Author

Johann Kruschwitz

Humboldt-Universität zu Berlin

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Andreas HeinziD· 3 shared Henrik WalteriD· 3 shared Michael N. SmolkaiD· 2 shared Pablo ReinhardtiD· 2 shared Alannah Smyth· 1 shared Antoine GrigisiD· 1 shared Arun L.W. BokdeiD· 1 shared Danilo BzdokiD· 1 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersNeuroscience

Andreas HeinzORCID

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3 papers

Recent·Most cited

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use

Yihe Weng, Johann Kruschwitz, Laura M. Rueda‐Delgado, Kathy L. Ruddy, Rory Thomas Boyle, Luisa Franzen, Emin Serin, Tochukwu Nweze, Jamie Lars Hanson, Alannah Smyth, Tom Farnan, Tobias Banaschewski, Arun L.W. Bokde, Sylvane Desrivières, Herta Flor, , Robert Whelan

This study shows that a brain network for sustained attention at age 14 predicts increased cannabis and cigarette use by age 23 in a longitudinal cohort.

eLife · Jun 5, 2024Peer-reviewedNeuroscience

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients

Justin Böhmer, Pablo Reinhardt, Maria Garbusow, Michael Marxen, Michael N. Smolka, Ulrich S. Zimmermann, Andreas Heinz, Danilo Bzdok, Eva Friedel, Johann Kruschwitz, Henrik Walter

The paper examines functional brain network organization in alcohol-dependent patients to identify predictors of relapse during a 12-month follow-up.

Addiction Biology · Oct 2, 2023Peer-reviewedNeuroscience

A Fast And Intuitive Method For Calculating Dynamic Network Reconfiguration And Node Flexibility

Narges Chinichian, Johann Kruschwitz, Pablo Reinhardt, Maximilian Palm, Sarah A. Wellan, Susanne Erk, Andreas Heinz, Henrik Walter, Ilya Milos Veer

The paper introduces a computationally efficient method to measure brain network flexibility using fixed modular frameworks, showing it matches results from a data-driven method with less computational cost.

Frontiers in Neuroscience · Feb 9, 2023Peer-reviewedNeuroscience