Lauren J. O’Donnell

8.6k total citations · 1 hit paper
142 papers, 4.6k citations indexed

About

Lauren J. O’Donnell is a scholar working on Radiology, Nuclear Medicine and Imaging, Pediatrics, Perinatology and Child Health and Cognitive Neuroscience. According to data from OpenAlex, Lauren J. O’Donnell has authored 142 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 125 papers in Radiology, Nuclear Medicine and Imaging, 51 papers in Pediatrics, Perinatology and Child Health and 31 papers in Cognitive Neuroscience. Recurrent topics in Lauren J. O’Donnell's work include Advanced Neuroimaging Techniques and Applications (124 papers), Advanced MRI Techniques and Applications (65 papers) and Fetal and Pediatric Neurological Disorders (50 papers). Lauren J. O’Donnell is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (124 papers), Advanced MRI Techniques and Applications (65 papers) and Fetal and Pediatric Neurological Disorders (50 papers). Lauren J. O’Donnell collaborates with scholars based in United States, China and Australia. Lauren J. O’Donnell's co-authors include Carl‐Fredrik Westin, Alexandra J. Golby, Fan Zhang, C.-F. Westin, K W Heaton, J. Virjee, Yogesh Rathi, Isaiah Norton, Ofer Pasternak and William M. Wells and has published in prestigious journals such as PLoS ONE, NeuroImage and Cancer Research.

In The Last Decade

Lauren J. O’Donnell

128 papers receiving 4.5k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

2022 149 citations Fan Zhang, Lauren J. O’Donnell et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lauren J. O’Donnell United States	36	3.4k	1.2k	1.1k	339	324	142	4.6k
Yonggang Shi United States	30	1.6k 0.5×	352 0.3×	743 0.7×	178 0.5×	334 1.0×	141	3.3k
Aaron S. Field United States	36	4.5k 1.3×	986 0.8×	1.8k 1.6×	802 2.4×	76 0.2×	93	6.6k
Andréia V. Faria United States	34	3.4k 1.0×	983 0.8×	2.5k 2.2×	812 2.4×	207 0.6×	135	5.9k
Matthan W.A. Caan Netherlands	37	1.3k 0.4×	482 0.4×	957 0.8×	548 1.6×	152 0.5×	140	4.2k
Yoshitaka Masutani Japan	35	3.2k 0.9×	737 0.6×	802 0.7×	918 2.7×	345 1.1×	141	5.1k
Hangyi Jiang United States	25	6.1k 1.8×	2.0k 1.7×	2.9k 2.6×	983 2.9×	158 0.5×	41	8.0k
Mariana Lazar United States	27	4.5k 1.3×	1.3k 1.1×	2.3k 2.0×	828 2.4×	54 0.2×	62	6.4k
Thomas R. Barrick United Kingdom	46	2.9k 0.9×	463 0.4×	2.0k 1.7×	977 2.9×	422 1.3×	115	6.5k
Jeffrey Duda United States	18	2.8k 0.8×	835 0.7×	1.2k 1.1×	353 1.0×	118 0.4×	44	4.0k
Kazi Akhter United States	9	2.1k 0.6×	516 0.4×	1.1k 1.0×	359 1.1×	82 0.3×	9	2.8k

Countries citing papers authored by Lauren J. O’Donnell

Since Specialization

Citations

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

Fields of papers citing papers by Lauren J. O’Donnell

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lauren J. O’Donnell. 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 Lauren J. O’Donnell. The network helps show where Lauren J. O’Donnell may publish in the future.

Co-authorship network of co-authors of Lauren J. O’Donnell

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Lan, Zhou, Yuqian Chen, Jarrett Rushmore, et al.. (2025). Fiber microstructure quantile (FMQ) regression: A novel statistical approach for analyzing white matter bundles from periphery to core. Imaging Neuroscience. 3.

Wang, Jinhui, Junyi Wang, Mingyu Gao, et al.. (2025). DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRI. IEEE Journal of Selected Topics in Signal Processing. 19(8). 1920–1929.

Cetin‐Karayumak, Suheyla, Fan Zhang, Tashrif Billah, et al.. (2024). Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. Scientific Data. 11(1). 249–249. 10 indexed citations

Zhang, Fan, Yuqian Chen, Lipeng Ning, et al.. (2024). Assessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI. Human Brain Mapping. 45(14). e70041–e70041.

Zhang, Fan, Kang Ik Kevin Cho, Johanna Seitz‐Holland, et al.. (2023). DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI. IEEE Transactions on Medical Imaging. 43(3). 1191–1202. 10 indexed citations

Zhang, Fan, Yuanjing Feng, Jarrett Rushmore, et al.. (2023). Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Human Brain Mapping. 44(17). 6055–6073. 10 indexed citations

Ahtam, Banu, Henry A. Feldman, Lauren J. O’Donnell, et al.. (2023). Abstract 66: Diffusion Imaging Demonstrates White Matter Alterations In Children With Moyamoya In Watershed Tracts. Stroke. 54(Suppl_1). 1 indexed citations

Li, Sipei, Guoqiang Xie, Shun Yao, et al.. (2023). DEEPRGVP: A novel microstructure-informed supervised contrastive learning framework for automated identification of the retinogeniculate pathway using dMRI tractography. 37. 1–5. 1 indexed citations

Zanão, Tamires, Johanna Seitz‐Holland, Lauren J. O’Donnell, et al.. (2023). Exploring the impact of hippocampal sclerosis on white matter tracts and memory in individuals with mesial temporal lobe epilepsy. Epilepsia Open. 8(3). 1111–1122. 3 indexed citations

10.

Xu, Hao, Dongnan Liu, Fan Zhang, et al.. (2023). A Registration- and Uncertainty-Based Framework for White Matter Tract Segmentation with Only One Annotated Subject. 1–5. 2 indexed citations

11.

Levitt, James J., Fan Zhang, Márk Vangel, et al.. (2023). The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Molecular Psychiatry. 28(6). 2301–2311. 7 indexed citations

12.

Goodlett, Benjamin D., Fan Zhang, Adam P. Vogel, et al.. (2023). Characterization of central manifestations in patients with Niemann-Pick disease type C. Genetics in Medicine. 26(3). 101053–101053. 7 indexed citations

13.

Xiao, Xu, Yuqian Chen, Leo Zekelman, et al.. (2023). Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method. 12. 1–4. 1 indexed citations

14.

Zhang, Fan, Steve Pieper, Nikos Makris, et al.. (2022). Model and Predict Age and Sex in Healthy Subjects Using Brain White Matter Features: A Deep Learning Approach. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1–5. 5 indexed citations

15.

Widge, Alik S., Fan Zhang, Angela M. Noecker, et al.. (2021). Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder. Neuropsychopharmacology. 47(4). 965–972. 22 indexed citations

16.

Norton, Isaiah, Walid Ibn Essayed, Fan Zhang, et al.. (2017). SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Research. 77(21). e101–e103. 91 indexed citations

17.

Shaffer, Joseph J., Jeffrey D. Long, Regina E. Y. Kim, et al.. (2017). Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration. PMC. 1 indexed citations

18.

Ning, Lipeng, Frederik B. Laun, Yaniv Gur, et al.. (2015). Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?. Medical Image Analysis. 26(1). 316–331. 61 indexed citations

19.

Panagiotaki, Eleftheria, et al.. (2012). CDMRI 2012. MICCAI 2012 Workshop on Computational Diffusion MRI.

20.

Knight, Melanie A., et al.. (2001). Adult mouse intrinsic laryngeal muscles express high levels of the myogenic regulatory factor, MYF-5. Queensland's institutional digital repository (The University of Queensland). 9(3). 157–167. 2 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.

Explore authors with similar magnitude of impact