Keiko Nakagaki

683 total citations
24 papers, 473 citations indexed

About

Keiko Nakagaki is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Social Psychology. According to data from OpenAlex, Keiko Nakagaki has authored 24 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 5 papers in Cellular and Molecular Neuroscience and 5 papers in Social Psychology. Recurrent topics in Keiko Nakagaki's work include Autism Spectrum Disorder Research (8 papers), Primate Behavior and Ecology (5 papers) and SARS-CoV-2 and COVID-19 Research (4 papers). Keiko Nakagaki is often cited by papers focused on Autism Spectrum Disorder Research (8 papers), Primate Behavior and Ecology (5 papers) and SARS-CoV-2 and COVID-19 Research (4 papers). Keiko Nakagaki collaborates with scholars based in Japan, United States and India. Keiko Nakagaki's co-authors include Fumihiro Taguchi, Noritaka Ichinohe, Heihachiro Arito, Hiroshi Tsuruta, Hideka Miura, Kazuya Shirato, Shutoku Matsuyama, Miyuki Kawase, Yoshio Imada and Nobuyuki Kawai and has published in prestigious journals such as Nature Communications, NeuroImage and Journal of Virology.

In The Last Decade

Keiko Nakagaki

23 papers receiving 465 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Keiko Nakagaki Japan 14 145 116 79 77 73 24 473
Jason T. Newman United States 12 110 0.8× 82 0.7× 188 2.4× 153 2.0× 70 1.0× 18 615
Zoltán Péterfi Hungary 17 180 1.2× 190 1.6× 181 2.3× 124 1.6× 37 0.5× 52 738
Cécile Delorme France 13 128 0.9× 132 1.1× 116 1.5× 128 1.7× 65 0.9× 49 772
Jaime Rofina Netherlands 8 67 0.5× 25 0.2× 152 1.9× 37 0.5× 182 2.5× 8 539
Stephanie S. Erlich United States 12 66 0.5× 84 0.7× 53 0.7× 144 1.9× 22 0.3× 14 675
Hang Zheng China 14 48 0.3× 25 0.2× 272 3.4× 51 0.7× 55 0.8× 29 633
Mariangela Iorio Italy 13 64 0.4× 192 1.7× 80 1.0× 44 0.6× 68 0.9× 26 626
Satomi Sonoda Japan 12 48 0.3× 23 0.2× 182 2.3× 131 1.7× 48 0.7× 37 647
Tracy Reynolds United States 12 41 0.3× 41 0.4× 177 2.2× 187 2.4× 34 0.5× 21 582
Nikki J. Kirkman United States 8 68 0.5× 47 0.4× 111 1.4× 77 1.0× 47 0.6× 8 380

Countries citing papers authored by Keiko Nakagaki

Since Specialization
Citations

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

Fields of papers citing papers by Keiko Nakagaki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Keiko Nakagaki. 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 Keiko Nakagaki. The network helps show where Keiko Nakagaki may publish in the future.

Co-authorship network of co-authors of Keiko Nakagaki

This figure shows the co-authorship network connecting the top 25 collaborators of Keiko Nakagaki. A scholar is included among the top collaborators of Keiko Nakagaki 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 Keiko Nakagaki. Keiko Nakagaki 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
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Noguchi, Jun, Satoshi Watanabe, Tomofumi Oga, et al.. (2024). Altered projection-specific synaptic remodeling and its modification by oxytocin in an idiopathic autism marmoset model. Communications Biology. 7(1). 642–642. 1 indexed citations
3.
Chao, Zenas C., Misako Komatsu, Madoka Matsumoto, et al.. (2024). Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder. Communications Biology. 7(1). 851–851. 5 indexed citations
4.
Nakagaki, Keiko, et al.. (2022). Prenatal valproic acid-induced autism marmoset model exhibits higher salivary cortisol levels. Frontiers in Behavioral Neuroscience. 16. 943759–943759. 4 indexed citations
5.
Nakagaki, Keiko, et al.. (2022). Reduced childhood social attention in autism model marmosets predicts impaired social skills and inflexible behavior in adulthood. Frontiers in Psychiatry. 13. 885433–885433. 7 indexed citations
6.
Watanabe, Satoshi, Tohru Kurotani, Tomofumi Oga, et al.. (2021). Functional and molecular characterization of a non-human primate model of autism spectrum disorder shows similarity with the human disease. Nature Communications. 12(1). 5388–5388. 24 indexed citations
7.
Mimura, Koki, Tomofumi Oga, Tetsuya Sasaki, et al.. (2019). Abnormal axon guidance signals and reduced interhemispheric connection via anterior commissure in neonates of marmoset ASD model. NeuroImage. 195. 243–251. 20 indexed citations
8.
Sanagi, Tomomi, Tetsuya Sasaki, Keiko Nakagaki, et al.. (2019). Segmented Iba1-Positive Processes of Microglia in Autism Model Marmosets. Frontiers in Cellular Neuroscience. 13. 18 indexed citations
9.
Nakagaki, Keiko, et al.. (2018). Inequity aversion is observed in common marmosets but not in marmoset models of autism induced by prenatal exposure to valproic acid. Behavioural Brain Research. 343. 36–40. 23 indexed citations
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Sasaki, Tetsuya, Tomofumi Oga, Keiko Nakagaki, et al.. (2014). Developmental expression profiles of axon guidance signaling and the immune system in the marmoset cortex: Potential molecular mechanisms of pruning of dendritic spines during primate synapse formation in late infancy and prepuberty (I). Biochemical and Biophysical Research Communications. 444(3). 302–306. 23 indexed citations
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Hirai, Asuka, Toshio Ikeda, Dianna M. Blau, et al.. (2010). Role of Mouse Hepatitis Virus (MHV) Receptor Murine CEACAM1 in the Resistance of Mice to MHV Infection: Studies of Mice with Chimeric mCEACAM1a and mCEACAM1b. Journal of Virology. 84(13). 6654–6666. 20 indexed citations
14.
Sumida, Kayo, Tōru Yamada, Keiko Nakagaki, et al.. (2009). Gene expression profiles in the common marmoset brain determined using a newly developed common marmoset-specific DNA microarray. Neuroscience Research. 66(1). 62–85. 7 indexed citations
15.
Ami, Yasushi, Noriyo Nagata, Kazuya Shirato, et al.. (2008). Co‐infection of respiratory bacterium with severe acute respiratory syndrome coronavirus induces an exacerbated pneumonia in mice. Microbiology and Immunology. 52(2). 118–127. 23 indexed citations
16.
Miura, Hideka, Keiko Nakagaki, & Fumihiro Taguchi. (2003). N-Terminal Domain of the Murine Coronavirus Receptor CEACAM1 Is Responsible for Fusogenic Activation and Conformational Changes of the Spike Protein. Journal of Virology. 78(1). 216–223. 37 indexed citations
17.
Inoue, Haruhisa, Makoto Sawada, Akihide Ryo, et al.. (1999). Serial analysis of gene expression in a microglial cell line. Glia. 28(3). 265–271. 46 indexed citations
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19.
Arito, Heihachiro, Hiroshi Tsuruta, & Keiko Nakagaki. (1984). Acute effects of toluene on circadian rhythms of sleep-wakefulness and brain monoamine metabolism in rats. Toxicology. 33(3-4). 291–301. 30 indexed citations
20.
Arito, Heihachiro, Ayako SUDO, Noboru Hara, Keiko Nakagaki, & Shizuo Torii. (1982). Changes in circadian sleep-waking rhythms of rats following administration of methylmercury chloride.. Industrial Health. 20(1). 55–65. 7 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.

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