Brain Network Visualization

Brain Network Visualization is a computational method that enables the graphical representation and structural interpretation of anatomical or functional neural connectivity patterns derived from neuroimaging data within the framework of graph-theoretical models.

50

Publications

9.7K

Citations

257

Authors

123

Institutions

Publications per year

2017–2026

12

Authors

257

  1. PM
    Paul M. Thompson

    University of California, Los Angeles, United States

    3 publications · 160 citations

    3

    h-index

  2. AG
    Angus G. Forbes

    University of Illinois Chicago, United States

    3 publications · 54 citations

    3

    h-index

  3. TB
    Tanya Berger‐Wolf

    University of Illinois Chicago, United States

    2 publications · 29 citations

    2

    h-index

  4. JB
    Joachim Böttger

    Max Planck Institute for Human Cognitive and Brain Sciences, Germany

    2 publications · 97 citations

    2

    h-index

  5. GL
    Gabriele Lohmann

    Max Planck Institute for Human Cognitive and Brain Sciences, Germany

    2 publications · 97 citations

    2

    h-index

1–5 of 257

Venues

  1. NeuroImage

    5 publications · 2.2K citations

    5

    h-index

  2. PLoS ONE

    3 publications · 4.4K citations

    3

    h-index

  3. Neuroinformatics

    2 publications · 294 citations

    2

    h-index

  4. Procedia Computer Science

    2 publications · 30 citations

    2

    h-index

  5. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 117 citations

    2

    h-index

1–5