Francesco Musso

Cognitive Neuroscience · 2010

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Francesco Musso trusts overall

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

  1. Christian F. Beckmann
  2. Stephen M. Smith
  3. Aapo Hyvärinen
  4. Tom Minka
  5. Roberto D. Pascual-Marqui
  6. John C. Mazziotta
  7. Marcus E. Raichle
  8. Robert Oostenveld
  9. Mark Jenkinson
  10. Mark William Woolrich

Who trusts Francesco Musso overall

  1. C. Michel
  2. Vince D. Calhoun
  3. R. Matthew Hutchison
  4. Arjun R. Khanna
  5. Elena A. Allen
  6. T. Adalı
  7. Thomas Koenig
  8. Thilo Womelsdorf
  9. Alvaro Pascual-Leone

Papers from Francesco Musso's perspective

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

  1. Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data (NeuroImage)
  2. Spontaneous low‐frequency BOLD signal fluctuations: An fMRI investigation of the resting‐state default mode of brain function hypothesis (Human Brain Mapping)
  3. fMRI resting state networks define distinct modes of long-distance interactions in the human brain (NeuroImage)
  4. Simultaneous EEG and fMRI of the alpha rhythm (NeuroReport)
  5. Probabilistic independent component analysis for functional magnetic resonance imaging (IEEE Transactions on Medical Imaging)
  6. Tensorial extensions of independent component analysis for multisubject FMRI analysis (NeuroImage)
  7. 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)
  8. Consistent resting-state networks across healthy subjects (Proceedings of the National Academy of Sciences of the United States of America)
  9. A default mode of brain function: A brief history of an evolving idea (NeuroImage)
  10. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images (NeuroImage)

Their papers