Author

Julius Steiglechner

Universitätsklinikum Tübingen

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

Biology Disciplines

Click to filter author papers

Neuroscience· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:experimental assay· 2 Assay:imaging· 2 Research Focus:database / resource· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Gabriele LohmanniD· 3 shared Klaus ScheffleriD· 3 shared Lucas Mahler· 3 shared Vinod KumariD· 2 shared Benjamin BenderiD· 1 shared Dana Ramadan· 1 shared Edyta Charyasz· 1 shared Florian BirkiD· 1 shared

Activity by Year

2023-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersNeuroscience

Gabriele LohmannORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

Lucas Mahler, Julius Steiglechner, Benjamin Bender, Tobias M. Lindig, Dana Ramadan, J Bause, Florian Birk, Rahel Heule, Edyta Charyasz, Michael Erb, Vinod Kumar, Gisela Elisabeth Hagberg, Pascal Martin, Gabriele Lohmann, Klaus Scheffler

UltraCortex provides high-resolution 9.4 T brain MRI data and validated segmentations, addressing gaps in existing repositories.

Scientific Data · Apr 15, 2025Peer-reviewedNeuroscience

Beyond ROIs: Laminar fMRI Mapping with Cylarim

Gabriele Lohmann, Julius Steiglechner, Lucas Mahler, Klaus Scheffler

Cylarim enables unbiased laminar fMRI mapping by eliminating reliance on predefined ROIs, allowing large-scale, depth-resolved analysis of cortical activity.

bioRxiv · Mar 25, 2025 · PreprintNeuroscience

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Gabriele Lohmann, Samuel Heczko, Lucas Mahler, Qi Wang, Julius Steiglechner, Vinod Kumar, Michelle Roost, Jürgen Jost, Klaus Scheffler

This paper improves fMRI-based intelligence prediction by using semi-blind machine learning with educational level as supplementary data, achieving better accuracy even with small sample sizes.

bioRxiv · Nov 3, 2023 · PreprintNeuroscience