Ying Xing
- Cognitive Neuroscience top 10%
- Radiology, Nuclear Medicine and Imaging
- Experimental and Cognitive Psychology
- Artificial Intelligence
- Psychiatry and Mental health
- Co-authors
- Vince D. CalhounYuhui DuPeter KochunovZening FuDongdong LinAnees AbrolL. Elliot HongMustafa S. Salman
- Topics
- Functional Brain Connectivity Studies (4 papers)Machine Learning in Healthcare (3 papers)Neural dynamics and brain function (2 papers)
- Cited by
- Cognitive NeuroscienceRadiology, Nuclear Medicine and ImagingExperimental and Cognitive Psychology
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNeuroImageSchizophrenia Bulletin
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Ying Xing
8 papers receiving 279 citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Cognitive Neuroscience 226
- Radiology, Nuclear Medicine and Imaging 91
- Experimental and Cognitive Psychology 45
- Artificial Intelligence 41
- Psychiatry and Mental health 39
Countries citing papers authored by Ying Xing
This map shows the geographic impact of Ying Xing'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 Ying Xing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Xing more than expected).
Fields of papers citing papers by Ying Xing
This network shows the impact of papers produced by Ying Xing. 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 Ying Xing. The network helps show where Ying Xing may publish in the future.
Co-authorship network of co-authors of Ying Xing
This figure shows the co-authorship network connecting the top 25 collaborators of Ying Xing. A scholar is included among the top collaborators of Ying Xing 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 Ying Xing. Ying Xing is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 4 | |
| 6 | 15 | |
| 7 | 35 | |
| 8 | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disordersbreakdown → | 215 |
About Ying Xing
Ying Xing is a scholar working on Cognitive Neuroscience, Biophysics and Artificial Intelligence, having authored 8 papers that have together received 280 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (4 papers), Machine Learning in Healthcare (3 papers) and Neural dynamics and brain function (2 papers). The work is most often cited by research in Cognitive Neuroscience (226 citations), Radiology, Nuclear Medicine and Imaging (91 citations) and Experimental and Cognitive Psychology (45 citations). Ying Xing has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Vince D. Calhoun, Yuhui Du, Peter Kochunov, Zening Fu, Dongdong Lin, Anees Abrol, L. Elliot Hong, Mustafa S. Salman, Jing Sui and Md Abdur Rahaman. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, NeuroImage and Schizophrenia Bulletin.
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.