Mimi Dang
Impact in
- Psychiatry and Mental health top 0.5%
- Dementia and Cognitive Impairment Research
- Physiology top 1%
- Alzheimer's disease research and treatments
Papers in
-
- Dementia and Cognitive Impairment Research 55
- Physiology 22
- Alzheimer's disease research and treatments 22
Mimi Dang
73 papers receiving 5.7k citations
Mimi Dang's Hit Papers
Peers
Comparison fields: 5 of 148
- Psychiatry and Mental health 2.8k
- Physiology 1.6k
- Neurology 456
- Health Informatics 99
- Cognitive Neuroscience 675
Countries citing papers authored by Mimi Dang
This map shows the geographic impact of Mimi Dang'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 Mimi Dang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mimi Dang more than expected).
Fields of papers citing papers by Mimi Dang
This network shows the impact of papers produced by Mimi Dang. 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 Mimi Dang. The network helps show where Mimi Dang may publish in the future.
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All Works
Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis Hit paper breakdown → | 2016 | 947 |
| 2 | Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference Hit paper breakdown → | 2018 | 411 |
| 3 | Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease Hit paper breakdown → | 2020 | 390 |
| 4 | Predicting Alzheimer’s disease progression using multi-modal deep learning approach Hit paper breakdown → | 2019 | 319 |
| 5 | Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images Hit paper breakdown → | 2018 | 315 |
| 6 | 2015 | 295 | |
| 7 | 2016 | 264 | |
| 8 | Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease Hit paper breakdown → | 2020 | 264 |
| 9 | 2016 | 139 | |
| 10 | 2017 | 125 | |
| 11 | 2022 | 103 | |
| 12 | 2019 | 99 | |
| 13 | 2020 | 96 | |
| 14 | 2022 | 92 | |
| 15 | 2017 | 91 | |
| 16 | 2018 | 77 | |
| 17 | 2023 | 77 | |
| 18 | 2018 | 75 | |
| 19 | 2019 | 74 | |
| 20 | 2019 | 72 |
About Mimi Dang
Mimi Dang is a scholar working on Psychiatry and Mental health, Physiology, Cognitive Neuroscience, Statistics and Probability and Genetics, having authored 76 papers that have together received 5.8k indexed citations. Recurring topics across this work include Dementia and Cognitive Impairment Research (55 papers), Alzheimer's disease research and treatments (22 papers), Functional Brain Connectivity Studies (13 papers), Genetic Associations and Epidemiology (4 papers), Epigenetics and DNA Methylation (1 paper), Topological and Geometric Data Analysis (1 paper), Advanced Neuroimaging Techniques and Applications (1 paper) and Traumatic Brain Injury Research (1 paper). The work is most often cited by research in Psychiatry and Mental health (2.8k citations), Physiology (1.6k citations), Neurology (456 citations), Health Informatics (99 citations) and Cognitive Neuroscience (675 citations). Mimi Dang has collaborated with scholars based in United States, Canada and Hong Kong. Their work appears in journals such as Scientific Reports, Nature Communications, GeroScience, Proceedings of the National Academy of Sciences and JAMA Neurology.
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.