Mingzhou Ding

Cognitive Neuroscience · 2000–2017

A researcher in the Science corpus: 17 papers; trusts 25 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

Whom Mingzhou Ding trusts overall

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

  1. Clive W. J. Granger
  2. Hirotugu Akaike
  3. Steven L. Bressler
  4. Yonghong Chen
  5. Andrea Brovelli
  6. Thomas Schreiber
  7. Maciej Kaminski
  8. Andrew T. Walden
  9. Yuzo Hosoya
  10. Michael R. Chernick

Who trusts Mingzhou Ding overall

  1. Jerry Quispe
  2. Yonghong Chen
  3. Xiao-Jing Wang
  4. Anil K. Seth
  5. Steven L. Bressler
  6. M. Dhamala
  7. Karl J. Friston
  8. Lionel C. Barnett
  9. André Moraes Bastos
  10. Rafal Kus

Papers from Mingzhou Ding's perspective

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

  1. Partial directed coherence: a new concept in neural structure determination (Biological cybernetics)
  2. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance (Biological cybernetics)
  3. Investigating causal relations by econometric models and cross-spectral methods
  4. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. (Magnetic Resonance Imaging)
  5. Elimination of Third‐series Effect and Defining Partial Measures of Causality
  6. Time Series Modelling and Interpretation
  7. Mapping directed influence over the brain using Granger causality and fMRI (NeuroImage)
  8. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment (Biological cybernetics)
  9. Dynamic causal modelling
  10. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. (Journal of Neuroscience Methods)

Their papers