Michael A. Skeide

1.2k total citations
28 papers, 660 citations indexed

About

Michael A. Skeide is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience and Statistics and Probability. According to data from OpenAlex, Michael A. Skeide has authored 28 papers receiving a total of 660 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Developmental and Educational Psychology, 16 papers in Cognitive Neuroscience and 9 papers in Statistics and Probability. Recurrent topics in Michael A. Skeide's work include Reading and Literacy Development (16 papers), Cognitive and developmental aspects of mathematical skills (9 papers) and Neurobiology of Language and Bilingualism (7 papers). Michael A. Skeide is often cited by papers focused on Reading and Literacy Development (16 papers), Cognitive and developmental aspects of mathematical skills (9 papers) and Neurobiology of Language and Bilingualism (7 papers). Michael A. Skeide collaborates with scholars based in Germany, United States and United Kingdom. Michael A. Skeide's co-authors include Angela D. Friederici, Jens Bräuer, Gesa Schaadt, Holger Kirsten, Nicole E. Neef, Arndt Wilcke, Bent Müller, Indra Kraft, Johannes Boltze and Frank Emmrich and has published in prestigious journals such as Nature reviews. Neuroscience, NeuroImage and Brain.

In The Last Decade

Michael A. Skeide

25 papers receiving 646 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael A. Skeide Germany 15 438 418 151 82 53 28 660
Olumide A. Olulade United States 11 465 1.1× 361 0.9× 178 1.2× 62 0.8× 43 0.8× 16 605
Brian C. Donohue United States 5 510 1.2× 411 1.0× 185 1.2× 87 1.1× 42 0.8× 7 664
Eileen M. Napoliello United States 13 581 1.3× 605 1.4× 292 1.9× 47 0.6× 86 1.6× 15 759
Marion Grande Germany 15 495 1.1× 407 1.0× 155 1.0× 25 0.3× 42 0.8× 38 630
Karen Gross-Glenn United States 10 439 1.0× 460 1.1× 218 1.4× 87 1.1× 39 0.7× 12 699
Jun Ren Lee Taiwan 8 787 1.8× 816 2.0× 347 2.3× 56 0.7× 112 2.1× 14 1.1k
Ann Meyler United States 11 708 1.6× 848 2.0× 447 3.0× 56 0.7× 89 1.7× 11 990
Laura K. Halderman United States 12 261 0.6× 190 0.5× 37 0.2× 77 0.9× 81 1.5× 18 505
Valérie Chanoine France 11 700 1.6× 801 1.9× 373 2.5× 62 0.8× 162 3.1× 20 1.1k
Sanne van der Mark Switzerland 6 558 1.3× 539 1.3× 256 1.7× 23 0.3× 46 0.9× 6 650

Countries citing papers authored by Michael A. Skeide

Since Specialization
Citations

This map shows the geographic impact of Michael A. Skeide'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 Michael A. Skeide with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael A. Skeide more than expected).

Fields of papers citing papers by Michael A. Skeide

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael A. Skeide. 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 Michael A. Skeide. The network helps show where Michael A. Skeide may publish in the future.

Co-authorship network of co-authors of Michael A. Skeide

This figure shows the co-authorship network connecting the top 25 collaborators of Michael A. Skeide. A scholar is included among the top collaborators of Michael A. Skeide 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 Michael A. Skeide. Michael A. Skeide is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jeong, Gi Seok, Joram Soch, Robert Trampel, Andreas Nieder, & Michael A. Skeide. (2025). High-field fMRI at 7 Tesla reveals topographic responses tuned to number in the developing human brain. Developmental Cognitive Neuroscience. 75. 101598–101598.
2.
Skeide, Michael A., et al.. (2025). How EEG preprocessing shapes decoding performance. Communications Biology. 8(1). 1039–1039. 2 indexed citations
3.
Skeide, Michael A., et al.. (2023). A meta‐analysis of mental rotation in the first years of life. Developmental Science. 26(6). e13381–e13381. 8 indexed citations
4.
Skeide, Michael A., et al.. (2021). Mathematical learning deficits originate in early childhood from atypical development of a frontoparietal brain network. PLoS Biology. 19(9). e3001407–e3001407. 13 indexed citations
5.
Rahman, Rasha Abdel, et al.. (2021). A meta-analysis of fMRI studies of semantic cognition in children. NeuroImage. 241. 118436–118436. 14 indexed citations
6.
Friederici, Angela D., Nicole E. Neef, Frank Emmrich, et al.. (2020). Auditory brainstem measures and genotyping boost the prediction of literacy: A longitudinal study on early markers of dyslexia. Developmental Cognitive Neuroscience. 46. 100869–100869. 6 indexed citations
7.
Friederici, Angela D., et al.. (2020). A meta-analysis of fMRI studies of language comprehension in children. NeuroImage. 215. 116858–116858. 47 indexed citations
8.
Skeide, Michael A., et al.. (2020). Neurobiological origins of individual differences in mathematical ability. PLoS Biology. 18(10). e3000871–e3000871. 12 indexed citations
9.
Friederici, Angela D., Frank Emmrich, Jens Bräuer, et al.. (2019). Early cortical surface plasticity relates to basic mathematical learning. NeuroImage. 204. 116235–116235. 14 indexed citations
10.
Qi, Ting, et al.. (2019). The emergence of long-range language network structural covariance and language abilities. NeuroImage. 191. 36–48. 16 indexed citations
11.
Skeide, Michael A., Uttam Kumar, Ramesh Kumar Mishra, et al.. (2017). Learning to read alters cortico-subcortical cross-talk in the visual system of illiterates. Science Advances. 3(5). e1602612–e1602612. 52 indexed citations
12.
Müller, Bent, Gesa Schaadt, Johannes Boltze, et al.. (2017). ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR. Brain and Behavior. 7(11). e00851–e00851. 7 indexed citations
13.
Müller, Bent, Arndt Wilcke, Peter Ahnert, et al.. (2016). Association, characterisation and meta-analysis of SNPs linked to general reading ability in a German dyslexia case-control cohort. Scientific Reports. 6(1). 27901–27901. 11 indexed citations
14.
Kraft, Indra, Jan Schreiber, Gesa Schaadt, et al.. (2016). Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRI. NeuroImage. 143. 378–386. 39 indexed citations
15.
Skeide, Michael A., Indra Kraft, Bent Müller, et al.. (2016). NRSN1associated grey matter volume of the visual word form area reveals dyslexia before school. Brain. 139(10). 2792–2803. 36 indexed citations
16.
Skeide, Michael A., et al.. (2016). The ontogeny of the cortical language network. Nature reviews. Neuroscience. 17(5). 323–332. 1 indexed citations
17.
Skeide, Michael A., Holger Kirsten, Indra Kraft, et al.. (2015). Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children. NeuroImage. 118. 414–421. 36 indexed citations
18.
Skeide, Michael A., Jens Bräuer, & Angela D. Friederici. (2015). Brain Functional and Structural Predictors of Language Performance. Cerebral Cortex. 26(5). 2127–2139. 122 indexed citations
19.
Skeide, Michael A., Jens Bräuer, & Angela D. Friederici. (2014). Syntax gradually segregates from semantics in the developing brain. NeuroImage. 100. 106–111. 68 indexed citations
20.
Skeide, Michael A.. (2012). Syntax and semantics networks in the developing brain. MPG.PuRe (Max Planck Society).

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.

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