Nick C Fox

Physiology · 2000–2020

A researcher in the Science corpus: 50 papers; trusts 15 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

Whom Nick C Fox trusts overall

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

  1. Kenneth H. Rubin
  2. John Ashburner
  3. Stephen M. Smith
  4. Joseph E LeDoux
  5. E. Fox
  6. Michael I. Posner
  7. S. Calkins
  8. A. Mitchell
  9. Clifford R. Jack Jr.
  10. Karl J. Friston

Who trusts Nick C Fox overall

  1. Kenneth H. Rubin
  2. Robert J. Coplan
  3. Dana Liebermann
  4. J. Stamps
  5. D. McAdams
  6. David J. Lewkowicz
  7. G. Bohlin
  8. Amie A Hane
  9. N. Fox
  10. Yong Fan

Papers from Nick C Fox's perspective

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

  1. Voxel-based morphometry--the methods.
  2. A voxel-based morphometric study of ageing in 465 normal adult human brains (5th IEEE EMBS International Summer School on Biomedical Imaging, 2002.)
  3. Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory (Journal of Neuroscience)
  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. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI (Proc. Natl. Acad. Sci. USA)
  6. 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)
  7. Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate (NeuroImage)
  8. Current concepts in mild cognitive impairment. (Archives of Neurology)
  9. Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis (NeuroImage)
  10. Pattern Recognition and Machine Learning

Their papers