Ming Song

5.5k citations
69 papers · 3.7k indexed · 1 hit paper · h-index 29
Topics
Functional Brain Connectivity Studies (41 papers)Advanced Neuroimaging Techniques and Applications (25 papers)Neural dynamics and brain function (19 papers)

In The Last Decade

Ming Song

65 papers receiving 3.7k citations

Hit Papers

Disrupted small-world networks in schizophrenia20082026201420202008250500750

Peers

Ming Song
Comparison fields: 5 of 137
  • Cognitive Neuroscience 2.5k
  • Radiology, Nuclear Medicine and Imaging 997
  • Experimental and Cognitive Psychology 759
  • Psychiatry and Mental health 400
  • Social Psychology 377
Replace Noriaki Yahata with:
Noriaki Yahata Japan
Rachel M. Brouwer Netherlands
Aaron Alexander‐Bloch United States
Sheila G. Crewther Australia
Mario Džemidžić United States
Michael T. Alkire United States
Gerd Wagner Germany
Daphne J. Holt United States
Christiane M. Thiel Germany
Casey Paquola Canada
Ming Song relative to Noriaki Yahata Japan Noriaki Yahata's profile →
Citations per field
00.5×
Noriaki Yahata · 1×
Citations per year

Countries citing papers authored by Ming Song

Since Specialization
Citations

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

Fields of papers citing papers by Ming Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Song

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Song. A scholar is included among the top collaborators of Ming Song 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 Ming Song. Ming Song 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
#WorkIndexed citations
1 6
2 0
3 14
4 2
5 1
6 1
7 18
8 68
9 52
10 41
11 27
12 61
13 25
14 57
15 87
16 19
17 69
18 261
19 115
20 69

About Ming Song

Ming Song is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Neurology, having authored 69 papers that have together received 3.7k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (41 papers), Advanced Neuroimaging Techniques and Applications (25 papers) and Neural dynamics and brain function (19 papers). The work is most often cited by research in Cognitive Neuroscience (2.5k citations), Experimental and Cognitive Psychology (759 citations) and Behavioral Neuroscience (180 citations). Ming Song has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Tianzi Jiang, Chunshui Yu, Yong Liu, Yuan Zhou, Yihui Hao, Zhening Liu, Meng Liang, Haihong Liu, Yong He and Jingyu Yang. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

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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