Chin Song Lu

786 citations
9 papers · 551 indexed · h-index 8

Chin Song Lu

9 papers receiving 541 citations

Peers

Chin Song Lu
Comparison fields: 5 of 50
  • Neurology 497
  • Cellular and Molecular Neuroscience 322
  • Neurology 94
  • Cognitive Neuroscience 84
  • Complementary and alternative medicine 27
Replace K. Lohmann-Hedrich with:
K. Lohmann-Hedrich Germany
Thora Lohnau Germany
D. G. Healy United Kingdom
Hisamasa Imai Japan
Z.K. Wszolek United States
John Nutt United States
S. Gancher United States
Sara Carrideo Italy
Vivian Altmann Brazil
Hilary Ayling United Kingdom
Chin Song Lu relative to K. Lohmann-Hedrich Germany K. Lohmann-Hedrich's profile →
Citations per field
00.5×
K. Lohmann-Hedrich · 1×
Citations per year

Countries citing papers authored by Chin Song Lu

Since Specialization
Citations

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

Fields of papers citing papers by Chin Song Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Chin Song Lu, 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 Chin Song Lu Line = papers co-authored together Chin Song Lu links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 20189
2 20132
3 201147
4 2010112
5 2007125
6 2005192
7
Short-term effect of bilateral subthalamic stimulation for advanced Parkinson's disease.
200315
8
Extrapontine myelinolysis in a patient with systemic lupus erythematosus: a case report.
200211
9
Dopamine transporter concentration is reduced in asymptomatic Machado-Joseph disease gene carriers.
200238

About Chin Song Lu

Chin Song Lu is a scholar working on Neurology, Cellular and Molecular Neuroscience and Neurology, having authored 9 papers that have together received 551 indexed citations. Recurring topics across this work include Parkinson's Disease Mechanisms and Treatments (8 papers), Neurological disorders and treatments (6 papers), Genetic Neurodegenerative Diseases (4 papers), Neuroscience and Neural Engineering (2 papers), Banana Cultivation and Research (1 paper), Transcranial Magnetic Stimulation Studies (1 paper), Neurological diseases and metabolism (1 paper) and Lanthanide and Transition Metal Complexes (1 paper). The work is most often cited by research in Neurology (497 citations), Cellular and Molecular Neuroscience (322 citations) and Neurology (94 citations). Chin Song Lu has collaborated with scholars based in Taiwan, United Kingdom and United States. Frequent co-authors include Chiung Chu Chen, Shih Tseng Lee, Peter Brown, Hsiao Lung Chan, Yi‐Ting Hsu, Alexandre Eusébio, Patricia Limousin, Marwan Hariz, Jan Aasly and Matthew J. Farrer. Their work appears in journals such as Experimental Brain Research, Experimental Neurology and Frontiers in Human 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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