Jens C. Pruessner

Behavioral Neuroscience · 2000–2016

A researcher in the Science corpus: 15 papers; trusts 8 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

Whom Jens C. Pruessner trusts overall

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

  1. John G. Sled
  2. Alex P. Zijdenbos
  3. Alan C. Evans
  4. John Ashburner
  5. Clemens Kirschbaum
  6. Rachael I. Scahill
  7. Karl J. Friston
  8. Richard J. Davidson
  9. Bruce S. McEwen
  10. Bruce Fischl

Who trusts Jens C. Pruessner overall

  1. M. Enoch
  2. Oliver D. Howes
  3. Bruce S. McEwen
  4. Lars Schwabe
  5. A. Clow
  6. Christine Marcelle Heim
  7. Florin Dolcos
  8. Yong He
  9. Alexis R. Harris
  10. Urs M. Nater

Papers from Jens C. Pruessner's perspective

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

  1. A nonparametric method for automatic correction of intensity nonuniformity in MRI data
  2. Voxel-based morphometry--the methods.
  3. Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate (NeuroImage)
  4. 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)
  5. Neuropathological stageing of Alzheimer-related changes
  6. Mapping the evolution of regional atrophy in Alzheimer's disease: Unbiased analysis of fluid-registered serial MRI (Proceedings of the National Academy of Sciences of the United States of America)
  7. Automatic Differentiation of Anatomical Patterns in the Human Brain: Validation with Studies of Degenerative Dementias (NeuroImage)
  8. Cortical thickness analysis examined through power analysis and a population simulation (NeuroImage)
  9. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis (IEEE Transactions on Medical Imaging)
  10. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. (Archives of Neurology)

Their papers