Marom Bikson

35.2k total citations · 6 hit papers
297 papers, 18.7k citations indexed

About

Marom Bikson is a scholar working on Neurology, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Marom Bikson has authored 297 papers receiving a total of 18.7k indexed citations (citations by other indexed papers that have themselves been cited), including 246 papers in Neurology, 131 papers in Cognitive Neuroscience and 123 papers in Cellular and Molecular Neuroscience. Recurrent topics in Marom Bikson's work include Transcranial Magnetic Stimulation Studies (233 papers), Neuroscience and Neural Engineering (116 papers) and Muscle activation and electromyography studies (59 papers). Marom Bikson is often cited by papers focused on Transcranial Magnetic Stimulation Studies (233 papers), Neuroscience and Neural Engineering (116 papers) and Muscle activation and electromyography studies (59 papers). Marom Bikson collaborates with scholars based in United States, Brazil and Germany. Marom Bikson's co-authors include Abhishek Datta, Lucas C. Parra, John G. R. Jefferys, Daniel R. Merrill, Davide Reato, Felipe Fregni, Asif Rahman, Preet Minhas, Dennis Q. Truong and Varun Bansal and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Marom Bikson

283 papers receiving 18.4k citations

Hit Papers

Electrical stimulation of excitable tissue: design of eff... 2004 2026 2011 2018 2005 2015 2009 2004 2012 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marom Bikson United States 68 13.2k 10.0k 5.8k 3.6k 2.0k 297 18.7k
Andrea Antal Germany 62 14.6k 1.1× 11.8k 1.2× 3.1k 0.5× 2.7k 0.7× 1.4k 0.7× 226 19.1k
Alberto Priori Italy 71 12.8k 1.0× 9.1k 0.9× 4.8k 0.8× 2.9k 0.8× 6.2k 3.1× 394 21.2k
Vincenzo Di Lazzaro Italy 80 14.1k 1.1× 9.9k 1.0× 4.8k 0.8× 4.8k 1.3× 5.9k 3.0× 506 25.3k
Joseph Claßen Germany 56 9.0k 0.7× 7.3k 0.7× 2.0k 0.3× 3.3k 0.9× 2.8k 1.4× 229 14.6k
Ulf Ziemann Germany 96 23.4k 1.8× 16.6k 1.7× 4.6k 0.8× 7.6k 2.1× 5.8k 2.9× 489 32.7k
Hartwig R. Siebner Denmark 72 7.2k 0.5× 9.4k 0.9× 2.6k 0.4× 2.2k 0.6× 3.5k 1.8× 482 17.9k
Eric M. Wassermann United States 77 18.5k 1.4× 13.2k 1.3× 2.4k 0.4× 4.4k 1.2× 3.8k 1.9× 207 24.7k
Mark Hallett United States 82 19.4k 1.5× 15.0k 1.5× 3.7k 0.6× 6.4k 1.8× 5.5k 2.8× 201 28.9k
Paulo S. Boggio Brazil 61 14.8k 1.1× 10.0k 1.0× 1.9k 0.3× 2.5k 0.7× 1.8k 0.9× 199 18.8k
Sarah H. Lisanby United States 66 10.0k 0.8× 6.6k 0.7× 1.9k 0.3× 1.1k 0.3× 2.7k 1.4× 245 16.5k

Countries citing papers authored by Marom Bikson

Since Specialization
Citations

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

Fields of papers citing papers by Marom Bikson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marom Bikson

This figure shows the co-authorship network connecting the top 25 collaborators of Marom Bikson. A scholar is included among the top collaborators of Marom Bikson 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 Marom Bikson. Marom Bikson 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.
Bikson, Marom. (2025). Wearable Disposable Brain Stimulation. Brain stimulation. 18(1). 614–614.
2.
Khadka, Niranjan, et al.. (2025). Wearable disposable electrotherapy. Nature Communications. 16(1). 9060–9060.
3.
Jeong, Hyeonseok, Seunghee Na, Jin Kyoung Oh, et al.. (2025). Repeated neuromodulation with low-intensity focused ultrasound in patients with Alzheimer's disease. Journal of Alzheimer s Disease. 105(3). 955–965. 2 indexed citations
5.
Charvet, Leigh, Judith D. Goldberg, Xiaochun Li, et al.. (2025). Enhanced cognitive outcomes with telehealth-based tDCS in multiple sclerosis: Results from a sham-controlled RCT. Multiple Sclerosis Journal - Experimental Translational and Clinical. 11(3). 3120719904–3120719904.
6.
Ho, Johnson, et al.. (2024). A Visual and Narrative Timeline Review of Spinal Cord Stimulation Technology and US Food and Drug Administration Milestones. Neuromodulation Technology at the Neural Interface. 27(6). 1020–1025. 2 indexed citations
7.
Khadka, Niranjan, et al.. (2024). Transcranial electric stimulation modulates firing rate at clinically relevant intensities. Brain stimulation. 17(3). 561–571. 11 indexed citations
8.
Khadka, Niranjan, Cynthia Poon, Limary M. Cancel, John M. Tarbell, & Marom Bikson. (2023). Multi-scale multi-physics model of brain interstitial water flux by transcranial Direct Current Stimulation. Journal of Neural Engineering. 20(4). 46014–46014. 4 indexed citations
9.
Sabé, Michel, Chaomei Chen, Joshua Hyde, et al.. (2023). A century of research on neuromodulation interventions: A scientometric analysis of trends and knowledge maps. Neuroscience & Biobehavioral Reviews. 152. 105300–105300. 24 indexed citations
11.
Sharma, Mahima, et al.. (2021). Weak DCS causes a relatively strong cumulative boost of synaptic plasticity with spaced learning. Brain stimulation. 15(1). 57–62. 14 indexed citations
13.
14.
Foster, Adriana, Yair Bar‐Haim, Daniel S. Pine, et al.. (2020). Case Series of Transcranial Direct Current Stimulation as an Augmentation Strategy for Attention Bias Modification Treatment in Adolescents with Anxiety Disorders. SHILAP Revista de lepidopterología. 9(3). 105–126. 2 indexed citations
15.
Esmaeilpour, Zeinab, Greg Kronberg, Davide Reato, Lucas C. Parra, & Marom Bikson. (2020). Temporal interference stimulation targets deep brain regions by modulating neural oscillations. Brain stimulation. 14(1). 55–65. 88 indexed citations
16.
Machado, Daniel Gomes da Silva, Gözde Ünal, Suellen Marinho Andrade, et al.. (2018). Effect of transcranial direct current stimulation on exercise performance: A systematic review and meta-analysis. Brain stimulation. 12(3). 593–605. 105 indexed citations
17.
Huang, Yu, Anli Liu, Belen Lafon, et al.. (2017). Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. eLife. 6. 389 indexed citations breakdown →
18.
Khadka, Niranjan, et al.. (2016). Transcranial direct current stimulation transiently increases the blood-brain barrier solute permeability in vivo. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9788. 97881X–97881X. 4 indexed citations
19.
Brunoni, André R., Bernardo Sampaio-Júnior, Adriano H. Moffa, et al.. (2015). The Escitalopram versus Electric Current Therapy for Treating Depression Clinical Study (ELECT-TDCS): rationale and study design of a non-inferiority, triple-arm, placebo-controlled clinical trial. Sao Paulo Medical Journal. 133(3). 252–263. 42 indexed citations
20.
Bikson, Marom, Jun Lian, & Dominique M. Durand. (1999). Effect of high frequency stimulation on epileptiform activity in the hippocampus. The Society for Neuroscience Abstracts. 25. 1870. 1 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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