Danielle Graham
- Behavioral Neuroscience top 0.2%
- Biological Psychiatry top 0.5%
- Cellular and Molecular Neuroscience top 0.5%
- Neurotransmitter Receptor Influence on Behavior 17
- Neuroscience and Neuropharmacology Research 13
- Developmental Neuroscience top 1%
- Neurology top 2%
- Parkinson's Disease Mechanisms and Treatments 9
- Amyotrophic Lateral Sclerosis Research 4
- Neurological disorders and treatments 4
-
- Alzheimer's disease research and treatments 16
-
- Computational Drug Discovery Methods 5
-
- S100 Proteins and Annexins 4
- Co-authors
- David W. SelfEric J. NestlerRalph DileoneMaribel RiosVaishnav KrishnanCarlos A. BolañosColleen A. McClungOlivier Berton
- Partner nations
- United StatesJapanFrance
In The Last Decade
Danielle Graham
60 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Behavioral Neuroscience 1.1k
- Biological Psychiatry 713
- Cellular and Molecular Neuroscience 2.0k
- Developmental Neuroscience 328
- Neurology 383
Countries citing papers authored by Danielle Graham
This map shows the geographic impact of Danielle Graham'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 Danielle Graham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Graham more than expected).
Fields of papers citing papers by Danielle Graham
This network shows the impact of papers produced by Danielle Graham. 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 Danielle Graham. The network helps show where Danielle Graham may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Danielle Graham, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2023 | 13 | |
| 7 | 2023 | 47 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 7 | |
| 11 | 2021 | 0 | |
| 12 | 2019 | 139 | |
| 13 | 2018 | 17 | |
| 14 | 2018 | 10 | |
| 15 | 2016 | 40 | |
| 16 | 2013 | 89 | |
| 17 | Integrating Behavioral and Molecular Approaches in Mouse: Self-Administration Studies | 2010 | 2 |
| 18 | 2010 | 67 | |
| 19 | 2007 | 55 | |
| 20 | 2007 | 329 |
About Danielle Graham
Danielle Graham is a scholar working on Cellular and Molecular Neuroscience, Behavioral Neuroscience and Neurology, having authored 68 papers that have together received 4.5k indexed citations. Recurring topics across this work include Neurotransmitter Receptor Influence on Behavior (17 papers), Alzheimer's disease research and treatments (16 papers), Neuroscience and Neuropharmacology Research (13 papers), Parkinson's Disease Mechanisms and Treatments (9 papers), Computational Drug Discovery Methods (5 papers), Amyotrophic Lateral Sclerosis Research (4 papers), S100 Proteins and Annexins (4 papers) and Neurological disorders and treatments (4 papers). The work is most often cited by research in Behavioral Neuroscience (1.1k citations), Biological Psychiatry (713 citations) and Cellular and Molecular Neuroscience (2.0k citations). Danielle Graham has collaborated with scholars based in United States, Japan and France. Frequent co-authors include David W. Self, Eric J. Nestler, Ralph Dileone, Maribel Rios, Vaishnav Krishnan, Carlos A. Bolaños, Colleen A. McClung, Olivier Berton, Lisa M. Monteggia and Scott J. Russo. Their work appears in journals such as Alzheimer s & Dementia, Neurology, Biological Psychiatry, Journal of Neuroscience and Nature Neuroscience.
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.