Nicha C. Dvornek
- Cognitive Neuroscience top 2%
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Psychiatry and Mental health top 10%
- Neurology top 10%
- Co-authors
- James S. DuncanPamela VentolaXiaoxiao LiLawrence H. StaibJuntang ZhuangYufeng GuKevin A. PelphreyMuhan Zhang
- Topics
- Functional Brain Connectivity Studies (22 papers)Radiomics and Machine Learning in Medical Imaging (13 papers)Medical Imaging Techniques and Applications (10 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNeuropsychopharmacologyIEEE Transactions on Medical Imaging
- Partner nations
- United StatesChinaCanada
In The Last Decade
Nicha C. Dvornek
45 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Cognitive Neuroscience 824
- Artificial Intelligence 503
- Radiology, Nuclear Medicine and Imaging 424
- Psychiatry and Mental health 141
- Neurology 129
Countries citing papers authored by Nicha C. Dvornek
This map shows the geographic impact of Nicha C. Dvornek'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 Nicha C. Dvornek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicha C. Dvornek more than expected).
Fields of papers citing papers by Nicha C. Dvornek
This network shows the impact of papers produced by Nicha C. Dvornek. 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 Nicha C. Dvornek. The network helps show where Nicha C. Dvornek may publish in the future.
Co-authorship network of co-authors of Nicha C. Dvornek
This figure shows the co-authorship network connecting the top 25 collaborators of Nicha C. Dvornek. A scholar is included among the top collaborators of Nicha C. Dvornek 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 Nicha C. Dvornek. Nicha C. Dvornek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 35 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 16 | |
| 11 | MALI: A memory efficient and reverse accurate integrator for Neural ODEs | 1 |
| 12 | BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysisbreakdown → | 351 |
| 13 | 7 | |
| 14 | 12 | |
| 15 | 25 | |
| 16 | 18 | |
| 17 | 8 | |
| 18 | 63 | |
| 19 | 51 | |
| 20 | 42 |
About Nicha C. Dvornek
Nicha C. Dvornek is a scholar working on Health Informatics, Cognitive Neuroscience and Radiology, Nuclear Medicine and Imaging, having authored 52 papers that have together received 1.5k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (22 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Medical Imaging Techniques and Applications (10 papers). The work is most often cited by research in Health Informatics (77 citations), Cognitive Neuroscience (824 citations) and Radiology, Nuclear Medicine and Imaging (424 citations). Nicha C. Dvornek has collaborated with scholars based in United States, China and Canada. Frequent co-authors include James S. Duncan, Pamela Ventola, Xiaoxiao Li, Lawrence H. Staib, Juntang Zhuang, Yufeng Gu, Kevin A. Pelphrey, Muhan Zhang, Yuan Zhou and Siyuan Gao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neuropsychopharmacology and IEEE Transactions on Medical Imaging.
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.