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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.