Kun Qin

987 total citations
47 papers, 574 citations indexed

About

Kun Qin is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Psychiatry and Mental health. According to data from OpenAlex, Kun Qin has authored 47 papers receiving a total of 574 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Cognitive Neuroscience, 19 papers in Experimental and Cognitive Psychology and 19 papers in Psychiatry and Mental health. Recurrent topics in Kun Qin's work include Functional Brain Connectivity Studies (35 papers), Mental Health Research Topics (18 papers) and Bipolar Disorder and Treatment (15 papers). Kun Qin is often cited by papers focused on Functional Brain Connectivity Studies (35 papers), Mental Health Research Topics (18 papers) and Bipolar Disorder and Treatment (15 papers). Kun Qin collaborates with scholars based in China, United States and United Kingdom. Kun Qin's co-authors include Qiyong Gong, Du Lei, John A. Sweeney, Nanfang Pan, Xueling Suo, Graham J. Kemp, Song Wang, Wenbin Li, Jing Yang and Walter Hugo Lopez Pinaya and has published in prestigious journals such as Biological Psychiatry, The British Journal of Psychiatry and Cerebral Cortex.

In The Last Decade

Kun Qin

43 papers receiving 565 citations

Peers

Kun Qin
Eva Mennigen United States
Richelle Stiffler United States
Xiaojun Huang China
Saige Rutherford United States
Nanfang Pan China
Kristin Klahr United States
Jeanette Lockovich United States
Knut Øverbye Norway
Pauline Favre France
María J. Escartí Spain
Eva Mennigen United States
Kun Qin
Citations per year, relative to Kun Qin Kun Qin (= 1×) peers Eva Mennigen

Countries citing papers authored by Kun Qin

Since Specialization
Citations

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

Fields of papers citing papers by Kun Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kun Qin

This figure shows the co-authorship network connecting the top 25 collaborators of Kun Qin. A scholar is included among the top collaborators of Kun Qin 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 Kun Qin. Kun Qin 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.
Pan, Nanfang, et al.. (2025). Noninvasive Brain Stimulation in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis of Treatment Efficacy and Targeting Strategies. Journal of the American Academy of Child & Adolescent Psychiatry. 65(3). 369–391.
2.
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Pan, Nanfang, Kun Qin, L. Rodrigo Patino, et al.. (2024). Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task‐based fMRI connectome study. Journal of Child Psychology and Psychiatry. 65(8). 1072–1086. 4 indexed citations
4.
Xie, Hongsheng, Baolin Wu, Kun Qin, et al.. (2024). Body Satisfaction, Exercise Dependence, and White Matter Microstructure in Young Adults. Journal of Magnetic Resonance Imaging. 61(2). 749–755. 1 indexed citations
5.
Li, Huiru, Huawei Zhang, Kun Qin, et al.. (2024). Disrupted small-world white matter networks in patients with major depression and recent suicide plans or attempts. Brain Imaging and Behavior. 18(4). 741–752. 4 indexed citations
6.
Zhang, Lisha, Kun Qin, Nanfang Pan, Haoran Xu, & Qiyong Gong. (2024). Shared and distinct patterns of default mode network dysfunction in major depressive disorder and bipolar disorder: A comparative meta-analysis. Journal of Affective Disorders. 368. 23–32. 3 indexed citations
7.
Long, Jingyi, Kun Qin, Nanfang Pan, Wenliang Fan, & Yi Li. (2024). Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set. The British Journal of Psychiatry. 224(5). 170–178. 8 indexed citations
8.
Qin, Kun, Huiru Li, Huawei Zhang, et al.. (2024). Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder. Biological Psychiatry. 96(6). 435–444. 21 indexed citations
9.
Pan, Nanfang, Song Wang, Xun Zhang, et al.. (2024). Multivariate patterns of brain functional connectome associated with COVID-19-related negative affect symptoms. Translational Psychiatry. 14(1). 49–49. 2 indexed citations
10.
Qin, Kun, Wenbin Li, Maxwell J. Tallman, et al.. (2023). Regional microstructural differences in ADHD youth with and without a family history of bipolar I disorder. Journal of Affective Disorders. 334. 238–245. 1 indexed citations
11.
Pan, Nanfang, et al.. (2023). ChatGPT: a promising AI technology for psychoradiology research and practice. PubMed. 3. kkad018–kkad018. 2 indexed citations
12.
Pan, Nanfang, Kun Qin, Ying Chen, et al.. (2023). Shared and Distinct Neurobiological Bases of Bipolar Disorder and Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: A Comparative Meta-Analysis of Structural Abnormalities. Journal of the American Academy of Child & Adolescent Psychiatry. 63(6). 586–604. 6 indexed citations
13.
Suo, Xueling, Du Lei, Huaiqiang Sun, et al.. (2023). Characterizing White Matter Changes along Fibers in Treatment-Naive Pediatric Posttraumatic Stress Disorder. Depression and Anxiety. 2023. 1–9. 1 indexed citations
14.
Qin, Kun, Du Lei, Walter Hugo Lopez Pinaya, et al.. (2022). Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. EBioMedicine. 78. 103977–103977. 59 indexed citations
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Pan, Nanfang, Kun Qin, Xun Zhang, et al.. (2022). Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic. Psychological Medicine. 53(11). 5155–5166. 12 indexed citations
17.
Lei, Du, Wenbin Li, Maxwell J. Tallman, et al.. (2021). Changes in the brain structural connectome after a prospective randomized clinical trial of lithium and quetiapine treatment in youth with bipolar disorder. Neuropsychopharmacology. 46(7). 1315–1323. 29 indexed citations
18.
Pan, Nanfang, Song Wang, Yajun Zhao, et al.. (2021). Brain gray matter structures associated with trait impulsivity: A systematic review and voxel‐based meta‐analysis. Human Brain Mapping. 42(7). 2214–2235. 68 indexed citations
19.
Li, Junying, Xueling Suo, Nannan Li, et al.. (2021). Disrupted Brain Network Topology in Drug-naïve Essential Tremor Patients with and Without Depression. Clinical Neuroradiology. 31(4). 981–992. 10 indexed citations
20.
He, Haoqiang, Hui Zhang, Kun Qin, et al.. (2020). A randomized controlled trial for gualou danshen granules in the treatment of unstable angina pectoris patients with phlegm-blood stasis syndrome. Medicine. 99(33). e21593–e21593. 5 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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