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Publications

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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

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

🌐︎

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
  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., Kincses, B., Zunhammer, M., Schlitt, F., Asan, L., Schmidt-Wilcke, T., Kincses, Z. T., Bingel, U., & Spisak, T. (2023). Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach. Pain, 164(11), 2516–2527. 10.1097/j.pain.0000000000002958
  8. Kotikalapudi, R., Kincses, B., Gallitto, G., Englert, R., Hoffschlag, 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
  9. 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
  10. 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
  11. 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
  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