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TitleFirstLastJournalYearMore
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., 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
  2. 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
  3. Spisak, T., Bingel, U., & Wager, T. (2022). Replicable multivariate BWAS with moderate sample sizes. 10.1101/2022.06.22.497072
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. Kotikalapudi, R., Kincses, B., Gallitto, G., Englert, R., Hoffschlag, K., Li, J., Bingel, U., & Spisak, T. (2024). On the replicability of diffusion weighted MRI-based brain-behavior models. 10.1101/2024.07.08.602202
  12. Kincses, B., Forkmann, K., Schlitt, F., Pawlik, R., Schmidt, K., Timmann, D., Elsenbruch, S., Wiech, K., Bingel, U., & Spisak, T. (2023). RCPL preprint: An externally validated resting-state brain connectivity signature of pain-related learning. 10.31219/osf.io/utkbv
  13. 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. (2023). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). 10.48550/ARXIV.2309.05768
  14. Steiner, K. M., Timmann, D., Bingel, U., Kunkel, A., Spisak, T., Schedlowski, M., Benson, S., Engler, H., Scherbaum, N., & Koelkebeck, K. (2023). Study protocol: effects of treatment expectation toward repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder—a randomized controlled clinical trial. Trials, 24(1). 10.1186/s13063-023-07579-4
  15. Labrenz, F., Spisák, T., Ernst, T. M., Gomes, C. A., Quick, H. H., Axmacher, N., Elsenbruch, S., & Timmann, D. (2022). Temporal dynamics of fMRI signal changes during conditioned interoceptive pain-related fear and safety acquisition and extinction. Behavioural Brain Research, 427, 113868. 10.1016/j.bbr.2022.113868