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Publications¶

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Title	First	Last	Journal	Year	More
Self-orthogonalizing attractor neural networks emerging from the free energy principle.	T. Spisak	K. Friston	arXiv	2025	🌐︎
Multivariate BWAS can be replicable with moderate sample sizes	T. Spisak	TD. Wager	Nature	2023	🌐︎ 🎦
Pain-free resting-state functional brain connectivity predicts individual pain sensitivity	T. Spisak	U. Bingel	Nature Communications	2020	🌐︎
Statistical quantification of confounding bias in machine learning models	T. Spisak	sole author	GigaScience	2022	🌐︎
Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data	M. Zunhammer	U. Bingel	Nature Communications	2021	🌐︎
Probabilistic TFCE: a generalised combination of cluster size and voxel intensity to increase statistical power	T. Spisak	TZ. Kincses	NeuroImage	2019	🌐︎
Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach	R. Kotikalapudi	T. Spisak	Pain	2023	🌐︎
On the replicability of diffusion weighted MRI-based brain-behavior models	R. Kotikalapudi	T. Spisak	Pain	2025
Machine learning and artificial intelligence in neuroscience: A primer for researchers	F. Badrulhisham	Jan Vollert	Brain, Behavior, and Immunity	2023
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)	RA. Poldrack	KJ. Gorgolewski	Imaging Neuroscience	2023
An externally validated resting-state brain connectivity signature of pain-related learning	B. Kincses	T. Spisak	Communications Biology	2024	🌐︎
Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease	R. Englert	T. Spisak	BioRxiv preprint, under review in eLife, website	2023	🌐︎

References¶

Spisak, T., & Friston, K. (2025). Self-orthogonalizing attractor neural networks emerging from the free energy principle. arXiv. 10.48550/ARXIV.2505.22749
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
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
Spisak, T., Bingel, U., & Wager, T. (2022). Replicable multivariate BWAS with moderate sample sizes. 10.1101/2022.06.22.497072
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
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
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
Kotikalapudi, R., Kincses, B., Gallitto, G., Englert, R., Hofffschlag, K., Li, J., Büchel, C., Bingel, U., & Spisak, T. (2025). On the replicability of diffusion weighted MRI-based brain-behavior models. Communications Biology, 8(1). 10.1038/s42003-025-09048-x
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
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. 10.1162/imag_a_00103
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
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
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
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
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

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