Ming Song

5.5k total citations · 1 hit paper
69 papers, 3.7k citations indexed

About

Ming Song is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Experimental and Cognitive Psychology. According to data from OpenAlex, Ming Song has authored 69 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Cognitive Neuroscience, 29 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Experimental and Cognitive Psychology. Recurrent topics in Ming Song's work include Functional Brain Connectivity Studies (41 papers), Advanced Neuroimaging Techniques and Applications (25 papers) and Neural dynamics and brain function (19 papers). Ming Song is often cited by papers focused on Functional Brain Connectivity Studies (41 papers), Advanced Neuroimaging Techniques and Applications (25 papers) and Neural dynamics and brain function (19 papers). Ming Song collaborates with scholars based in China, Australia and United States. Ming Song's co-authors include Tianzi Jiang, Chunshui Yu, Yong Liu, Yuan Zhou, Yihui Hao, Zhening Liu, Meng Liang, Haihong Liu, Yong He and Jingyu Yang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

In The Last Decade

Ming Song

65 papers receiving 3.7k citations

Hit Papers

Disrupted small-world networks in schizophrenia 2008 2026 2014 2020 2008 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming Song China 29 2.5k 997 759 400 377 69 3.7k
Michael T. Alkire United States 28 2.7k 1.1× 541 0.5× 488 0.6× 301 0.8× 326 0.9× 38 4.5k
Urs Ribary Canada 29 3.9k 1.6× 316 0.3× 495 0.7× 436 1.1× 391 1.0× 86 6.1k
Gerd Wagner Germany 41 3.1k 1.3× 1.1k 1.1× 1.1k 1.4× 1.2k 3.0× 261 0.7× 141 5.4k
David P. Crewther Australia 35 2.0k 0.8× 570 0.6× 476 0.6× 284 0.7× 290 0.8× 176 4.0k
Aaron Alexander‐Bloch United States 32 4.2k 1.7× 2.1k 2.1× 883 1.2× 761 1.9× 168 0.4× 83 5.8k
Biyu J. He United States 30 4.7k 1.9× 835 0.8× 292 0.4× 267 0.7× 164 0.4× 58 5.1k
Sheila G. Crewther Australia 36 1.6k 0.7× 611 0.6× 530 0.7× 385 1.0× 215 0.6× 217 4.1k
Andrew S. Fox United States 38 2.0k 0.8× 458 0.5× 1.1k 1.4× 291 0.7× 791 2.1× 79 3.7k
Gabriele Ende Germany 42 1.7k 0.7× 1.0k 1.0× 385 0.5× 1.3k 3.3× 164 0.4× 140 4.9k
Jong H. Yoon United States 32 2.4k 1.0× 464 0.5× 503 0.7× 947 2.4× 208 0.6× 62 3.4k

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
1.
Liu, Hao, Jin Fang, Yihang Wang, et al.. (2025). A wearable repetitive transcranial magnetic stimulation device. Nature Communications. 16(1). 2731–2731. 6 indexed citations
3.
Luo, Na, et al.. (2024). Multimodal Fusion of Brain Imaging Data: Methods and Applications. 21(1). 136–152. 14 indexed citations
4.
Zhang, Xiaoru, Ming Song, Jin Li, & Tianzi Jiang. (2022). EM-fMRI: A Promising Method for Mapping the Brain Functional Connectome. Neuroscience Bulletin. 39(4). 707–709. 2 indexed citations
5.
Song, Ming, Zhengyi Yang, & Tianzi Jiang. (2022). Multimodal Brain Imaging Fusion for the White-Matter Fiber Architecture in the Human Brain. Neuroscience Bulletin. 38(5). 561–564. 1 indexed citations
6.
Song, Ming, Jianghong He, Yi Yang, & Tianzi Jiang. (2021). Combination of Biomedical Techniques and Paradigms to Improve Prognostications for Disorders of Consciousness. Neuroscience Bulletin. 37(7). 1082–1084. 1 indexed citations
7.
Wu, Dongya, Lingzhong Fan, Ming Song, et al.. (2020). Hierarchy of Connectivity–Function Relationship of the Human Cortex Revealed through Predicting Activity across Functional Domains. Cerebral Cortex. 30(8). 4607–4616. 18 indexed citations
8.
Song, Ming, Yi Yang, Jianghong He, et al.. (2018). Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics. eLife. 7. 68 indexed citations
9.
Jiang, Rongtao, Chris Abbott, Tianzi Jiang, et al.. (2017). SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacology. 43(5). 1078–1087. 52 indexed citations
10.
Wu, Yan, Yuan Zhou, Yuanchao Zhang, et al.. (2016). Sex-specific neural circuits of emotion regulation in the centromedial amygdala. Scientific Reports. 6(1). 23112–23112. 41 indexed citations
11.
Liao, Yanhui, Jinsong Tang, Mei Yang, et al.. (2016). Decreased Thalamocortical Connectivity in Chronic Ketamine Users. PLoS ONE. 11(12). e0167381–e0167381. 27 indexed citations
12.
Tang, Jinsong, Yanhui Liao, Ming Song, et al.. (2013). Aberrant Default Mode Functional Connectivity in Early Onset Schizophrenia. PLoS ONE. 8(7). e71061–e71061. 61 indexed citations
13.
Jiang, Tianzi, Yuan Zhou, Bing Liu, Yong Liu, & Ming Song. (2013). Brainnetome-wide association studies in schizophrenia: The advances and future. Neuroscience & Biobehavioral Reviews. 37(10). 2818–2835. 25 indexed citations
14.
Song, Ming, Hanjian Du, Nan Wu, et al.. (2011). Impaired Resting-State Functional Integrations within Default Mode Network of Generalized Tonic-Clonic Seizures Epilepsy. PLoS ONE. 6(2). e17294–e17294. 57 indexed citations
15.
Yin, Yan, Lingjiang Li, Changfeng Jin, et al.. (2011). Abnormal baseline brain activity in posttraumatic stress disorder: A resting-state functional magnetic resonance imaging study. Neuroscience Letters. 498(3). 185–189. 87 indexed citations
16.
Xu, Cunlu, Zhenhua Wang, Ming Fan, et al.. (2010). Effects of BDNF Val66Met polymorphism on brain metabolism in Alzheimer's disease. Neuroreport. 21(12). 802–807. 19 indexed citations
17.
Song, Ming, et al.. (2010). Regional homogeneity of the resting-state brain activity correlates with individual intelligence. Neuroscience Letters. 488(3). 275–278. 69 indexed citations
18.
Song, Ming, Yuan Zhou, Jun Li, et al.. (2008). Brain spontaneous functional connectivity and intelligence. NeuroImage. 41(3). 1168–1176. 261 indexed citations
19.
Qi, Jia, Jingyu Yang, Ming Song, et al.. (2007). Inhibition by oxytocin of methamphetamine-induced hyperactivity related to dopamine turnover in the mesolimbic region in mice. Naunyn-Schmiedeberg s Archives of Pharmacology. 376(6). 441–448. 115 indexed citations
20.
Tian, Lixia, Tianzi Jiang, Yong Liu, et al.. (2007). The relationship within and between the extrinsic and intrinsic systems indicated by resting state correlational patterns of sensory cortices. NeuroImage. 36(3). 684–690. 69 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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