Marc-André Schulz

593 total citations
11 papers, 298 citations indexed

About

Marc-André Schulz is a scholar working on Cognitive Neuroscience, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Marc-André Schulz has authored 11 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cognitive Neuroscience, 3 papers in Statistics and Probability and 2 papers in Molecular Biology. Recurrent topics in Marc-André Schulz's work include Functional Brain Connectivity Studies (5 papers), Cognitive and developmental aspects of mathematical skills (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Marc-André Schulz is often cited by papers focused on Functional Brain Connectivity Studies (5 papers), Cognitive and developmental aspects of mathematical skills (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Marc-André Schulz collaborates with scholars based in Germany, United Kingdom and Canada. Marc-André Schulz's co-authors include Danilo Bzdok, Joshua T Vogelstein, Jakob Nikolas Kather, B.T. Thomas Yeo, Konrad P. Körding, Blake A. Richards, Kerstin Ritter, Karsten Witt, Peter Brugger and Fabian Eitel and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Marc-André Schulz

11 papers receiving 291 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Marc-André Schulz Germany	7	136	66	62	36	30	11	298
Byung-Hoon Kim South Korea	8	101 0.7×	46 0.7×	61 1.0×	47 1.3×	55 1.8×	28	256
Md Abdur Rahaman United States	8	220 1.6×	71 1.1×	113 1.8×	50 1.4×	37 1.2×	17	355
Lijun An Singapore	5	99 0.7×	73 1.1×	59 1.0×	21 0.6×	63 2.1×	9	238
Jessica Dafflon United Kingdom	8	122 0.9×	27 0.4×	54 0.9×	26 0.7×	29 1.0×	10	216
Rogers F. Silva United States	11	350 2.6×	76 1.2×	162 2.6×	44 1.2×	43 1.4×	42	494
Vishnu Bashyam United States	5	71 0.5×	37 0.6×	65 1.0×	16 0.4×	21 0.7×	9	188
Johanna Bayer Australia	6	159 1.2×	25 0.4×	98 1.6×	48 1.3×	34 1.1×	7	284
Andrés Hoyos-Idrobo France	4	366 2.7×	58 0.9×	111 1.8×	89 2.5×	76 2.5×	8	515
Fabian Eitel Germany	5	48 0.4×	114 1.7×	50 0.8×	16 0.4×	33 1.1×	9	231

Countries citing papers authored by Marc-André Schulz

Since Specialization

Citations

This map shows the geographic impact of Marc-André Schulz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marc-André Schulz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc-André Schulz more than expected).

Fields of papers citing papers by Marc-André Schulz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marc-André Schulz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marc-André Schulz. The network helps show where Marc-André Schulz may publish in the future.

Co-authorship network of co-authors of Marc-André Schulz

This figure shows the co-authorship network connecting the top 25 collaborators of Marc-André Schulz. A scholar is included among the top collaborators of Marc-André Schulz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marc-André Schulz. Marc-André Schulz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Schulz, Marc-André, et al.. (2025). Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection. PLoS Biology. 23(10). e3003451–e3003451. 1 indexed citations

Kainmueller, Dagmar, et al.. (2025). Do Transformers and CNNs Learn Different Concepts of Brain Age?. Human Brain Mapping. 46(8). e70243–e70243. 2 indexed citations

Schulz, Marc-André, Danilo Bzdok, Stefan Haufe, John–Dylan Haynes, & Kerstin Ritter. (2023). Performance reserves in brain-imaging-based phenotype prediction. Cell Reports. 43(1). 113597–113597. 19 indexed citations

Schulz, Marc-André, Stefan Hetzer, Fabian Eitel, et al.. (2023). Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis. iScience. 26(9). 107679–107679. 5 indexed citations

Schulz, Marc-André, et al.. (2021). A cognitive fingerprint in human random number generation. Scientific Reports. 11(1). 20217–20217. 7 indexed citations

Eitel, Fabian, et al.. (2021). Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Experimental Neurology. 339. 113608–113608. 32 indexed citations

Bhatt, Umang, et al.. (2021). FIMAP: Feature Importance by Minimal Adversarial Perturbation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(13). 11433–11441. 10 indexed citations

Schulz, Marc-André, et al.. (2020). Inferring disease subtypes from clusters in explanation space. Scientific Reports. 10(1). 12900–12900. 16 indexed citations

Schulz, Marc-André, B.T. Thomas Yeo, Joshua T Vogelstein, et al.. (2020). Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nature Communications. 11(1). 4238–4238. 176 indexed citations

10.

Schulz, Marc-André, Christina Regenbogen, Carolin Moessnang, et al.. (2015). On utilizing uncertainty information in template‐based EEG‐fMRI ballistocardiogram artifact removal. Psychophysiology. 52(6). 857–863. 3 indexed citations

11.

Schulz, Marc-André, et al.. (2012). Analysing Humanly Generated Random Number Sequences: A Pattern-Based Approach. PLoS ONE. 7(7). e41531–e41531. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact