Jacob Levman

401 total citations
24 papers, 290 citations indexed

About

Jacob Levman is a scholar working on Radiology, Nuclear Medicine and Imaging, Pediatrics, Perinatology and Child Health and Cognitive Neuroscience. According to data from OpenAlex, Jacob Levman has authored 24 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Pediatrics, Perinatology and Child Health and 6 papers in Cognitive Neuroscience. Recurrent topics in Jacob Levman's work include Advanced Neuroimaging Techniques and Applications (10 papers), Fetal and Pediatric Neurological Disorders (7 papers) and Functional Brain Connectivity Studies (5 papers). Jacob Levman is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (10 papers), Fetal and Pediatric Neurological Disorders (7 papers) and Functional Brain Connectivity Studies (5 papers). Jacob Levman collaborates with scholars based in Canada, United States and Japan. Jacob Levman's co-authors include Emi Takahashi, Tadashi Shiohama, James Kennedy, Peter Jezzard, Michael A. Chappell, Yee Kai Tee, Fintan Sheerin, Nicholas P. Blockley, Nicole Baumer and Thomas W. Okell and has published in prestigious journals such as International Journal of Molecular Sciences, Cerebral Cortex and Human Brain Mapping.

In The Last Decade

Jacob Levman

21 papers receiving 288 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jacob Levman Canada 9 138 79 66 56 52 24 290
Rongwen Tain United States 10 162 1.2× 106 1.3× 13 0.2× 44 0.8× 10 0.2× 19 347
Angelika Mennecke Germany 11 185 1.3× 63 0.8× 55 0.8× 22 0.4× 9 0.2× 35 332
Pasi Tuunanen Finland 13 195 1.4× 28 0.4× 62 0.9× 19 0.3× 26 0.5× 16 405
Huiying Kang China 7 112 0.8× 27 0.3× 99 1.5× 79 1.4× 4 0.1× 17 255
Julia Reuter Germany 8 59 0.4× 40 0.5× 49 0.7× 39 0.7× 54 1.0× 13 282
Martin Buechert Germany 11 104 0.8× 8 0.1× 160 2.4× 30 0.5× 32 0.6× 23 407
Anna La Noce Italy 10 97 0.7× 73 0.9× 39 0.6× 23 0.4× 6 0.1× 29 318
Baohong Wen China 12 216 1.6× 37 0.5× 124 1.9× 12 0.2× 3 0.1× 54 427
Tomohide Akimitsu Japan 12 123 0.9× 10 0.1× 20 0.3× 104 1.9× 35 0.7× 19 470
Wibeke Nordhøy Norway 11 279 2.0× 39 0.5× 84 1.3× 34 0.6× 5 0.1× 16 407

Countries citing papers authored by Jacob Levman

Since Specialization
Citations

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

Fields of papers citing papers by Jacob Levman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jacob Levman

This figure shows the co-authorship network connecting the top 25 collaborators of Jacob Levman. A scholar is included among the top collaborators of Jacob Levman 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 Jacob Levman. Jacob Levman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gauthier, Camille, et al.. (2025). Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence. International Journal of Molecular Sciences. 26(14). 6878–6878. 2 indexed citations
2.
Kendall, J. M., et al.. (2025). Machine Learning and Feature Selection in Pediatric Appendicitis. Tomography. 11(8). 90–90. 3 indexed citations
3.
Guo, Xiaoyan, et al.. (2025). Machine Learning in Differentiated Thyroid Cancer Recurrence and Risk Prediction. Applied Sciences. 15(17). 9397–9397.
4.
5.
Mattie, David R., et al.. (2024). Baseline Structural Connectomics Data of Healthy Brain Development Assessed with Multi-Modal Magnetic Resonance Imaging. Information. 15(1). 66–66. 1 indexed citations
6.
Levman, Jacob, et al.. (2024). Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes. Information. 15(11). 687–687. 3 indexed citations
7.
Levman, Jacob, et al.. (2023). Morphological Abnormalities in Early-Onset Schizophrenia Revealed by Structural Magnetic Resonance Imaging. Biology. 12(3). 353–353. 1 indexed citations
8.
Akalan, Nejat, et al.. (2022). Preoperative and postoperative high angular resolution diffusion imaging tractography of cerebellar pathways in posterior fossa tumors. Clinical Anatomy. 35(8). 1085–1099. 3 indexed citations
9.
Levman, Jacob, et al.. (2021). Structural magnetic resonance imaging demonstrates volumetric brain abnormalities in down syndrome: Newborns to young adults. NeuroImage Clinical. 32. 102815–102815. 16 indexed citations
10.
Shiohama, Tadashi, et al.. (2020). Quantitative analyses of high‐angular resolution diffusion imaging (HARDI)‐derived long association fibers in children with sensorineural hearing loss. International Journal of Developmental Neuroscience. 80(8). 717–729. 3 indexed citations
11.
Shiohama, Tadashi, Jacob Levman, Lana Vasung, & Emi Takahashi. (2020). Brain morphological analysis in PTEN hamartoma tumor syndrome. American Journal of Medical Genetics Part A. 182(5). 1117–1129. 15 indexed citations
12.
Shiohama, Tadashi, et al.. (2019). Quantitative brain morphological analysis in CHARGE syndrome. NeuroImage Clinical. 23. 101866–101866. 10 indexed citations
13.
Vasung, Lana, Hyuk Jin Yun, Jae W. Song, et al.. (2019). Structural and Diffusion MRI Analyses With Histological Observations in Patients With Lissencephaly. Frontiers in Cell and Developmental Biology. 7. 124–124. 8 indexed citations
14.
Shiohama, Tadashi, Jacob Levman, Nicole Baumer, & Emi Takahashi. (2019). Structural Magnetic Resonance Imaging-Based Brain Morphology Study in Infants and Toddlers With Down Syndrome: The Effect of Comorbidities. Pediatric Neurology. 100. 67–73. 18 indexed citations
15.
Shiohama, Tadashi, et al.. (2019). The left lateral occipital cortex exhibits decreased thickness in children with sensorineural hearing loss. International Journal of Developmental Neuroscience. 76. 34–40. 15 indexed citations
16.
Levman, Jacob, et al.. (2019). Structural magnetic resonance imaging demonstrates abnormal cortical thickness in Down syndrome: Newborns to young adults. NeuroImage Clinical. 23. 101874–101874. 17 indexed citations
17.
Shiohama, Tadashi, Jacob Levman, & Emi Takahashi. (2019). Surface‐ and voxel‐based brain morphologic study in Rett and Rett‐like syndrome with MECP2 mutation. International Journal of Developmental Neuroscience. 73(1). 83–88. 19 indexed citations
18.
Levman, Jacob, et al.. (2018). Asymmetric Insular Connectomics Revealed by Diffusion Magnetic Resonance Imaging Analysis of Healthy Brain Development. Brain Connectivity. 9(1). 2–12. 5 indexed citations
19.
Levman, Jacob, et al.. (2017). A pediatric structural MRI analysis of healthy brain development from newborns to young adults. Human Brain Mapping. 38(12). 5931–5942. 54 indexed citations
20.
Levman, Jacob & Emi Takahashi. (2016). Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses. Frontiers in Pediatrics. 4. 5 indexed citations

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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