Aleksander A. Mathé

14.2k total citations · 1 hit paper
235 papers, 11.3k citations indexed

About

Aleksander A. Mathé is a scholar working on Cellular and Molecular Neuroscience, Behavioral Neuroscience and Physiology. According to data from OpenAlex, Aleksander A. Mathé has authored 235 papers receiving a total of 11.3k indexed citations (citations by other indexed papers that have themselves been cited), including 129 papers in Cellular and Molecular Neuroscience, 70 papers in Behavioral Neuroscience and 56 papers in Physiology. Recurrent topics in Aleksander A. Mathé's work include Neuropeptides and Animal Physiology (80 papers), Stress Responses and Cortisol (70 papers) and Neuroendocrine regulation and behavior (42 papers). Aleksander A. Mathé is often cited by papers focused on Neuropeptides and Animal Physiology (80 papers), Stress Responses and Cortisol (70 papers) and Neuroendocrine regulation and behavior (42 papers). Aleksander A. Mathé collaborates with scholars based in Sweden, United States and Italy. Aleksander A. Mathé's co-authors include Francesco Angelucci, Stefan Brené, Susanne Gruber, Gregers Wegener, Astrid Bjørnebekk, Per Hedqvist, Carina Stenfors, Elvar Theodorsson, Hagit Cohen and Aram El Khoury and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and The Lancet.

In The Last Decade

Aleksander A. Mathé

234 papers receiving 10.9k citations

Hit Papers

Understanding resilience 2013 2026 2017 2021 2013 100 200 300 400

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Aleksander A. Mathé 4.3k 3.1k 2.4k 2.2k 1.9k 235 11.3k
Rainer Rupprecht 4.1k 1.0× 4.0k 1.3× 3.2k 1.3× 1.2k 0.6× 2.4k 1.3× 374 15.1k
Marco Andrea Riva 4.7k 1.1× 3.8k 1.2× 3.0k 1.2× 1.2k 0.6× 3.2k 1.7× 299 13.0k
Michael J. Owens 3.3k 0.8× 4.3k 1.4× 1.9k 0.8× 891 0.4× 2.1k 1.1× 164 11.2k
Giorgio Racagni 8.0k 1.9× 3.0k 1.0× 4.7k 2.0× 2.0k 0.9× 2.6k 1.3× 363 16.4k
Paul J. Lucassen 4.0k 0.9× 5.9k 1.9× 3.0k 1.3× 3.1k 1.4× 2.9k 1.5× 247 16.9k
Undine E. Lang 4.1k 0.9× 2.6k 0.8× 2.9k 1.2× 2.2k 1.0× 3.5k 1.8× 377 17.4k
Victoria Arango 5.4k 1.3× 1.9k 0.6× 3.4k 1.4× 900 0.4× 2.3k 1.2× 153 12.9k
Ralph Dileone 5.4k 1.3× 2.5k 0.8× 3.5k 1.5× 2.1k 1.0× 1.9k 1.0× 101 13.9k
Hiroshi Kunugi 3.3k 0.8× 2.0k 0.6× 4.2k 1.8× 2.1k 1.0× 3.0k 1.6× 446 15.7k
Gregers Wegener 2.6k 0.6× 1.9k 0.6× 1.9k 0.8× 1.3k 0.6× 2.3k 1.2× 252 8.1k

Countries citing papers authored by Aleksander A. Mathé

Since Specialization
Citations

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

Fields of papers citing papers by Aleksander A. Mathé

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aleksander A. Mathé. 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 Aleksander A. Mathé. The network helps show where Aleksander A. Mathé may publish in the future.

Co-authorship network of co-authors of Aleksander A. Mathé

This figure shows the co-authorship network connecting the top 25 collaborators of Aleksander A. Mathé. A scholar is included among the top collaborators of Aleksander A. Mathé 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 Aleksander A. Mathé. Aleksander A. Mathé 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.
Zohar, Joseph, et al.. (2024). Exploring the Anxiolytic Potential of NPY by a Dipeptidyl Peptidase-IV Inhibitor in an Animal Model of PTSD. The International Journal of Neuropsychopharmacology. 27(12). 7 indexed citations
2.
Carboni, Lucia, Aram El Khoury, Daniela I. Beiderbeck, Inga D. Neumann, & Aleksander A. Mathé. (2022). Neuropeptide Y, calcitonin gene-related peptide, and neurokinin A in brain regions of HAB rats correlate with anxiety-like behaviours. European Neuropsychopharmacology. 57. 1–14. 15 indexed citations
3.
Efstathopoulos, Paschalis, et al.. (2021). Sirtuinsandneuropeptide ydownregulation in Flinders Sensitive Line rat model of depression. Acta Neuropsychiatrica. 34(2). 86–92. 7 indexed citations
4.
Carboni, Lucia, Francesca Pischedda, Giovanni Piccoli, et al.. (2020). Depression-Associated Gene Negr1-Fgfr2 Pathway Is Altered by Antidepressant Treatment. Cells. 9(8). 1818–1818. 17 indexed citations
5.
Mathé, Aleksander A., et al.. (2020). A Randomized Controlled Trial of Intranasal Neuropeptide Y in Patients With Major Depressive Disorder. The International Journal of Neuropsychopharmacology. 23(12). 783–790. 34 indexed citations
6.
Nasca, Carla, Benedetta Bigio, Kathleen Watson, et al.. (2020). Insulin receptor substrate in brain-enriched exosomes in subjects with major depression: on the path of creation of biosignatures of central insulin resistance. Molecular Psychiatry. 26(9). 5140–5149. 81 indexed citations
7.
Bigio, Benedetta, Aleksander A. Mathé, Vasco C. Sousa, et al.. (2016). Epigenetics and energetics in ventral hippocampus mediate rapid antidepressant action: Implications for treatment resistance. Proceedings of the National Academy of Sciences. 113(28). 7906–7911. 72 indexed citations
8.
Wei, Ya Bin, Philippe A. Melas, Jia Jia Liu, et al.. (2016). MicroRNA 101b Is Downregulated in the Prefrontal Cortex of a Genetic Model of Depression and Targets the Glutamate Transporter SLC1A1 (EAAT3)in Vitro. The International Journal of Neuropsychopharmacology. 19(12). pyw069–pyw069. 22 indexed citations
9.
Slattery, David A., Roshan Ratnakar Naik, Thomas Grund, et al.. (2015). Selective Breeding for High Anxiety Introduces a Synonymous SNP That Increases Neuropeptide S Receptor Activity. Journal of Neuroscience. 35(11). 4599–4613. 50 indexed citations
10.
Edemann-Callesen, Henriette, Mareike Voget, Martin Vögel, et al.. (2015). Medial Forebrain Bundle Deep Brain Stimulation has Symptom-specific Anti-depressant Effects in Rats and as Opposed to Ventromedial Prefrontal Cortex Stimulation Interacts With the Reward System. Brain stimulation. 8(4). 714–723. 36 indexed citations
11.
Soleimani, Laili, María A. Oquendo, Gregory M. Sullivan, Aleksander A. Mathé, & J. John Mann. (2014). Cerebrospinal Fluid Neuropeptide Y Levels in Major Depression and Reported Childhood Trauma. The International Journal of Neuropsychopharmacology. 18(1). pyu023–pyu023. 17 indexed citations
14.
Moreno, Francisco, Craig M. Palmer, Aram El Khoury, et al.. (2009). CSF neurochemicals during tryptophan depletion in individuals with remitted depression and healthy controls. European Neuropsychopharmacology. 20(1). 18–24. 46 indexed citations
15.
Garnier, Fabien, et al.. (2008). Electrical aspects of Brugada: hyperkalaemia and intoxication with phenothiazines. Heart. 94(12). 1578–1578. 4 indexed citations
18.
Murase, Sumio, et al.. (1995). Seasonal mood variation among Japanese residents of Stockholm. Acta Psychiatrica Scandinavica. 92(1). 51–55. 35 indexed citations
19.
Lemen, Richard J., et al.. (1978). Relationships among digital clubbing, disease severity, and serum prostaglandins F2alpha and E concentrations in cystic fibrosis patients.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 117(4). 639–46. 35 indexed citations
20.
Mathé, Aleksander A., Per Hedqvist, A. Holmgren, & N Svanborg. (1973). Bronchial Hyperreactivity to Prostaglandin F2α and Histamine in Patients with Asthma. BMJ. 1(5847). 193–196. 161 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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