Sunita Bava

2.7k citations
22 papers · 2.1k indexed · h-index 19

Sunita Bava

22 papers receiving 2.0k citations

Peers

Sunita Bava
Comparison fields: 5 of 90
  • Cognitive Neuroscience 929
  • Computational Mathematics 17
  • Pediatrics, Perinatology and Child Health 510
  • Psychiatry and Mental health 373
  • Radiology, Nuclear Medicine and Imaging 539
Replace Tim McQueeny with:
Tim McQueeny United States
Rachel E. Thayer United States
Richard Kanaan Australia
Jeong‐Ho Seok South Korea
Matthew D. Albaugh United States
Mace Beckson United States
Raquelle I. Mesholam‐Gately United States
Annchen R. Knodt United States
Simon Ducharme Canada
Lara C. Foland‐Ross United States
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Citations per field
00.5×2.8×
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Citations per year

Countries citing papers authored by Sunita Bava

Since Specialization
Citations

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

Fields of papers citing papers by Sunita Bava

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Sunita Bava, 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 Sunita Bava Line = papers co-authored together Sunita Bava links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201915
2 201389
3 201271
4 201156
5 201076
6 201046
7 2010317
8 2010169
9 200928
10 2009142
11 2009137
12 2009216
13 200964
14 2009150
15 200719
16
Reduced microstructural white matter integrity in a genetic metabolic disorder: A diffusion tensor MRI study
20071
17 2005159
18 200521
19 20055
20 2002132

About Sunita Bava

Sunita Bava is a scholar working on Computational Mathematics, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience, having authored 22 papers that have together received 2.1k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (12 papers), Traumatic Brain Injury Research (6 papers), Functional Brain Connectivity Studies (5 papers), Neonatal and fetal brain pathology (4 papers), Cannabis and Cannabinoid Research (3 papers), Prenatal Substance Exposure Effects (3 papers), Hemispheric Asymmetry in Neuroscience (2 papers) and Biomedical Research and Pathophysiology (2 papers). The work is most often cited by research in Cognitive Neuroscience (929 citations), Computational Mathematics (17 citations) and Pediatrics, Perinatology and Child Health (510 citations). Sunita Bava has collaborated with scholars based in United States, Finland and Israel. Frequent co-authors include Susan F. Tapert, Joanna Jacobus, Lawrence R. Frank, Rachel E. Thayer, Brian C. Schweinsburg, Tim McQueeny, Alecia D. Schweinsburg, Omar M. Mahmood, Tony T. Yang and Megan Ward. Their work appears in journals such as NeuroImage, Brain Research and Alcoholism Clinical and Experimental Research.

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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