Deborah A. Yurgelun-Todd

Cognitive Neuroscience · 2001–2018

A researcher in the Science corpus: 5 papers; trusts 4 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom Deborah A. Yurgelun-Todd trusts overall

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

  1. Ahmad R. Hariri
  2. Jay N. Giedd
  3. L. Spear
  4. Joaquín M. Fuster
  5. Nathalie Tzourio-Mazoyer
  6. Beatriz Luna

Who trusts Deborah A. Yurgelun-Todd overall

  1. Ruben C. Gur
  2. B. Casey
  3. Ben Alderson-Day
  4. Niklas Ziegler
  5. Leah H. Somerville
  6. Eric W. Owens
  7. Jean Decety
  8. Monica E. Calkins

Papers from Deborah A. Yurgelun-Todd's perspective

Ordered by how strongly the researchers Deborah A. Yurgelun-Todd trusts endorse each paper. Same corpus, a different order for every researcher.

  1. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest (NeuroImage)
  2. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. (Archives of General Psychiatry)
  3. 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)
  4. A hybrid approach to the skull stripping problem in MRI (NeuroImage)
  5. Multiband Multislice GE-EPI at 7 Tesla, With 16-Fold Acceleration Using Partial Parallel Imaging With Application to High Spatial and Temporal Whole-Brain FMRI (Magnetic Resonance in Medicine)
  6. Advances in functional and structural MR image analysis and implementation as FSL.
  7. The B‐matrix must be rotated when correcting for subject motion in DTI data (Magnetic Resonance in Medicine)
  8. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters (NeuroImage)
  9. A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T) (NeuroImage)
  10. The Human Connectome Project: A data acquisition perspective (NeuroImage)

Their papers