Andrew R. Mitz

2.3k total citations
34 papers, 1.5k citations indexed

About

Andrew R. Mitz is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Genetics. According to data from OpenAlex, Andrew R. Mitz has authored 34 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cognitive Neuroscience, 10 papers in Cellular and Molecular Neuroscience and 4 papers in Genetics. Recurrent topics in Andrew R. Mitz's work include Neural dynamics and brain function (12 papers), Memory and Neural Mechanisms (9 papers) and Neural and Behavioral Psychology Studies (9 papers). Andrew R. Mitz is often cited by papers focused on Neural dynamics and brain function (12 papers), Memory and Neural Mechanisms (9 papers) and Neural and Behavioral Psychology Studies (9 papers). Andrew R. Mitz collaborates with scholars based in United States, Netherlands and Czechia. Andrew R. Mitz's co-authors include Elisabeth A. Murray, Bruno B. Averbeck, Moshe Godschalk, Peter H. Rudebeck, Steven P. Wise, Aldo Genovesio, Peter J. Brasted, S.P. Wise, Ravi V. Chacko and Richard C. Saunders and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and Journal of Neuroscience.

In The Last Decade

Andrew R. Mitz

34 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew R. Mitz United States 19 1.2k 280 222 157 149 34 1.5k
Mathieu Bourguignon Belgium 28 1.9k 1.6× 221 0.8× 176 0.8× 242 1.5× 130 0.9× 99 2.3k
Markus Bauer United Kingdom 25 1.8k 1.5× 324 1.2× 243 1.1× 68 0.4× 92 0.6× 45 2.2k
He Cui China 17 1.1k 0.9× 259 0.9× 244 1.1× 159 1.0× 109 0.7× 43 1.4k
Nobuhiro Hagura Japan 13 772 0.6× 145 0.5× 272 1.2× 89 0.6× 318 2.1× 27 1.3k
Alexandre Zénon Belgium 20 1.2k 1.0× 217 0.8× 181 0.8× 133 0.8× 192 1.3× 52 1.7k
Xiaofeng Lu Japan 16 1.4k 1.2× 374 1.3× 402 1.8× 149 0.9× 308 2.1× 22 1.7k
Pietro Avanzini Italy 22 1.2k 1.0× 232 0.8× 538 2.4× 66 0.4× 155 1.0× 82 1.9k
Aldo Genovesio Italy 25 2.2k 1.8× 214 0.8× 402 1.8× 103 0.7× 165 1.1× 83 2.4k
Valeria Della‐Maggiore Argentina 22 1.2k 1.0× 154 0.6× 331 1.5× 172 1.1× 264 1.8× 41 1.6k
Anna Floyer-Lea United Kingdom 6 881 0.7× 270 1.0× 148 0.7× 225 1.4× 354 2.4× 6 1.3k

Countries citing papers authored by Andrew R. Mitz

Since Specialization
Citations

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

Fields of papers citing papers by Andrew R. Mitz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew R. Mitz. 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 Andrew R. Mitz. The network helps show where Andrew R. Mitz may publish in the future.

Co-authorship network of co-authors of Andrew R. Mitz

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew R. Mitz. A scholar is included among the top collaborators of Andrew R. Mitz 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 Andrew R. Mitz. Andrew R. Mitz 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.
Mitz, Andrew R., Luigi Boccuto, & Audrey Thurm. (2024). Evidence for common mechanisms of pathology between SHANK3 and other genes of Phelan‐McDermid syndrome. Clinical Genetics. 105(5). 459–469. 8 indexed citations
2.
Bartolo, Ramón, Richard C. Saunders, Andrew R. Mitz, & Bruno B. Averbeck. (2020). Dimensionality, information and learning in prefrontal cortex. PLoS Computational Biology. 16(4). e1007514–e1007514. 35 indexed citations
3.
Costa, Vincent D., Andrew R. Mitz, & Bruno B. Averbeck. (2019). Subcortical Substrates of Explore-Exploit Decisions in Primates. Neuron. 103(3). 533–545.e5. 76 indexed citations
4.
Hwang, Jaewon, Andrew R. Mitz, & Elisabeth A. Murray. (2019). NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB. Journal of Neuroscience Methods. 323. 13–21. 71 indexed citations
5.
Kaskan, Peter M., et al.. (2018). Gustatory responses in macaque monkeys revealed with fMRI: Comments on taste, taste preference, and internal state. NeuroImage. 184. 932–942. 18 indexed citations
6.
Mitz, Andrew R., Luigi Boccuto, Aleksandr Shcheglovitov, et al.. (2018). Identification of 22q13 genes most likely to contribute to Phelan McDermid syndrome. European Journal of Human Genetics. 26(3). 293–302. 44 indexed citations
7.
Rudebeck, Peter H., et al.. (2017). Amygdala Contributions to Stimulus–Reward Encoding in the Macaque Medial and Orbital Frontal Cortex during Learning. Journal of Neuroscience. 37(8). 2186–2202. 59 indexed citations
8.
Mitz, Andrew R., et al.. (2017). Keeping Communications Flowing During Large-scale Disasters: Leveraging Amateur Radio Innovations for Disaster Medicine. Disaster Medicine and Public Health Preparedness. 12(2). 257–264. 9 indexed citations
9.
Mitz, Andrew R., Ramón Bartolo, Richard C. Saunders, et al.. (2017). High channel count single-unit recordings from nonhuman primate frontal cortex. Journal of Neuroscience Methods. 289. 39–47. 27 indexed citations
10.
Mitz, Andrew R., et al.. (2014). Real-Time Dopamine Measurement in Awake Monkeys. PLoS ONE. 9(6). e98692–e98692. 36 indexed citations
11.
Rudebeck, Peter H., Andrew R. Mitz, Ravi V. Chacko, & Elisabeth A. Murray. (2013). Effects of Amygdala Lesions on Reward-Value Coding in Orbital and Medial Prefrontal Cortex. Neuron. 80(6). 1519–1531. 116 indexed citations
12.
Mitz, Andrew R., et al.. (2008). A method for recording single-cell activity in the frontal-pole cortex of macaque monkeys. Journal of Neuroscience Methods. 177(1). 60–66. 15 indexed citations
13.
Genovesio, Aldo, Peter J. Brasted, Andrew R. Mitz, & Steven P. Wise. (2005). Prefrontal Cortex Activity Related to Abstract Response Strategies. Neuron. 47(2). 307–320. 160 indexed citations
14.
Mitz, Andrew R.. (2005). A liquid-delivery device that provides precise reward control for neurophysiological and behavioral experiments. Journal of Neuroscience Methods. 148(1). 19–25. 24 indexed citations
15.
Mitz, Andrew R., et al.. (2001). A novel food-delivery device for neurophysiological and neuropsychological studies in monkeys. Journal of Neuroscience Methods. 109(2). 129–135. 5 indexed citations
16.
Wise, S.P., et al.. (1998). Changes in motor cortical activity during visuomotor adaptation. Experimental Brain Research. 121(3). 285–299. 146 indexed citations
17.
Godschalk, Moshe, et al.. (1995). Somatotopy of monkey premotor cortex examined with microstimulation. Neuroscience Research. 23(3). 269–279. 141 indexed citations
18.
Mitz, Andrew R. & Moshe Godschalk. (1989). Eye-movement representation in the frontal lobe of rhesus monkeys. Neuroscience Letters. 106(1-2). 157–162. 68 indexed citations
19.
Mitz, Andrew R. & Donald R. Humphrey. (1986). Intracortical stimulation in pyramidotomized monkeys. Neuroscience Letters. 64(1). 59–64. 13 indexed citations
20.
Yaar, Israel, et al.. (1985). Fatigue trends in and the diagnosis of myasthenia gravis by frequency analysis of EMG interference patterns. Muscle & Nerve. 8(4). 328–335. 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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