Author

Tobias Schmidt‐Wilcke

Heinrich Heine University DüsseldorfORCID 0000-0002-7929-1079Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Bálint KincsesiD· 2 shared Frederik SchlittiD· 2 shared Matthias ZunhammeriD· 2 shared Tamás SpisákiD· 2 shared Ulrike BingeliD· 2 shared Zsigmond Tamás KincsesiD· 2 shared Livia AsaniD· 1 shared Raviteja KotikalapudiiD· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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2 shared papersNeuroscience

Bálint KincsesORCID

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2 papers

Recent·Most cited

Gray matter cortical thickness predicts individual pain sensitivity: a multi-center machine learning approach

Raviteja Kotikalapudi, Bálint Kincses, Matthias Zunhammer, Frederik Schlitt, Tobias Schmidt‐Wilcke, Zsigmond Tamás Kincses, Livia Asan, Ulrike Bingel, Tamás Spisák

This study develops a multi-center machine learning model using gray matter cortical thickness to predict individual pain sensitivity, achieving a moderate predictive performance.

bioRxiv · Jun 14, 2022 · PreprintNeuroscience

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Tamás Spisák, Bálint Kincses, Frederik Schlitt, Matthias Zunhammer, Tobias Schmidt‐Wilcke, Zsigmond Tamás Kincses, Ulrike Bingel

The paper identifies a resting-state brain connectivity pattern predictive of individual pain sensitivity without painful stimulation.

Nature Communications · Jan 10, 2020Peer-reviewedNeuroscience