Amy W. Lasek

2.6k total citations
69 papers, 1.9k citations indexed

About

Amy W. Lasek is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Amy W. Lasek has authored 69 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 34 papers in Cellular and Molecular Neuroscience and 14 papers in Cell Biology. Recurrent topics in Amy W. Lasek's work include Neurotransmitter Receptor Influence on Behavior (20 papers), Neuroscience and Neuropharmacology Research (15 papers) and Receptor Mechanisms and Signaling (12 papers). Amy W. Lasek is often cited by papers focused on Neurotransmitter Receptor Influence on Behavior (20 papers), Neuroscience and Neuropharmacology Research (15 papers) and Receptor Mechanisms and Signaling (12 papers). Amy W. Lasek collaborates with scholars based in United States, Spain and United Kingdom. Amy W. Lasek's co-authors include Ulrike Heberlein, Donghong He, Rosalba Satta, Hu Chen, Wei-Yang Chen, Mark S. Brodie, Chang You, Linus Tsai, Viktor Kharazia and David A. Jones and has published in prestigious journals such as Cell, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Amy W. Lasek

67 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amy W. Lasek United States 28 833 812 252 214 200 69 1.9k
Eric F. Schmidt United States 20 1.2k 1.5× 1.2k 1.5× 195 0.8× 282 1.3× 269 1.3× 30 2.5k
Rochelle M. Hines United States 21 1.2k 1.5× 982 1.2× 94 0.4× 218 1.0× 350 1.8× 35 2.2k
Julieta Alfonso Germany 21 526 0.6× 637 0.8× 175 0.7× 121 0.6× 188 0.9× 30 1.5k
Zsuzsanna Callaerts‐Vegh Belgium 25 767 0.9× 919 1.1× 162 0.6× 179 0.8× 223 1.1× 66 2.0k
John Marshall United States 28 1.3k 1.5× 1.8k 2.2× 193 0.8× 378 1.8× 211 1.1× 44 2.9k
Natalia V. Gounko Belgium 23 495 0.6× 1.1k 1.3× 114 0.5× 324 1.5× 149 0.7× 43 1.9k
Jessica L. Ables United States 18 1.2k 1.4× 1.4k 1.8× 306 1.2× 132 0.6× 347 1.7× 22 2.9k
Yun‐Li Ma Taiwan 31 798 1.0× 1.2k 1.4× 241 1.0× 97 0.5× 209 1.0× 68 2.4k
Shanting Zhao China 29 1.3k 1.5× 997 1.2× 98 0.4× 333 1.6× 274 1.4× 83 2.6k
Koko Ishizuka United States 21 714 0.9× 1.4k 1.7× 110 0.4× 144 0.7× 300 1.5× 59 2.5k

Countries citing papers authored by Amy W. Lasek

Since Specialization
Citations

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

Fields of papers citing papers by Amy W. Lasek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amy W. Lasek. 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 Amy W. Lasek. The network helps show where Amy W. Lasek may publish in the future.

Co-authorship network of co-authors of Amy W. Lasek

This figure shows the co-authorship network connecting the top 25 collaborators of Amy W. Lasek. A scholar is included among the top collaborators of Amy W. Lasek 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 Amy W. Lasek. Amy W. Lasek 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.
Yamaguchi, Takashi, et al.. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. 28(12). 2502–2514. 2 indexed citations
2.
Nguyen, Rachel, et al.. (2025). Estrogenic regulation of perineuronal nets in the mouse insular cortex and hippocampus. Neuropharmacology. 279. 110641–110641.
3.
Lasek, Amy W., et al.. (2024). Modulation of stress-, pain-, and alcohol-related behaviors by perineuronal nets. Neurobiology of Stress. 33. 100692–100692. 2 indexed citations
4.
Chen, Hu, et al.. (2024). Histone deacetylase inhibitor decreases hyperalgesia in a mouse model of alcohol withdrawal‐induced hyperalgesia. Alcohol Clinical and Experimental Research. 48(3). 478–487. 7 indexed citations
5.
You, Chang, Harish R. Krishnan, Ying Chen, et al.. (2023). Transcriptional Dysregulation of Cholesterol Synthesis Underlies Hyposensitivity to GABA in the Ventral Tegmental Area During Acute Alcohol Withdrawal. Biological Psychiatry. 95(3). 275–285. 2 indexed citations
6.
Chen, Hu, et al.. (2023). Conserved role for PCBP1 in altered RNA splicing in the hippocampus after chronic alcohol exposure. Molecular Psychiatry. 28(10). 4215–4224. 3 indexed citations
7.
Chen, Wei-Yang, et al.. (2020). Epigenetic mechanisms underlying stress-induced depression. International review of neurobiology. 156. 87–126. 19 indexed citations
8.
Chen, Hu & Amy W. Lasek. (2019). Perineuronal nets in the insula regulate aversion‐resistant alcohol drinking. Addiction Biology. 25(6). e12821–e12821. 50 indexed citations
9.
Hamada, Kana & Amy W. Lasek. (2019). Receptor Tyrosine Kinases as Therapeutic Targets for Alcohol Use Disorder. Neurotherapeutics. 17(1). 4–16. 7 indexed citations
10.
You, Chang, et al.. (2018). Histone Deacetylase Inhibitor Suberanilohydroxamic Acid Treatment Reverses Hyposensitivity to γ‐Aminobutyric Acid in the Ventral Tegmental Area During Ethanol Withdrawal. Alcoholism Clinical and Experimental Research. 42(11). 2160–2171. 9 indexed citations
11.
Harris, R. Adron, Michal Bajo, Richard L. Bell, et al.. (2017). Genetic and Pharmacologic Manipulation of TLR4 Has Minimal Impact on Ethanol Consumption in Rodents. PMC. 2 indexed citations
12.
Lasek, Amy W., Hu Chen, & Wei-Yang Chen. (2017). Releasing Addiction Memories Trapped in Perineuronal Nets. Trends in Genetics. 34(3). 197–208. 55 indexed citations
13.
Lasek, Amy W., et al.. (2013). Sex differences in cocaine conditioned place preference in C57BL/6J mice. Neuroreport. 25(2). 105–109. 41 indexed citations
14.
Hamida, Sami Ben, Jérémie Neasta, Amy W. Lasek, et al.. (2012). The Small G Protein H-Ras in the Mesolimbic System Is a Molecular Gateway to Alcohol-Seeking and Excessive Drinking Behaviors. Journal of Neuroscience. 32(45). 15849–15858. 30 indexed citations
15.
Maiya, Rajani, Viktor Kharazia, Amy W. Lasek, & Ulrike Heberlein. (2012). Lmo4 in the Basolateral Complex of the Amygdala Modulates Fear Learning. PLoS ONE. 7(4). e34559–e34559. 12 indexed citations
16.
Lasek, Amy W., Jana P. Lim, Christopher L. Kliethermes, et al.. (2011). An Evolutionary Conserved Role for Anaplastic Lymphoma Kinase in Behavioral Responses to Ethanol. PLoS ONE. 6(7). e22636–e22636. 80 indexed citations
17.
Lasek, Amy W., et al.. (2011). AlkIs a Transcriptional Target of LMO4 and ERα That Promotes Cocaine Sensitization and Reward. Journal of Neuroscience. 31(40). 14134–14141. 25 indexed citations
18.
Lasek, Amy W., et al.. (2009). Virus-Delivered RNA Interference in Mouse Brain to Study Addiction-Related Behaviors. Methods in molecular biology. 602. 283–298. 12 indexed citations
19.
Lesscher, Heidi M. B., Thomas McMahon, Amy W. Lasek, et al.. (2007). Amygdala protein kinase C epsilon regulates corticotropin‐releasing factor and anxiety‐like behavior. Genes Brain & Behavior. 7(3). 323–333. 41 indexed citations
20.
Heighway, Jim, John K. Field, Daniel Betticher, et al.. (2002). Expression profiling of primary non-small cell lung cancer for target identification. Oncogene. 21(50). 7749–7763. 121 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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