Giorgio Bonmassar

5.6k total citations · 1 hit paper
112 papers, 4.0k citations indexed

About

Giorgio Bonmassar is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Neurology. According to data from OpenAlex, Giorgio Bonmassar has authored 112 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Cognitive Neuroscience, 49 papers in Radiology, Nuclear Medicine and Imaging and 33 papers in Neurology. Recurrent topics in Giorgio Bonmassar's work include Advanced MRI Techniques and Applications (44 papers), Neurological disorders and treatments (32 papers) and Neuroscience and Neural Engineering (27 papers). Giorgio Bonmassar is often cited by papers focused on Advanced MRI Techniques and Applications (44 papers), Neurological disorders and treatments (32 papers) and Neuroscience and Neural Engineering (27 papers). Giorgio Bonmassar collaborates with scholars based in United States, Italy and Japan. Giorgio Bonmassar's co-authors include John W. Belliveau, Leonardo M. Angelone, Bruce R. Rosen, Jon̈athan R. Polimeni, Nina E. Fultz, Laura D. Lewis, Robert Stickgold, Kawin Setsompop, Jyrki Ahveninen and Laleh Golestanirad and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Giorgio Bonmassar

101 papers receiving 3.9k citations

Hit Papers

Coupled electrophysiological, hemodynamic, and cerebrospi... 2019 2026 2021 2023 2019 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giorgio Bonmassar United States 32 2.2k 1.3k 1.0k 779 491 112 4.0k
Werner Doyle United States 42 3.9k 1.8× 563 0.4× 2.1k 2.0× 823 1.1× 490 1.0× 136 6.7k
A Oeltermann Germany 16 5.7k 2.6× 2.0k 1.6× 1.7k 1.6× 323 0.4× 286 0.6× 28 6.9k
Olivier David France 41 4.3k 2.0× 815 0.6× 1.5k 1.4× 1.0k 1.3× 167 0.3× 203 6.2k
John R. Ives United States 36 2.7k 1.2× 889 0.7× 579 0.6× 244 0.3× 161 0.3× 89 3.7k
Uri T. Eden United States 36 3.7k 1.7× 409 0.3× 2.0k 1.9× 312 0.4× 237 0.5× 131 4.9k
M Augath Germany 29 7.4k 3.4× 2.4k 1.9× 1.7k 1.7× 323 0.4× 284 0.6× 65 9.0k
J Pauls Germany 8 5.1k 2.3× 1.7k 1.4× 1.1k 1.0× 246 0.3× 203 0.4× 21 6.4k
Richard C. Burgess United States 38 2.9k 1.3× 624 0.5× 1.2k 1.2× 650 0.8× 326 0.7× 167 4.8k
Krish D. Singh United Kingdom 53 7.5k 3.4× 1.4k 1.1× 2.1k 2.0× 354 0.5× 354 0.7× 180 10.2k
Ashesh D. Mehta United States 47 6.7k 3.0× 585 0.5× 1.7k 1.6× 440 0.6× 401 0.8× 105 8.7k

Countries citing papers authored by Giorgio Bonmassar

Since Specialization
Citations

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

Fields of papers citing papers by Giorgio Bonmassar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giorgio Bonmassar

This figure shows the co-authorship network connecting the top 25 collaborators of Giorgio Bonmassar. A scholar is included among the top collaborators of Giorgio Bonmassar 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 Giorgio Bonmassar. Giorgio Bonmassar 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
2.
Kouwe, André van der, et al.. (2023). The MotoNet: An MRI-Compatible EEG Net with Embedded Motion Sensors. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations
3.
Warbrick, Tracy, Manfred Jaschke, Rajiv Gupta, et al.. (2023). Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction. Sensors. 23(7). 3633–3633.
4.
Bonmassar, Giorgio, et al.. (2023). Multi-Segment Leads To Reduce RF Heating in MRI: A Computational Evaluation at 1.5T and 3T. PubMed. 2023. 1–4. 3 indexed citations
5.
Fultz, Nina E., Daniel E. P. Gomez, Stephanie D. Williams, et al.. (2022). A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state. Nature Communications. 13(1). 5442–5442. 33 indexed citations
6.
Zöllei, Lilla, et al.. (2022). A high-resolution pediatric female whole-body numerical model with comparison to a male model. Physics in Medicine and Biology. 68(2). 25022–25022.
7.
Golestanirad, Laleh, Leonardo M. Angelone, John E. Kirsch, et al.. (2019). Reducing RF-Induced Heating Near Implanted Leads Through High-Dielectric Capacitive Bleeding of Current (CBLOC). IEEE Transactions on Microwave Theory and Techniques. 67(3). 1265–1273. 43 indexed citations
8.
Fultz, Nina E., Giorgio Bonmassar, Kawin Setsompop, et al.. (2019). Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep. Science. 366(6465). 628–631. 683 indexed citations breakdown →
9.
Serano, Peter, et al.. (2018). Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T. IEEE Transactions on Electromagnetic Compatibility. 61(3). 852–859. 13 indexed citations
10.
Golestanirad, Laleh, Maria Ida Iacono, Boris Keil, et al.. (2017). Construction and modeling of a reconfigurable MRI coil for lowering SAR in patients with deep brain stimulation implants. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations
11.
Bonmassar, Giorgio & Laleh Golestanirad. (2017). EM fields comparison between planar vs. solenoidal μMS coil designs for nerve stimulation. PubMed. 16. 3576–3579. 8 indexed citations
12.
Ahveninen, Jyrki, Wei‐Tang Chang, Samantha Huang, et al.. (2016). Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using 7T fMRI. NeuroImage. 143. 116–127. 31 indexed citations
13.
Bonmassar, Giorgio, Catherine Poulsen, Eric T. Pierce, et al.. (2015). Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. DSpace@MIT (Massachusetts Institute of Technology). 6 indexed citations
15.
Bonmassar, Giorgio, Peter Serano, & Leonardo M. Angelone. (2013). Specific absorption rate in a standard phantom containing a Deep Brain Stimulation lead at 3 Tesla MRI. 747–750. 6 indexed citations
16.
Lee, Seung Woo, Giorgio Bonmassar, & Shelley I. Fried. (2012). Activation of Retinal Ganglion Cells By Microcoil-Induced Magnetic Stimulation. Investigative Ophthalmology & Visual Science. 53(14). 5530–5530. 1 indexed citations
17.
Bonmassar, Giorgio, Seung Woo Lee, D. Freeman, et al.. (2012). Microscopic magnetic stimulation of neural tissue. Nature Communications. 3(1). 921–921. 149 indexed citations
18.
Franceschini, Maria Angela, Ilkka Nissilä, Wei‐Cheng Wu, et al.. (2008). Coupling between somatosensory evoked potentials and hemodynamic response in the rat. NeuroImage. 41(2). 189–203. 61 indexed citations
19.
Angelone, Leonardo M., Christos E. Vasios, Graham C. Wiggins, Patrick L. Purdon, & Giorgio Bonmassar. (2006). On the effect of resistive EEG electrodes and leads during 7 T MRI: simulation and temperature measurement studies. Magnetic Resonance Imaging. 24(6). 801–812. 42 indexed citations
20.
Bonmassar, Giorgio, et al.. (2001). Spatiotemporal Brain Imaging of Visual-Evoked Activity Using Interleaved EEG and fMRI Recordings. NeuroImage. 13(6). 1035–1043. 116 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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