Donna Munic
Impact in
- Psychiatry and Mental health top 0.5%
- Dementia and Cognitive Impairment Research
- Physiology top 0.5%
- Alzheimer's disease research and treatments
Papers in
-
- Dementia and Cognitive Impairment Research 78
- Physiology 27
- Alzheimer's disease research and treatments 27
Donna Munic
100 papers receiving 7.7k citations
Donna Munic's Hit Papers
Peers
Comparison fields: 5 of 154
- Psychiatry and Mental health 4.2k
- Physiology 2.1k
- Neurology 555
- Health Informatics 103
- Cognitive Neuroscience 802
Countries citing papers authored by Donna Munic
This map shows the geographic impact of Donna Munic'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 Donna Munic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donna Munic more than expected).
Fields of papers citing papers by Donna Munic
This network shows the impact of papers produced by Donna Munic. 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 Donna Munic. The network helps show where Donna Munic may publish in the future.
Co-authors
The 2 scholars most cited alongside Donna Munic, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 105 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 | 306 | |
| 7 | 2015 | 295 | |
| 8 | 2016 | 264 | |
| 9 | Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease Hit paper breakdown → | 2020 | 264 |
| 10 | 2010 | 212 | |
| 11 | 2016 | 161 | |
| 12 | 2016 | 140 | |
| 13 | 2012 | 139 | |
| 14 | 2016 | 139 | |
| 15 | 2019 | 130 | |
| 16 | 2013 | 126 | |
| 17 | 2017 | 125 | |
| 18 | 2015 | 124 | |
| 19 | 2016 | 115 | |
| 20 | 2012 | 104 |
About Donna Munic
Donna Munic is a scholar working on Psychiatry and Mental health, Physiology, Cognitive Neuroscience, Genetics and Statistics and Probability, having authored 105 papers that have together received 7.8k indexed citations. Recurring topics across this work include Dementia and Cognitive Impairment Research (78 papers), Alzheimer's disease research and treatments (27 papers), Functional Brain Connectivity Studies (14 papers), Genetic Associations and Epidemiology (6 papers), Medical Image Segmentation Techniques (2 papers), Advanced Neuroimaging Techniques and Applications (2 papers), Epigenetics and DNA Methylation (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Psychiatry and Mental health (4.2k citations), Physiology (2.1k citations), Neurology (555 citations), Health Informatics (103 citations) and Cognitive Neuroscience (802 citations). Donna Munic has collaborated with scholars based in United States, Sweden and Canada. Frequent co-authors include James E. Johnson and Thomas P. Petzel. Their work appears in journals such as Scientific Reports, Neurology, Nature Communications, Proceedings of the National Academy of Sciences and GeroScience.
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.