Author

Vera Moliadze

Christian-Albrechts-Universität zu KielORCID 0000-0002-9072-6539Unclaimed 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 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Assay:electronic health records· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexander Prehn‐KristenseniD· 1 shared Astrid DempfleiD· 1 shared Aureli Soria‐FrischiD· 1 shared Carolin Breitling-Ziegler· 1 shared Claire BraboszcziD· 1 shared Eleni KroupiiD· 1 shared Frauke NeesiD· 1 shared Hannah BraueriD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperNeuroscience

Alexander Prehn‐KristensenORCID

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2 papers

Recent·Most cited

A Real-Time Functional Localization Method Based on Dynamic Connectivity for Coupled Brain Regions

Nils Jannik Heukamp, Vera Moliadze, Frauke Nees

This paper develops a functional localization method for dynamic connectivity in rtfMRI neurofeedback, showing improved signal-to-noise ratio and correlation with anxiodepressive symptoms.

bioRxiv · Feb 4, 2026 · PreprintNeuroscience

Stratification of responses to tDCS intervention in a healthy paediatric population based on resting-state EEG profiles

Paulina Clara Dagnino, Claire Braboszcz, Eleni Kroupi, Maike Splittgerber, Hannah Brauer, Astrid Dempfle, Carolin Breitling-Ziegler, Alexander Prehn‐Kristensen, Kerstin Krauel, Michael Siniatchkin, Vera Moliadze, Aureli Soria‐Frisch

The paper uses resting-state EEG and machine learning to stratify responses to tDCS in healthy children, identifying electrophysiological profiles linked to positive or negative responses.

Scientific Reports · May 25, 2023Peer-reviewedNeuroscience