Nandakumar S. Narayanan

6.7k total citations
104 papers, 4.4k citations indexed

About

Nandakumar S. Narayanan is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Nandakumar S. Narayanan has authored 104 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Cognitive Neuroscience, 46 papers in Cellular and Molecular Neuroscience and 35 papers in Neurology. Recurrent topics in Nandakumar S. Narayanan's work include Neural dynamics and brain function (37 papers), Parkinson's Disease Mechanisms and Treatments (26 papers) and Neuroscience and Music Perception (22 papers). Nandakumar S. Narayanan is often cited by papers focused on Neural dynamics and brain function (37 papers), Parkinson's Disease Mechanisms and Treatments (26 papers) and Neuroscience and Music Perception (22 papers). Nandakumar S. Narayanan collaborates with scholars based in United States, Brazil and China. Nandakumar S. Narayanan's co-authors include Mark Laubach, James F. Cavanagh, K L Parker, Young-Cho Kim, Ralph Dileone, Eric B. Emmons, Stephanie L. Alberico, Arun Singh, Robert L. Rodnitzky and Douglas J. Guarnieri and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Neuron.

In The Last Decade

Nandakumar S. Narayanan

97 papers receiving 4.3k citations

Peers

Nandakumar S. Narayanan

CMN

NEURO

Kebreten F. Manaye United States

Alexxai V. Kravitz United States

Jan Gründemann Switzerland

Carmen Cavada Spain

Vikram R. Rao United States

Léon Tremblay France

Bin Hu Canada

Daniela Popa France

René Drucker‐Colín Mexico

Susanne E. Ahmari United States

Kebreten F. Manaye United States

Nandakumar S. Narayanan

1.1k ×0.4

1.6k ×1.1

CMN

847 ×0.9

NEURO

771 ×1.5

475 ×1.3

NEURO

Citations per year, relative to Nandakumar S. Narayanan Nandakumar S. Narayanan (= 1×) peers Kebreten F. Manaye

Countries citing papers authored by Nandakumar S. Narayanan

Since Specialization

Citations

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

Fields of papers citing papers by Nandakumar S. Narayanan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nandakumar S. Narayanan. 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 Nandakumar S. Narayanan. The network helps show where Nandakumar S. Narayanan may publish in the future.

Co-authorship network of co-authors of Nandakumar S. Narayanan

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

All Works

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20 of 20 papers shown

Espinoza, Arturo I., Rachel C. Cole, Arun Singh, et al.. (2024). Resting-state EEG measures cognitive impairment in Parkinson’s disease. npj Parkinson s Disease. 10(1). 12 indexed citations

Thangavel, Ramasamy, et al.. (2024). Mice Expressing A53T/A30P Mutant Alpha-Synuclein in Dopamine Neurons Do Not Display Behavioral Deficits. eNeuro. 11(2). ENEURO.0170–23.2023.

Narayanan, Nandakumar S., et al.. (2024). Cognition falters at ~4 Hz in Parkinson’s disease. Trends in Cognitive Sciences. 28(9). 789–791. 4 indexed citations

Harris, Shana, Nandakumar S. Narayanan, & Daniel Tranel. (2023). Does Black vs. White race affect practitioners’ appraisal of Parkinson’s disease?. npj Parkinson s Disease. 9(1). 106–106. 8 indexed citations

Singh, Arun, Rachel C. Cole, Arturo I. Espinoza, et al.. (2023). Evoked mid-frontal activity predicts cognitive dysfunction in Parkinson’s disease. Journal of Neurology Neurosurgery & Psychiatry. 94(11). 945–953. 19 indexed citations

Thangavel, Ramasamy, Serena B. Gumusoglu, Hisham Abdelmotilib, et al.. (2023). Alpha-Synuclein Pre-Formed Fibrils Injected into Prefrontal Cortex Primarily Spread to Cortical and Subcortical Structures. Journal of Parkinson s Disease. 14(1). 81–94. 10 indexed citations

Chan, Aubrey C., Rong Fan, Jeffrey D. Long, et al.. (2022). Intoxicating effects of alcohol depend on acid-sensing ion channels. Neuropsychopharmacology. 48(5). 806–815. 7 indexed citations

Espinoza, Arturo I., Patrick May, Arun Kumar Singh, et al.. (2022). A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease. Clinical Parkinsonism & Related Disorders. 7. 100166–100166. 4 indexed citations

Leidinger, Mariah, et al.. (2022). Mice expressing P301S mutant human tau have deficits in interval timing. Behavioural Brain Research. 432. 113967–113967. 8 indexed citations

10.

Emmons, Eric B., et al.. (2021). Experience‐related enhancements in striatal temporal encoding. European Journal of Neuroscience. 54(3). 5063–5074. 8 indexed citations

11.

Groth, Christopher L., Arun Singh, Qiang Zhang, Brian D. Berman, & Nandakumar S. Narayanan. (2021). GABAergic Modulation in Movement Related Oscillatory Activity: A Review of the Effect Pharmacologically and with Aging. Tremor and Other Hyperkinetic Movements. 11(1). 48–48. 4 indexed citations

12.

Singh, Arun, et al.. (2021). Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson’s disease. npj Parkinson s Disease. 7(1). 14–14. 40 indexed citations

13.

Schultz, Jordan L., Jia Xu, Sarah E. Ernst, et al.. (2021). A pilot to assess target engagement of terazosin in Parkinson's disease. Parkinsonism & Related Disorders. 94. 79–83. 28 indexed citations

14.

Singh, Arun, et al.. (2020). Frontal theta and beta oscillations during lower-limb movement in Parkinson’s disease. Clinical Neurophysiology. 131(3). 694–702. 78 indexed citations

15.

Cai, Rong, Y Zhang, Jacob E. Simmering, et al.. (2019). Enhancing glycolysis attenuates Parkinson’s disease progression in models and clinical databases. Journal of Clinical Investigation. 129(10). 4539–4549. 196 indexed citations

16.

Johnson, Shane B., Eric B. Emmons, Rachel M. Anderson, et al.. (2018). Prefrontal–Bed Nucleus Circuit Modulation of a Passive Coping Response Set. Journal of Neuroscience. 39(8). 1405–1419. 39 indexed citations

17.

Parker, K L, et al.. (2013). Executive dysfunction in Parkinson’s disease and timing deficits. Frontiers in Integrative Neuroscience. 7. 75–75. 80 indexed citations

18.

Salardini, Arash, Nandakumar S. Narayanan, Jagriti Arora, R. Todd Constable, & Bahman Jabbari. (2012). Ipsilateral synkinesia involves the supplementary motor area. Neuroscience Letters. 523(2). 135–138. 12 indexed citations

19.

Sears, Robert M., Nandakumar S. Narayanan, Rakefet Sharf, et al.. (2010). Regulation of Nucleus Accumbens Activity by the Hypothalamic Neuropeptide Melanin-Concentrating Hormone. Journal of Neuroscience. 30(24). 8263–8273. 87 indexed citations

20.

Narayanan, Nandakumar S., Eyal Y. Kimchi, & Mark Laubach. (2005). Redundancy and Synergy of Neuronal Ensembles in Motor Cortex. Journal of Neuroscience. 25(17). 4207–4216. 93 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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