World's Best Scientists 2026 revealed!

D-Index & Metrics

Neuroscience

D-Index
74
Citations
22755
World Ranking
2088
National Ranking
993

Gang Li publication distribution in Neuroscience in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Neuroscience in 2026. The highlighted bar marks where Gang Li sits on this spectrum.

38–47 publications: 18 scientists 48–57 publications: 79 scientists 58–67 publications: 193 scientists 68–77 publications: 323 scientists 78–87 publications: 406 scientists 88–97 publications: 452 scientists 98–107 publications: 539 scientists 108–117 publications: 505 scientists 118–127 publications: 522 scientists 128–137 publications: 469 scientists 138–147 publications: 456 scientists 148–157 publications: 459 scientists 158–167 publications: 397 scientists 168–177 publications: 383 scientists 178–187 publications: 350 scientists 188–197 publications: 302 scientists 198–207 publications: 306 scientists 208–217 publications: 262 scientists 218–227 publications: 242 scientists 228–237 publications: 220 scientists 238–247 publications: 203 scientists 248–257 publications: 174 scientists 258–267 publications: 176 scientists 268–277 publications: 175 scientists 278–287 publications: 125 scientists 288–297 publications: 116 scientists 298–307 publications: 127 scientists 308–317 publications: 128 scientists 318–327 publications: 99 scientists 328–337 publications: 89 scientists 338–347 publications: 78 scientists 348–357 publications: 96 scientists 358–367 publications: 66 scientists 368–377 publications: 59 scientists 378–387 publications: 65 scientists 388–397 publications: 54 scientists 398–407 publications: 48 scientists 408–417 publications: 49 scientists 418–427 publications: 34 scientists 428–437 publications: 31 scientists 438–447 publications: 30 scientists 448–457 publications: 31 scientists 458–467 publications: 36 scientists 468–477 publications: 40 scientists 478–487 publications: 35 scientists 488–497 publications: 30 scientists 498–507 publications: 23 scientists 508–517 publications: 26 scientists 518–527 publications: 20 scientists 528–537 publications: 23 scientists 538–547 publications: 20 scientists 548–557 publications: 20 scientists 558–567 publications: 17 scientists 568–577 publications: 14 scientists 578–587 publications: 20 scientists 588–597 publications: 20 scientists 598–607 publications: 19 scientists 608–617 publications: 18 scientists 618–627 publications: 17 scientists 628–637 publications: 11 scientists 638–647 publications: 11 scientists 648–657 publications: 11 scientists 658–667 publications: 8 scientists 668–677 publications: 7 scientists 678–687 publications: 11 scientists 688–697 publications: 10 scientists 698–707 publications: 4 scientists 708–717 publications: 6 scientists 718–727 publications: 5 scientists 728–737 publications: 5 scientists 738–747 publications: 9 scientists 748–757 publications: 9 scientists 758–767 publications: 3 scientists 768–777 publications: 7 scientists 778–787 publications: 7 scientists 788–797 publications: 6 scientists 798–807 publications: 2 scientists 808–817 publications: 2 scientists 818–827 publications: 7 scientists 828–837 publications: 0 scientists 838–847 publications: 9 scientists 848–857 publications: 3 scientists 858–867 publications: 1 scientists 868–877 publications: 3 scientists 878–886 publications: 6 scientists 887+ publications: 100 scientists
38 publications 887+

This scientist: 622 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 887 publications or more.

Gang Li D-index placement in Neuroscience in 2026

The chart shows the D-index (discipline H-index) distribution of Neuroscience scientists ranked by Research.com in 2026. The highlighted bar marks where Gang Li sits on this spectrum.

30–31 D-Index: 42 scientists 32–33 D-Index: 172 scientists 34–35 D-Index: 296 scientists 36–37 D-Index: 435 scientists 38–39 D-Index: 459 scientists 40–41 D-Index: 456 scientists 42–43 D-Index: 467 scientists 44–45 D-Index: 478 scientists 46–47 D-Index: 512 scientists 48–49 D-Index: 435 scientists 50–51 D-Index: 425 scientists 52–53 D-Index: 418 scientists 54–55 D-Index: 392 scientists 56–57 D-Index: 357 scientists 58–59 D-Index: 334 scientists 60–61 D-Index: 328 scientists 62–63 D-Index: 260 scientists 64–65 D-Index: 278 scientists 66–67 D-Index: 239 scientists 68–69 D-Index: 250 scientists 70–71 D-Index: 210 scientists 72–73 D-Index: 200 scientists 74–75 D-Index: 189 scientists 76–77 D-Index: 170 scientists 78–79 D-Index: 146 scientists 80–81 D-Index: 113 scientists 82–83 D-Index: 126 scientists 84–85 D-Index: 100 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 99 scientists 90–91 D-Index: 84 scientists 92–93 D-Index: 85 scientists 94–95 D-Index: 72 scientists 96–97 D-Index: 76 scientists 98–99 D-Index: 45 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 43 scientists 104–105 D-Index: 32 scientists 106–107 D-Index: 45 scientists 108–109 D-Index: 50 scientists 110–111 D-Index: 32 scientists 112–113 D-Index: 39 scientists 114–115 D-Index: 32 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 27 scientists 120–121 D-Index: 19 scientists 122–123 D-Index: 23 scientists 124–125 D-Index: 27 scientists 126–127 D-Index: 16 scientists 128–129 D-Index: 24 scientists 130–131 D-Index: 13 scientists 132–133 D-Index: 21 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 14 scientists 138–139 D-Index: 15 scientists 140–141 D-Index: 10 scientists 142–143 D-Index: 10 scientists 144–145 D-Index: 13 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 8 scientists 150–151 D-Index: 6 scientists 152–153 D-Index: 6 scientists 154–155 D-Index: 7 scientists 156–157 D-Index: 7 scientists 158–159 D-Index: 10 scientists 160–161 D-Index: 4 scientists 162 D-Index: 8 scientists 163+ D-Index: 100 scientists
30 D-Index 163+

This scientist: 74 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 163 D-Index or more.

Overview

Gang Li is affiliated with the University of North Carolina at Chapel Hill in the United States. Their research primarily spans the fields of Medicine and Neuroscience, with a focus on pediatrics, radiology, cognitive neuroscience, computer vision, and artificial intelligence.

Their main research topics include functional brain connectivity studies, advanced neuroimaging techniques and applications, neonatal and fetal brain pathology, fetal and pediatric neurological disorders, advanced MRI techniques and applications, domain adaptation and few-shot learning, and medical image segmentation techniques.

Frequent coauthors collaborating with Gang Li are:

  • Li Wang
  • Weili Lin
  • Zhengwang Wu
  • Dinggang Shen
  • Fenqiang Zhao

The scientist's publication venues typically include:

  • UNC Libraries
  • Lecture Notes in Computer Science
  • IEEE Transactions on Medical Imaging
  • arXiv (Cornell University)
  • Medical Image Analysis

Recent research papers authored or coauthored by Gang Li are:

  • iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction, 2023, Nature Protocols
  • Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge, 2021, IEEE Transactions on Medical Imaging
  • Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment, 2021, Medical Image Analysis
  • A deep learning model for detection and tracking in high-throughput images of organoid, 2021, Computers in Biology and Medicine
  • Synergetic effect of O-POSS and T-POSS to enhance ablative resistant of phenolic-based silica fiber composites via strong interphase strength and ceramic formation, 2022, Composites Part A Applied Science and Manufacturing

Best Publications

  • The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.

    Brittany R. Howell;Martin A. Styner;Wei Gao;Wei Gao;Pew-Thian Yap

  • Dynamic Development of Regional Cortical Thickness and Surface Area in Early Childhood

    Amanda E. Lyall;Feng Shi;Xiujuan Geng;Sandra Woolson

  • Review of methods for functional brain connectivity detection using fMRI

    Kaiming Li;Lei Guo;Jingxin Nie;Gang Li

  • Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age

    Gang Li;Li Wang;Feng Shi;Amanda E. Lyall

  • LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

    Li Wang;Yaozong Gao;Feng Shi;Gang Li

  • Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection

    Gang Li;Boon-Leng Lee;Wan-Young Chung

  • High-order resting-state functional connectivity network for MCI classification

    Xiaobo Chen;Han Zhang;Yue Gao;Chong Yaw Wee

  • Mapping Region-Specific Longitudinal Cortical Surface Expansion from Birth to 2 Years of Age

    Gang Li;Jingxin Nie;Li Wang;Feng Shi

  • Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

    Yongchang Chen;Yongchang Chen;Juehua Yu;Yuyu Niu;Yuyu Niu;Dongdong Qin

  • Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

    Li Wang;Dong Nie;Guannan Li;Elodie Puybareau

  • Segmentation of neonatal brain MR images using patch-driven level sets.

    Li Wang;Feng Shi;Gang Li;Yaozong Gao

  • Computational neuroanatomy of baby brains: A review.

    Gang Li;Li Wang;Pew Thian Yap;Fan Wang

  • High glucose-induced expression of inflammatory cytokines and reactive oxygen species in cultured astrocytes.

    J. Wang;G. Li;Z. Wang;X. Zhang

  • Mapping Longitudinal Hemispheric Structural Asymmetries of the Human Cerebral Cortex From Birth to 2 Years of Age

    Gang Li;Jingxin Nie;Li Wang;Feng Shi

  • Spatial Patterns, Longitudinal Development, and Hemispheric Asymmetries of Cortical Thickness in Infants from Birth to 2 Years of Age

    Gang Li;Weili Lin;John H. Gilmore;Dinggang Shen

  • Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

    Gang Li;Li Wang;Feng Shi;John H. Gilmore

  • iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

    Unknown

  • Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants

    Yu Meng;Gang Li;Weili Lin;John H. Gilmore

  • Axonal Fiber Terminations Concentrate on Gyri

    Jingxin Nie;Lei Guo;Kaiming Li;Kaiming Li;Yonghua Wang

  • Structural and Maturational Covariance in Early Childhood Brain Development

    Xiujuan Geng;Gang Li;Zhaohua Lu;Wei Gao

  • Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces.

    Gang Li;Jingxin Nie;Li Wang;Feng Shi

  • Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation.

    Li Wang;Feng Shi;Yaozong Gao;Gang Li

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Weili Lin
Weili Lin University of North Carolina at Chapel Hill
Tianming Liu
Tianming Liu University of Georgia
Feng Shi
Feng Shi United Imaging Intelligence (China)
Lei Guo
Lei Guo Beijing University of Posts and Telecommunications
Stephen T. C. Wong
Stephen T. C. Wong Houston Methodist
Han Zhang
Han Zhang ShanghaiTech University
Kaiming Li
Kaiming Li Sichuan University
Yaozong Gao
Yaozong Gao United Imaging Healthcare (China)
Pew Thian Yap
Pew Thian Yap University of North Carolina at Chapel Hill

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Related Online Degrees & Career Pathways

When considering a future in neuroscience, it’s important to explore related online degrees and possible career pathways. Students interested in flexible learning options will find a variety of online college courses that can help build foundational skills for neuroscience and related fields, such as biology, psychology, or cognitive science.

Financial accessibility is another crucial factor. Many online college courses with financial aid are available, allowing learners to pursue their studies without incurring overwhelming debt.

For those seeking career advancement or enhanced job prospects, online certificates in data science, health sciences, or medical technology can complement a neuroscience background and increase earning potential.

Students balancing passion and practicality may also want to review easiest college majors with high pay. This can help identify alternative or dual-degree pathways that lead to rewarding, high-paying careers while pursuing an interest in neuroscience.

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