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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🌐︎
ALIIAS: Anonymization/Pseudonymization with LimeSurvey integration and II-factor Authentication for Scientific researchR. EnglertT. SpisakSoftwareX2023🌐︎
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. GorgolewskiArXiv2023
An externally validated resting-state brain connectivity signature of pain-related learningB. KincsesT. SpisakOSF preprint2023🌐︎
Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and DiseaseR. EnglertT. SpisakBioRxiv preprint, 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., 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