Dan Ehninger

87 papers receiving 4.9k citations

Dan Ehninger's Hit Papers

Reversal of learning deficits in a Tsc2+/− mouse model of tuberous sclerosis 2008 · 707 citations
7070+6+12Years since publication200400600

Peers

Dan Ehninger
Comparison fields: 5 of 137
  • Developmental Neuroscience 1.1k
  • Aging 205
  • Neurology 571
  • Cellular and Molecular Neuroscience 1.2k
  • Behavioral Neuroscience 226
Replace Farahnaz Sananbenesi with:
Farahnaz Sananbenesi Germany
Jinsoo Seo South Korea
Diane C. Lagace Canada
Marcelo A. Wood United States
Alexander A. Sosunov United States
Victoria M. Perreau Australia
Farah D. Lubin United States
Marı́a Llorens-Martı́n Spain
Dan Ehninger relative to Farahnaz Sananbenesi Germany Farahnaz Sananbenesi's profile →
Citations per field
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Farahnaz Sananbenesi · 1×
Citations per year

Countries citing papers authored by Dan Ehninger

Since Specialization
Citations

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

Fields of papers citing papers by Dan Ehninger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dan Ehninger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Ehninger Line = papers co-authored together Dan Ehninger links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 88 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Reversal of learning deficits in a Tsc2+/− mouse model of tuberous sclerosis
Hit paper breakdown →
2008707
2 2005374
3 2006316
4 2003270
5 2010261
6 2007198
7 2010185
8 2007173
9 2008153
10 2007150
11 2014144
12 2010137
13 2014121
14 200995
15 202092
16 200592
17 201091
18 201791
19 201189
20 201386

About Dan Ehninger

Dan Ehninger is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Physiology, Cognitive Neuroscience and Aging, having authored 88 papers that have together received 5.1k indexed citations. Recurring topics across this work include Neuroscience and Neuropharmacology Research (23 papers), Alzheimer's disease research and treatments (12 papers), Neurogenesis and neuroplasticity mechanisms (9 papers), Genetics, Aging, and Longevity in Model Organisms (9 papers), Memory and Neural Mechanisms (9 papers), Genetics and Neurodevelopmental Disorders (8 papers), Mitochondrial Function and Pathology (8 papers) and Ion channel regulation and function (7 papers). The work is most often cited by research in Developmental Neuroscience (1.1k citations), Aging (205 citations), Neurology (571 citations), Cellular and Molecular Neuroscience (1.2k citations) and Behavioral Neuroscience (226 citations). Dan Ehninger has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Alcino J. Silva, Gerd Kempermann, Weidong Li, Yu Zhou, Carrie Shilyansky, Golo Kronenberg, Vijaya Ramesh, David J. Kwiatkowski, Sangyeul Han and Kan Xie. Their work appears in journals such as Neural Plasticity, Scientific Reports, Mechanisms of Ageing and Development, Data in Brief and Journal of Visualized Experiments.

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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