Srikanth Ryali

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. ncbi Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings
    S Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Neuroimage 48:348-61. 2009
  2. ncbi Sparse logistic regression for whole-brain classification of fMRI data
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 51:752-64. 2010
  3. ncbi A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 65:83-96. 2013
  4. ncbi Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 59:3852-61. 2012
  5. ncbi Multivariate dynamical systems models for estimating causal interactions in fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Neuroimage 54:807-23. 2011
  6. ncbi Hippocampal-prefrontal engagement and dynamic causal interactions in the maturation of children's fact retrieval
    Soohyun Cho
    Stanford University, Stanford, CA, USA
    J Cogn Neurosci 24:1849-66. 2012
  7. ncbi Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development
    Lucina Q Uddin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
    J Neurosci 31:18578-89. 2011
  8. ncbi Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns
    Daniel A Abrams
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Cereb Cortex 21:1507-18. 2011
  9. ncbi How does a child solve 7 + 8? Decoding brain activity patterns associated with counting and retrieval strategies
    Soohyun Cho
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305 5719, USA
    Dev Sci 14:989-1001. 2011

Detail Information

Publications9

  1. ncbi Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings
    S Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Neuroimage 48:348-61. 2009
    ..We provide new insights into the strengths and weaknesses of each method using a unified subspace framework...
  2. ncbi Sparse logistic regression for whole-brain classification of fMRI data
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 51:752-64. 2010
    ..These findings suggest that our method is not only computationally efficient, but it also achieves the twin objectives of identifying relevant discriminative brain regions and accurately classifying fMRI data...
  3. ncbi A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 65:83-96. 2013
    ..Taken together, our findings suggest that our method is a powerful tool for investigating functional subdivisions in the human brain...
  4. ncbi Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 59:3852-61. 2012
    ..Taken together, our findings suggest that SPC-EN provides a powerful tool for characterizing connectivity involving a large number of correlated regions that span the entire brain...
  5. ncbi Multivariate dynamical systems models for estimating causal interactions in fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Neuroimage 54:807-23. 2011
    ..Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data...
  6. ncbi Hippocampal-prefrontal engagement and dynamic causal interactions in the maturation of children's fact retrieval
    Soohyun Cho
    Stanford University, Stanford, CA, USA
    J Cogn Neurosci 24:1849-66. 2012
    ....
  7. ncbi Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development
    Lucina Q Uddin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
    J Neurosci 31:18578-89. 2011
    ....
  8. ncbi Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns
    Daniel A Abrams
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Cereb Cortex 21:1507-18. 2011
    ....
  9. ncbi How does a child solve 7 + 8? Decoding brain activity patterns associated with counting and retrieval strategies
    Soohyun Cho
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305 5719, USA
    Dev Sci 14:989-1001. 2011
    ..More generally, our study illustrates how brain imaging and developmental research can be integrated to investigate fundamental aspects of neurocognitive development...