Ma Feilong

798 total citations
22 papers, 330 citations indexed

About

Ma Feilong is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ma Feilong has authored 22 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cognitive Neuroscience, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ma Feilong's work include Functional Brain Connectivity Studies (11 papers), Neural dynamics and brain function (9 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Ma Feilong is often cited by papers focused on Functional Brain Connectivity Studies (11 papers), Neural dynamics and brain function (9 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Ma Feilong collaborates with scholars based in United States, Italy and China. Ma Feilong's co-authors include James V. Haxby, J. Swaroop Guntupalli, Samuel A. Nastase, M. Ida Gobbini, Guo Jiahui, Matteo Visconti di Oleggio Castello, Andrew C. Connolly, Isabella Hansen, Jeremy F. Huckins and Xufeng Qi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Ma Feilong

22 papers receiving 328 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ma Feilong United States 9 289 90 37 27 16 22 330
Bradley Caron United States 8 278 1.0× 117 1.3× 23 0.6× 51 1.9× 14 0.9× 12 398
Jessica Dafflon United Kingdom 8 122 0.4× 54 0.6× 26 0.7× 13 0.5× 7 0.4× 10 216
Yudan Ren China 9 256 0.9× 105 1.2× 40 1.1× 9 0.3× 11 0.7× 25 288
Francesca Strappini Italy 9 233 0.8× 24 0.3× 21 0.6× 26 1.0× 11 0.7× 23 279
Binke Yuan China 10 250 0.9× 119 1.3× 37 1.0× 3 0.1× 8 0.5× 22 312
Seyedeh-Rezvan Farahibozorg United Kingdom 9 303 1.0× 87 1.0× 22 0.6× 5 0.2× 14 0.9× 13 341
Sonia Poltoratski United States 8 278 1.0× 56 0.6× 43 1.2× 68 2.5× 24 1.5× 15 304
Daniel Bullock United States 9 175 0.6× 177 2.0× 16 0.4× 7 0.3× 11 0.7× 14 310
Svyatoslav Vergun United States 5 341 1.2× 157 1.7× 44 1.2× 9 0.3× 4 0.3× 5 375
Hervé Glasel France 4 139 0.5× 65 0.7× 27 0.7× 20 0.7× 11 0.7× 5 233

Countries citing papers authored by Ma Feilong

Since Specialization
Citations

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

Fields of papers citing papers by Ma Feilong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ma Feilong

This figure shows the co-authorship network connecting the top 25 collaborators of Ma Feilong. A scholar is included among the top collaborators of Ma Feilong 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 Ma Feilong. Ma Feilong 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.
Chen, Su, et al.. (2025). Function of R2R3-Type Myeloblastosis Transcription Factors in Plants. Rice Science. 32(3). 307–321. 1 indexed citations
2.
Feilong, Ma, Guo Jiahui, M. Ida Gobbini, & James V. Haxby. (2024). A cortical surface template for human neuroscience. Nature Methods. 21(9). 1736–1742. 3 indexed citations
3.
Jiahui, Guo, Ma Feilong, Samuel A. Nastase, James V. Haxby, & M. Ida Gobbini. (2023). Cross-movie prediction of individualized functional topography. eLife. 12. 3 indexed citations
4.
Rapuano, Kristina M., Kevin Anderson, Monica D. Rosenberg, et al.. (2023). Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development. Journal of Neuroscience. 44(6). e0735232023–e0735232023. 2 indexed citations
5.
6.
Feilong, Ma, Samuel A. Nastase, Guo Jiahui, et al.. (2023). The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains. Imaging Neuroscience. 1. 5 indexed citations
7.
Jiahui, Guo, Ma Feilong, Matteo Visconti di Oleggio Castello, et al.. (2023). Modeling naturalistic face processing in humans with deep convolutional neural networks. Proceedings of the National Academy of Sciences. 120(43). e2304085120–e2304085120. 13 indexed citations
8.
Feilong, Ma, Samuel A. Nastase, Guo Jiahui, et al.. (2022). Precise and generalizable cartography of functional topographies in individual brains. Journal of Vision. 22(14). 3813–3813. 1 indexed citations
9.
10.
Jiahui, Guo, Ma Feilong, Matteo Visconti di Oleggio Castello, et al.. (2022). Modeling naturalistic face processing in humans with deep convolutional neural networks. 1 indexed citations
11.
12.
Jiahui, Guo, Ma Feilong, Matteo Visconti di Oleggio Castello, et al.. (2022). Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing. Journal of Vision. 22(14). 3714–3714. 1 indexed citations
13.
Feilong, Ma, J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, et al.. (2021). Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage. 233. 117975–117975. 10 indexed citations
14.
Sippel, Lauren M., Paul E. Holtzheimer, Jeremy F. Huckins, et al.. (2021). Neurocognitive mechanisms of poor social connection in posttraumatic stress disorder: Evidence for abnormalities in social working memory. Depression and Anxiety. 38(6). 615–625. 7 indexed citations
15.
Haxby, James V., J. Swaroop Guntupalli, Samuel A. Nastase, & Ma Feilong. (2020). Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies. eLife. 9. 94 indexed citations
16.
Jiahui, Guo, Ma Feilong, Matteo Visconti di Oleggio Castello, et al.. (2019). Predicting individual face-selective topography using naturalistic stimuli. NeuroImage. 216. 116458–116458. 13 indexed citations
17.
Nastase, Samuel A., Andrew C. Connolly, Ma Feilong, et al.. (2018). Modeling Semantic Encoding in a Common Neural Representational Space. Frontiers in Neuroscience. 12. 437–437. 17 indexed citations
18.
Guntupalli, J. Swaroop, Ma Feilong, & James V. Haxby. (2018). A computational model of shared fine-scale structure in the human connectome. PLoS Computational Biology. 14(4). e1006120–e1006120. 55 indexed citations
19.
Feilong, Ma, Samuel A. Nastase, J. Swaroop Guntupalli, & James V. Haxby. (2018). Reliable individual differences in fine-grained cortical functional architecture. NeuroImage. 183. 375–386. 45 indexed citations
20.
Li, Xueting, Alain De Beuckelaer, Guo Jiahui, et al.. (2014). The Gray Matter Volume of the Amygdala Is Correlated with the Perception of Melodic Intervals: A Voxel-Based Morphometry Study. PLoS ONE. 9(6). e99889–e99889. 6 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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