Li‐Chun Lin

1.2k total citations
17 papers, 830 citations indexed

About

Li‐Chun Lin is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Neurology. According to data from OpenAlex, Li‐Chun Lin has authored 17 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cellular and Molecular Neuroscience, 5 papers in Molecular Biology and 4 papers in Neurology. Recurrent topics in Li‐Chun Lin's work include Neuroscience and Neuropharmacology Research (5 papers), Neuroinflammation and Neurodegeneration Mechanisms (4 papers) and Single-cell and spatial transcriptomics (3 papers). Li‐Chun Lin is often cited by papers focused on Neuroscience and Neuropharmacology Research (5 papers), Neuroinflammation and Neurodegeneration Mechanisms (4 papers) and Single-cell and spatial transcriptomics (3 papers). Li‐Chun Lin collaborates with scholars based in United States, Taiwan and Switzerland. Li‐Chun Lin's co-authors include Etienne Sibille, Lih‐Chu Chiou, Wei‐Shiung Yang, Keng‐Chen Liang, Ing‐Kang Ho, Chiung‐Tong Chen, C.-Y. Wang, William W. Seeley, Alissa L. Nana and Stephanie E. Gaus and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Cerebral Cortex.

In The Last Decade

Li‐Chun Lin

17 papers receiving 825 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Li‐Chun Lin United States	10	271	233	194	152	144	17	830
Eleni Païzanis France	14	149 0.5×	312 1.3×	149 0.8×	177 1.2×	146 1.0×	17	857
Jenica D. Tapocik United States	19	186 0.7×	434 1.9×	446 2.3×	101 0.7×	120 0.8×	21	983
Ajaykumar N. Sharma United States	13	150 0.6×	194 0.8×	130 0.7×	120 0.8×	53 0.4×	14	671
Molly Brennan United States	7	183 0.7×	513 2.2×	255 1.3×	254 1.7×	134 0.9×	16	869
J. Brent Kuzmiski Canada	16	195 0.7×	455 2.0×	349 1.8×	142 0.9×	126 0.9×	18	1.2k
Victoria A. Macht United States	16	133 0.5×	130 0.6×	86 0.4×	101 0.7×	75 0.5×	28	565
In Se Lee South Korea	16	188 0.7×	218 0.9×	134 0.7×	122 0.8×	79 0.5×	42	821
Cristina Lemos Portugal	16	278 1.0×	366 1.6×	201 1.0×	82 0.5×	96 0.7×	27	940
Flavia Carreño United States	20	174 0.6×	281 1.2×	214 1.1×	106 0.7×	276 1.9×	29	1.1k
Jan N. Keijser Netherlands	19	176 0.6×	385 1.7×	218 1.1×	91 0.6×	253 1.8×	24	952

Countries citing papers authored by Li‐Chun Lin

Since Specialization

Citations

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

Fields of papers citing papers by Li‐Chun Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Li‐Chun Lin. 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 Li‐Chun Lin. The network helps show where Li‐Chun Lin may publish in the future.

Co-authorship network of co-authors of Li‐Chun Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Li‐Chun Lin. A scholar is included among the top collaborators of Li‐Chun Lin 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 Li‐Chun Lin. Li‐Chun Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Bahl, Ethan, Snehajyoti Chatterjee, Yann Vanrobaeys, et al.. (2024). Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data. Nature Communications. 15(1). 779–779. 7 indexed citations

Vanrobaeys, Yann, Snehajyoti Chatterjee, Li‐Chun Lin, et al.. (2023). Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation. Nature Communications. 14(1). 7095–7095. 26 indexed citations

Voigt, Andrew P., Nathaniel K. Mullin, E Navratil, et al.. (2023). Gene Expression Within a Human Choroidal Neovascular Membrane Using Spatial Transcriptomics. Investigative Ophthalmology & Visual Science. 64(13). 40–40. 5 indexed citations

Vanrobaeys, Yann, Ethan Bahl, Li‐Chun Lin, et al.. (2023). Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation. Nature Communications. 14(1). 6100–6100. 12 indexed citations

Lin, Li‐Chun, Rachel C. Cole, Jeremy D.W. Greenlee, & Nandakumar S. Narayanan. (2023). A Pilot Study of Ex Vivo Human Prefrontal RNA Transcriptomics in Parkinson’s Disease. Cellular and Molecular Neurobiology. 43(6). 3037–3046. 1 indexed citations

Lin, Li‐Chun, et al.. (2022). An 18‐month‐old with white matter calcifications and seizures. Brain Pathology. 32(5). e13082–e13082. 1 indexed citations

Lin, Li‐Chun, et al.. (2022). Neuropathology of COVID-19.. SHILAP Revista de lepidopterología. 65(Supplement). S146–S152. 3 indexed citations

Rexach, Jessica E., Damon Polioudakis, Vivek Swarup, et al.. (2020). Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression. Cell Reports. 33(7). 108398–108398. 59 indexed citations

Lin, Li‐Chun, Alissa L. Nana, Jihye Hwang, et al.. (2019). Preferential tau aggregation in von Economo neurons and fork cells in frontotemporal lobar degeneration with specific MAPT variants. Acta Neuropathologica Communications. 7(1). 159–159. 31 indexed citations

10.

Dijkstra, Anke A., Li‐Chun Lin, Alissa L. Nana, Stephanie E. Gaus, & William W. Seeley. (2016). Von Economo Neurons and Fork Cells: A Neurochemical Signature Linked to Monoaminergic Function. Cerebral Cortex. 28(1). 131–144. 33 indexed citations

11.

Lin, Li‐Chun & Etienne Sibille. (2015). Somatostatin, neuronal vulnerability and behavioral emotionality. Molecular Psychiatry. 20(3). 377–387. 134 indexed citations

12.

Lin, Li‐Chun & Etienne Sibille. (2015). Transcriptome changes induced by chronic psychosocial/environmental or neuroendocrine stressors reveal a selective cellular vulnerability of cortical somatostatin (SST) neurons, compared with pyramidal (PYR) neurons. Molecular Psychiatry. 20(3). 285–285. 4 indexed citations

13.

Lin, Li‐Chun & Etienne Sibille. (2013). Reduced brain somatostatin in mood disorders: a common pathophysiological substrate and drug target?. Frontiers in Pharmacology. 4. 90 indexed citations

14.

Lin, Li‐Chun, David A. Lewis, & Etienne Sibille. (2011). A human-mouse conserved sex bias in amygdala gene expression related to circadian clock and energy metabolism. Molecular Brain. 4(1). 18–18. 21 indexed citations

15.

Lin, Li‐Chun, Sabine Kolczewski, Eric Prinssen, et al.. (2010). Functional heterogeneity of nociceptin/orphanin FQ receptors revealed by (+)-5a Compound and Ro 64-6198 in rat periaqueductal grey slices. The International Journal of Neuropsychopharmacology. 14(7). 977–989. 9 indexed citations

16.

Wang, C.-Y., Li‐Chun Lin, Chiung‐Tong Chen, et al.. (2009). Sex Differences in High‐fat Diet‐induced Obesity, Metabolic Alterations and Learning, and Synaptic Plasticity Deficits in Mice. Obesity. 18(3). 463–469. 329 indexed citations

17.

Chen, Hsiun‐ing, Li‐Chun Lin, Lung Yu, et al.. (2007). Treadmill exercise enhances passive avoidance learning in rats: The role of down-regulated serotonin system in the limbic system. Neurobiology of Learning and Memory. 89(4). 489–496. 65 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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