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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	Neurocomputing	2026	🌐︎
Multivariate BWAS can be replicable with moderate sample sizes	T. Spisak	TD. Wager	Nature	2023	🌐︎ 🎦
Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease	R. Englert	T. Spisak	eLife, website	2026	🌐︎
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	Communications Biology	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	🌐︎

References¶

Spisak, T., & Friston, K. (2026). Self-orthogonalizing attractor neural networks emerging from the free energy principle. Neurocomputing, 682, 133472. 10.1016/j.neucom.2026.133472
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
Englert, R., Kincses, B., Kotikalapudi, R., Gallitto, G., Li, J., Hoffschlag, K., Woo, C.-W., Wager, T. D., Timmann, D., Bingel, U., & Spisak, T. (2026). Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease. eLife, 13. 10.7554/elife.98725
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. (2022). Statistical quantification of confounding bias in machine learning models. GigaScience, 11. 10.1093/gigascience/giac082
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., Spisák, Z., Zunhammer, M., Bingel, U., Smith, S., Nichols, T., & Kincses, T. (2019). Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power. NeuroImage, 185, 12–26. 10.1016/j.neuroimage.2018.09.078
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
Li, J., Kincses, B., Schmidt, K., Forkmann, K., Busch, L., Kaur, J., Schlitt-Nguyen, F., Wiech, K., Bingel, U., & Spisak, T. (2026). Beyond pain: a brain-based biomarker predicts individual pain relief. 10.64898/2026.08.21.746216
Gallitto, G., Englert, R., Kincses, B., Kotikalapudi, R., Li, J., Hoffschlag, K., Ali, S., Bingel, U., & Spisak, T. (2026). Reinforcement Learning via Brain Feedback for real-time fMRI-based adaptive stimulus generation. 10.64898/2026.08.08.743648
Kincses, B., Pfaffenrot, V., Püchner, K., Spisak, T., Wiech, K., Koopmans, P., & Bingel, U. (2026). Layer-specific cortical processing dissociates sensory and cognitive influences on pain. 10.64898/2026.05.12.724526
Wei, Z., Spisak, T., Timmann, D., Scherrer, G., Bingel, U., & Wager, T. D. (2026). Cerebellar activation in human placebo analgesia: Bridging findings from mice to humans. 10.64898/2026.04.07.717067
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
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. (2026). Predicting individual differences of fear and cognitive learning and extinction. Nature Communications, 17(1). 10.1038/s41467-026-71830-0

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