Large quantities of imaging data enable us to generalise our findings and make predictions. © solvod/stock.adobe.com
Can imaging techniques help to predict the effect of treatment expectations on therapeutic outcome?
Imaging techniques such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG) enable us to visualise the anatomy, blood flow and function of the brain. This central research project analyses imaging data from ten collaborative research centre (CRC) projects, with the objective of pooling the findings and improving our understanding of the mechanisms underlying the effect of expectations on treatment outcome. Another objective is to identify characteristic biological features (predictive biomarkers), which in future will enable us to predict the effect of treatment expectations and optimise treatment for individual patients.
Central research project: neural imaging
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.
Büchel C, Peters J, Banaschewski T, Bokde ALW, Bromberg U, Conrod PJ, Flor H, Papadopoulos D, Garavan H, Gowland P, Heinz A, Walter H, Ittermann B, Mann K, Martinot J-L, Paillère-Martinot M-L, Nees F, Paus T, Pausova Z, Poustka L, Rietschel M, Robbins TW, Smolka MN, Gallinat J, Schumann G, Knutson B, IMAGEN consortium (2017) Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents. Nat Commun 8:14140. PubMed
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
In close cooperation with these projects
Prof. Dr. Ulrike Bingel
Neurologist and Neuroscientist
Prof. Dr. Christian Büchel
Dr. Tamas Spisak
Dr. Raviteja Kotikalapudi
Dr. Balint Kincses
PhD Student, Neuroscientist
PhD Student, Medical Technician