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Predictive models

We develop MRI-based brain signatures of behaviour and clinical traits — from functional connectivity and task activity to morphology — often with external validation. Alongside the signatures themselves, we build tools that improve statistical power, quantify confounding, and make discovery–validation designs more credible.

Signatures & applications

ResourceOne-line summary
RPN-signatureResting-state Pain susceptibility Network: pain-free FC predicts individual pain sensitivity (Nat Commun, 2020).
RCPL signatureExternally validated resting-state connectivity signature of pain-related learning (Commun Biol, 2024).
CTP / brain morphologyMulticenter ML model: brain morphology predicts individual pain sensitivity (Pain, 2023).
PARESTask-fMRI signature of bidirectional pain change (exacerbation and relief), generalising to chronic back pain.

Methods & tooling

ResourceOne-line summary
AdaptiveSplitRegistered models + adaptive sample splitting for credible external validation (GigaScience, 2025).
mlconfoundQuantify confounding bias in ML predictions (GigaScience, 2022).
pTFCEProbabilistic threshold-free cluster enhancement for more powerful neuroimaging inference (NeuroImage, 2019).

Related work also appears under Brain Models, Software, and Publications.

References
  1. 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