Can imaging techniques help to predict the effect of treatment expectations on therapeutic outcome?

This project is a pooled multicentre analysis of all neural imaging data generated by the CRC. The objective is to combine mechanistic data from individual projects with key general information in the form of biomarkers that are predictive of individual expectation effects on treatment outcome. The research strategy of this project focuses on a comprehensive data management plan, which includes the standardisation of data and metadata structure, imaging sequences and experimental protocols, automatic quality assessment and coordination of data transmission.

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Spisak T, Spisak 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. PubMed

Spisak T, Kincses B, Schlitt F, Zunhammer M, Schmidt-Wilcke T, Kincses ZT, Bingel U (2019) Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nature Communications volume 11: 187 (2020) PubMed

Zunhammer M, Bingel U, Wager TD (2018) Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA neurology 75(11):1321-30. PubMed