakK. FristonarXiv2025🌐︎
Multivariate BWAS can be replicable with moderate sample sizesT. SpisakTD. WagerNature2023🌐︎ 🎦
Pain-free resting-state functional brain connectivity predicts individual pain sensitivityT. SpisakU. BingelNature Communications2020🌐︎
Statistical quantification of confounding bias in machine learning modelsT. Spisaksole authorGigaScience2022🌐︎
Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI dataM. ZunhammerU. BingelNature Communications2021🌐︎
Probabilistic TFCE: a generalised combination of cluster size and voxel intensity to increase statistical powerT. SpisakTZ. KincsesNeuroImage2019🌐︎
Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approachR. KotikalapudiT. SpisakPain2023🌐︎
Machine learning and artificial intelligence in neuroscience: A primer for researchersF. BadrulhishamJan VollertBrain, Behavior, and Immunity2023
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)RA. PoldrackKJ. GorgolewskiImaging Neuroscience2023
An externally validated resting-state brain connectivity signature of pain-related learningB. KincsesT. SpisakCommunications Biology2024🌐︎
Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and DiseaseR. EnglertT. SpisakBioRxiv preprint, under review in eLife, website2023🌐︎
References
  1. Spisak, T., & Friston, K. (2025). Self-orthogonalizing attractor neural networks emerging from the free energy principle. arXiv. 10.48550/ARXIV.2505.22749
  2. Spisak, T., Bingel, U., & Wager, T. D. (2023). Multivariate BWAS can be replicable with moderate sample sizes. Nature, 615(7951), E4–E7. 10.1038/s41586-023-05745-x
  3. Spisak, T., Kincses, B., Schlitt, F., Zunhammer, M., Schmidt-Wilcke, T., Kincses, Z. T., & Bingel, U. (2020). Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nature Communications, 11(1). 10.1038/s41467-019-13785-z
  4. Spisak, T. (2022). Statistical quantification of confounding bias in machine learning models. GigaScience, 11. 10.1093/gigascience/giac082
  5. Zunhammer, M., Spisák, T., Wager, T. D., Bingel, U., Atlas, L., Benedetti, F., Büchel, C., Choi, J. C., Colloca, L., Duzzi, D., Eippert, F., Ellingsen, D.-M., Elsenbruch, S., Geuter, S., Kaptchuk, T. J., Kessner, S. S., Kirsch, I., Kong, J., Lamm, C., … Zeidan, F. (2021). Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data. Nature Communications, 12(1). 10.1038/s41467-021-21179-3
  6. Spisák, T., Román, V., Papp, E., Kedves, R., Sághy, K., Csölle, C. K., Varga, A., Gajári, D., Nyitrai, G., Spisák, Z., Kincses, Z. T., Lévay, G., Lendvai, B., & Czurkó, A. (2019). Purkinje cell number-correlated cerebrocerebellar circuit anomaly in the valproate model of autism. Scientific Reports, 9(1). 10.1038/s41598-019-45667-1
  7. Kotikalapudi, R., Moser, D. A., Dricu, M., Spisak, T., & Aue, T. (2023). Predictive modeling of optimism bias using gray matter cortical thickness. Scientific Reports, 13(1). 10.1038/s41598-022-26550-y
  8. Badrulhisham, F., Pogatzki-Zahn, E., Segelcke, D., Spisak, T., & Vollert, J. (2024). Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain, Behavior, and Immunity, 115, 470–479. 10.1016/j.bbi.2023.11.005
  9. Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Benar, C. G., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., … Gorgolewski, K. J. (2024). The past, present, and future of the brain imaging data structure (BIDS). Imaging Neuroscience, 2, 1–19. 10.1162/imag_a_00103
  10. Kincses, B., Forkmann, K., Schlitt, F., Jan Pawlik, R., Schmidt, K., Timmann, D., Elsenbruch, S., Wiech, K., Bingel, U., & Spisak, T. (2024). An externally validated resting-state brain connectivity signature of pain-related learning. Communications Biology, 7(1). 10.1038/s42003-024-06574-y
  11. Englert, R., Schedlowski, M., Engler, H., Rief, W., Büchel, C., Bingel, U., & Spisak, T. (2023). ALIIAS: Anonymization/Pseudonymization with LimeSurvey integration and II-factor Authentication for Scientific research. SoftwareX, 24, 101522. 10.1016/j.softx.2023.101522
  12. Caliskan, E. B., Schmidt, K., Hellmann, A., Spisák, T., Bingel, U., & Kleine-Borgmann, J. (2025). From catastrophizing to catalyzing: does pain catastrophizing modulate the beneficial impact of open-label placebos for chronic low back pain? A secondary analysis. Frontiers in Psychology, 16. 10.3389/fpsyg.2025.1522634
  13. Gomes, C. A., Bach, D. R., Razi, A., Batsikadze, G., Elsenbruch, S., Engler, H., Ernst, T. M., Fellner, M. C., Fraenz, C., Genç, E., Klass, A., Labrenz, F., Lissek, S., Merz, C. J., Metzen, D., Nostadt, A., Pawlik, R. J., Schneider, J. E., Tegenthoff, M., … Axmacher, N. (2025). Predicting individual differences of fear and cognitive learning and extinction. 10.1101/2025.05.04.651880
  14. Li, J., Schmidt, K., Busch, L., Forkmann, K., Spisak, T., Kaur, J., Schlitt-Nguyen, F., Wiech, K., & Bingel, U. (2025). Common and distinct neural mechanisms of aversive and appetitive pain-related learning. 10.1101/2025.04.02.646781
  15. Schneider Penate, J. E., Gomes, C. A., Spisak, T., Genc, E., Merz, C. J., Wolf, O. T., Quick, H. H., Elsenbruch, S., Engler, H., Fraenz, C., Metzen, D., Ernst, T. M., Thieme, A., Batsikadze, G., Hagedorn, B., Timmann, D., Güntürkün, O., Axmacher, N., & Kumsta, R. (2025). Polygenic prediction of fear learning is mediated by brain connectivity. 10.1101/2025.03.12.25323754