R. Craddock

Cognitive Neuroscience · NeuroImage · 2008–2018

A researcher in the Science corpus: 15 papers; trusts 54 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

Whom R. Craddock trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Karl J. Friston
  2. Stephen M. Smith
  3. Helen S. Mayberg
  4. Jianbo Shi
  5. Daniel S. Margulies
  6. Ulrike von Luxburg
  7. Torben Ellegaard Lund
  8. Isabelle M Guyon
  9. Yufeng Zang
  10. Michael D. Fox

Who trusts R. Craddock overall

  1. Stephen M. Smith
  2. Bilwaj Gaonkar
  3. Alexandre Abraham
  4. A. di Martino
  5. Michael P. Milham
  6. Russell A. Poldrack
  7. F. X. Castellanos
  8. Salim Arslan
  9. Carlton Chu
  10. Maxwell A. Bertolero

Papers from R. Craddock's perspective

Ordered by how strongly the researchers R. Craddock trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Mapping the Structural Core of Human Cerebral Cortex (PLoS Biology)
  2. ImageNet classification with deep convolutional neural networks
  3. Correspondence of the brain's functional architecture during activation and rest
  4. Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function (Proceedings of the National Academy of Sciences of the United States of America)
  5. Consistent resting-state networks across healthy subjects (Proceedings of the National Academy of Sciences of the United States of America)
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. The Human Connectome: A Structural Description of the Human Brain
  8. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis (Proceedings of the National Academy of Sciences of the United States of America)
  9. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease
  10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers