Christian Gaser

Cognitive Neuroscience · 2001–2024

A researcher in the Science corpus: 17 papers; trusts 53 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

Whom Christian Gaser trusts overall

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

  1. John Ashburner
  2. Karl J. Friston
  3. Bruce Fischl
  4. Catriona D. Good
  5. Stephen M. Smith
  6. Ingrid S. Johnsrude
  7. Jussi Tohka
  8. A. M. Dale
  9. Yoav Benjamini
  10. Bogdan Draganski

Who trusts Christian Gaser overall

  1. Ellen Bialystok
  2. Eran Dayan
  3. Bogdan Draganski
  4. Eileen Luders
  5. Sylvain Moreno
  6. Cyrille L Magne
  7. Soo-Eun Chang
  8. K. Anders Ericsson
  9. Arne May
  10. Mikkel Wallentin

Papers from Christian Gaser's perspective

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

  1. Voxel-based morphometry--the methods.
  2. Measuring the thickness of the human cerebral cortex from magnetic resonance images. (Proceedings of the National Academy of Sciences of the United States of America)
  3. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest (NeuroImage)
  4. A voxel-based morphometric study of ageing in 465 normal adult human brains (5th IEEE EMBS International Summer School on Biomedical Imaging, 2002.)
  5. A fast diffeomorphic image registration algorithm (NeuroImage)
  6. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm (IEEE Transactions on Medical Imaging)
  7. Navigation-related structural change in the hippocampi of taxi drivers. (Proceedings of the National Academy of Sciences of the United States of America)
  8. A nonparametric method for automatic correction of intensity nonuniformity in MRI data
  9. Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate (NeuroImage)
  10. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

Their papers