Shuli Liang

66 papers receiving 960 citations

Peers

Shuli Liang
Comparison fields: 5 of 79
  • Psychiatry and Mental health 332
  • Cellular and Molecular Neuroscience 314
  • Cognitive Neuroscience 278
  • Neurology 107
  • Neurology 171
Replace Virginie Lambrecq with:
Virginie Lambrecq France
Petr Marusič Czechia
Xiao Luo China
Luca De Palma Italy
Aimée F. Luat United States
Annelies Van Dycke Belgium
Wenhan Hu China
Peter Bergin New Zealand
Kotaro Hiraoka Japan
Silvia Bonelli Austria
Shuli Liang relative to Virginie Lambrecq France Virginie Lambrecq's profile →
Citations per field
00.5×1.5×2.3×
Virginie Lambrecq · 1×
Citations per year

Countries citing papers authored by Shuli Liang

Since Specialization
Citations

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

Fields of papers citing papers by Shuli Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shuli Liang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shuli Liang Line = papers co-authored together Shuli Liang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202084
2 201377
3 201948
4 201748
5 202147
6 201839
7 201936
8 202235
9 201034
10 201831
11 201630
12 201427
13 202026
14 201625
15 201225
16 201723
17 201922
18 201621
19 201820
20 201320

About Shuli Liang

Shuli Liang is a scholar working on Psychiatry and Mental health, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Neurology and Pediatrics, Perinatology and Child Health, having authored 70 papers that have together received 966 indexed citations. Recurring topics across this work include Epilepsy research and treatment (25 papers), Neurological disorders and treatments (14 papers), Tuberous Sclerosis Complex Research (12 papers), EEG and Brain-Computer Interfaces (11 papers), Neuroscience and Neuropharmacology Research (11 papers), Pharmacological Effects and Toxicity Studies (11 papers), Genetic and Kidney Cyst Diseases (8 papers) and Polyomavirus and related diseases (7 papers). The work is most often cited by research in Psychiatry and Mental health (332 citations), Cellular and Molecular Neuroscience (314 citations), Cognitive Neuroscience (278 citations), Neurology (107 citations) and Neurology (171 citations). Shuli Liang has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Shaohui Zhang, Guojun Zhang, Ru Liu, Xiaofeng Yang, Hongwei� Jiang, Zhiqi Mao, Longsheng Pan, Zhipei Ling, Liang Wang and Wenjing Zhou. Their work appears in journals such as Frontiers in Neurology, Epilepsy Research, World Neurosurgery, Stereotactic and Functional Neurosurgery and Science Advances.

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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